Reviewerly/exHarmony huggingface dataset:
The Organization of Pedagogical Communication in the E-Learning of the Mathematical Disciplines

In the current sanitary and epidemiological situation and the total transition to a distance format of communication between the teacher and the student, the problem of maintaining the quality of the educational services provided at the proper level becomes especially urgent. One of the options for solving this problem is seen by the authors in a well-established feedback loop with students and a competent organization of pedagogical communication within the framework of e-learning. This article describes the experience of organizing distance communication in the classroom in the disciplines of the mathematical cycle with junior-year students of economic specialties, conducted using video conferencing, electronic information and educational environment and author's workbooks on the subject. The effectiveness of this method is confirmed by the results of checking the residual knowledge of students in mathematics, carried out when licensing their specialty.
Performance Modeling for Distributed Training of Convolutional Neural Networks

We perform a theoretical analysis comparing the scalability of data versus model parallelism, applied to the distributed training of deep convolutional neural networks (CNNs), along five axes: batch size, node (floating-point) arithmetic performance, node memory bandwidth, network link bandwidth, and cluster dimension. Our study relies on analytical performance models that can be configured to reproduce the components and organization of the CNN model as well as the hardware configuration of the target distributed platform. In addition, we provide evidence of the accuracy of the analytical models by performing a validation against a Python library for distributed deep learning training.
Recommender Systems Beyond E-Commerce: Presence and Future

Recommender systems are supporting users in the identification of items that fulfill their wishes and needs and are also helping to foster consumer happiness. These systems have been successfully applied in different application domains—examples thereof are the recommendation of movies, books, digital cameras, points of interest, financial services, and software requirements. The major objectives of this chapter are to provide an overview of recommendation approaches including criteria when to use which algorithm, to show different applications of recommendation algorithms going beyond standard e-commerce scenarios and to discuss issues for future research.
A New Design of Metaheuristic Search Called Improved Monkey Algorithm Based on Random Perturbation for Optimization Problems

The aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters and is constructed off the proposed algorithm. The main advantage of the control parameters is that they more generally prevented the proposed algorithm from getting stuck in optimal solutions. Many optimization problems at the maximum allowable number of iterations can sometimes lead to an inferior local optimum. However, the search strategy in the proposed algorithm has proven to have a successful global optimal solution, convergence optimal solution, and much better performance on many optimization problems for the lowest number of iterations against the original monkey algorithm. All details in the improved monkey algorithm have been represented in this study. The performance of the proposed algorithm was first evaluated using 12 benchmark functions on different dimensions. These different dimensions can be classified into three different types: low-dimensional (30), medium-dimensional (60), and high-dimensional (90). In addition, the performance of the proposed algorithm was compared with the performance of several metaheuristic algorithms using these benchmark functions on many different types of dimensions. Experimental results show that the improved monkey algorithm is clearly superior to the original monkey algorithm, as well as to other well-known metaheuristic algorithms, in terms of obtaining the best optimal value and accelerating convergence solution.
On the Understandability of Language Constructs to Structure the State and Behavior in Abstract State Machine Specifications: A Controlled Experiment

Abstract State Machine (ASM) theory is a well-known state-based formal method to analyze and specify software and hardware systems. As in other state-based formal methods, the proposed modeling languages for ASMs still lack easy-to-comprehend abstractions to structure state and behavior aspects of specifications. Modern object-oriented languages offer a variety of advanced language constructs, and most of them either offer interfaces, mixins, or traits in addition to classes and inheritance. Our goal is to investigate these language constructs in the context of state-based formal methods using ASMs as a representative of this kind of formal methods. We report on a controlled experiment with 105 participants to study the understandability of the three language constructs in the context of ASMs. Our hypotheses are influenced by the debate of object-oriented communities. We hypothesized that the understandability (measured by correctness and duration variables) shows significantly better understanding for interfaces and traits compared to mixins, as well as at least a similar or better understanding for traits compared to interfaces. The results indicate that understandability of interfaces and traits show a similar good understanding, whereas mixins shows a poorer understanding. We found a significant difference for the correctness of understanding comparing interfaces with mixins.
Efficient Segmentation of Lymphoblast in Acute Lymphocytic Leukemia

Microscopic examination of peripheral blood smears and bone marrow is the preliminary step for the diagnosis of several life-threatening diseases. Acute lymphocytic leukemia (ALL) is the most common disease in children that also needs an early diagnosis for on-time treatment as it spreads rapidly in the blood and forms immature lymphocytes. This might cause death in some weeks if left untreated. Manual methods in clinical laboratory being applied for the diagnosis of these diseases are inefficient and expensive, and the results are less accurate. A computer-aided system is the need of the day in which the most important step is segmenting the region of interest in blood or bone marrow for the detection and cure of the diseases which is the most challenging task. This study aims to propose a simple threshold-based segmentation technique by processing the S component of the HSV color space to segment the lymphoblasts in the bone marrow images of ALL patients. The technique was applied to 230 RGB bone marrow images having all the three types of ALL, i.e., L1, L2, and L3 resulted in the overall accuracy of 96.8%.
Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction

To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, and using distributional reinforcement learning to generate optimal relation information representation. This method is called Dual Attention Graph Convolutional Network (DAGCN), to adaptively integrate local features with their global dependencies. Specifically, the samples are represented as nodes on the graph, and the relationships within and between nodes are studied. We consider the influence between node feature locations and associate each location information of the feature with other features. This allows the feature vector to contain a wider range of semantic information to enhance the ability of feature representation. We consider the information features of node dependence, use adjacent nodes to represent their own nodes, and encode the features of node relation, so as to enhance the global dependence between nodes. We sum the outputs of the two attention modules and use reinforcement learning to predict the classification of nodes relationship to further improve feature representation which contributes to more precise extraction results. The results on the common datasets show that the model can obtain more useful information for relational extraction tasks, and achieve better performances on various evaluation indexes.
Measurement Challenges for Spectrum Sensing in Communication Networks

We summarize a few key spectrum sensing measurement challenges and recent advances. Laboratory tests of sensing are complicated by their inseparable and often imbedded role in modern hardware. Results are difficult to calibrate because physical parameters are often specified with ad-hoc or unclear definitions. The scope of testing is increased dramatically by sensors that demand more complex signal classification in addition to binary occupancy detection. Tests of spectrum sharing are encumbered even further by a lack of accepted, testable parameters for assessing the contribution of spectrum sensing to spectrum sharing between systems. The measurement needs and approaches we discuss here cross the domains of guided-wave and radiated physical measurements, network measurements, and commercial and government spectrum use.
Using Paragraph Vectors to improve our existing code review assisting tool-CRUSO

Code reviews are one of the effective methods to estimate defectiveness in source code. However, the existing methods are dependent on experts or inefficient. In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool–CRUSO. The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts.
DiHi: Distributed and Hierarchical Performance Modeling of Multi-VM Cloud Running Applications

Performance fluctuation of cloud running applications, due to collocations of different tenants on the same machine, is one of the cloud users' concerns. To alleviate users' concerns with insightful information, performance modeling and prediction of their applications is deemed necessary. In this paper, we propose two hierarchical and a monolithic performance modeling and prediction frameworks for multi-VM applications running on clouds. Hierarchical frameworks have been considered to distribute the required processing power for performance modeling and prediction over a group of collaborating VMs for better scalability, and to reduce the load of transferring unprocessed profiling data. Several unsupervised learning algorithms and existing single-VM models have been incorporated into the hierarchical frameworks to evaluate their effects on models' accuracy. Then, the results are compared with a baseline approach where the most intuitive monolithic framework requires the accumulation of all profiling data on a central VM. We have considered a comprehensive set of micro-benchmarks to probe the contention in various resources of all collaborating VMs. Then, using the proposed frameworks, the experienced contention levels by each VM and the overall performance of the applications are modeled and predicted. The results of our experiments in a private and two public clouds show that the distributed and hierarchical frameworks can predict the overall applications' performance effectively with comparable accuracy to the monolithic framework with an average prediction error of 5% for different cluster sizes and clouds.
A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures.
Adaptive Power Iteration Clustering

Power iteration has been applied to compute the eigenvectors of the similarity matrix in spectral clustering tasks. However, these power iteration based clustering methods usually suffer from the following two problems: (1) the power iteration usually converges very slowly; (2) the singular value decomposition method adopted to obtain the eigenvectors of the similarity matrix is time-consuming. To solve these problems, we propose a novel clustering method named Adaptive Power Iteration Clustering (AdaPIC). Specifically, AdaPIC employs a sequence of rank-one matrices to approximate the normalized similarity matrix. Then, the first K+1 eigenvectors can be computed in parallel, and the stopping condition of power iteration can be automatically yielded based on the target clustering error. We performed extensive experiments on public datasets to demonstrate the effectiveness of the proposed AdaPIC method, comparing with leading baseline methods. The experimental results indicate that the proposed AdaPIC algorithm has a competitive advantage in running time. The running time taken by spectral clustering baseline methods is usually more than 2.52 times of that taken by AdaPIC. For clustering accuracy, AdaPIC outperforms classic PIC by 97% on average, over all experimental datasets. Moreover, AdaPIC achieves comparable clustering accuracy with other 3 baseline methods, and achieves 6%–15% better clustering accuracy than the remaining 6 state-of-the-art baseline methods.
Database Security as a Gateway to Privacy Preserving Data Mining

