Reviewerly/exHarmony huggingface dataset:
A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification

Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as the frequency of each class is typically different. The performance of current mainstream learning algorithms in text classification suffers when the training data are highly imbalanced. The problem can get worse when the categories with fewer data are severely undersampled to the extent that the variation within each category is not fully captured by the given data. At present, there are a few studies on long-tailed text classification which put forward effective solutions. Encouraged by the progress of handling long-tailed data in the field of image, we try to integrate effective ideas into the field of long-tailed text classification and prove the effectiveness. In this paper, we come up with a novel approach of feature space reconstruction with the help of three-way decisions (3WDs) for long-tailed text classification. In detail, we verify the rationality of using a 3WD model for feature selection in long-tailed text data classification, propose a new feature space reconstruction method for long-tailed text data for the first time, and demonstrate how to effectively generate new samples for tail classes in reconstructed feature space. By adding new samples, we enrich the representing information of tail classes, to improve the classification results of long-tailed text classification. After some comparative experiments, we have verified that our model is an effective strategy to improve the performance of long-tailed text classification.
Knowledge-Guided Sequential Recommendation with Reinforcement Learning Using Empirical Distribution Function

This paper presents a novel reinforcement learning and knowledge graph-based sequential recommendation method using an empirical distribution function. Conventional sequential recommendation methods have used the reward function combining sequence-level and knowledge-level rewards. These methods have never considered the variations in the possible values of each level reward. In the proposed method, we introduce an empirical distribution function into the reward function to achieve robust learning to variations in the possible values of each level reward. The proposed method can provide more accurate sequential recommendation than baseline and state-of-the-art recommendation methods.
Proposal for Display System Superimposing Operating Instructions on Home Appliances

Home appliances are indispensable in our daily lives. However, various home appliances have multiple functions and operating methods. In general, the instruction manuals describe their use. However, as a problem with instruction manuals, it is not easy to obtain the required information. Instruction manuals are also incredibly unfriendly to the elderly. Therefore, this study developed a method that allows the naked eye to directly check the instructions and explain how to operate the device. With this method, translucent screens are placed on the control panels of the home appliances. A projector is used to project the instructions on the screen. When the user needs assistance in operating the device, this new system teaches the user how to do so on the control panel. This method does not require the wearing of head-mounted displays or smartphones. This paper describes the proposed method and develops a prototype. This study is an ongoing project.
Encoding and Decoding of Picture Embedded barcodes by Threshold

Barcodes are being used in almost all the fields, since it is the key to automation, assisting faster processing. The design of barcodes has been levelled-up with the introduction of image-based barcodes that can facilitate visual quality and storage capacity. The barcode was designed with the main aim of not interrupting the essential features of an image. To make it less visually distortive, the luminance component of the image is chosen for the message embedding and the proposed threshold-based modulation has made it easy for data to withstand the intensity fluctuations.
End-to-End Automated Crossword Solver using Image Processing, Natural Language Processing and Neural Network

Solving newspaper crosswords improves human brain function and mental health. It also helps decrease depression and anxiety that occurs due to social isolation and loneliness in the COVID-19 pandemic. In this game, players are enthusiastic about finding the correct answer after solving the puzzle. However, to check the answers, players have to wait until the newspaper publishes the solution. Due to this delay, players are moving to online crosswords. Mainly this issue occurs in Sri Lanka. There is no quick way to verify the answers using existing methods because players need the technical knowledge to use the solver. Also, existing solvers are unable to provide accurate solutions for different clue types. This paper provided an automated crossword puzzle solver which can solve British-Style crosswords using an image of the crossword puzzle. Image Processing, Natural Language Processing, and Neural Networking technologies were used to implement the proposed solution. The final answer list was prepared using the neural network model and filled the puzzle grid using OpenCV. The proposed system achieved more than 90% of word and letter accuracy in evaluation with human experts.
Compute Query and Document Similarity using Explicit Semantic Analysis

