Reviewerly/exHarmony huggingface dataset:
Food Image Recognition for Price Calculation using Convolutional Neural Network

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the café of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Café, UTP with a trained CNN classification model at the backend.
An Approach to Suggest Code Smell Order for Refactoring

Code smell is an indicator of issues in source code qualities that may hinder maintenance, and evolution. Source code metrics are used to measure the quality of the code. In the literature, there are many code smells, refactoring techniques, and refactoring tools. However, a software project often contains thousands of code smells and many of them have no relation with design quality. It is a challenge for developers to decide which kind of code smell should be refactored first. We have proposed an approach that suggests a code smell order based on two aspects: (1) finding relevant metrics for each code smell dataset with the help of feature selection technique (2) analyzing the internal relation among the code smells with those relevant metrics. With this analysis, we are suggesting code smell order for developers to save their effort in the refactoring stage. The suggested order is evaluated on simple java source code.
Self-balanced multi-view orthogonality correlation analysis for image feature learning

Images belong to high-dimensional data with noise and redundant information. How to learn effective image features is still an important and challenging task in computer vision. In this paper, we propose a novel image feature learning method based on correlation analysis theory, called self-balanced multi-view orthogonality correlation analysis (SMOCA). The method can automatically balance between-view correlations and within-view scatters, and correlation features with well discriminative power can be learned from a few images. Besides, correlation features of SMOCA preserve metric structures of raw data as much as possible and are insensitive to data distribution. Extensive experiments on four categories of images such as thermal images and NIR images have showed the effectiveness and robustness of the method.
A practical algorithm for solving the sparseness problem of short text clustering

Dirichlet Multinomial Mixture (DMM) models have been successful in clustering short texts. However, the word co-occurrence information that can be captured by these models is limited to the short text corpus itself. If two words have strong relatedness but rarely co-occurring in short texts, these models can not fully capture the semantic relatedness between the two words. In this paper, we propose a novel model by incorporating word-word correlation into DMM, called WDMM. By constructing a sparse graph using word-word relationship, our model expands each short text using their neighboring words in each text that can help to solve the problem of sparseness in short texts. Therefore, the cluster label of each text is not only influenced by its words, but decided by their similar words in this corpus. Experimental results on real-world datasets demonstrated the substantial superiority of our WDMM model over the state-of-the-art methods.
On Functional Test Generation for Deep Neural Network IPs

Machine learning systems based on deep neural networks (DNNs) produce state-of-the-art results in many applications. Considering the large amount of training data and know-how required to generate the network, it is more practical to use third-party DNN intellectual property (IP) cores for many designs. No doubt to say, it is essential for DNN IP vendors to provide test cases for functional validation without leaking their parameters to IP users. To satisfy this requirement, we propose to effectively generate test cases that activate parameters as many as possible and propagate their perturbations to outputs. Then the functionality of DNN IPs can be validated by only checking their outputs. However, it is difficult considering large numbers of parameters and highly non-linearity of DNNs. In this paper, we tackle this problem by judiciously selecting samples from the DNN training set and applying a gradient-based method to generate new test cases. Experimental results demonstrate the efficacy of our proposed solution.
Anomaly Identification in A Liquid-Coffee Vending Machine Using Electrical Current Waveforms

This paper proposes an anomaly identification method for a liquid-coffee vending machine using electrical current waveforms. The method consists of preprocessing of a series of current values collected from the machine, training of multiple classifiers corresponding to individual target anomalous operations, and anomaly detection by means of the classifiers. Preprocessing improves detection accuracy by excluding current values that represent non-target operations. Multiple classifiers corresponding to individual target operations are trained using pre-processed data and the ground truth. An operation with the maximum likelihood normalized by the total number of individual operations is identified as the current anomaly. Evaluations using electrical current values obtained from an actual coffee vending machine shows a false positive rate and a false negative rate of, respectively, 0% and 6.7%, for lack of beans and 2% and 0% for water leakage, both of which are major reasons for degraded coffee quality.
Prototype: User Authentication Scheme for IoT Using NFC

