Reviewerly/exHarmony huggingface dataset:
Type Classes

You’ve seen that there are some common functions, like == and show, that are available for many but not all types. For instance, neither == nor show will work on function types. The type of a polymorphic function can require that it be applied only on types that have such functions, and these requirements can be inferred automatically.
The Coaching Participation Experience of College Students: Career Barriers and Career Preparation Behaviors

This study explores in-depth how college students overcome career barrier factors through coaching participation experience and reach their own career search through establishing career preparation behavior. To this end, students who participated in coaching five times in a row as college students were asked what motivated them to participate in coaching in college life, and what effect did coaching participation have on career barriers and career preparation behavior. As a result of the search, college students participate in coaching to solve various problems and stress caused by college life and career, and through coaching with the coach, they experience recognizing what kind of person I am and what my strengths are. In addition, through the career exploration process, I experience what kind of work that I am interested in and what psychological and environmental factors of college students slow down my career path. Finally, college students have a positive effect on their own career search and college life through coaching, such as establishing career preparation behavior, which is a practical factor, necessary for preparing and deciding their careers.
The Digital Virus Against Democracy

Abstract The notion of digital virus covers, in our view, two points: computer viruses that infect our computers and technological solutionism, the unreasonable passion that consists in considering that the solution to all social problems lies in the digital world. Yet the digital world is as vulnerable as the biological world. Moreover, it is dangerous because it pushes us into a digital bondage that undermines democracy. The solution to the crisis is not less democracy, but more democracy. More precisely, we will argue that digital devices do not strengthen democratic communication, they promote a digital connection that hinders deliberation and threatens the very idea of democracy
Covid-19 Vaccine Tweets Sentiment Analysis and Topic Modelling for Public Opinion Mining

The world is facing the major crisis in the form of coronavirus pandemic. Since it’s been more than a year of Covid-19 pandemic, there has been a significant call in social media regarding the requirement and feasibility for COVID-19 Vaccine. This paper aims at analyzing tweets related to Covid-19 Vaccine, determining the sentiments about vaccination and extracting the significant topics. We performed multi-class sentiment analysis, steps comprising of pre-processing followed by training three different classification models: Gaussian Naïve Bayes, Support Vector Machine and LSTM. Results of the model obtained was one the three (Positive, Negative, Neutral) sentiment. Based on the outcomes, accuracy and F1- scores were computed to draw comparison between distinct models. Topic Modeling was performed using LDA on the combined tweets dataset to derive top seven important topics. In addition, Exploratory Data Analysis was also performed on dataset consisting of Vaccination Progress worldwide to bring out popularity of vaccines.
A New Model Dehazing Algorithm Based on Atmospheric Scattering Model and Retinex Algorithm

Aiming at the problems of low contrast, dark image and serious loss of detail information after directly using atmospheric scattering model to remove fog, this paper proposes a new defogging model, which is composed of Retinex model and atmospheric scattering model. A new model. The model absorbs the advantages of the two, Retinex can enhance the contrast of the image, so that the contrast of the processed image increases and the detail information of the image increases. Qualitative and quantitative experimental results show that the proposed algorithm can well restore the real scene of the image, and highlight the details of the image, confirming the progress of the algorithm.
An Automated Modeling Method and Visualization Implementation of Smart Contracts

Smart contracts are one of the core components of the block-chain system and have been widely used across various fields. Since a smart contract cannot be easily changed or updated once instantiated, one has to be absolutely sure that the program code works as expected. However, there are no uniform definitions for smart contracts, and the programming of smart contracts requires professional developers with expert domain knowledge. This paper proposed a formal modeling method for start contracts. First, the formal definition of smart contracts is proposed. Second, we introduce an EFSM based modeling method for smart contracts. Finally, we design a visual modeling tool EFSMSolid for creating EFSM on an easy-to-use graphical platform. To verify the effectiveness of the method, we conduct experiments on smart contracts of five blockchain applications, and the experimental results show that the proposed method can automatically and effectively create smart contracts models.
An Adaptive Weight A* Algorithm Applied to UAV Path Planning