There is a lot of data and information that exists all around us. With the increase in data around us, it is essential to find a way to retrieve and organize the data in an efficient manner. Using generic search engines would not be the best use case for domain specific data. Thus, customized search engines based on different domains are required. The submodule used in the search engine like spell checkers can be used in various other real-world problems like grammar correction in documents and so on. In this paper Natural Language Processing based technique is proposed to achieve efficient and accurate information retrieval. It calculates nDCG scores based on several analyses such as Spell Check, Information Retrieval, Query Autocomplete. The IR System in this paper reaches a nDCG score of 0.32. Since the concept space of the Semantic Interpreter is spanned by only 100 articles. It is mainly focused to compute scores based on the query being entered and results in the form of a graph.
The Success of Conversational AI and the AI Evaluation Challenge It Reveals

Research interest in Conversational artificial intelligence (ConvAI) has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more “human” tasks that involve creativity and variation, evaluation strategies need to mature with them.
Anomaly Identification Performed Independently in Explanatory Machine using Log-based Method

In programming frameworks, logging is customarily acquainted with record data about the execution of a program. Normally, logs are broken down by people after a conspicuous blunder happens or is accounted for, by an end client. In any case, as programming develops, it is at this point not feasible to screen application conduct and investigate mistakes with the unaided eye. AI based abnormality recognition can beat these issues and at last give an apparatus to identify bugs at a beginning phase while they are still somewhat innocuous. In this postulation, time series of log information delivered by Motorola Solution's Smart Connect is broken down. It is intended to demonstrate that it is feasible to distinguish peculiarities in a log dataset created by a true framework comprising of two primary entertainers - the foundation and the push-to-talk radios associated with Smart Connect. A peculiarity discovery design has been proposed, that comprises of gathering information from the framework, applying log parsing with Drain3, extricating occasion count and TF-IDF highlights, and taking care of the removed fixed-size time window structure into four oddity location AI models: Isolation Forest, PCA, Invariants Mining and Log Clustering. Since this is a profoundly classified area, it is required to track down an effective method for preparing AI models dependent exclusively upon datasets created in a test stage, which might be not the same as the datasets created in the creation climate. Two of the four calculations, PCA and Log Clustering, accomplishes ideal precision on the test dataset in recognizing typical and irregular conduct. To assess the models on the obscure dataset, the specialists from the Smart Connect group is requested to assess the expectations. They affirmed that the PCA model has the option to recognize one more irregularity that was not known before the investigation. Nonetheless, because of the intricacy and the enormous measure of logs they were given to review, they couldn't determine if the models accurately characterized non-atypical examples. At long last, it is observed that the models could likewise be utilized to acquire knowledge into the code inclusion of the framework tests.
The Maximum Number of Appearances of a Word in a Grid

How can you fill a 3 × 3 grid with the letters A and M so that the word “AMM” appears as many times as possible in the grid? More generally, given a word w of length n, how can you fill an n × n grid so that w appears as many times as possible? We solve this problem exactly for several families of words, and we asymptotically solve this problem in higher-dimensional grids.
Popularity Debiased Entity Linking by Adversarial Attack

Entity linking is critical for many Natural Language Processing (NLP) tasks, which aims to map textual mentions to the corresponding entities in KBs. Existing approaches have achieved promising results, however, these approaches are limited by the presence of entity long tail and tend to link popular entities while showing poor performance on the tail. In this work, we address this issue by proposing a popularity-unaware entity linking framework which applies adversarial attack to dynamically overcome popularity bias. Specifically, we add the popularity perturbation to the entity representations to decouple popularity attributes. Extensive experimental results demonstrate that our model consistently outperforms baselines. Especially on the tail entities, our method outperforms the previous state-of-the-art approaches by a large margin.
Application of the Hungarian Method and Software Quality Management (QM) Testing in Determining Optimal Wage Costs at OneTop Frozen Food Stores

Linear programming that can be found in life around is an assignment problem. Common assignment problems include n tasks that must be assigned to m workers assuming each worker has different competencies in completing each task. One of the methods in solving this problem is the Hungarian method. The purpose of this research is to optimize the assignment of employees by looking at the cost of daily wages. The problems that occur at the OneTop Frozen Store are the ineffectiveness of the work process and the swelling of operational costs, especially at work, Order Sorting, Packing, Labeling, Delivery with 4 workers. The application of the Hungarian method and testing of Software Quality Management obtains optimal wage costs so that operational costs can be reduced to a minimum without reducing the quality of service to consumers. Based on data processing in the Hungarian method, an assignment that is in accordance with the work in preparing Frozen Food orders at the OneTop Store can cost a daily wage of Rp.130,000 (one hundred and thirty thousand) to be the optimal wage cost per day. when compared with the previous calculation without using the Hungarian method by paying wages of Rp. 175,000 (one hundred and seventy five) per day. Based on the application of the Hungarian method, it is effective in determining an assignment and placement of workers so that they can work more effectively on a better and optimal Frozen Food ordering process.
A Similarity Measure for Classification of Text Extracted from Image using Machine Learning

