Reviewerly/exHarmony huggingface dataset:
Research on the current situation of user interface design in Thai electronic product

This study was concerned about the problems as mentioned above. Therefore, we focused on the problem of user interface design in Thailand’s electronics industry. To understand the current situation, this research is conducted to investigate the current design of user interface on electrical products sold in Thai electric appliance stores. A questionnaire was used to ask the opinions of users about the top five electrical appliances that are important for their lives and are complex in terms of use. The results indicated that the top five electronic devices that are most important for Thai users are refrigerators, air conditioners, TVs, washing machines, and electronic fans. However, washing machines are the most sophisticated devices in terms of use. The experiment was conducted to analyze and examine the design of user interfaces on the control panel screens used in washing machines. Data were then collected and evaluated by 2-way ANOVA and t-test. The results were statistically significant which indicated the differences between groups. The findings indicated that almost all Thai electronic products rely on Korean user interface design. In addition, more than 70% of the functions that appear on the control panel of washing machines use words to describe operations rather than symbols. The findings were used to identify the proper interface design words on washing machines in the next step.
Unified Clustering and Outlier Detection on Specialized Hardware

Clustering and outlier detection are often studied as separate problems. However, previous work has shown that a unified approach can lead to better performance. Unified clustering and outlier detection is a hard combinatorial problem that has received significant attention in recent years. The recent emergence of specialized optimization hardware capable of solving combinatorial problems formulated as Quadratic Unconstrained Binary Optimization (QUBO) models has led to increased interest in harnessing these platforms in core data mining tasks. In this work, we present a novel QUBO formulation of the unified clustering and outlier detection problem and use the Fujitsu Digital Annealer, a specialized CMOS hardware, to solve it. Experiments on synthetic and real datasets demonstrate the effectiveness of our approach.
Research and development for increased application of data science in sustainability analysis

This chapter summarizes research needs in developing and enhancing data sets, data sources, and data science methods for applying data science to the broad field of sustainability. Overall, while there is a great need to develop environmental science, manufacturing and technology systems, and societal systems data at greater spatial and temporal resolution, there is also a need to consider what sustainability questions can be addressed by creative use of the data sets currently available. Furthermore, advances in data science remain foundational to realizing the potential of many techniques (including machine learning, artificial intelligence) to enhancing environmental and societal sustainability. Advances in explainable AI, edge computing, and applying the advantages of the 5G technology that is on the horizon are all required. Importantly, engagement of multiple disciplines – particularly computer science – along with multiple science and engineering disciplines will be foundational to bringing the power of data science methods to bear on the pressing sustainability challenges.
Efficient Algorithm for Mining Probabilistic Frequent Itemsets of Uncertain Data

At present, uncertain data has become a hot research topic for scholars. For the current mining algorithms, there are still many shortcomings in terms of time and memory. How to improve or find efficient mining algorithms and quickly mine them Information that is more valuable to people has also become a thorny problem for researchers. At present, the existing algorithms for mining frequent itemsets of uncertain data probabilities with pattern growth have many shortcomings in terms of memory. Aiming at the problem that the existing PUFP-Growth algorithm consumes too much memory, this paper proposes a HUFP-Growth algorithm. This algorithm adds a candidate item set judgment mechanism to the original algorithm, which can judge the item set in advance Is it necessary to do linking to save a lot of memory. At the same time, it can also save the time consumed when the itemsets in the original algorithm are connected, and to a certain extent, it also saves the running time of the algorithm. Finally, the effectiveness of the method is proved through experiments.
From Certain to Uncertain: Toward Optimal Solution for Offline Multiple Object Tracking

Affinity measure in object tracking outputs a similarity or distance score for given detections. As an affinity measure is typically imperfect, it generally has an uncertain region in which regarding two groups of detections as the same object or different objects based on the score can be wrong. How to reduce the uncertain region is a major challenge for most similarity-based tracking methods. Early mistakes often result in distribution drifts for tracked objects and this is another major issue for object tracking. In this paper, we propose a new offline tracking method called agglomerative hierarchical clustering with ensemble of tracking experts (AHC_ETE), to tackle the uncertain region and early mistake issues. We conduct tracking from certain to uncertain to reduce early mistakes. Meanwhile, we ensemble multiple tracking experts to reduce the uncertain region as the final uncertain region is the intersection of those of all tracking experts. Experiments on the MOT15 and MOT16 datasets demonstrated the effectiveness of our method. The code is publicly available at https://github.com/cyoukaikai/ahc_ete.
Homemade XR: Enriching conversations and shortening social distancing