In the internet of things (IoT), the user authentication process is becoming more important with the growing number the services provided by IoT. Securing access to these services by the user authentication process leads to high security to prevent any attack on the IoT network. In this paper, we shall construct a lightweight local user authentication scheme for IoT using NFC technology. The approach will try to keep the private information secured in an efficient way, ensuring that only authorized users can access this information.
Process and Application of Data Mining in the University Library

In view of characteristics of users' data in the university library and based on big data technology, in this paper we propose a data mining process and discuss some applications of data mining in the university library. Besides, we inveterate the problems in the application of big data mining in the university library and provide some suggestions to solve these problems.
Development of Creative Potential of Students of Engineering Specialties Within the Framework of Mathematical and Information Disciplines

Trends in the development of the industry require specialists capable of designing smart networks, as well as those who can model “smart environments” for various types of tasks, develop technological and infrastructure requirements for these environments throughout the entire life cycle. This fact requires the development of the ability of students of engineering specialties of higher education to creativity in the conditions of technologies of big data. Future professionals who will work in the new digital reality must have the following qualities: to be able to identify complex systems and work with them; understand technology, processes and market situation. For this, it is necessary to have the skills of non-standard solutions, which can be formed within the framework of mathematical disciplines. Creative skills are formed during the study of mathematics in the re-water description of the processes of objective reality in the language of formulas. When constructing models, the choice of a variant of the mathematical description expresses a creative attitude to the process. The specialist of the new format should distinguish a constant desire to eliminate all types of losses, which can be achieved only if it is involved in the process of optimizing the business, as well as maximum customer orientation. These capabilities are provided by large data technologies. The training of future engineers in the principle positions of working with large data makes it possible to use the selected technologies as a basis for further processes of constructing mathematical models in their subject area. Thus, the developed methodology for using the technology of big data for modeling processes and systems, allows to form the competence of future specialists on the basis of developing the creative abilities of students and to be a source of inspiration for the creation of new technical products.
Categorizing Emails Using Machine Learning with Textual Features

We developed an application that automates the process of assigning emails received in a generic request inbox to one of fourteen predefined topic categories. To build this application, we compared the performance of several classifiers in predicting the topic category, using an email dataset extracted from this inbox, which consisted of 8,841 emails over three years. The algorithms ranged from linear classifiers operating on n-gram features to deep learning techniques such as CNNs and LSTMs. For our objective, we found that the best-performing model was a logistic regression classifier using n-grams with TF-IDF weights, achieving 90.9% accuracy. The traditional models performed better than the deep learning models for this dataset, likely in part due to the small dataset size, and also because this particular classification task may not require the ordered sequence representation of tokens that deep learning models provide. Eventually, a bagged voting model was selected which combines the predictive power of the top eight models, with accuracy of 92.7%, surpassing the performance of any of the individual models.
GhostLink: Latent Network Inference for Influence-aware Recommendation

Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation?
Capturing Expert Arguments from Medical Adjudication Discussions in a Machine-readable Format

Group-based discussion among human graders can be a useful tool to capture sources of disagreement in ambiguous classification tasks and to adjudicate any resolvable disagreements. Existing workflows for panel-based adjudication, however, capture graders' arguments and rationales in a free-form, unstructured format, limiting the potential for automatic analysis of the discussion contents. We designed and implemented a structured adjudication system that collects graders' arguments in a machine-readable format without limiting graders' abilities to provide free-form justifications for their classification decisions. Our system enables graders to cite instructions from a set of labeling guidelines, specified in the form of discrete classification rules and conditions that need to be met in order for each rule to be applicable. In the present work, we outline the process of designing and implementing this adjudication system, and report preliminary findings from deploying our system in the context of medical time series analysis for sleep stage classification.
Research on theoretical analysis of human capital of labor economics based on artificial intelligence

In order to deepen the analysis method of human resource theory in labor economics, artificial intelligence technology is utilized to complement and develop. Firstly, the corresponding development of artificial intelligence technology is elaborated. Then the labor economics human resource theory m odel is constructed according to artificial intelligence technology, combining economics with traditional human capital theory, with the standardization of theoretical analysis of human resources market further improved. An optimization and updating scheme is put forward from algorithm flow and evaluation model, and an effective evaluation model is established by effectively evaluating the status of human resources development. Secondly, the algorithm technology support is added to the research to reflect the optimization of artificial intelligence. In order to further test the application effect of the artificial intelligence model power resources theory, different labor economics data are used for simulation testing. It can make the human capital theoretical program more suitable for economic development and has a positive role in promoting the development of the labor economy.
Augmented Reality Indoor Navigation Using Handheld Devices

Any user can navigate outdoors by using online maps with the help of a GPS signal, but navigation in an indoor environment is difficult as the GPS signals can be weak inside a building. In this article, a system for providing a solution for indoor navigation with the help of augmented reality technology based on a computer vision approach is developed so as to provide navigational assistance to users in any new or unknown environment. This is done with an android based mobile phone application. This can be done by using augmented reality technology along with a computer vision-based approach to find where the user is and what is present in front of the user. Using this information, the user can get to navigate inside the building.
Human activity recognition using skeleton data and support vector machine

In this paper, we propose a method for recognizing human activities using skeleton data by RGB-D camera, namely Kinect device. The human activity recognition is a learning in the field of computer vision. In its application, the recognition of human activity can be used for a sign language learning, human-computer interaction, surveillance of the elderly, image processing and etc. Our approach is based on skeleton data with coordinate value of each joints in human body, that will be classified using support vector machine algorithm when performing a movement to predict the activities name. Experiments were performed with a new training data that we've create manual from capturing movement while human target are doing activities. Experiments result show that the system best average accuracy is 93.75% of all activities prediction with the optimal distance of object to the devices is 2 meters.
Analysis of the Stability of the Chemical Composition of Wastewater in the Production of Printed Circuit Boards

A method for analyzing the stability of the chemical composition of wastewater in the manufacture of printed circuit boards using statistical methods of process control is proposed. The correlation and normal distribution of the monitored indicators is evaluated. Types of control charts are selected that provide control of both the average level of the process and its dispersion. To control the stability of independent indicators, Shewhart charts are used. Monitoring of correlated indicators is carried out using the Hotelling control chart. If it is necessary to control the multidimensional dispersion of the process, generalized variance charts can be used. The proposed approach ensures the detection of process violations before the monitored indicators are out of tolerance.
On the Use of Multi-Modal Sensing in Sign Language Classification

In literature, sign language recognition (SLR) has been proposed using multi-channel data acquisition devices with various sensing modalities. When using wearable sensors, multimodality data acquisition has been shown to be particularly useful for improving the classification accuracies as compared to single modality data acquisition. In this work, a statistical analysis is presented to quantify the performance of different combinations of wearable sensors such as surface electromyogram (sEMG), accelerometers and gyroscopes in the classification of isolated signs. Twelve signs from the Indian sign language are considered such that the signs consist of static hand postures, as well as complex motion of forearm and simple wrist motions. Following four combinations of sensor modalities are compared for classification accuracies using statistical tests: 1) accelerometer and gyroscope 2) sEMG and accelerometer, 3) sEMG and gyroscope and finally, 4) sEMG, accelerometer and gyroscope. Results obtained on actual data indicate that the combination of all three modalities, namely sEMG, accelerometer and gyroscope yield the best classification accuracy of 88.25% as compared to the remaining sensor combinations. However, the statistical analysis of the classification accuracies using analysis of variance (ANOVA) indicates that the use of sEMG sensors is particularly useful in the classification of static hand postures. Moreover, the classification of signs involving dynamic motion of hands either with simple wrist motion or motion of hand along a complex trajectory is comparatively better with any sensing modality as compared to the classification of static hand postures.
Toward an Effective Igbo Part-of-Speech Tagger

Part-of-speech (POS) tagging is a well-established technology for most Western European languages and a few other world languages, but it has not been evaluated on Igbo, an agglutinative African language. This article presents POS tagging experiments conducted using an Igbo corpus as a test bed for identifying the POS taggers and the Machine Learning (ML) methods that can achieve a good performance with the small dataset available for the language. Experiments have been conducted using different well-known POS taggers developed for English or European languages, and different training data styles and sizes. Igbo has a number of language-specific characteristics that present a challenge for effective POS tagging. One interesting case is the wide use of verbs (and nominalizations thereof) that have an inherent noun complement , which form “linked pairs” in the POS tagging scheme, but which may appear discontinuously. Another issue is Igbo’s highly productive agglutinative morphology, which can produce many variant word forms from a given root. This productivity is a key cause of the out-of-vocabulary (OOV) words observed during Igbo tagging. We report results of experiments on a promising direction for improving tagging performance on such morphologically-inflected OOV words.
EyeCom-An Innovative Approach for Computer Interaction

