Reviewerly/exHarmony huggingface dataset:
Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression

Constructing a binary tree, the Huffman algorithm introduced the method of text compression that helps to reduce the size keeping the original message of the file. Nowadays, Huffman-based algorithm assessment can be measured in two ways; one in terms of space, another is decoding speed. The requirement of memory for a text file is going to be reasonable while the time effectiveness of Huffman decoding is being more significant. Meanwhile, this research is introducing the adjacent distance array with Huffman principle as a new data structure for encoding, and decoding the Bengali text compression using transliterated English text. Since the transliterated English text accommodates to reduce the unit of symbols accordingly, we transliterated the Bengali text into English and then applied the Huffman principle with adjacent distance array. By calculating the ASCII values, adjacent distance array is used to save the distances for each adjacent symbols. Apart from the regular Huffman algorithm, a codeword has produced by traversing the whole Huffman tree for a character in case, respectively adopting the threshold value and adjacent distance array can skip the lengthy codeword and perform the decoding manner to decode estimating the distances for all adjacent symbols except traversing the whole tree. Our findings have acquired 27.54% and 20.94% compression ratios for some specimen transliterated Bengali texts, as well as accomplished a significant ratio on different corpora.
Toward Transparent and Accountable Privacy-Preserving Data Classification

Machine learning provides an effective approach to execute big data analysis. As a branch of machine learning, classification has been widely adopted in data processing. However, the sensitivity of data raises the concern of data privacy. How to balance data utility and data privacy is a challenging issue. Privacy-preserving data classification, which supports flexible and privacy-friendly access to datasets and data classification, enables users' data to be collected in an authenticated manner. However, the priva-cy-preserving data classification approach has a limitation in that the correctness of data classification cannot be guaranteed. As a consequence, it is possible for a malicious classifier to manipulate the classification result. To solve these problems, in this article, we propose a transparent and accountable privacy-preserving data classification framework, which involves a tracer to assert the behavior of the classifier and maintains the utility and privacy of data. Specifically, we take advantage of cryptography techniques to balance data privacy and data utility, and use blockchain to achieve transparency and accountability for the behavior of the classifier. To illustrate the practicability of this framework, we implement concrete cryptographic algorithms and develop a prototype system to evaluate and test its performance.
Video Conferencing in the Age of Covid-19: Engaging Online Interaction Using Facial Expression Recognition and Supplementary Haptic Cues

More than 50 years since its mass market introduction the core user interfaces of Video Conferencing (VC) systems have essentially been unchanged. Relaying real time audio and video over distance is inherently productive. However, it lacks the sense of in-person interaction. With the current global pandemic, additional privacy concerns over the extended use of video and audio-conferencing systems, there is a need to redefine how VC Systems function and what information they communicate. To resolve these issues, we propose a VC system that utilizes facial recognition to identify and catalog participant's expressions and communicates their emotional states to other participants on the VC system using encoded haptic cues. In our testing we found that the approach was able to provide summarized haptic feedback of facial expressions and reduce the time it took for the participants to react to ongoing discussions without increasing mental or physical strain on the user.
A Mobile Augmented Reality Interface on Additive Manufacturing

The increasing customized and complexity of products makes the handling of additive manufacturing becomes more sophisticated. Amidst the growing demands of personalized product, interpretation of input from customer to the specification of product is now a challenge for the industry. In view of this, augmented reality (AR), a technology which projects virtual content of target object on real world scenery can be used to interpret the design information and project the detail onto display accordingly. Through visual interaction, the technical information on additive manufacturing can be easily visualised and flow of the process can be managed and planned on preliminary before actual production. This helps prevent the misunderstanding of information conveyed through verbal communication and subsequently satisfactory of the product fabricated is of definite guarantee. As such, a mobile AR interface to visualise additive manufacturing is proposed. In this work, cloud database is integrated to the AR system to enable online information retrieval on the product material. Overall, a prototype system has been developed and evaluation of the usability of the AR interface has been conducted. The mobile application is essential to guide operators and could possibly be used to design additive manufacturing process.
Chinese Clinical Named Entity Recognition Based on Stroke-Level and Radical-Level Features