This work shows an alternative way to shortening social distancing without breaking physical distancing, a medical recommendation in some health conditions, as the current COVID-19 pandemic. As an alternative to traditional video conferencing, this proposal allows persons to interact with their natural space while actively participating in a remote call. Each participant's reality is extended with a visual representation of the other and binaural sound, enriching the conversation by the sense of the other's proximity. This proposal uses quotidian technology, i.e., smartphones, and an immersive homemade device, to provide an affordable system to display and capture audio stereo and video.
Development of a Realistic Simulator for Driving Education of a Disaster-Responding Special Purpose Machinery

Due to natural disasters such as earthquakes, ground collapse, typhoons and floods, building collapses continue to occur. In order to rescue the buried people, disaster-responding special purpose machines have been developed to quickly and safely move piles of rubble of building. The disaster-responding special purpose machine is a new concept machine unlike the existing excavator. Therefore, there is a need for an educational simulator for driving training. This paper proposes a realistic simulator based on a 3D virtual environment and motion platform for effective driving training special purpose machines. We implemented a driving training test against disaster response specialists. As a result, it was confirmed that the proposed simulator is effective in driving education.
Trust Is All You Need: From Belief Revision to Information Revision

Belief revision is a hallmark of knowledge representation, logic, and philosophy. However, despite the extensive research in the area, we believe a fresh take on belief revision is needed. To that end, it is our conviction that believing a piece of information depends on trust in information sources that conveyed said piece and that trust in information sources is affected by changes in beliefs. Trust is also an impress of philosophy and all time favorite of psychology and multi-agent systems. Hence, many approaches were developed for trust representation, yet, in isolation from belief revision. While admittedly crucial to a realistic treatment of belief revision, trust revision, to our dismay, did not receive the same level of attention. In this paper, we argue that a formal treatment for the joint interdependent revision of belief and trust is called for. Moreover, we propose a new framework called information revision that captures the joint revision of belief and trust. Further, we provide postulates that govern such process of revision. Finally, we provide a class of operators called relevant change propagation operators and provide their representation theorem.
A Novel Analog Modulation Classification: Discrete Wavelet Transform-Extreme Learning Machine (DWT-ELM)

The aim of this study is to propose a method using discrete wavelet transform and extreme learning machine (DWT-ELM) in classification of communication signals. Six types of analog modulated signals as “AM”, “DSB”, “USB”, “LSB”, “FM” and “PM” are used for classification and analog modulated signal dataset consists of 1920 signals. These signals are also added white noise. Feature extraction is performed using different DWT filters. The feature vector obtained from DWT is used in classification. ELM is preferred due to its advantages over conventional back-propagation based classification. The feature vector is fed by the inputs of the ELM. The performance of the proposed method is evaluated for different types of DWT filters. In addition, compared results with similar study are presented to be able to determine the success of the proposed method.
An Empirical Study on Task-Oriented Dialogue Translation

Translating conversational text, in particular task-oriented dialogues, is an important application task for machine translation technology. However, it has so far not been extensively explored due to its inherent characteristics including data limitation, discourse, informality and personality. In this paper, we systematically investigate advanced models on the task-oriented dialogue translation task, including sentence-level, document-level and non-autoregressive NMT models. Be-sides, we explore existing techniques such as data selection, back/forward translation, larger batch learning, finetuning and domain adaptation. To alleviate low-resource problem, we transfer general knowledge from four different pre-training models to the downstream task. Encouragingly, we find that the best model with mBART pre-training pushes the SOTA performance on WMT20 English-German and IWSLT DIALOG Chinese-English datasets up to 62.67 and 23.21 BLEU points, respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Algorithm of Detecting Abnormal Behavior Based on Optical Flow Method

In this paper, an algorithm of detecting abnormal behavior based on optical flow method for the automatic monitoring is proposed. By comparing the "momentum" of the image and the threshold, the algorithm can determine whether abnormal motion occurs or not. For improving the real-time performance, the algorithm which can be applied to real-time video detection of abnormal behavior, reduces the image resolution, sets the region of interest (ROI) and ignores the tiny changes of optical flow points. Furthermore, calculating accumulating alarm is used instead of single frame image alarm, so the misjudgment is avoided effectively. The feasibility and accuracy of the algorithm are verified through the simulation of abnormal behavior video in elevator.
Using time-to-click to identify websites with robot traffic

This paper shows how to use statistics to identify automated browsing leveraging time between events, such as clicks on a website. The methodology was applied on web advertising traffic, aiming to identify websites with a high share of non-human traffic by using the time between the timestamp an ad is displayed and the timestamp it is clicked.
Integrating Multi-threading into Query-Subquery Nets