An improved A* algorithm based on adaptive weighting strategy is proposed to address the problems of long search time, large range, and a large number of redundant points in a complex environment of traditional A* algorithm. Firstly, the Manhattan distance is used as the heuristic function to extend the traditional A* algorithm from 8-way search to 16-way search and reduce the turning angle; secondly, the UAV position information is introduced and the adaptive weight evaluation function is designed to accelerate the convergence speed of the algorithm; finally, the path optimization method is used to remove a large number of redundant nodes and reduce the number of turning points based on the information of the turning points by rewiring. The experimental results show that compared with the traditional A* algorithm, the improved A* algorithm reduces the search area by 92.10%, the planning time by 86.63%, and the number of inflection points by 57.14%, while the path cost is reduced by 2.13%. The proposed algorithm is verified in different environments and the results show that it can quickly complete the path planning in different environments.
Corporeal Musical Structure: A Gestural-Kinesthetic Approach to Toru Takemitsu’s <i>Rain Tree Sketch II</i>

The music of Tōru Takemitsu’s Rain Tree Sketch II (1994) entails a procession of discrete gestures that are delineated by moments of repose. The performer’s grasp of the piece lies in its physicality of movement: each gesture and in-between stillness are both heard and felt as an aggregate of velocities, directions, and intentions of the body. Drawing upon Carrie Noland’s concept of “vitality affects,” I take the performative gesture, encompassing both visually accessible movement and inwardly felt kinesthesia, as a starting point for the analysis of Rain Tree Sketch II . Concepts of effort and shape taken from Rudolf Laban’s dance theory provide a framework for creating a new methodology of enhanced trace-forms to analyze gesture and kinesthesia. The analysis of gestures reveals the coexistence of opposite effort qualities and shapes in an expanded corporeal space, resonating with Takemitsu’s ideal of reconciling contradictory sounds, as noted in his collection of essays Confronting Silence (1995). Husserl’s notions of retention and protention, viewed through the lens of embodiment, and Laban’s concepts of effort states and effort recovery are brought to bear on the still moments, showing the piece to have a throbbing, embodied rhythmic structural arc. This new methodology centering on gestural-kinesthetic details provides the tools to articulate structural sensations that are often overlooked but lie at the center of musical experience.
Applied Interval Analysis of Big Data Using Linear Programming Methods

The article describes the problems of mathematical modeling of processes using an experimental database and a knowledge base. This research relates to multidimensional dependency building. It uses regression analysis and machine learning techniques within the framework of probability theory and mathematical statistics. A large observation table often cannot be processed on a single computer. The analysis of such data requires parallel computations and in this article it is carried out by the method of interval mathematics, which allows performing such computations. The analysis of linear dependences on parameters is reduced to solving systems of interval linear algebraic equations. Among the approaches to systems study known in the literature, an approach was chosen that takes into account the so-called “single set of solutions”. This method provides a guaranteed estimate of the required dependencies and allows the use of linear programming in some cases. Using this method, interval forecasts of the output variable of the modeled process are calculated. Interval estimates of the parameters of the studied dependence were also obtained. Two methods of sequential and parallel analysis of a large database are proposed, using methods for solving large-scale linear programming problems. The optimality of the algorithms is substantiated using the well-known technique of removing constraints in optimization problems of large dimension. The research was carried out on model processes and on real data of statistics of road traffic accidents in England.
A variant RSA acceleration with parallelisation

The standard RSA relies on multiple big-number modular exponentiation operations and a longer key-length is required for better protection. This imposes a hefty time penalty for encryption and decryption. In this study, we analysed and developed an improved parallel algorithm (PMKRSA) based on the idea of splitting the plaintext into multiple chunks and encrypt the chunks using multiple key-pairs. The algorithm in our new scheme is so natural for parallelised implementation that we also investigated its parallelisation in a GPU environment. In the following, the structure of our new scheme is outlined and its correctness is proved mathematically. Then, with the algorithm implemented and optimised on both CPU and CPU+GPU platforms, we showed that our algorithm shortens the computational time considerably, and it has a security advantage over the standard RSA as it is invulnerable to the common attacks. Finally, we also proved the feasibility of using our algorithm to encrypt large files through simulation. The results show that over the set of file size: 1 MB, 10 MB, 25 MB, 50 MB, 100 MB, the average encryption and decryption time of the CPU version is 0.2476 and 9.4476 s, and for the CPU+GPU version, it is 0.0009 and 0.0618 s, respectively.
Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.
Smart Recommendation of Telecommunication Services