Clinical Named Entity Recognition (CNER) is an important step for mining clini-cal text. Aiming at the problem of insufficient representation of potential Chinese features, we propose the Chinese clinical named entity recognition model based on stroke level and radical level features. The model leverages Bidirectional Long Short-term Memory (BiLSTM) neural network to extract the internal semantic in-formation of Chinese characters (i.e., strokes and radicals). Our method can not only capture the dependence of the internal strokes of Chinese characters, but also enhance the semantic representation of Chinese characters, thereby improving the entity recognition ability of the model. Experimental results show that the accuracy of the model on the CCKS-2017 task 2 benchmark data set reaches 93.66%, and the F1 score reaches 94.70%. Comp ared with the basic BiLSTM-CRF mod-el, the precision of model is increased by 3.38%, the recall is increased by 1.05% and F1 value is increased by 1.91%.
An Efficient Criminal Segregation Technique Using Computer Vision

In the contemporary world, where the population has been growing rapidly, it has become difficult to identify suspicious persons. Given the abundance of population in public places, it is difficult to identify a culprit post-crime activity because one (in general, the investigator) has to go through the entire CCTV footage to track and pin down people who seem suspicious for further investigation. These traditional methods are very time-consuming and laborious since each footage can be at least hours long. This proposed method takes advantage of the fact that the culprit tries to hide their identity by either evading the camera or by masking themselves. In places like shopping malls, movie theaters, restaurants, etc. these cameras are placed at the entrance and at security checks. Hence, it is not plausible for them to completely evade the cameras. This shifts our concentration to the latter idea that they hide their identity by masking themselves. We build our model on this flaw and combine video surveillance with machine intelligence to provide an efficient interface than unprocessed video feed. Furthermore, this system is not only useful for post-crime scenarios but can also be deployed for real-time analysis.
Multi-layer PCA Network for Image Classification

PCANet is a simple deep learning baseline for image classification, which learns the filters banks by PCA instead of stochastic gradient descent (SGD) in each layer. It shows a good performance for image classification tasks with only a few parameters and no backpropagation procedure. However, PCANet suffers from two main problems. The first problem is the features explosion which limits its depth to two layers. The second issue is the binarization process which leads to discriminative information loss. To handle these problems, we adopted CNN-like convolution layers to learn the PCA filter-bank and reduce the number of dimensions. We also used second-order pooling with z-score normalization to replace the histogram descriptor. The late fusion method is used to combine the class posteriors generated each layer. The proposed network has been tested on image classification tasks including MNIST, Cifar10, Cifar100 and Tiny ImageNet databases. The experimental results show that our model achieves better performance than standard PCANet and is competitive with some CNN methods.
A Smartphone App Based Model for Classification of Users and Reviews (A Case Study for Tourism Application)

Classification of reviews provided by the users plays a vital role in many real world applications. Many industries in the current era depend on the reviews of the customers/ users for planning their business and providing better customer care services. It deals with the classification of reviews to validate the objectives of the organization. The evaluations and follow up goals can be determined as positive or negative types of reviews. This paper tried to propose a model that performs the classification of users or customer reviews using ratings provided by users or reviewers. In the proposed model, users can give feedback on the location through the Android App, which will be stored in a cloud platform. This real time dataset can be used for tourism applications in the proposed work. The algorithm is used to classify a review as either an honest review or a fake review. It also tries to classify the users as honest, suspicious, and malicious. Feedbacks classified as honest and given by honest users only will be considered authentic information by the other users during the search operation.
Labelled cyclic proofs for separation logic

Abstract Separation logic (SL) is a logical formalism for reasoning about programs that use pointers to mutate data structures. It is successful for program verification as an assertion language to state properties about memory heaps using Hoare triples. Most of the proof systems and verification tools for ${\textrm{SL}}$ focus on the decidable but rather restricted symbolic heaps fragment. Moreover, recent proof systems that go beyond symbolic heaps are purely syntactic or labelled systems dedicated to some fragments of ${\textrm{SL}}$ and they mainly allow either the full set of connectives, or the definition of arbitrary inductive predicates, but not both. In this work, we present a labelled proof system, called ${\textrm{G}_{\textrm{SL}}}$, that allows both the definition of cyclic proofs with arbitrary inductive predicates and the full set of SL connectives. We prove its soundness and show that we can derive in ${\textrm{G}_{\textrm{SL}}}$ the built-in rules for data structures of another non-cyclic labelled proof system and also that ${\textrm{G}_{\textrm{SL}}}$ is strictly more powerful than that system.
Data Mining Technology in University Computer Management Information System

