Reviewerly/exHarmony huggingface dataset:
Case study of vision systems: Optimized compression architecture for wireless endorobots

Using endoscopy method is very efficient in medical domains due to its aptitude to examine the digestive disease precisely. Nevertheless, it is difficult to be used in old wire-based technique since it causes huge discomfort to patients. For this reason, a wireless endoscopic capsule has been presented in the past few years. Several performances criteria need to be optimized for this kind of capsules such as power consumption, area, speed, and quality of the received images. In this chapter, we focus on the minimization of the cited criteria. The presented approach aims to explore the advantages of the discrete cosine transform (DCT) and discrete wavelet transform (DWT) coding methods by merging them into a unique design. The modified look-ahead carry (MCLA) approach is used for arithmetic computations. A context adaptive binary arithmetic coding (CABAC) entropy coder that allows all possible binarization patterns is used with this architecture. The communication inside the capsule architecture is based on the adaptation of the parallel/serial AMBA/I2C busses while ensuring an efficient data transferring. The proposed design is compared with a set of well-known approaches. Experimental results prove that this design is high-performance-oriented in terms of power consumption, area, speed, and images quality.
A Framework for Accelerating Graph Convolutional Networks on Massive Datasets

In recent years, there has been much interest in Graph Convolutional Networks (GCNs). There are several challenges associated with training GCNs. Particularly among them, because of massive scale of graphs, there is not only a large computation time, but also the need for partitioning and loading data multiple times. This paper presents a different framework in which existing GCN methods can be accelerated for execution on large graphs. Building on top of ideas from meta-learning we present an optimization strategy. This strategy is applied to three existing frameworks, resulting in new methods that we refer to as GraphSage++, ClusterGCN++, and GraphSaint++. Using graphs with order of 100 million edges, we demonstrate that we reduce the overall training time by up to 30%, while not having a noticeable reduction in F1 scores in most cases.
Image Parallel Processing by Using MapReduce

In recent years, big data processing platform Hadoop and parallel computing model MapReduce have achieved good results in mass data processing. However, since Hadoop does not design the input data streams for image files and MapReduce does not define an interface to match the data types of image files, the technology that running MapReduce program on Hadoop cluster to process massive image files in parallel is not mature. In addition, as image characteristic data (such as pixel intensive) is often skewness, image parallel processing on heterogeneous Hadoop cluster triggers a large data transfer and load imbalance among nodes, so that reducing the efficiency of MapReduce job. To tackle aforementioned problems, this paper carried out the following work, (1) The Hadoop platform is optimized to efficiently process the massive image; (2) The MapReduce model is extended and the interface is designed to support parallel image processing. (3) A new partition, namely PIHA, is proposed to mitigate data skew in heterogeneous Hadoop 3.2.0. Experiments show that the method proposed in this paper can effectively improve the efficiency of parallel image processing using MapReduce on Hadoop.
Applications of grammatical inference methods and future development

This chapter focuses on applications of grammatical inference (GI) and discusses future applications. First, the author discusses results obtained from algorithms implemented for GI using a genetic algorithm. Then, various application areas are highlighted in which GI algorithms have had a central role. Finally, opportunities for future research are explored.
A Personnel Rating Optimization Algorithm

In view of the enterprise personnel management (EPM), a novel nature-inspired optimization algorithm called personnel rating optimization (PRO) is proposed in this paper. The design of PRO mimics the workflow of management activities from EPM to achieve the aim for bolstering the team's competence level. Built on the mechanisms of PRO, including personal promotion, swarm interaction, and coordinated management, it could quickly lock onto the solution region in high-dimensional space. In this study, we analyzed the essential similarities between PRO and EMP about management strategies, then presented the detailed procedures and mathematical model applied in PRO. A set of various numerical benchmark functions have been conducted on PRO and some other state-of-art natural computation algorithms including whale optimization algorithm (WOA), differential evolution algorithm (DE), genetic algorithm (GA) and particle swarm optimization (PSO). Experimental results show that PRO has high precision and good stability, and the premature phenomenon is effectively restrained.
An Efficient Method to Extract Data from Bank Statements Based on Image-Based Table Detection

