MUSINGWAY Ltd. (Currently suspended.)
My name is Kaveh Bakhtiyari, & I was born in September 1984. I am an artificial intelligence specialist on high-tech systems, and founder of Musingway Ltd. . Currently, I am doing research in Artificial Intelligence (AI), Cognitive Science & System Engineering at The National University of Malaysia and University of Duisburg-Essen .
Human natural languages are my interest. I learn, practice and speak in many languages such as Persian/Farsi (Native), English, French, German, Spanish, Italian, Esperanto, Hebrew, Malay, Mandarin (Chinese) and Japanese. Beside the languages, I have few other hobbies such as travelling (which I truly love it), playing music (mostly Guitar and Violin) and reading books.
Well, I am a technology lover, who hates the technology. I believe that this technology has slaved us to work for it. Apparently, it sounds great, but it sometimes solves only the problems which have been made by itself.
Finally, I feel successful and happy, because I followed a very simple rule in my life:
"Do what you can, & Try what you can not."
And this is my mystery, but of course not the only one.
Here is my personal web site to be connected with you. If you would like to contact me, just feel free.
MUSINGWAY Ltd. (Currently suspended.)
Sayan Soft Co. Ltd., Tehran, Iran
It was in September 1984 in Tehran, IRAN that I was born. I am the only child of my family. When I was 6 years old, I started the elementary school. In 1993, when I just was in 4th grade, I began to play the keyboard (ORG). My first keyboard was a simple small CASIO. After a while, I bought a CASIO 670 TONE BANK.
In summer 1995, I started learning the English language at Shokouh English School in Tehran; in summer 1996, learning the computer sciences, & in the winter 1997, I bought my first PC (Pentium 166 MHz).
In 1998, I used the Internet for the first time. The first website which I personally browsed was Titanic Movie Web Site , & the first e-mail which I sent, was to the Glasgow University in Scotland, Great Britain. In 1999, learning more programming languages began, and also I had a course on AutoDesk AutoCAD R.14 at Fine Arts College in University of Tehran.
I designed my first homepage at 'http://bakhtiyari.homepage.com' in September 2009, and in this year, I got my own personal Internet access. I do remember that it was a metered Internet, 5 IRRials/KB from Kavosh ISP. On 12th December 2000, I registered Bakhtiyari.com for the first time. In fall 2000, I finished the English school, and I got my TOEFL certificate.
I got my high school diploma in winter 2001, and in the next year, I graduated the Pre-University in Mathematics & Physics. Summer 2002, I sold my Keyboard, then I bought and started playing the classic guitar.
After sometime, in summer 2004, I passed the university entrance exam for BSc. Computer - Software Engineering in Islamic Azad University - Tafresh Branch (IAUTB) . I have been living in Tafresh with 3 house mates. It was a great experience, beautiful memories, and nice friends in campus life. My college took around 4 years, and I was qualified as the top research student for 4 times at the college. Meanwhile, I worked in several companies part-time. I was also doing the university website and network, and it was a great opportunity to learn more about the network hardware. Company ownership was my old dream to develop my ideas. Therefore I did register my company (MUSINGWAY Ltd. ) in spring 2005 as a Hi-Tech service provider.
Throughout my college, I started learning French and, after a year, German languages; but it did not take a long time, because of my time was limited by university courses, and I could not continue the foreign languages at that time.
Winter 2008, I got graduated from university in Bachelor of Software Engineering at CGPA of 3.20/4. So it was a good time in 2009 to continue my incomplete tasks. Developing my own company, learning the foreign languages, practising violin, and applying for a university abroad for Master education. Then in January 2010, I entered the National University of Malaysia (UKM) in Master of Information Technology - Artificial Intelligence.
During my master education, I worked on a human emotion recognition system. I was thinking of an intelligent computer to interact with naturally. It is a very long time dream. During my stay in Malaysia, I found a student group called AIESEC . I joined this group and later the network of international friends began to grow. AIESEC gave me the chance of knowing the world differently. This international mixed group of people showed me different aspects, and it changed my mind, my look, and my expectations to have a better, easier and more efficient life.
In winter 2011, I had an opportunity to travel to Germany as an exchange student in University of Duisburg-Essen . I did my research on human emotions and affective computing in Germany. I used this chance to travel to a few European countries (as it is typical for all exchange students to travel around), to expand my friends' network, and to experience the AIESEC in Germany. In UDE, I got to know a professor who introduced me a new research area on Recommender Systems, and as he suggested, I intended to work on Affective Computing and Recommender Systems for my PhD studies.