In this paper, we propose a new method, named QSQN-MT, for the evaluation of queries to Horn knowledge bases. Particularly, we integrate multi-threading into query-subquery nets to reduce the execution time for evaluating a query over a logic program regardless of the order of clauses. The usefulness of the proposed method is indicated by the experimental results.
Assessment of Lifestyle and Mental Health: Case Study of the FST Beni Mellal

Lifestyle habits are defined as behaviors of a sustainable nature which are based on a set of elements incorporating cultural heritage, social relations, geographic and socio-economic circumstances as well as personality. Mental health encompasses the promotion of well-being, the prevention of mental disorders, and the treatment and rehabilitation of people with these disorders. In order to address this issue, we propose a solution which consists of the development of an extended autonomous computer model for large textual data. This model will make it possible to give a psychological, emotional or even a lifestyle character from tweets or a web forum. So we turned to the notions of sentiment analysis and Text Mining using Deep Learning. This work (which will be limited to a Moroccan context) concerns the development of a computer model that allows to determine the habits of life and the Health of the students of the Faculty of Sciences and Technologies at the Sultan Moulay Slimane university in Beni Mellal. We started by developing a script to retrieve posts made by students from a Facebook group. The choice of Facebook and not Twitter is due to the fact that the twitter community among the students is relatively small. Afterwards, we built our deep learning model and we tested it with data from twitter comprising of thirteen (13) classes (anger, joy, sadness, disgust etc.). We also submitted these textual data to automatic learning algorithms (naive Bayesian, K nearest neighbors).
Induction and Skolemization in saturation theorem proving

We consider a typical integration of induction in saturation-based theorem provers and investigate the effects of Skolem symbols occurring in the induction formulas. In a practically relevant setting we establish a Skolem-free characterization of refutation in saturation-based proof systems with induction. Finally, we use this characterization to obtain unprovability results for a concrete saturation-based induction prover.
Organization of Knowledge Engineering Subject Using TTD Model Based on Stepwise Refinement Framework

Knowledge engineering is a new subject of engineering technology. As an interdisciplinary subject of social science and natural science, it is the result of the interpenetration of science and engineering. Knowledge engineering aims to obtain large quantity of knowledge and information in high efficiency with modem technology. In that case, the technology would promote the intelligence the creativity of people in highest level. Moreover, it would make people grasp knowledge and skills to promote the ability of using information with modern tools. The emergence of knowledge engineering shows that the special culture, science, knowledge and thought of human beings are closely related to modern machines. This not only promotes the renewal of electronic computer products, but also has a profound impact on the new leap of social productivity and the new changes of social life. As a kind of engineering technology knowledge engineering, its main object is to study how to form a new communication, education and control system which is composed of electronic computer and modem communication technology. Therefore, this project is of great significance to the development of social economy, science and technology, culture and education, and to accelerate the process of social informatization. In order to meet the social needs, we have set up the course named “Knowledge Engineering”. This course is an elective course for students in the school of computer science and technology in our university. This paper introduces the organization of the knowledge engineering subject using TTD modle based on stepwise refinement framework including properties, teaching purposes, teaching content and examination and performance evaluation method. Using TTD model the organization of this subject will be more reasonable. The contents will be more completed and better effect will be achieved.
The Comparison of Deep Learning Driven Optical Character Recognition for Hard Disk Head Slider Serial Number

This paper presents the development of an optical character recognition system by using deep learning which is trained by the Faster_Rcnn_Inception_V2 and SSD_Mobilenet_V1 model through the Tensorflow object detection API library. To build the character set recognition system, 466 images of the character set of hard disk head slider were used for training the model to build the character set recognition system. For the Faster_Rcnn_Inception_V2 model, the model was trained by 12570 epochs with the 0.0456 of loss value. In case of the SSD_Mobilenet_V1 model, the model was trained by 99051 epochs with the 0.9329 of loss value. From the results, the character set recognition system using Faster_Rcnn_Inception_V2 model, SSD_Mobilenet_V1 model and the template matching method, the accuracy in character set recognition of these methods are 83.5%, 1.5% and 4.5% respectively. The software creation of this paper focuses on the Python language and the TensorFlow library.
Speaker forensic identification using joint factor analysis and i-vector

