Reviewerly/exHarmony huggingface dataset:
The Impact of the Digital Interaction Network on the Future Professionals Behavior

The paper focuses on the social and psychological factors of human-computer interactions in students' preparation for responsible engineering activities. Engineering in a digital society requires computer skills in any professional field. The authors emphasize the need for a systematic analysis of the students' readiness to predict real situations, to make responsible decisions and to evaluate their consequences independently. The study of factors influencing the behavioral deviancy of future specialists is especially important within the framework of applied computer ethics, which emphasizes the potential negative aspects of the interactive virtual environment impact on perception of information while making responsible decisions. The digital and professional competence combination implies the socio-psychological context inclusion in the assessment of the possibility of introducing and future use of technological innovations into network environment, taking into account the consequences that lead to the transformation of mental and behavioral actions. The authors' goals are determined by the analysis of the digital interactions' influence on shaping a prospective readiness of a future specialist for adequate professional activity within complex conditions of the modern info sphere. The empiric study was conducted utilizing focus groups and online survey of a representative sample of the St. Petersburg Polytechnic University students.
Application of Machine Learning In Forensic Science

In this chapter, the authors explore the use of machine learning methodology for cyber forensics as machine learning has proven its importance and efficiency. For classification and identification purposes in forensic science, pattern recognition algorithms can be very helpful.
Accelerating Large Integer Multiplication Using Intel AVX-512IFMA

In this study, we implemented large integer multiplication with Single Instruction Multiple Data (SIMD) instructions. We evaluated the implementation on a processor with Cannon Lake microarchitecture, containing Intel AVX-512IFMA (Integer Fused Multiply-Add) instructions. AVX-512IFMA can compute multiple 52-bit integer multiplication and addition operations through one instruction and it has the potential to process large integer multiplications faster than its conventional AVX-512 counterpart. Furthermore, the AVX-512IFMA instructions take three 52-bit integers of 64-bit spaces as operands, and we can use the remaining 12 bits effectively to accumulate carries (reduced-radix representation). For multiplication in the context of larger integers, we applied the Karatsuba and Basecase methods to our program. The former is known to be a faster algorithm than the latter. For evaluation purposes, we compared execution times against extant alternatives and the GNU Multiple Precision Arithmetic Library (GMP). This comparison showed that we were able to achieve a substantive improvement in performance. Specifically, our proposed approach was up to approximately 3.07 times faster than AVX-512F (Foundation) and approximately 2.97 times faster than GMP.
A note on the stability of trinomials over finite fields

A polynomial f(x) over a field K is called stable if all of its iterates are irreducible over K. In this paper, we study the stability of trinomials over finite fields. We show that if f(x) is a trinomial of even degree over the binary field F2, then f(x) is not stable. We prove similar results for some families of monic trinomials over finite fields of odd characteristic. We also study the stability of polynomials of higher weights and prove some results and pose a new conjecture.
Can Generative Adversarial Networks Teach Themselves Text Segmentation?

In the information age in which we live, text segmentation from scene images is a vital prerequisite task used in many text understanding applications. Text segmentation is a difficult problem because of the potentially vast variation in text and scene landscape. Moreover, systems that learn to perform text segmentation usually need non-trivial annotation efforts. We present in this work a novel unsupervised method to segment text at the pixel-level from scene images. The model we propose, which relies on generative adversarial neural networks, segments text intelligently; and does not therefore need to associate the scene image that contains the text to the ground-truth of the text. The main advantage is thus skipping the need to obtain the pixel-level annotation dataset, which is normally required in training powerful text segmentation models. The results are promising, and to the best of our knowledge, constitute the first step towards reliable unsupervised text segmentation. Our work opens a new research path in unsupervised text segmentation and poses many research questions with a lot of trends available for further improvement.
An Ultra-Fast and Parallelizable Algorithm for Finding $k$-Mismatch Shortest Unique Substrings

This paper revisits the k-mismatch shortest unique substring finding problem and demonstrates that a technique recently presented in the context of solving the k-mismatch average common substring problem can be adapted and combined with parts of the existing solution, resulting in a new algorithm which has expected time complexity of O(n logk n), while maintaining a practical space complexity at O(kn), where n is the string length. When , which is the hard case, our new proposal significantly improves the any-case O(n2) time complexity of the prior best method for k-mismatch shortest unique substring finding. Experimental study shows that our new algorithm is practical to implement and demonstrates significant improvements in processing time compared to the prior best solution's implementation when k is small relative to n. For example, our method processes a 200 KB sample DNA sequence with k=1 in just 0.18 seconds compared to 174.37 seconds with the prior best solution. Further, it is observed that significant portions of the adapted technique can be executed in parallel, using two different simple concurrency models, resulting in further significant practical performance improvement. As an example, when using 8 cores, the parallel implementations both achieved processing times that are less than 1/4 of the serial implementation's time cost, when processing a 10 MB sample DNA sequence with k=2. In an age where instances with thousands of gigabytes of RAM are readily available for use through Cloud infrastructure providers, it is likely that the trade-off of additional memory usage for significantly improved processing times will be desirable and needed by many users. For example, the best prior solution may spend years to finish a DNA sample of 200MB for any , while this new proposal, using 24 cores, can finish processing a sample of this size with k=1 in 206.376 seconds with a peak memory usage of 46 GB, which is both easily available and affordable on Cloud. It is expected that this new efficient and practical algorithm for k-mismatch shortest unique substring finding will prove useful to those using the measure on long sequences in fields such as computational biology. We also give a theoretical bound that the k-mismatch shortest unique substring finding problem can be solved using O(n logk n) time and O(n) space, asymptotically much better than the one we implemented, serving as a new discovery of interest.
Comparison of the social network weight measurements

Abstract To extract social networks from the information sources, it is necessary to measure the similarities. There are several measurements that can become application to form a social network based on information sources. On the other side, various the information sources with various conditions and are not homogeneous, but it is possible to cluster them into a homogeneous form. However, measurement about it require proof. To produce the proof, a comparison of results from various measurements gives one of the answers.
Integer ConvNets on Embedded CPUs: Tools and Performance Assessment on the Cortex-A Cores

Quantization via fixed-point representation is commonly used to reduce the complexity of Convolutional Neural Networks (ConvNets). It is particularly suited for accelerating edge-inference on embedded devices as it enables to reduce resource requirements with no loss of prediction quality. However, porting integer ConvNets on low-end CPUs is not straightforward: it calls for proper software design and organization with a high degree of hardware awareness. Today there are plenty of fixed-point libraries integrated into different inference engines which provide design support. The aim of this work is to review the most stable tools and analyze their performance on different use-cases processed on embedded boards powered by Arm Cortex-A cores. The collected results provide an interesting analysis with useful guidelines for developers and hardware designers.
Protecting Privacy in Digital Era on Cloud Architecture for Banking and Financial Services Industry

With an intention to provide easy to use and convenience to customers, Banking and Financial Services Corporations have enabled key services over handheld and wearable digital devices. With use of these devices on everyday basis for several other purpose along with financial transaction, customers are leaving digital foot prints. This digital data can be collected, stored, analysed and used to learn consumer behaviour, location, taste & preferences and monetary spending habits. Based on historical information collected from several areas (social media, transaction analytics, cloud services utilized) and developing suggestions for customer for better choices available in everyday life is where Banking and Financial Services Corporations are competing and willing to provide personalized experience. The use of data to make prediction is through consent from customer, however, not all customer would like their data being collected or used or both. The user or customer consent form over digital application comes with a “take it or leave it” approach. This is where the customer needs and their privacy related aspects are concerned and compromised. In this paper, we have identified the way digitization is breaching customer privacy, changes required over digital platform and data collection frequency to preserve customer's (an individual's) right of being let alone. This paper covers the methods to collect private data, methods to use private data for marketing of goods & services, harm to an individual due these unauthorized promotional activities and solution to protect an individual's privacy.
A Secure and Self-tallying E-voting System Based on Blockchain

E-voting has been studied for many years. Recently, researchers find that blockchain can provide an alternative secure platform for e-voting systems, because of its properties of tamper resistance and transparency. However, existing schemes either require centralized authorities to tally ballots or can only handle a limited number of voters. This paper tries to propose a self-tallying e-voting system, i.e., the public can verify the validity of all ballots and tally the ballots. To achieve this goal, technically, we design a new method on blockchain that can generate and distribute random numbers for ballot security and these random numbers will be cancelled out when multiplying all ballots to allow counting the ballots. Secondly, we adopt non-interactive zero knowledge proof to make sure these ballots are valid. Our scheme is proved to be secure.
An Automatic Generation of Android Application for WooCommerce

Smartphone technology affects every aspect of human life nowadays. Especially there is an increasing demand for online shopping through smartphones instead of websites. Therefore, to preserve the customers in the business, it is essential to keep up with the latest trends and convert the business websites to the equivalent mobile applications. Transforming a website to a mobile application efficiently and rapidly is of high demand from sellers with no programming knowledge. However, it is not a trivial task with the existence of complex websites for a variety of domains. WooCommerce with many E-Commerce functionalities and special customization features is one of the commonly-used E-Commerce platforms in the community. It is built on top of WordPress and operates over a large number of online stores. This research provides a unified framework for the transformation of WooCommerce websites to Android as the most widely-used mobile operating system. The Model-Driven Engineering (MDE) is applied for the development of the framework, which provides an efficient way to automatically generate an Android implementation by focusing the attention of software developers from the low level of implementation to the high level of design.
DBNet: A New Generalized Structure Efficient for Classification

In this paper, we propose a new deep learning structure named deep-broad network (DBNet) that is efficient for classification task. By modifying the decision-making mechanism of the deep structure, the proposed method can improve the testing efficiency while maintaining the testing accuracy. Specifically, the modified convolutional neural networks (CNNs) are first pre-trained and used to extract high-quality features. And then the dimension of extracted features is reduced by linear mapping. Finally, the broad learning system (BLS) uses processed features to make decisions. Compared with the previous deep structure, the efficiency of the proposed model is improved. Compared with the BLS, the DBNet has better performance. The proposed model is evaluated by using the CIFAR-10, CIFAR-100 and MNIST datasets. And experimental results show that the DBNet is effective and efficient. Code and models are available at https://github.com/YHDang/DBNet.
Bidirectional Long Short-Term Memory-Based Spatio-Temporal in Community Question Answering

Community-based question answering (CQA) is an online-based crowdsourcing service that enables users to share and exchange information in the field of natural language processing. A major challenge of CQA service is to determine the high-quality answer with respect to the given question. The existing methods perform semantic matches between a single pair of a question and its relevant answer. In this paper, a Spatio-Temporal bidirectional Long Short-Term Memory (ST-BiLSTM) method is proposed to predict the semantic representation between the question–answer and answer–answer. ST-BiLSTM has two LSTM network instead of one LSTM network (i.e., forward and backward LSTM). The forward LSTM controls the spatial relationship and backward LSTM for examining the temporal interactions for accurate answer prediction. Hence, it captures both the past and future context by using two networks for accurate answer prediction based on the user query. Initially, preprocessing is carried out by name-entity recognition (NER), dependency parsing, tokenization, part of speech (POS) tagging, lemmatization, stemming, syntactic parsing, and stop word removal techniques to filter out the useless information. Then, a par2vec is applied to transform the distributed representation of question and answer into a fixed vector representation. Next, ST-BiLSTM cell learns the semantic relationship between question–answer and answer–answer to determine the relevant answer set for the given user question. The experiment performed on SemEval 2016 and Baidu Zhidao datasets shows that our proposed method outperforms than other state-of-the-art approaches.
Generating Datasets for Classification Task and Predicting Best Classifiers with Conditional Generative Adversarial Networks

We focus on the algorithm selection problem and closely related dataset synthesis problem. We present conditional deep convolutional generative adversarial network we call LM-GAN, generator of which is capable of synthesizing dataset for classification in the matrix form with numeric features and discriminator of which can perform the best classifier prediction for a new never seen dataset. We also suggest a technique for transforming matrices representing datasets to a canonical form. Experimental evaluation shows that the presented network working with matrices in the canonical form outperforms baseline solutions in dataset synthesis and the best classifier prediction.
Constructive Alignment: Investigating Student's Use of Mobile Technologies in University Classrooms

The purpose of the present study was to inspect the impact of information and communication technologies (ICTs), while attempting to learn from real-time university classroom lectures. The numerous adoptions of mobile phones are one aspect of ICT pleasant appearance. Mobile usage is banned in most classrooms globally. However, students still use them during their lectures and tutorials. It is anticipated that this usage is askew from the motivation of the lectures and classrooms. Synchronizing students' mobile usage in classrooms with the incentives of their academic activities remains a significant avenue of research. A sample of undergraduate nd graduate Computer Science (CS) students and another sample of undergraduate and graduate English literature students were surveyed to establish their frequency of mobile ICT use in the classrooms and the students' motivations and basis for undertaking those activities unrelated to classrooms culture, in UMT Lahore, Pakistan. In spite of mobile usage being banned in class rooms, it was determined that a large number of students use mobile phones during lectures and tutorials. This study helped to revamp classroom activities, to actively involve digital technologies to lend a hand in valuable coalition with the learning outcomes and widen the student learning awareness.
Analysis of the effect early cluster centre points on the combination of k-means algorithms and sum of squared error on k centroid

Abstract K-Means clustering is a clustering algorithm based on a partition with data only entered into one grub K, the algorithm determines the number of grub at the beginning and defines the set of K centroid. The initial determination of the cluster center is very influential on the results of the clustering process in determining the quality of clustering. The results of better clustering are often obtained after several trials. Sum of Squared Error (SSE) is a representation of homogeneity or uniformity within a cluster. In this study, the Sum of Squared Error (SSE) was used as an approach to determine the center point of the initial cluster of the K-Means algorithm. Tests were carried out on 2 datasets and the number of centroids 2,3,4,5,6,7,8, and 9 obtained values of centroids 3 and 4 in iris data had better number of iterations using a combination of K-Means and Sum of Squared Error ( SSE). These results prove that the grouping with the method of determining the cluster center starting with the K-Means algorithm is based on the minimum Sum of Squared Error value that can improve clustering results and increase the value of Sum of Squared Error (SSE), compared to conventional cluster center points.
Considerations for advancing nephrology research and practice through natural language processing

Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation.
Incremental Learning of Fetal Heart Anatomies Using Interpretable Saliency Maps

While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured in machine learning methods deployed for medical imaging. To explore interpretable lifelong learning for deep neural networks in medical imaging, we introduce a perspective of understanding forgetting in neural networks used in ultrasound image analysis through the notions of attention and saliency. Concretely, we propose quantification of forgetting as a decline in the quality of class specific saliency maps after each subsequent task schedule. We also introduce a knowledge transfer from past tasks to present by a saliency guided retention of past exemplars which improve the ability to retain past knowledge while optimizing parameters for current tasks. Experiments on a clinical fetal echocardiography dataset demonstrate a state-of-the-art performance for our protocols.
Optimization of De-duplication Technology Based on CDC Blocking Algorithm

All kinds of information data show exponential growth, and there are a lot of duplicate data in the mass data, which seriously affects the efficiency of data processing and causes serious waste of storage space. How to delete duplicate data has become a research hotspot in the current storage field. The content-defined chunking(CDC) algorithm is one of the most widely used data de-duplication algorithm, but it often produces too small or too large data blocks, resulting in low data processing efficiency and waste of storage space. In this paper, CDC chunking algorithm is optimized, and data processing efficiency and data de-duplication rate are improved in three experimental data sets through experiments.
Color Space Volume and Superpixel Based Leukocyte Image Segmentation

In this paper, we propose a novel method based on color space volume and multimodal histogram thresholding (MHT) for leukocyte segmentation in human blood smear images under the rapid staining condition. The proposed method first defines a new color space volume to highlight the leukocyte's nucleus region, and it then uses the MHT method to extract the nucleus. Then, the proposed method takes the central region in the superpixel segmentation result as an initial leukocyte region, and it then uses the MHT method to extract the leukocyte region. Finally, the cytoplasm is obtained by subtracting the nucleus region from the leukocyte region. Experimental results on a dataset containing 300 leukocyte images under rapid staining condition show that the proposed method provides a higher segmentation accuracy than the counterparts.
Research on the Influence of Distributed Accounting Technology on Accounting

In order to study the influence and application of blockchain technology in the field of accounting, this paper will start from the distributed of blockchain technology, combine the traditional accounting retesting accounting method for comparative analysis and introduce the DID model for empirical analysis to study the impact of the blockchain distributed accounting method on the quality of accounting information in listed companies. The result proves that in regions where blockchain technology is applied, the quality of corporate information will be better than that of regions where blockchain technology is not applicable. Then it discusses the subversion of the traditional theory of double-entry bookkeeping, which is reflected in the advantages and disadvantages of accounting and auditing. It also predicts and analyzes the application of the future blockchain distributed accounting method in the accounting industry, proposes solutions to the related issues of “Internet + Finance” and provide suggestions for the quickly understanding of the blockchain technology, the impact of insights into emerging technologies and development trends on existing business and how to establish a development strategy.