The world is innovating rapidly, and there is a need for continuous interactions with the technology. Sadly, there do not exist promising options for paralyzed people to interact with the machine, i.e., laptops, smartphones, and notepads. A few commercial solutions such as Google Glasses are costly and cannot be afforded by every paralyzed person to interact with the machine. Towards this end, the paper proposes a retina-controlled device called EyeCom. The proposed device is constructed from off-the-shelf cost-effective yet robust IoT devices (i.e., Arduino microcontrollers, Xbee wireless sensors, infrared light emitting (IR) diodes, and accelerometer). The EyeCom can be easily mounted on to the glasses. The paralyzed person using this device can interact with the machine, using simple head movements and eye blinks. The IR detector is located in front of the EyeCom to illuminate the eye region. As a result of the illumination, the eye reflects the IR light, which includes electrical signals. As eyelids close, the reflected light, over all of the eye surface is disrupted, and the change in reflected value is recorded. On the other hand, to enable cursor movement onto the computer screen on behalf of the paralyzed person a device named accelerometer is used. A microcontroller processes the inputs from the IR sensors and accelerometer and transmit them wirelessly via Xbee wireless sensor (i.e., a radio) to another microcontroller attached to the computer. With the help of the proposed algorithm, the microcontroller attached to the computer, on receiving the signal moves the cursor onto the computer screen and facilitate performing actions, as simple as opening a document, to operating a word-to-speech software.
Dimensions Affecting Representation Styles in Ontologies

There are different ways to formalise roughly the same knowledge, which negatively affects ontology reuse and alignment and other tasks such as formalising competency questions automatically. We aim to shed light on, and make more precise, the intuitive notion of such ‘representation styles’ through characterising their inherent features and the dimensions by which a style may differ. This has led to a total of 28 different traits that are partitioned over 10 dimensions. The operationalisability was assessed through an evaluation of 30 ontologies on those dimensions and applicable values. It showed that it is feasible to use the dimensions and values and resulting in three easily recognisable types of ontologies. Most ontologies had clearly one or the other trait, whereas some were inherently mixed due to inclusion of different and conflicting design decisions.
Analyzing Robust Stability of an Interval Control System on the Basis of Vertex Polynomials

In the paper, a characteristic polynomial of an interval control system, whose coefficients are unknown or may vary within certain ranges of values, is considered. Parametric variations cause migration of interval characteristic polynomial roots within their allocation areas, whose borders determine robust stability degree of the interval control system. To estimate a robust stability degree, a projection of a polytope of interval characteristic polynomial coefficients on a complex plane must be examined. However, in order to find a robust stability degree it is enough to examine some vertices of a coefficient polytope and not the whole polytope. To find these vertices, which fully determine a robust stability degree, it is proposed to use a basic phase equation of a root locus method. Considering the requirements to placing allocation areas of system poles an interval extension of expressions for angles included to the phase equation. The set of statements, allowing to find a sum of pole angles intervals in the case of degree of oscillating robust stability, were formulated and proved. From these statements, a set of double interval angular inequalities was derived. The inequalities determine ranges of angles of all root locus edge branches departure from every pole. Considered research resulted in a procedure of finding coordinates of verifying vertices of a coefficients polytope and vertex polynomials according to these vertices. Such polynomials were found for oscillating robust stability degree analysis of interval control systems of the second, the third and the forth order. Also, similar statements were derived for aperiodical robust stability degree analysis. Numerical examples of vertex analysis of oscillating and aperiodical robust stability degree were provided for interval control systems of the second, the third and the fourth order. Obtained results were proved by examining root allocation areas of interval characteristic polynomials examined in application examples of proposed methods.