Reviewerly/exHarmony huggingface dataset:
Development of Different Word Vectors and Testing Using Text Classification Algorithms for Telugu

Word embedding methods are used to represent words in a numerical way. Text data cannot be directly processed by machine learning or deep learning algorithms. It is very efficient to process numerical data, so by using word embedding techniques, we need to transform the text data into numerical form. One hot encoding vectors of real-valued numbers are simple and easy to generate. The researchers are now managing Word2vec for semantic representation words. In the literature review, we found that there are fewer tools and resources available for Indian languages compared to European languages. We want to construct a word embedding (vectors) using one hot encoding and Word2vec strategy. In this paper, we evaluate these vectors using supervised machine learning algorithms for sentiment classification. We pursue a two-step approach in this article. The first step is to generate vocabulary using News Corpus and to create word vectors using various word embedding methods. Validating the vector quality is the second step, using machine learning algorithms. We did preprocessing on the corpus we received. We got 178,210 types and 929,594 tokens after preprocessing. The size of our vocabulary is 178,210 unique words. We used labeled corpus, i.e.; movie review sentences and vocabulary, for developing sentence vector. Using the one hot encoding vector and word2vec vector model, we translated sentences to vectors. Once label sentences were translated into vectors, three machine learning algorithms were trained and evaluated. We finally compared the outcome.
Digital Transformation of Business Education

The relevance of the study is due to the importance of accessible, quality education that meets modern challenges and leads to scientific and technological progress of the economy, civilized society. The aim of the study: the article aims to comprehend the importance of legal registration of digital and distance education, investing in this sphere as factors of economic and technological progress acceleration, renewal of production capacities in general. Methods of research: general scientific, as well as general, private scientific, private legal methods, which allowed a comprehensive substantiation of the thesis that the digitalization of the educational process is forced in the conditions of COVID-19 and that at the same time it is a progressive, inevitable step into the future. Results of the study: the digitalization of the educational sphere is analyzed; the necessity of accepting the Education Code in the Russian Federation is justified, which could absorb the entire legal and regulatory framework in the field of education and science, as well as contribute to the transformation of educational law in the digital economy. Practical Importance: Materials of the article are of practical value for lawmakers, politicians, researchers in the field of development of digital formats of the educational process.
Research on Vector Structure of Neural Network Algorithm Based on RISC-V

To solve the problem of low computational efficiency and energy efficiency in the general-purpose processor neural network, and low flexibility of the dedicated processor, a scalar plus vector processor is designed based on the RISC-V instruction set Structure. Through software and hardware co-design, a dedicated access instruction design is adopted for the algorithm structure of the neural network, and vector instructions are used to accelerate operations such as activation, pooling, and convolution. The extended instructions of the RISC-V processor are decoded in the processor decoding stage, and then the vector unit is called at the execution stage to accelerate the neural network. Experimental results show that the computing performance of the processor at 200MHz reaches 60.18 GOP/s, which is an increase of 417.91% compared to the processor, and the energy consumption ratio reaches the mainstream level of similar designs.
Context Dependency with User Action in Recommendation System

Recommendation systems recommend the users with the items which they may prefer in future. Context-based recommendation uses the user, item, or interaction context in recommendation process. Tagging action of user as interaction can be used as implicit action for recommendation generation, whereas the rating action can be used as explicit action. The context which influences the tagging action or the rating action can also be used to generate the recommendation in addition to user profile and item profile. The prefiltering method with context is used in recommendation systems to reduce the data for processing by recommendation system and to increase the quality of recommendation. A method to determine the context dependency for incorporating context to increase relevance of recommendation is proposed with prefiltering approach with implicit user tagging action with class association rule mining. The evaluation of the method on MovieLens dataset with tagging action shows that the context variable selected will not improve the relevance which can be used to make the decision of dependency of context with recommendation.
Leveraging deep contrastive learning for semantic interaction

The semantic interaction process seeks to elicit a user's mental model as they interact with and query visualizations during a sense-making activity. Semantic interaction enables the development of computational models that capture user intent and anticipate user actions. Deep learning is proving to be highly effective for learning complex functions and is, therefore, a compelling tool for encoding a user's mental model. In this paper, we show that deep contrastive learning significantly enhances semantic interaction in visual analytics systems. Our approach does so by allowing users to explore alternative arrangements of their data while simultaneously training a parametric algorithm to learn their evolving mental model. As an example of the efficacy of our approach, we deployed our model in Z-Explorer, a visual analytics extension to the widely used Zotero document management system. The user study demonstrates that this flexible approach effectively captures users' mental data models without explicit hyperparameter tuning or even requiring prior machine learning expertise.
Model Selection Procedure in Alleviating Drawbacks of The Electronic Whiteboard

Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem.
Preserving Democracy and the Rule of Law in a Pandemic. Some Lessons from the Venice Commission

This chapter analyses the impact the COVID-19 pandemic is having on the European legal systems, and on Spain in particular, through the prism of the studies elaborated in the context of the health crisis by the Venice Commission. These documents explicitly recognise the relevance of the current health crisis in our societies and its impact on the founding objectives of the Council of Europe: the safeguarding of democracy, human rights and the rule of law. The analysis of the criteria of the Venice Commission in relation to the application, regulation and the redistribution of powers during states of emergency is indeed a useful tool to assess the use of the exceptional powers by States during the COVID-19 pandemic. In particular, reference to these standards may be useful to identify possible abuses by populist governments.
An Integrated Geochemical Spatial and Temporal Survey of Low Temperature Thermal Springs to Characterize the Geothermal Resource of a Volcano (Piton Des Neiges, Réunion Island)

Geothermal exploration can be challenging in the absence of obvious surface markers of a high enthalpy geothermal resource such as geysers, fumaroles or thermal springs with temperatures exceeding 100°C. This is the case at the Piton des Neiges volcano (Réunion Island), which displays evidence of hydrothermal activity mostly as thermal springs with temperatures not exceeding 50°C, affected by mixing and reequilibration, and emerging in a complex geological framework, resulting from the polygenic history of this old dormant volcano. Yet, these thermal springs keep a partial record of the reservoir geochemistry. Here, we propose an exploration method to set this thermal signature appart from the contribution of other processes and trace back the equilibrium conditions, enabling us to locate thermal fluid circulations, and evaluate their temperature and availability. We report its application at Piton des Neiges volcano. We led an extensive geochemical survey which nearly doubled the number of thermal springs reported on the volcano, and dramatically extended the existing geochemical database in space, time and number of parameters. In tandem, we also monitored δ 18 O and δD values in rainwaters and thermal springs over two years to compare their temporal variations.Our surveys show that the repartition of thermal springs is controlled by only one of the volcanic rift zones (the N30°E axis), which is thus identified as the heat source of hydrothermal circulations. This N30°E rift zone is also a preferential pathway for the degassing of mantellic CO 2 . In addition, the composition of thermal waters is influenced by syenite intrusions along the rim of the caldera, acting as drains for the uprise of hydrothermal waters. O, H and Sr isotopic signatures of springs show that the geothermal resource of Piton des Neiges is in fact made of two independent and unconnected hydrothermal systems on the northern and southern circular depressions of the volcano (the cirques of Salazie and Cilaos, respectively). Their recharge is meteoric, plurennial, and mostly achieved by cyclonic events. Thermal waters experience reequilibration during their ascent and various degrees of CO 2 input or degassing. Last equilibrium temperatures of water/rock interaction, estimated by multicomponent geothermometry, reach up to 125 ± 6°C in the cirque of Salazie, and up to 141 ± 15°C in the cirque of Cilaos, with possibility for hotter waters before reequilibration. Taken together, our results suggest that the most promising target for future exploration is located in the cirque of Cilaos, along the caldera rim, and in proximity of syenite intrusions.Our study demonstrates that a four steps methodology that combines a thorough prospecting of thermal springs with a comprehensive geochemical study, including temporal variations of the different water reservoirs, is a powerful tool to characterize hidden geothermal resources and plan future exploration targets.
Expression Recognition Based on Image Fusion and Transfer Learning

There are two problems in facial expression recognition: one is that single-channel network can only extract a single feature, the other is that the multi-channel network can extract a variety of features, but it increases the amount of computation. This paper proposes an expression recognition method based on image fusion and transfer learning. It combines the knowledge and experience of traditional methods with the computing power of deep learning methods. The method firstly locates and segments the face according to human experience of knowing things. The segmented area is the face, eyes and mouth which affect expression most. Then the features of the three perspectives are fused to form a three-channel picture containing the face, eye and mouth area. Finally, it is sent to the network which has preliminary experience in understanding things for transfer learning. Experimental results show that this method has achieved good results on CK + and JAFFE datasets with the average accuracy 100% and 98.55%. Compared with single-channel and multi-channel convolutional neural networks, the presented method can effectively improve the accuracy of facial expression recognition.
Fusion Transformer-Based Salient Object Detection in Multispectral Polarimetric Imagery