Text processing plays a very important role in acquisition of data, data mining and web search. Estimating the comparability between texts is a significant undertaking in the text handling field. In this work, a normalized gauge of the similarity between the two texts in terms of features is proposed. Maxwell Boltzmann's is used to find similarities between 2 documents within the corpus document. A corpus document as a master plan, unique files as containers, attributes as sub- containers, and individual words as particles. The proposed measurement method is called the Maxwell - Boltzmann Similarity Measure (MBSM) [1]. It is found in the distribution of feature values and the total number of non-zero elements between texts. It is indicated that MBSM satisfies all document parallel rating structures. The MBSM is installed in the K nearest neighbors (KNN). The proposed scale will be extended to measure similarities between the 2 sets of texts.
Gamification elements and online resources for teaching english lexics to future engineers at technical university

The paper is devoted to gamification as one of the leading trends in all branches of human activity, including education. A modern young professional should have a formed foreign language communicative competence to perform professional duties in the most effective way. In addition, knowledge of a foreign language contributes to the harmonious development of the individual and provides a basis for further achievements. The aim of this paper is to define the concept of gamification in educational discourse, to identify its key characteristics, as well as the differences from the concept of game. In the practical part of the paper, the authors made an attempt to determine the effectiveness of gamification and online sources for teaching English vocabulary to engineering students. In addition, a list of specific online resources that can be used in the classroom is given. This research was carried out using theoretical and practical methods. The theoretical methods are the analysis of the Federal State Educational Standard of Higher Education and other regulatory documents, regulating the learning process at the university, as well as reviewing and systematizing the theoretical and practical works of researchers in this area. The results of the practical part of the research were obtained through pedagogical observation, questionnaires, testing and experiment, as well as through a frontal conversation and an individual interview. The research results can be applied in teaching English to bachelor students of all technical areas of training, as part of a refreshment course for undergraduates of non-linguistic specialties. In addition, it should be noted that the use of gamification is possible both in classroom lessons and in the independent work of students.
A Novel Hybrid Algorithm for Securing Data Over Wireless Transfer in Machine Learning Based Prediction System

Data security refers to the path toward protecting information from unauthorized user access and data corruption all through its lifecycle. Most of security system deployed in the current system leads to loss of confidential data as the key is easily hackable because of a single algorithm usage. Diabetes-related complexities incorporate harm to large and little blood vessels. The danger of most diabetes-related inconveniences can be diminished whenever analyzed early. To overcome these two problems this project presents a medical application that analyses a patient’s medical data to give a diagnosis to check if he/she is diabetic with an efficient data security system where two security algorithms will be merged to secure the patient’s medical data stored and accessed in cloud. This research work attempts to provide an effective end to end security for medical applications.
Facial Feature Identification in the Deep Learning Based Apparent Personality Detection

Literature proves that the deep learning methods show significant results in apparent personality detection; however, lack of knowledge on how/why they perform well is a problem in the apparent personality detection. Plenty of explanation methods has been introduced to understand the performance of the convolutional neural network models. This work aims to describe the performance of the apparent personality detection models using Grad-CAM, Guided Backpropagation, and Guided Grad-CAM techniques. The results prove that facial features such as eyes, forehead, eyebrows, nose, and mouth are mainly involved in personality detection. However, the Guided Backpropagation method mainly highlights edges in the face area and detects background data. It was difficult for some input data with low scores to identify the facial features that impact the results. Further, the explanation methods used in this study have some limitations in describing the outputs of the apparent personality detection.
Implementation of Tilt angle computing in IoT Based PV System