Abstract Speaker Forensic is a process to determine the identity between a person’s voice (known speaker) and the investigated voice (suspect speaker). To improve accuracy in speaker forensic analysis is used a combination of 2 forensic methods, joint factor analysis and i-vector methods. The forensic approach by adding noise signal with SNR value as a representation of a tapping situation to measure speaker identification performance. Classification on verification using i-vector is done by comparing i-vector model tests and targets. Both models calculated vector similarities using cosine similarity score. Verification is done with compare the same speaker and verification between different speakers The testing process on the program performance is indicated by an equal rate error value. While the system’s sensibility is indicated by the threshold value. The results showed that the EER value of the Graz dataset (1.87%) compared to the Indonesia dataset (10%).
Megakaryocyte Images Segmentation Using Wavelet Transform and Modified Fuzzy C-means Techniques

The image segmentation is crucial and effective step in the description of megakaryocyte cell because it allows to identify the main details (cytoplasm and nucleus) and processed it separately from unnecessary details (background). This paper is proposed a state of art technique for the automatic segmentation for megakaryocyte structure image. The proposed method consists of several steps and it combines different tools: wavelet transform technique and modified fuzzy c-means technique. The first step is data collection, data will be collected as photograph of microscope sample which illustrates megakaryocytes of subject’s sample. In this work, 25 samples (aspirates stained by gemsa stain) are collected from 10 recruited patients. Wavelet transform technique was used to differentiate between image content and noise as pre-processing step, also to prepare image to the next step. Implementation proposed modified fuzzy c-means technique is the third step. The modification of fuzzy c-means technique deals uncertainty of the image details based on measure of fuzziness and Euclidean distance via fuzzy inference technique. Based on quantitative evaluations tools (sensitivity, specificity and accuracy) the proposed method exhibits remarkably high level of accuracy compared with Otsu’s segmentation method in the case of detection nucleus and cytoplasm (8% and 24% respectively).
Analysis of The Relationship between Ambiguous Sentences and Diagrams in Technical Manuals

The purpose of this study is to clarify the relationship between ambiguous sentences and diagrams in technical manuals. It is thought that ambiguous sentences are sentences that do not express tacit knowledge sufficiently, and it expect that the diagram will help to extract tacit knowledge by considering the presence or absence of ambiguity complementation. As a result, it was found that figures and tables have a role of complementing the ambiguity of ambiguous sentences, and the number of figures and tables is proportional to the number of ambiguous sentences. Furthermore, it was found that a figure far from the ambiguous sentence was also used to resolve the ambiguity.
Interactive Graph Exploration for Comprehension of Static Analysis Results

Static analysis results can be overwhelming depending on their complexity and the total number of results. Interactive graph visualization can help engineers explore the connections between different code entities while visually supporting insights about the code's behaviour. In our doctoral research, we aim to investigate how a graphical model of a program and its analysis results can support the engineer's understanding. We expect that a graphical interface can ease the diagnose of faults and reduce the cognitive load required to comprehend reported control and data flows present in the codebase.
A 3D Gesture Modeling Method and Its Application in 3D Gesture Prediction

In three-dimensional gesture recognition, it has always been a difficult problem to determine the accurate three-dimensional coordinates of the real hand. Direct use of image recognition cannot obtain complete information of hand joints. Even with the help of external equipment (such as data gloves and infrared recognition), there will be errors, which affect the accuracy of gesture recognition. However, on the other hand, the establishment of the virtual 3D model of the hand avoids this limitation and provides a new scheme for the 3D coordinate acquisition of the real hand. The research results of this paper have application value in sign language recognition, virtual environment interaction, user interface, robot arm grabbing, etc. In the field of sign language recognition, the recognized sign language can be translated to help deaf-mutes communicate with most people in sign language. In the interaction of the virtual environment, gestures in the real environment can be used to control the virtual hands in the virtual environment to make corresponding actions. In the application of user interface, users can communicate instructions to computers through gestures. In the field of grasping by robot manipulators, gestures can be used to control manipulators to complete tasks.
Development of Inter-ethnic Harmony Search Algorithm Based on Inter-ethnic Reconciliation

Harmony search (HS) has been applied in various fields and presented good results. However, since HS has several drawbacks (i.e., parameter settings, solution stagnation), various improved versions of HS developed to overcome these drawbacks. In this study, a new improved HS is proposed called inter-ethnic harmony search (IeHS). IeHS is developed to mimic the concept of the reconciliation of various ethnicities from Turkish history, and this algorithm considers the self-adaptive parameters and decision variables setting, balancing local and global search, overcoming the solution stagnation problem using the historical concept (i.e., Millet and Jannissary). To verify the performance of IeHS, the mathematical benchmark functions from CEC 2014 are applied and compared representative improved version of HS using performance indices.