In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This publication has emanated from research conducted with the financial support of the Bulgarian National Science Fund (BNSF) under the Grant No. KP-06-IP-CHINA/1 (КП-06-ИП-КИТАЙ/1)., a new vision is presented for highly contextualized, customized, and personalized recommendation of telecommunication services to mobile users (consumers) by considering the current consumer-, network-, and service context. A framework for smart recommendation of telecommunication services, which builds up and dynamically manages personal profiles of consumers, is elaborated. This seeks to facilitate service discovery and recommendation for the consumers, in support of the consumer-choice optimization process, and achieving the best quality of experience (QoE) when utilizing different telecommunication services, accessible through any kind of mobile devices (e.g., smartphones, tablets, laptops, etc.) via heterogeneous wireless access networks, anytime-anywhere-anyhow. The smart algorithm-driven recommended services range from classic telecommunication services (e.g., outgoing voice calls) to Internet services (e.g., multimedia streaming) and to Internet of Things (IoT) services. The algorithms also could be adapted and expanded to cover more sophisticated services working towards satisfying the health and safety needs of consumers, such as finding (with subsequent dynamic changing, if required) the most ‘healthy’ or ‘safe’ driving/biking/jogging/walking route to follow in order to avoid areas posing particular, consumer-specific, health or safety risk.
The Critical Role of Education and ICT in Promoting Environmental Sustainability in Eastern and Southern Africa: A Panel VAR Approach

The struggle to combat climate change remains complex and challenging. Currently, two climate change approaches, namely, mitigation and adaptation, have been widely supported. These are empirical, requiring further explanation of the main drivers of carbon emissions. This research seeks to tackle this problem by providing a strategy to reduce climate change impacts. This study contributes to the existing empirical literature in several ways. It investigates whether education and information and communication technology (ICT) matter to promote environmental sustainability in the Eastern and Southern Africa. The empirical evidence is based on the third-generation panel unit root test and panel cointegration tests that account for the potential issue of structural breaks in the series. We further dissect the long and short run dynamics using the panel Granger causality approach. Our findings show the possibility of using education and clean technology investment in a complementary strategy for mitigating carbon emissions and promoting environmental sustainability in the sampled countries.
The Relevance of Communication Design and the Undeniable Power of Brands

Communication design can well be framed as the heartland of effective communication. Brands are the result of a communication design exercise and their impact on lifestyles, either in terms of behaviour change or adaptation, ways of communicating, or one’s own perception of the human condition and the world, is well established. On the other hand, research fulfils the purpose of questioning, understanding, and transforming the world. This text presents a set of articles that open different perspectives on how brands can engage with ground-breaking communication design and how they transform the world we live in.
Method of features analysis on transition data

Detection of atypical data and outliers is an important and difficult task. Data that are considered atypical in turn are characterized by a set of features that determine their informativeness. Therefore, the search for features due to which data elements are considered atypical is relevant and necessary. An even more difficult task is to find atypical features on a set of small data, which can be considered transient data, i.e. those data that are located at the boundaries of classes in the classification of data. Transition zone data between classes create difficulties in classifying and constructing discriminant separation. This paper proposes a method for determining outliers and irrelevant data based on the clustering of class data using a minimal spanning tree. Outliers are detected by minimizing the set of bipartite graph data of adjacent groups. This leads to spatial local delimitation and determination of the set of outlier characteristics. The proposed method allows detecting outliers on different sets of features, both common and on the features of individual classes.
FPGA Accelerated Secure Data Communication

Many methods of encryption and authentication have been used in communication systems, while having many vulnerabilities and shortcomings. This inspired researchers to explore new algorithms for unbreakable security and privacy codes. Moreover, most of the recent sophisticated algorithms suffer from the mathematical complexity that requires many CPU cycles to be dedicated for encryption, decryption, as well as authentication. Also, implementing hardware accelerated security algorithms is dangerous, due to its sensitivity to flaws in the algorithms. This paper explores feasible solutions to this problem by allowing communication devices to use configurable hardware, such as FPGAs, while offloading CPU resources, to implement security tasks. This will enable the possibility of reprogramming when a vulnerability is found in an algorithm, much like a simple replacement of a device within a computer but done automatically through a software update. One vendor that manufactures open-source computer mainboard designs using IBM&#x2019;s OpenPOWER architecture, has used an FPGA to control many of the board&#x2019;s features, allowing the FPGA to be updated for security, bug fixes, as well as adding new features, something that is impossible on non-programmable hardware. This was our motivation to use open-source implementation of poly1305&#x002B;Salsa20, while translating an implementation from VHDL to SystemVerilog, porting it from a Xilinx board to a more universal implementation, writing a full Linux kernel driver for the new device, and demonstrating the throughput advantage of the FPGA when compared to many computers that will be tested.
Error bounds of adversarial bipartite ranking

Generalization analysis for learning models with adversarial examples has attracted increasing attentions recently. However, the previous theoretical results are usually limited to learning models with the pointwise loss. In this paper, we beyond this restriction by investigating generalization ability of bipartite ranking associated with adversarial perturbations. The upper bounds of adversarial ranking risk are established by formulating the pairwise learning in a minimax framework and introducing the transfer mapping to relate data distributions. In particular, our results are suitable to general loss satisfying Lipschitz conditions, e.g, the logistic loss and the least squared loss.
An Aspect Term Extraction Method Based on BiLSTM-CRF with BERT Embedding

Aspect term extraction is a key task in aspect-level sentiment analysis, aiming at extracting aspect term from text. However, most of the existing aspect-level sentiment analysis methods focus on the matching of aspect term and sentiment polarity, and neglect the research of aspect term extraction. In this paper, an aspect term extraction method based on BiLSTM-CRF with BERT embedding is proposed. Firstly, the embedded layer in Bert model is used to represent the vector, then it is sent to BiLSTM layer for context feature learning, and finally, the aspect term are extracted by CRF. Therefore, the model based on BiLSTM-CRF with BERT embedding is constructed. The experimental results show that this method has high precision and recall rate in Restaurants and Laptop of the SemEval-2014 dataset. The average value of F1 is 85.74%, and the performance of this method has the best comprehensive performance and has more outstanding performance than other models.
ВАЛЬДОРФСЬКА ШКОЛА — СВІТ ПРАЦІ

The role and features of labor training in the Waldorf school and the importance of labor lessons during 12-year training are considered. The importance of labor lessons in the formation of a harmoniously developed personality, the development of intellectual, emotional and volitional spheres is clarified
Exploiting Features with Split-and-Share Module

Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing convolutional blocks of the feature extractors, but less on the classifiers that exploit extracted features. In this work, we propose Split-and-Share Module (SSM),a classifier that splits a given feature into parts, which are partially shared by multiple sub-classifiers. Our intuition is that the more the features are shared, the more common they will become, and SSM can encourage such structural characteristics in the split features. SSM can be easily integrated into any architecture without bells and whistles. We have extensively validated the efficacy of SSM on ImageNet-1K classification task, andSSM has shown consistent and significant improvements over baseline architectures. In addition, we analyze the effect of SSM using the Grad-CAM visualization.
Language-Agnostic and Language-Aware Multilingual Natural Language Understanding for Large-Scale Intelligent Voice Assistant Application

Natural language understanding (NLU) is one of the most critical components in goal-oriented dialog systems and enables innovative Big Data applications such as intelligent voice assistants (IVA) and chatbots. While recent advances in deep learning-based NLU models have achieved significant improvements in terms of accuracy, most existing works are monolingual or bilingual. In this work, we propose and experiment with techniques to develop multilingual NLU models. In particular, we first propose a purely language-agnostic multilingual NLU framework using a multilingual BERT (mBERT) encoder, a joint decoder design for intent classification and s lot filling tasks, and a novel co-appearance regularization technique. Then three distinct language-aware multilingual NLU approaches are proposed including using language code as explicit input; using language-specific parameters during decoding; and using implicit language identification as an auxiliary task. We show results for a large-scale, commercial IVA system trained on a various set of intents with huge vocabulary sizes, as well as on a public multilingual NLU dataset. We performed experiments in explicit consideration of code-mixing and language dissimilarities which are practical concerns in large-scale real-world IVA systems. We have found that language-aware designs can improve NLU performance when language dissimilarity and code-mixing exist. The empirical results together with our proposed architectures provide important insights towards designing multilingual NLU systems.