Data mining is a process where relevant data that are primarily stored in a database or multiple databases are examined or tested properly in order to have a useful information that can be strategic or statistical. Such examined databases may contain several exquisite attributes or information through which attack may possible to reveal one's sensitive information. Again, at the same time, a trade-off must be needed between utility of data and privacy. PPDM can be considered as a study where prevention of such disclosure is illustrated. This paper deals with a method where privacy preservation of data is endurable with the help of database access control method as well as introducing inference control of databases with reasonable efficiency and security.
Deep Residual Networks for User Authentication via Hand-Object Manipulations

With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 &gt; ResNet-101 &gt; ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively.
Establishment and Discussion of the Design Criteria for Training Chopsticks for Children

For children living in East Asia, chopsticks are important eating utensils. Providing children with appropriate training chopsticks to help them learn how to hold chopsticks properly facilitates not only better hand muscle development, but also fine motor skills. However, commercially available children’s training chopsticks come in a variety of designs and styles that have different effects on the way children hold them. Because of the lack of scientific research exploring chopstick design criteria, there are no data for designers to refer to when designing chopsticks. To help resolve this issue, we collected and compiled chopstick design studies and analyzed the designs of commercially available chopsticks to develop 10 systematic design criteria for the design of training chopsticks. Subsequently, we used the analytic hierarchy process (AHP) and conducted interviews with three experts who were familiar with children’s chopstick usage behavior and/or who had experience in designing children’s tableware to derive the weights of the criteria. The results showed that operating part design (the design of the upper-chopstick), bridge size, and chopstick material were the primary design criteria that designers should consider when designing children’s training chopsticks.
TreeRNN: Topology-Preserving Deep Graph Embedding and Learning

General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized graph kernels, but few of them are effective for the integration of such local patterns into global features. In contrast, in this paper we study the methods to transfer the graphs into trees so that explicit orders are learned to direct the feature integration from local to global. To this end, we apply the breadth first search (BFS) to construct trees from the graphs, which adds direction to the graph edges from the center node to the peripheral nodes. In addition, we proposed a novel projection scheme that transfer the trees to image representations, which is suitable for conventional convolution neural networks (CNNs) and recurrent neural networks (RNNs). To best learn the patterns from the graph-tree-images, we propose TreeRNN, a 2D RNN architecture that recurrently integrates the image pixels by rows and columns to help classify the graph categories. We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.
Sentiment Analysis Using Machine Learning Approaches

Sentiment analysis (SA) or opinion mining or emotion AI is an on-going field which refers to the use of Natural Language Processing (NLP), analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic NLP techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.
OL-HeatMap: Effective Density Visualization of Multiple Overlapping Rectangles

Visualization of the density of multiple overlapping axis-aligned objects is a challenging computational problem that can inform large-scale visual analytics, in diverse domains. For example, when dealing with crowd simulations, we care about constructing interaction maps, and in urban planning we care about city areas mostly frequented by people, to name a few. The primary objective of this research is, given a large set of axis-aligned two-dimensional (2D) objects, or simply rectangles, to devise efficient and effective data visualization methods that inform whether, where and how much these rectangles overlap. Currently, such visualizations rely on inefficient implementations of determining the size of the overlapping rectangles that do not scale well and are hard to accomplish. Approximate methods have also been proposed in the literature. To the contrary of these approaches, we aim to address this problem by exploiting state-of-the-art computational geometry methods based on the sweep line paradigm. These methods are fast and can determine the exact size of the overlap of multiple axis-aligned objects, therefore can effectively inform the visualization method. Towards that end, we present OL-HeatMap, a novel type of a heat-map visualization that can be used to represent and perceive density of overlapping rectangles. Our experimental evaluation demonstrates the effectiveness of the proposed method in terms of both accuracy and running time for synthetic and real-world data-sets.
Detection of Compatibility, Proximity and Expectancy of Bengali Sentences using Long Short Term Memory

Text classification is known to be a supervised machine learning technique used in one or more predefined categories to classify sentences or text archives. To be a perfect phrase to convey one's feelings or to be significant, a Bengali sentence must have three properties i.e. Compatibility, Proximity and Expectancy. In this paper, we have proposed a method that is able to detect whether a Bengali sentence has compatibility, proximity and expectancy using Long Short Term Memory network. Our model is trained with word embedding layer and LSTM layer for the detection of Compatibility of a sentence but POS tagging is included for assuring the syntactic structure in case of Proximity and Expectancy detection. The model is tested on around 75000 Bengali simple sentences. The proposed framework achieves an accuracy of 97.5 percent, 85.5 percent and 97 percent for Compatibility, Proximity and Expectancy respectively. The result analysis proves that our model gives better performance.
Data-Debugging Through Interactive Visual Explanations

Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.