Salient object detection (SOD) is widely applied in image segmentation, image fusion and adaptive compression. However, SOD of visible image in complex scenes remains a prominent problem due to the lack of low-level features. To solve this problem, a visible image and polarized mask combined (RGB-M) method is proposed. Firstly, in order to extract precise polarization components from the multispectral polarimetric images, the polarimetric parameters of each band are fused based on the image fusion transformer. Secondly, a saliency mask M composed of strong polarization mask, high entropy mask and super dim mask is generated by polarization components. Finally, the saliency map is obtained by fusing RGB saliency and M saliency calculated via contrast prior algorithm. The indexes of the salient object detection results of this paper are obviously superior to the basic visible image algorithms, especially for objects in dim, low contrast or high transparency and other complex scenarios.
Applying AI to Manage Acute and Chronic Clinical Condition

Abstract Computer systems deployed in hospital environments, particularly physiological and biochemical real-time monitoring of patients in an Intensive Care Unit (ICU) environment, routinely collect a large volume of data that can hold very useful information. However, the vast majority are either not stored and lost forever or are stored in digital archives and seldom re-examined. In recent years, there has been extensive work carried out by researchers utilizing Machine Learning (ML) and Artificial Intelligence (AI) techniques on these data streams, to predict and prevent disease states. Such work aims to improve patient outcomes, to decrease mortality rates and decrease hospital stays, and, more generally, to decrease healthcare costs. This chapter reviews the state of the art in that field and reports on our own current research, with practicing clinicians, on improving ventilator weaning protocols and lung protective ventilation, using ML and AI methodologies for decision support, including but not limited to Neural Networks and Decision Trees. The chapter considers both the clinical and Computer Science aspects of the field. In addition, we look to the future and report how physiological data holds clinically important information to aid in decision support in the wider hospital environment.
Authenticated Multi-proxy Accumulation Schemes for Delegated Membership Proofs

Proving ownership (or possibly non-ownership) of an attribute associated with an individual or device can be used in many different use cases. For a user to prove age, credibility, medical records or other type of attributes, cryptographic accumulators can be used. Also, in a federated authentication architecture, a user may prove such ownership via one or many proxies, e.g. a trusted party such as a bank or government institution. We propose an authenticated multi-proxy accumulation (AMPA) scheme for solving these types of scenarios without the need for encryption and still preserve privacy and remove data set leakage during set membership proving. We illustrate how an AMPA scheme easily can be constructed and present initial results from a proof of concept implementation.
Hidden Gold for IT Professionals, Educators, and Students: Insights From Stack Overflow Survey

It is undeniable that technological advancements have taken significant changes in the nature of work, resulting in the loss of many jobs and the emergence of new ones. As a result, information technology (IT) job seekers and professionals must be aware of changes required in their respective fields. This study identified and ranked the most-used technologies, such as programming languages, databases, operating systems (OSs), and collaborative tools, specific to twenty-three (23) different IT roles. Furthermore, this study advocates that individuals focus on critical skills required in various domains to achieve career success. Similarly, individuals with a set of valued technical skills may decide whether to change jobs or roles if they are interested in a position that requires their current competence. The findings of this study can be helpful to career counselors, instructors, students, professionals, recruiters, curriculum designers, and decision-makers.
Context-dependent Features Fusion with BERT for Evaluating Multi-Turn Customer-Helpdesk Dialogues

With the growth of online text data in recent years, the research on automated dialogue systems has made more progress than before. In this paper, we propose a new model DepBERT. This model uses BERT pre-training model and integrates Syntactic Dependency Feature to extract the key features of customer and helpdesk data in the dialogue content to enhance the prediction of evaluating multiple turns of dialogue. The contribution of this research is to optimize the method of automated evaluation dialogue system. The F1-score of DepBERT has a 4% increase in customer dataset and has a 10% increase in helpdesk dataset compared to BERT, indicating that it can effectively predict the task behavior in the dialogue between the customer and the helpdesk.
Optimizing Data Locality by Executor Allocation in Reduce Stage for Spark Framework