With the development and progress of practice and technology, computer management information systems in colleges and universities have become more and more complex, and data mining has become more and more important. In order to study the application of data mining in computer management information in colleges and universities, this paper adopts method of case analysis and literature analysis to collect data from databases such as HowNet, Wanfang database, SSCI, etc., and build a computer management model. The literature survey method reads and analyzes a large number of related literature. The study found that through the introduction of data mining technology, the operating effect of the university computer management information system has been improved, the operating efficiency has been increased by about 20%, and the classification and tracking of data have been greatly improved, which has increased by about 40%. This shows that data mining technology can play a great role in improving the computer management information system of colleges and universities. With the complexity of computer management information in colleges and universities, the application of data mining has become a trend.
Recent Trends in Patient Registries for Health Services Research

Patient registries are an established methodology in health services research. Since more than 150 years, registries collect information concerning groups of similar patients to answer research questions. Elaborated recommendations about an appropriate development and an efficient operation of registries are available. However, the scene changes rapidly.The aim of the study is to describe current trends in registry research for health services research.Registries developed within a German funding scheme for model registries in health services research were analyzed. The observations were compared with recent recommendations of the Agency for Healthcare Research and Quality (AHRQ) on registries in the 21st century.Analyzing six registries from the funding scheme revealed the following trends: recruiting healthy individuals, representing familial or other interpersonal relationships, recording of patient-reported experiences or outcomes, accepting participants as study sites, active informing of participants, integrating the registry with other data collections, and transferring data from the registry to electronic patient records. This list partly complies with the issues discussed by the AHRQ. The AHRQ structured its ideas in five chapters, increasing focus on the patient, engaging patients as partners, digital health and patient registries, direct-to-patient registry, and registry networks.For the near future, it can be said that the concept and the design of a registry should place the patient in the center. Registries will be increasingly linked together and interconnected with other data collections. New challenges arise regarding the management of data quality and the interpretation of results from less controlled settings. Here, further research related to the methodology of registries is needed.
Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimisation

Previously a theoretical study of the average convergence rate was conducted for discrete optimisation. This paper extends it to a further analysis for continuous optimisation. First, the strategies of generating new solutions are classified into two categories: landscape-invariant and landscape-adaptive. Then, it is proven that the average convergence rate of evolutionary algorithms using positive-adaptive generators is asymptotically positive, but that of algorithms using landscape-invariant generators and zero-adaptive generators asymptotically converges to zero. A case study is made to validate the applicability of theoretical results. Besides the theoretical study, numerical simulations are presented to show the feasibility of the average convergence rate in practical applications. In case of unknown optimum, an alternative definition of the average convergence rate is also considered.
Towards a harm-based framework to evaluate Cyberhate

Communication over the internet has removed historical and social challenges such as distance, noise, or prolonged delivery time, thus, moving communication from the physical space to cyberspace. It has led to an increase in the use of the internet to commit hate crimes. The impact of hateful crimes is documented in the literature, with limited success stories on currently deployed intervention. A framework that classifies the harmful effect of Cyberhate is proposed. It aims to enrich our knowledge of the harm from Cyberhate crimes, enhance existing intervention, and provide harm metrics to prosecute cyberhate offenders. To date, limited research has quantified the harmful impact of Cyberhate crime; hence, understanding the harm from Cyberhate would provide a new perspective, with the harm concepts an area argued to currently being understudied.
ECG Data Modeling and Analyzing via Deep Representation Learning and Nonparametric Hidden Markov Models

In modern clinical medicine, electrocardiogram (ECG) is a common diagnosis technique of cardiovascular diseases. The purpose of this paper is to propose a novel model-based clustering approach for analyzing ECG data. Our approach is composed of two modules: representation learning and ECG data clustering. In the module of representation learning, a deep generative model referred to as the hyperspherical variational recurrent autoencoder (HVRAE) is developed to extract the representation of observed ECG data, based on the variational autoencoder (VAE) with long short-term memory (LSTM) networks. In the module of ECG data clustering, we develop a nonparametric hidden Markov model (NHMM) based on Dirichlet process in which the number of hidden states is inferred automatically during the learning process. Moreover, the emission density of each hidden state of our NHMM follows a mixture of von Mises-Fisher (VMF) distributions which have better capability for modeling ECG representations than other commonly used distributions (such as the Gaussian distribution). To learn the proposed VMF-based NHMM, we theoretically develop an effective learning algorithm based on variational Bayes. The merits of our model-based clustering approach for analyzing ECG data are verified through experiments on publicly available ECG data sets.
Image Authentication Using Block Truncation Coding in Lifting Wavelet Domain