In this research, a quick survey was conducted in a Vietnam university and it revealed the outdated paperwork handling in the financial department. Specifically, the bank statement, which is a financial transaction tracking document from banking partners sending monthly to customers, is currently inputted into financial software completely manually. Therefore, this study aims to automatize the extracting data stage by analyzing the table structure on the document, which may reduce the effort for the accountants. The output of this study is extracted text, which can then be added to the software by robotic automation process or other technologies. In this paper, the used methodology is the imaged-based approach only. The bank statement soft copy was converted into an image before being processed through table detection, cell recognition, and text extraction. The text is displayed in a spreadsheet as the output of the process. The measurement on the experiment dataset returned an accuracy of over 93% in most cases. These results suggest that the imaged-based method is applicable for extracting data from the university’s bank statements without performing more complicated technologies. However, the output must be put in review by the users to eliminate unwarranted financial errors.
Instantiation of Java Generics

Type parametrization is an essential construct in modern programming languages. On one hand, Java offers generics, on the other hand, C++ offers templates for highly reusable code. The mechanism between these constructs differs and affects usage and runtime performance, as well. Java uses type erasure, C++ deals with instantiations.In this paper, we argue for an approach in Java which is similar to C++ template construct. We evaluate the runtime performance of instantiated code and we present our tool which is able to use Java generics as templates. This tool generates Java source code. We present how this approach improves the usage of Java generics.
Digital Transformation in Higher Education: Impact of Instructor Training on Class Effectiveness During COVID-19

Digital technology is transforming society and business like never before. Digital technology has made inroads into all sections of society, especially with the pandemic restricting interaction and movement in the physical space. Education systems and institutions have witnessed a drastic change in their pedagogy. Education institutions adopting digital technologies can become drivers of growth and development for their ecosystems bringing significant changes in education, engagement, and management of class activities of educational institutions. The education system will have to adapt and evolve to take advantage of the new technologies and tools and develop strategies to play an active role in the digital transformation process. In the wake of the COVID-19 situation, higher education institutions have adopted digital platforms for teaching and learning. The study attempts to understand the instructors/academician/teachers training process adopted by selected higher education institutions in India to facilitate migration to digital platforms.Further, the study analyses the challenges faced in the new normal of education and the levels of training process initiated by institutions for teaching faculty. The authors have tried to analyse how this has enabled instructors to meet the challenges of conducting online classes and increase class effectiveness. The study unfolded the impact of high-level institutional training on class effectiveness and how individual digital preparedness is essential in engaging virtual classrooms. Further, the positive impact of training in reducing anxiety in engaging online sessions and the extent of motivation to continue online teaching as it has become inevitable with the second wave of the pandemic were examined across age and gender. An attempt is made to suggest few strategies for continued effective online class engagement as India battles through the second wave of the pandemic.
Asymmetric cryptographic protocols with a blockchain core: development problems and their solutions

The problem of axiomatic construction of secure cryptographic protocols is closely related to the choice of basic cryptographic blocks from which a cryptographic protocol of arbitrary complexity can be built. Let’s call such blocks primitive cryptographic protocols. Along with a traditional choice as primitive secret sharing protocols and non-interactive proof protocols today blockchain is considered to be a primitive cryptographic protocol. The security of such cryptographic protocols with a blockchain core is studied a bit today. We consider the methods for increasing the security of protocols with blockchain core by using a new agreement protocol in the blockchain, which is secure in the information theoretically sense.
An Efficient Skeleton-based Action Recognition Approach with View Transformation

Recently, human action recognition has made satisfactory progress on almost daily activities. The emergence of depth sensors makes it more convenient to obtain human skeleton information from videos. Many problems in action recognition can be solved by using joint points, such as light changes and background noise. However, the unfixed viewpoint is one of the difficulties that affect action recognition, which is still under-explored for most existing skeleton-based works. Besides, training a complex deep model increases hardware complexity and cost. The proposed model uses a view adaptive asymmetric convolutional network (VA-ACN) to extract skeleton features from the skeleton data and transform the original body perspective into a new observable viewpoint. The ResNet50-based backbone is improved for high-performance classification. Compared with recent state-of-the-art models, the proposed model improves feature extraction and classification performance without increasing extra computation cost. Hence, it saves training time while improving the performance of action recognition under certain conditions. Experimental results show that the proposed model outperforms state-of-the-art methods.
AMLChain: Supporting Anti-money Laundering, Privacy-Preserving, Auditable Distributed Ledger

Anti-money laundering (AML), as an important measure to ensure financial security and social stability, has attracted more and more attention. However, due to customer privacy protection, market competition, and other reasons, the current regulatory authorities and financial institutions have not yet formed a customer information sharing mechanism, which makes it difficult to monitor suspicious transactions. In paper, we use the distributed digital identity based on the blockchain to improve over the traditional know your customer (KYC) authentication process by combining KYC and suspicious transaction identification. With the help of cryptographic tools such as zero-knowledge proofs, suspicious interbank transactions can be effectively identified while the legal privacy is preserved.
<tt>3coSoKu</tt> and its declarative modeling

Abstract In this paper, we analyze the physical puzzle IcoSoKu, a game about placing some given triangular tiles on the faces of an icosahedron in order to fill the capacities of its vertices, and we propose its generalization called 3coSoKu, admitting an arbitrary playing field with triangular faces, arbitrary capacities and an arbitrary set of triangular tiles. First, we prove the strong NP-completeness of 3coSoKu, even when the playing field is a convex polyhedron with equilateral triangles as faces. Second, we encode 3coSoKu both in the constraint modeling language MiniZinc and in the logic programming paradigm known as Answer Set Programming and we develop a visual tool for an accessible interface to the solver. Finally, we use our encodings to verify experimentally that every initial state for IcoSoKu admits a solution.
A Multi-view Multiobjective Partitioning Technique for Search Results Clustering

The internet has been one of the fast-growing technologies allowing users to search for information, communicate and collaborate, social networking, promote business, and much more. There are over 1 billion sites on the world-wide web <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . It is important to understand the user’s query and return the top results that are majorly related to the query. In Search Result Clustering, results returned by one or more search engines are clustered into meaningful groups.The Search Results Clustering problem is modelled as a multi-view clustering problem in this study. The problem is then solved using an optimization framework. Experiments have been performed considering various combinations of four views including word-mover distance, BERT, TFIDF, and universal sentence encoder. The multi-objective binary differential evolution (MBDE) approach is used to optimise multiple quality measures computed on clusters produced by different views at the same time. We considered a set of possible solutions where each solution represents partitioning information obtained using different views. An agreement index that checks the consistency of partitionings of multiple views is also optimised. To get a single ensembled partitioning after considering various views, consensus partitioning technique and generative modeling are explored. The proposed model can automatically detect the number of clusters for any query. Our approach outperforms by 3.33% F1-score on three widely-used datasets over state-of-the-art techniques.
A modified Normalized Cross-Correlation algorithm for Embedded Systems

Problems related to image matching, such as processing time, performance, and changes in rotation and scale are widely studied. However, creating simple solutions for image matching that can be implemented in an embedded system has been overlooked. Therefore, this paper proposes a modified Normalized Cross-Correlation algorithm for embedded systems aimed at creating a low-cost solution for a cement tile application. The algorithm relies on the concept of template matching and it makes use of cross-correlation, zero-mean normalization, and thresholding for its implementation. Experimental results show that the algorithm is suitable for a cement tile application and appropriate to detect defective cement tiles during an on-site installation or manufacturing process. Finally, it is shown that the algorithm can act as a discriminator to identify quality non-conformance issues.
Improved learning of k-parities

We consider the problem of learning k-parities in the online mistake-bound model: given a hidden vector x∈{0,1}n where the hamming weight of x is k and a sequence of “questions” a1,a2,…∈{0,1}n, where the algorithm must reply to each question with 〈ai,x〉(mod2), what is the best trade-off between the number of mistakes made by the algorithm and its time complexity? We improve the previous best result of Buhrman et al. [3] by an exp⁡(k) factor in the time complexity. Next, we consider the problem of learning k-parities in the PAC model in the presence of random classification noise of rate η∈(0,12). Here, we observe that even in the presence of classification noise of non-trivial rate, it is possible to learn k-parities in time better than (nk/2), whereas the current best algorithm for learning noisy k-parities, due to Grigorescu et al. [9], inherently requires time (nk/2) even when the noise rate is polynomially small.
Error in Organization Name for Reported Recommendations

repeated statistical review and requested reanalysis and revision by the original investigators.We now publish a letter of correction, 2 a correction notice, 3 and the corrected article.
Role of CSR in Addressing Human Development Indices Inequalities

Aggressive, result oriented, comprehensive, co-operative and co-ordinated approach is needed on part of companies in their CSR activities to have an impact on the stakeholders living in the region where the industries are operating. While companies are spending huge amounts in areas of their own interest, there is very little co-ordination and co-operation among the companies spending on CSR activities. There is duplication of CSR activities which in turn reduces the chance to take up high impact CSR activities.
Overview of the III International Conference on Applied Physics, Information Technologies and Engineering – APITECH-III 2021

The overview describes the main directions and results of the III International Conference APITECH-III 2021 held in Krasnoyarsk from September 24 to October 3, 2021. It gives the details about the participants and the proceedings. The purpose of the Conference is to share the experience of leading experts in the application of modern methods of applied physics and information technology in high-tech production, in the fields of aerospace, energy, chemical and oil and gas engineering, as well as in modern areas of neuroinformatics and modeling of nonequilibrium systems. The JSC “ISS-Reshetnev Company” and Siberian Scientific Centre DNIT sponsored the Conference.
Hand Gesture Image Enhancement for Improved Recognition and Subsequent Analysis

This manuscript entails the preprocessing of hand gesture images for subsequent gesture recognition and cognitive communication. From this detailed analysis, it is obvious that the preprocessing stage is important for hand gesture recognition and its enhancement. Key to achieving the system design goal is optimizing this stage’s performance. Through the techniques that are described in this stage, the quality of the image for the subsequent stages will be assured. The techniques discussed in literature are rarely successful when they seek to retain the image quality and provide a low noise result. At the segmentation stage, the image is filtered to include only the Region of Interest, which is a clear visual representation of the hand portion, separated from the rest of the information. These techniques are implemented on the images acquired from the dataset and various pre-processing techniques are compared and identified which technique yields the best possible results for optimizing the preprocessing of hand sign images.
Chinese Electric Power Equipment Fault Recognition Based on Graph Convolutional Networks

Electric power equipment fault recognition is an important problem in power system. Traditional methods focus on the various indicators produced by power equipment automatically while the textual information artificially generated is rarely paid attention to. In this paper, we transform this problem into text-based power fault recognition task. This task usually faces two difficulties: lack of data and complex grammar in the electric power documents. To overcome the difficulties, proposed a method based on graph convolutional network for the task, i.e., C-TextGCN. Our C-TextGCN is built based on point-wise mutual information and term frequency between words and documents. To evaluate our C-TextGCN, we collected a new dataset, i.e., CPTF. The experimental results on the CPTF dataset show that our method has significant advantages and outperforms state-of-the-art methods.
Inner Product Similarity Pruning Optimization based on Imbalanced Datasets in Deep Forest

The deep forest model is a deep learning model comparable to deep learning proposed by Zhou and Feng, which has good performance in classifying small-scale datasets. The deep forest cascade part consists of multiple sets of random forests, in which decision trees with different classification strengths and different correlations between decision trees can lead to degradation of the overall model performance. To solve this problem, this paper proposes a low cohesion high coupling deep forest classifier (LCHCDF), which is an improvement on deep forest for imbalanced datasets. The algorithm prunes the forest by comparing the AUC values and inner product sums between decision trees in the forest, and aims to improve the strength of individual decision trees in deep forest while reducing the correlation of decision trees. In this paper, several experiments were conducted on the UCI dataset using Random Forest, Deep Forest, Adaboost, XGBoost, Decision Tree, and LCHCDF algorithms. The experimental results show that LCHCDF has good performance compared with other algorithms.
Detecting Anomaly Features in Facial Expression Recognition

In facial expression recognition task, humans and machines may give very different result on the same facial expression image. To solve this problem, this paper analyzes the reason that causes this phenomenon. Our research find that expression features of anomaly samples often deviate from standard expression features, which makes the recognition results different from that of human’s. In order to detect anomaly of facial expression, we propose Emo-Encoder Network (EEN), which consists of three modules: feature extractor, classifier and anomaly detector. Feature extractor is used to extract facial expression features. Based on extracted features, we can get classification results by classifier and anomaly degree by anomaly detector, where classification results denotes what kind of expression is, while anomaly degree displays how likely is this expression anomaly. To demonstrate our idea, we build a mixed dataset consisting of images collected from the Internet as well as the RAFDB dataset, and we relabel them with anomaly labels. Through our experiments, our model achieve anomaly detection accuracy of 87.09% on the mixed dataset. We find that expressions with high classification confidence cannot be directly adopted as the final results, unless the results is at high confidence and low anomaly degree.
The Representation of Gender and Inflectional Class in Italian: A Reply to Kučerová 2018

Kučerová (2018) puts forward a novel theory of the morphology and interpretation of nominal gender in Italian. Here, we take issue with this theory from both empirical and theoretical standpoints. We first show that several generalizations presented as empirical support for it are incorrect. We then point out three fundamental theoretical challenges. First, the proposed three-way classification of nouns does not represent the full range of facts, because it does not take into account plural morphology or the interdependencies of class and gender features. Second, the account of gender mismatch in terms of “semicon-servativity” fails to capture the Italian data, once the full paradigm is considered. Finally, the use of phase theory to model contextual valuation of gender faces an insurmountable lookahead problem.