Because of depleting energy resources, to increase the reliability as well to reduce the capital expenses innovation has to be made to increase the power output from renewable energy. In order to meet the energy demand in future among the energy sources one of the propitious energy source is solar energy. The energy from the sun is abundant and is radiated on the earth surface in the form of heat and light. Some of the alterable factors which influence the production of electric power from solar energy are PV installation factors namely the angle of incidence of light from the sun, orientation, area of exposure etc. The factors that cannot be altered are ambient temperature, wind movements, solar insolation etc. Since the earth and the sun position differs for the month and season. Due to this PV panel tilt angle as well as absorption of sunlight vary. Thus, the energy generation can be increased by determining the optimum angle for PV panel to collect maximum amount of solar energy to increase the electrical energy generation. Thus, tilt angle decides the PV panel surface area exposed to the solar radiations. If the yearly average fixed tilt angle is used the loss of energy is around 8-10% for each month. In order to increase the efficiency, the tilt angle should be varied. This paper confers about the factors influencing the tilt angle, determination of tilt angle and and also about IoT-Internet of Things integrated smart methodology for varying the value of tilt angle for PV panel to extract maximum energy to make PV more efficient and reliable.
Underwater Image Enhancement Using Dual Convolutional Neural Network with Skip Connections

Underwater images in high quality are important for many applications but they are often in poor quality since they suffer from fog, low brightness, colour distortion, and reduced contrast. Underwater image quality is degraded with the depth of the water since the red light is absorbed more than blue and green lights and the light is scattered by the suspended particles. Although several traditional and deep learning based approaches are proposed to enhance and restore the image, producing a high quality enhanced image with natural colour is still challenging. In this paper, a novel convolutional neural network architecture is proposed and it has two identical branches to input a raw degraded image and a colour balanced image. Dense blocks are utilized to train the model with fewer parameters. In addition, skip connections are introduced over the dense blocks to preserve the spatial information. The proposed approach is evaluated on publicly available UIEB dataset and shows 28.67 of PSNR value, and 0.89 of SSIM index, which are better than the state-of-the-art approaches.
Breast Cancer and Cervical Cancer Detection Using Machine Learning

Women are seriously threatened by breast cancer and cervical cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare a treatment plan that may prolong patient survival time. Hence, the requirement of time is to develop the technique which gives minimum error to increase accuracy. Four algorithms SVM, Logistic Regression, Random Forest and KNN which predict the breast cancer outcome have been compared in the paper using different datasets. All experiments are executed within a simulation environment and conducted in JUPYTER platform. Aim of research categorises in three domains. First domain is prediction of cancer before diagnosis, second domain is prediction of diagnosis and treatment and third domain focuses on outcome during treatment. The proposed work can be used to predict the outcome of different technique and suitable technique can be used depending upon requirement. This research is carried out to predict the accuracy. The future research can be carried out to predict the other different parameters and breast cancer and cervical cancer research can be categorises on basis of other parameters.
Eye Tracking System Using Facial Image Recognition

Since human perception is mainly visual, eye-tracking has been used to evaluate and measure various real-world events such as landscapes and advertisements. There are several types of eye tracking devices, such as eyeglasses and cameras. However, because these devices are difficult to carry, their use for measuring effects is limited. In addition, the special equipment for eye tracking is expensive and the measurement method is not easy to use. In order to solve these problems, we developed a tablet-based eye tracking tool and a system that uses two cameras to record what the user is looking at. We measured the accuracy of the developed system and adjusted it to meet the actual specifications.
Construction and Application of University Education Management Model Based on Intelligent Programming Analysis Method

The intelligent programming analysis system based on Internet has attracted more and more attention in the field of education management. The problem of how to flexibly master the application capabilities of intelligent programming and development tools, debugging and optimizing programs, and project deployment is becoming more and more serious. Based on the intelligent programming analysis method, this paper systematically introduces the research and design process of education management model and designs modules such as distributed parallel PhpDig, information analyzer, and information resource database. The system adopts client/middleware/server (C/M/S) three-tier architecture planning and design. The server-side carries the resource manager, the middleware carries the intelligent work tasks (information analyzer, resource collection agent), and the client implements user interaction and data representation solves the storage load problem of a large amount of data. The experimental results show that the semantic processing of search object attributes can improve the retrieval performance, and the retrieval rate and tolerance factor reach 87.6% and 0.041, respectively, which effectively promotes the integration of the analysis data on the chain level and the education system.