So, I got back to UKM in Malaysia after 5 months in spring 2012, and I finished my master's in summer 2012 specialized in Artificial Intelligence with the CGPA of 3.60/4. At the same time, I got to know a person who has a strong influence to continue my journey. Respectively, I started my PhD in UKM working on the idea of Recommender Systems. Summer 2013, I got the offer letter from UDE, Germany to continue my PhD studies based on a joint doctoral program between UKM and UDE.
Within my personal approach, I have various fields of study because I am interested and willing to know many things in this fast moving world. Today the whole world has become a global village in terms of knowledge, cultures and traditions. So I like learning every new research topic and bridge with my past knowledge, which obviously it is not possible, however I try my best. Within this nexus, Artificial Intelligence, Human-Computer Interaction and Game Theory are my main concerns in research.
Recommender Systems and Affective Computing are among new research areas which I have mostly focused on intelligent autonomous sections. In data mining research area, I love Big Data challenges. Its challenges and their related research papers have given me lots of ideas directly and indirectly in my research work. I am more interested in the application of intelligent systems in real business, engineering and cognitive issues.
I enjoy a great professional collaboration with researchers across the world. We have a very professional, friendly, sympathetic and enthusiastic research members in a variety of research domains, countries and business or academic positions. We also welcome whoever is willing to share and promote the knowledge by joining our research group.
In this research, we propose a novel adaptive recommender system on computational advertising based on emotional and cultural intelligence. It processes the users’ interactions with the common input devices such as mouse, keyboard, touch-screen monitors (and alternatively EEG machines). The system recommends the relevant advertisements to the network users based on their favorites and emotional states. In addition it adapts to the users requirements and needs psychologically and culturally responsive. A metaphor semantic ontology is also developed to retrieve the users’ cultures based on the metaphorical meaning of the context. This ontology can be different with the traditional semantic and direct meanings of the words. For evaluation of the system, despite the traditional Precision/Recall methods, emotional intelligence system and fuzzy/probabilistic calculations are proposed to measure the performance and accuracy of the resulted recommendation. The available hypothesis say in theory that the suggested system should be working in a reliable state and efficiency.
Nowadays, emotions play an important role among human interactions. These emotions can be integrated into the computer system to make more effective interactions with users. Human Emotions Recognition (HER) is an innovative software-based method for detecting user’s emotions to determine proper responses in human-computer interaction. There are several methods to recognize the emotions like processing the facial expressions, human voice, body gestures and Electroencephalography (EEG) machines. However, there are still some challenges such as the accuracy of recognition, the number of supported emotions by the system, real-time processing, and recognition of emotions without any direct interaction between the user and computer. This thesis research aims to provide two solutions to increase the accuracy of recognition and to detect the emotion without direct interaction between human and computer. It is conducted in two phases. The first phase provides a solution based on HCI. It performs analysis on collected data of the users’ emotions and interactions with various input devices such as the mouse, keyboard and touch-screen. The system is trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. Further analysis is applied to find the collaboration of various emotions, which may happen at a same time with different emotion scales. Moreover, the data are plotted on a fuzzy model since each emotion data are collected in diverse scales with overlapping crisp spectrums. The second phase, proposes a solution to recognize the emotion when the user is not interacting with the machine. This solution is based on HCI studies and un-concentrated recognition by processing the environment and surrounding area. For this purpose, it is suggested that environment features can be collected, tagged and trained for the system to recognize the users’ emotions at the same environment. The result of the first solution shows an increase of around 5% in accuracy by comparing with the existing methods. Furthermore, this result also shows that the cultural and language backgrounds can make different emotional expressions for the same emotion. The result of the second solution proves the recognition possibility, but with less accuracy. These results are significant contributions to show new directions of future research in this topical area of emotion recognition in computer.
The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are deemed largely inefficient to be deployed in cloud computing environments due to their openness, dynamicity and virtualization in offered services. This research surveys and explores the possible solutions to detect and prevent intrusions in cloud computing systems by providing a comprehensive taxonomy of existing IDPS. It discusses the key features of IDPS that are challenging and crucial for choosing the right security measures for designing an IDPS. The research further reviews the current state of the art of developed IDPSs for cloud computing which uses advanced techniques in overcoming the challenges imposed by cloud computing requirements for more resilient, effective and efficient IDPSs, abbreviated as CIPDS.
The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This research presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.
Only authors' versions of papers are available to download, except those open access and not copyrighted research articles.
To access the published typeset paper, please follow the external link to the publisher's repository.
h-Index: | i10-Index: | Citations: | Co-Authors:
Educational growth is a fundamental infrastructure factor required to achieve sustainable development. Therefore, evaluation and measurement of educational growth is essential for establishing a development road map. Because of this, there are many organizations and databases that work to capture academic trends and provide the general view of institute achievements. Web of Science and Scopus are the two most popular and scientific. In this paper, we define the important effective factors in educational growth and discuss them; we then compare these defined factors across four different developing countries: Brazil, Iran, Malaysia, and Turkey. As well as the comparisons, this paper uses the Pearson product moment correlation coefficient method to analyze the factors and the strong or weak relationship between the factors are discussed.
Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction more effective. Affective Computing is an innovative computational modelling and detecting user’s emotions to optimize system responses in Human-Computer Interaction (HCI). However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real world applications such as Natural Language Processing (NLP) and Electroencephalography (EEG) signals. To accomplish a reliable, usable and high performance system, this paper proposes an intelligent hybrid approach to recognize users’ emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch-pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. The result shows an increase of accuracy of 6% (93.20%) by SVM in comparison to the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modelling and emotional intelligence.
Purpose – This research work investigates the recent status of the information and communication technology services industry in Iran. It proposes a systemic applicable approach at policy making level and appropriate strategic planning steps to enlighten developing countries toward achieving their target objectives of an Information Society.
Design/methodology/approach – Largely based on existing literature and usage statistics in ICT services, global technology trends, and results from a survey to obtain consistent and up-to-date information about current issues of ICT services in the public and private sectors in Iran.
Findings – This study elaborates on all issues, points, and best practices relevant to the ICT services industry in Iran which is addressed by recommending some documented policies.
Research limitations/implications - Majority of the experts who attended the workshop and responded to the questionnaire were service consumers rather than service providers.
Practical implications - This paper discusses some of the implications for the development of this ICT services strategy and provides policy recommendations.
Originality/value – This study provides a government refining process policy to address the common gaps in the ICT services industry in these developing countries and emphasizes a formidable policy foundation before implementing and monitoring of the Flagship ICT projects.
This paper discusses plagiarism origins, and the ethical solutions to prevent it. It also reviews some unethical approaches, which may be used to decrease the plagiarism rate in academic writings. We propose eight ethical techniques to avoid unconscious and accidental plagiarism in manuscripts without using online systems such as Turnitin and/or iThenticate for cross checking and plagiarism detection. The efficiency of the proposed techniques is evaluated on five different texts using students individually. After application of the techniques on the texts, they were checked by Turnitin to produce the plagiarism and similarity report. At the end, the “effective factor” of each method has been compared with each other; and the best result went to a hybrid combination of all techniques to avoid plagiarism. The hybrid of ethical methods decreased the plagiarism rate reported by Turnitin from nearly 100% to the average of 8.4% on 5 manuscripts.
To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93%. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.
Emotions play an important role in human interactions. Human Emotions Recognition (HER - Affective Computing) is an innovative method for detecting user’s emotions to determine proper responses and recommendations in Human-Computer Interaction (HCI). This paper discusses an intelligent approach to recognize human emotions by using the usual input devices such as keyboard, mouse and touch screen displays. This research is compared with the other usual methods like processing the facial expressions, human voice, body gestures and digital signal processing in Electroencephalography (EEG) machines for an emotional-aware system. The Emotional Intelligence system is trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. The result shows 93.20% in accuracy which is around 5% more than the existing methods. It is a significant contribution to show new directions of future research in this topical area of emotion recognition, which is useful in recommender systems.
In this paper, we propose a novel adaptive recommender system based on emotional and cultural intelligence by processing the users’ interactions with the common input devices such as mouse, keyboard, touch-screen monitors and EEG. This system can be employed in a computational advertising system. A metaphor semantic ontology should also be developed to retrieve the users’ cultures based on the metaphor meaning of the context which can be different with the traditional semantic and direct meanings of the words. For evaluation of the system, despite the traditional Precision/Recall methods, emotional intelligence system and fuzzy/probabilistic calculations are proposed to measure the performance and accuracy of the resulted recommendation.
The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.
This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touch-screen interactions. This model can also be used to detect the other possible emotions at the time of recognition. Accuracy measurements of human emotions by the fuzzy model are discussed through two methods; the first is accuracy analysis and the second is false positive rate analysis. This fuzzy model detects more emotions, but on the other hand, for some of emotions, a lower accuracy was obtained with the comparison with the non-fuzzy human emotions detection methods. This system was trained and tested by Support Vector Machine (SVM) to recognize the users’ emotions. Overall, this model represents a closer similarity between human brain detection of emotions and computer systems.
The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. This paper surveys, explores and informs researchers about the latest developed IDPSs and alarm management techniques by providing a comprehensive taxonomy and investigating possible solutions to detect and prevent intrusions in cloud computing systems. Considering the desired characteristics of IDPS and cloud computing systems, a list of germane requirements is identified and four concepts of autonomic computing self-management, ontology, risk management, and fuzzy theory are leveraged to satisfy these requirements.
The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are deemed largely inefficient to be deployed in cloud computing environments due to their openness, dynamicity and virtualization in offered services. This paper surveys and explores the possible solutions to detect and prevent intrusions in cloud computing systems by providing a comprehensive taxonomy of existing IDPS. It discusses the key features of IDPS that are challenging and crucial for choosing the right security measures for designing an IDPS. The paper further reviews the current state of the art of developed IDPSs for cloud computing which uses advanced techniques in overcoming the challenges imposed by cloud computing requirements for more resilient, effective and efficient IDPSs, abbreviated as CIPDS.
Purpose – This paper aims to focus on plagiarism and the consequences of anti-plagiarism services such as Turnitin.com, iThenticate, and PlagiarismDetect.com in detecting the most recent cheatings in academic and other writings.
Design/methodology/approach – The most important approach is plagiarism prevention and finding proper solutions for detecting more complex kinds of plagiarism through natural language processing and artificial intelligence self-learning techniques.
Findings – The research shows that most of the anti-plagiarism services can be cracked through different methods and artificial intelligence techniques can help to improve the performance of the detection procedure.
Research limitations/implications – Accessing entire data and plagiarism algorithms is not possible completely, so comparing is just based on the outputs from detection services. They may produce different results on the same inputs.
Practical implications – Academic papers and web pages are increasing over time, and it is very difficult to capture and compare documents with all available data on the network in an up to date manner.
Originality/value – As many students and researchers use the plagiarism techniques (e.g. PDF locking, ghost-writers, dot replacement, online translators, previous works, fake bibliography) to cheat in academic writing, this paper is intended to prevent plagiarism and find suitable solutions for detecting more complex kinds of plagiarism. This should also be of grave concern to teachers and librarians to provide up to date/standard anti-plagiarism services. The paper proposes some new solutions to overcome these problems and to create more resilient and intelligent future systems.
University of Concordia, Montreal, CANADA
Faculty of Engineering & Computer Science
Organized by Assoc. Prof. Dr. Bentahar, & Assoc. Prof. Dr. Yu
University of Duisburg – Essen, Duisburg, GERMANY
Interactive Systems, Faculty of Engineering
Organized by Prof. Dr-Ing. Jürgen Ziegler
Hotel Equatorial Bangi, Serdang, MALAYSIA
Annual Seminar of DSP Research Group, Faculty of Engineering, The National University of Malaysia
Swiss Garden Hotel, Cherating Beach, Kuantan, MALAYSIA
Organized by ISAUKM
University of Duisburg – Essen, Duisburg, GERMANY
Department of Psychology-Media and Communication, Faculty of Engineering
Organized by Prof. Dr. Nicole Krämer
Tizen OS for Wearables was initially available on Samsung Gear 2 smart watch. The following apps are developed for
Tizen smart watch. These apps are available via Samsung Apps for Gear .
My Puzzle, Mobile Shield & Gear Dictionary were nominated in the 1st round of Samsung Gear App Challenge listed among top 200 apps for Gear in July 2014.
The developed applications are partially listed in MUSINGWAY Ltd. web site.
This macro writes the names of the sections in the Powerpoint footer, and it changes the font/color of the current section in order to show which section the slide belongs to.
In addition, it writes the active slide number and total number of slides dynamically (e.g. x of X) without hardcoding the total number.
Download Source Code (4 KB)
This macro counts the words and marks the sentences with more than a specific
number of words (e.g. 30). According to the "English for Writing Research Papers" , the maximum of 30 words per sentence is acceptable for research articles.
This macro helps authors to recognize the long sentences thus to simplify them.
Requirements: It requires "Microsoft XML v6.0" to be added/checked in references (VBA Editor: Tools > References).
Download Source Code (6 KB)
This macro marks (DarkBlue) the in-line citations of EndNote .
EndNote software does not have any built-in feature to change the color of in-line citations in a Word document. This macro changes the color of all citations made by EndNote, and makes the references in a document clear.
Requirements: It requires "Microsoft XML v6.0" to be added/checked in references (VBA Editor: Tools > References).
Download Source Code (4 KB)
You can find me at my office located at the University of Duisburg-Essen at the following address:
University of Duisburg-Essen (Campus Duisburg)
Forsthausweg 2 (Building LF)
47057 Duisburg, North Rhine-Westphalia (NRW)
You may consider a call to fix an appointment.
At the moment, I am not always available in campus.
You can find me at my office located at the National University of Malaysia at the following address:
Digital Signal Processing (DSP) Lab
Level 2, Faculty of Engineering & Built Environment
Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor
You may consider a call to fix an appointment.