Data locality is a key factor influencing the performance of Spark systems. As the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation in reduce stage for Spark framework. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, an approximation algorithm is proposed and the approximate factor is proved to be 2. Finally, we evaluate the performance of our algorithm in a practical Spark cluster by using several representative benchmarks: sort, pageRank and LDA. Experimental results show that the proposed algorithm can help to improve the data locality and application/job performance obviously.
Amazon Product Alexa’s Sentiment Analysis Using Machine Learning Algorithms

One of the most important elements of NLP is emotion analysis. We used machine learning algorithms like SVM, random forest as well naive bayes to solve the problem of sentiment analysis on any amazon product in this paper. Sentiment analysis is a data analysis concept in which a list of reviews are considered, these reviews are analyzed, process, and suggested to the consumer. The knowledge is obtained from the product websites. If the preprocessing is completed, the qualified datasets are categorized using the naive bayes and SVM algorithms to delete unnecessary data stop sentences, be verbs, punctuation, and conjunctions are examples. These current algorithms provided accuracy that was inadequate. In this paper, naive bayes, random forest and SVM algorithms were used. The accuracy of a product is determined by the number of reviews it receives.
A Blockchain-Based Approach for Collaborative Formalization of Mathematics and Programs

Formalization of mathematics is the process of digitizing mathematical knowledge, which allows for formal proof verification as well as efficient semantic searches. Given the large and ever-increasing gap between the set of formalized and unformalized mathematical knowledge, there is a clear need to encourage more computer scientists and mathematicians to solve and formalize mathematical problems together. With blockchain technology, we are able to decentralize this process, provide time-stamped verification of authorship and encourage collaboration through implementation of incentive mechanisms via smart contracts. Currently, the formalization of mathematics is done through the use of proof assistants, which can be used to verify proofs as well as algorithms. Furthermore, with the advancement of artificial intelligence (AI), particularly machine learning, we can apply automated reasoning tools in these proof assistants and (at least partially) automate the process of synthesizing proofs. In our paper, we demonstrate a blockchain-based system for collaborative formalization of mathematics and programs incorporating both human labour as well as AI tools. We explain how Token-Curated Registries (TCR) and smart contracts are used to ensure appropriate documents are recorded and encourage collaboration through implementation of incentive mechanisms respectively. Using an illustrative example, we show how formalized proofs of different sorting algorithms can be produced collaboratively in our proposed blockchain system.
Decentralized Asset Custody Scheme with Security Against Rational Adversary

Asset custody is a core financial service in which the custodian holds in-safekeeping assets on behalf of the client. Although traditional custody service is typically endorsed by centralized authorities, decentralized custody scheme has become technically feasible since the emergence of digital assets, and furthermore, it is greatly needed by new applications such as blockchain and DeFi (Decentralized Finance). In this work, we propose a framework of decentralized asset custody scheme that is able to support a large number of custodians and safely hold customer assets of multiple times the value of the total security deposit. The proposed custody scheme distributes custodians and assets into many custodian groups via combinatorial designs, where each group fully controls the assigned assets. Since every custodian group is small, the overhead cost is significantly reduced. The liveness is also improved because even a single alive group would be able to process transactions. The security of this custody scheme is guaranteed under the rational adversary model, i.e. any adversary corrupting a bounded fraction of custodians cannot move assets more than the security deposit paid. We further analyze the security and performance of our constructions from both theoretical and experimental sides, and provide explicit examples with concrete numbers and figures for a better understanding.
Chinese Fine-Grained Sentiment Classification Based on Pre-trained Language Model and Attention Mechanism

With the development of technology and the popularization of the Internet, the use of online platforms is gradually rising in all walks of life. People participate in the use of the platform and post comments, and the information interaction generated by this will affect other people’s views on the matter in the future. It can be seen that the analysis of these subjective evaluation information is particularly important. Sentiment analysis research has gradually developed into specific aspects of sentiment judgment, which is called fine-grained sentiment classification. Nowadays, China has a large population of potential customers and Chinese fine-grained sentiment classification has become a current research hotspot. Aiming at the problem of low accuracy and poor classification effect of existing models in deep learning, this paper conducts experimental research based on the merchant review information data set of Dianping. The BERT-ftfl-SA model is proposed and integrate the attention mechanism to further strengthen the data characteristics. Compared with traditional models such as SVM and FastText, its classification effect is significantly improved. It is concluded that the improved BERT-ftfl-SA fine-grained sentiment classification model can achieve efficient sentiment classification of Chinese text.