Image watermarking is one of the most accepted solutions protecting image authenticity. The method presented in this paper not only provides the desired outcome also efficient in terms of memory requirements and preserving image characteristics. This scheme effectively utilizes the concepts of block truncation coding (BTC) and lifting wavelet transform (LWT). The BTC method is applied to observe the binary watermark image corresponding to its gray-scale image. Whereas, the LWT is incorporated to transform the cover image from spatial coordinates to corresponding transform coordinates. In this, a quantization-based approach for watermark bit embedding is applied. And, the extraction of binary watermark data from the attacked watermarked image is based on adaptive thresholding. To show the effectiveness of the proposed scheme, the experiment over different benchmark images is performed. The experimental results and the comparison with state-of-the-art schemes depict not only the good imperceptibility but also high robustness against various attacks.
Proposed eLearning Framework using Open Corpus Web Resources

Covid-19 has unsettled the traditional teaching-learning model to meet the learning needs of the students in absence of a classroom environment. Knowledge delivery through online classes has been accompanied by low user satisfaction. One possible antidote to supplement a teacher's efforts that merits attention is the utilization of freely available content on the web. Copious volumes of learning content are generated by numerous entities and are easily available for knowledge consumption. Modern search engines do provide access to this trove of data on virtually any topic. However, to consume the content presented by a search engine, a user is required to manually sieve through the content and identify the ones suitable to their needs. To a person possessing domain knowledge of the topic, this approach to knowledge consumption is acceptable. Now, take the case of a class eight student, he/she may not be able to benefit from this search, filter and consume content approach. This is primarily due to the lack of adequate domain and web search expertise on their part. In this paper, approaches to delivering knowledge using web-based resources to the student community taking into consideration the aforementioned concerns are explored.
Text Detection in Trademark Images under Semi-Supervision

Abstract The texts in trademark image usually contain characters, text lines and other different parts. Therefore, compared with the detectors that can detect text lines directly, the detector that can detect characters is more suitable for the detection of texts in trademark images However, training character detectors needs a huge number of characters with position annotation, the con-struction process of this type of dataset will take a lot of time. In order to overcome the limitations of the detectors trained by datasets with word-level annotations and the shortage of datasets with character-level annotations, we propose a text detect method that can detect the single character in trademark images. Our proposed model is pretrained by the synthetic image dataset with character level annotation, then the pretrained model is used to detect the unannotated trademark images dataset to find more annotations for retrain the pretrained model. We also construct a trademark text dataset, which includes 4500 images. Experimental results on our dataset demonstrate our method can achieve the state-of-the-art performance compared to other methods. The results show our detector can detect complicated texts in trademark images.
Research and Development of a Smart Contract Algorithm for the Implementation of Block-chain Technology on Mobile Devices

A formalized representation has been proposed and smart contract algorithms have been developed for the implementation of block-chain technology on mobile devices, which meet the following requirements: short block formation time, low energy consumption, no computations that require significant expenditures of device RAM. A model was built, for which, in the language of linear temporal logic, a constraint on the state of the model was formulated, reflecting the features of the registration process in conditions of a lack of resources. The results of checking the adequacy of the model showed that the model works correctly and the behavior of the system remains within the specified constraints. As a result of a parametric study, it can be concluded that the probability of completing the user registration process significantly depends on the execution time of the acquisition process in the range from 0 to 100 ms.
Overview of speech keyword recognition technology

Abstract Since the 1990s, speech keyword recognition technology has gradually separated from speech recognition technology and officially become an important branch of speech recognition technology. In this paper, several speech keyword recognition technologies are studied and reviewed, including sample recognition methods, filler model methods, basic speech recognition systems based methods, neural network classifiers and end-to-end methods. Finally, the development prospect and advanced technology of speech keyword recognition will be discussed.
Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads

Distributed Database Management Systems~(DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance.