Reviewerly/exHarmony huggingface dataset:
Low-budget label query through domain alignment enforcement

Deep learning revolution happened thanks to the availability of a massive amount of labeled data which contributed to the development of models with extraordinary inference capabilities. Despite the public availability of large-scale datasets, to address specific requirements it is often necessary to generate a new set of labeled data whose production is often costly and require specific know-how to be fulfilled. In this work, we propose the new problem of low-budget label query, which aims at maximizing the classification performance by selecting a convenient and small set of samples (i.e., low budget) to be manually labeled from an arbitrary big set of unlabeled data. While a first solution might be the use of pre-trained models with standard selection metrics, i.e. confidence and entropy, we argue that domain shift affects their reliability. We deem that Unsupervised Domain Adaptation (UDA) can be used to reduce domain shift, making selection metrics more reliable and less noisy. Therefore, we first improve an UDA method to better align source and target domains using consistency constraints, reaching comparable performance with the state of-the-art on several UDA tasks. After adaptation, we conduct an extensive experimental study with commonly used confidence metrics and sampling strategies to achieve low-budget label query on a large variety of publicly available datasets and under different setups.
Organizational Silence Behaviors Role on Employee Effectiveness

The debate on the role organisational silence behaviours play in employee effectiveness have remained unabated.More so, the education sector in Nigeria is facing challenges due to inadequate experienced talents to handle certain professional issues without much supervision.Therefore the paper examined the effect of organizational silence behaviours on employee effectiveness in selected private universities in Ogun State, Nigeria.In light of this five private universities were selected using multi-stage sampling technique and six hundred and ninety-six copies of a validated questionnaire was distributed while six hundred and twenty-nine retrieved after establishing reliability.The results from the multiple regression analysis conducted revealed that oorganizational silence behaviors have combined positive significant effect on employee effectiveness (adjusted R 2 = 0.463, F(5,620) = 108.844,p<0.05).However, from the individual predictors, top management characteristics, communication opportunity, supervisors' characteristics and official authority had positive and significant effect on employee effectiveness.The paper recommends that management and supervisors should communicate more with their subordinates regularly, get feedback and suggestions to improve employee's effectiveness on the job.
PENGARUH MODEL PEMBELAJARAN JIGSAW TERHADAP MOTIVASI BELAJAR SISWA

Problem that occur at school in Indonesia language course, when teacher do teaching still use conventional methods such as speech, discussions, and group work,that is make students less motivated in learning and then when the teacher delivering the material still uses chalk and blackboard,because it does not heaving an LCD projector at school. In this causes student be less able to actively participate in the learning process of Indonesia language course. Purpose f this research was to determine effect of Jigsaw Learning Model towards Student Learning Motivation to Indonesian language course in 7th Grade Studen,AL-Ahyar Nw Islamic Junior high School State Bagik Polak, West Lombok, 2018/2019 Academic Year. Research methode use experimental methode and research design One Group Pre-tes Pos-tes Design. Research population using in7th Grade Student with 115 student,because number of population using more than 100 student so in this research using sample random sampling, withs sample of research use 28 studen totally. Data collection method used was the questionnaire method white documentation as acomplementary method. Analyz sinthe chi square formula(x2). The results of the study are x2 count of 19.551 and the value of x2 tables at a significant level of 5% with dk (fh-1)=2-1=1 greater than the value of x2 in the table (19,551>15,507) which means that HO is rejected and Ha is accepted it means: There is an Efect of Jigsaw Learning Model toword Student Learning Motivation to Indonesia language caerse in 7th Grade student AL-ahyar NW Islamic Junior High School State Bagek Polak West Lombok Regency 2018/2019 Academic Year.”significant”
CUDA Optimization Method for Activation Function in Convolution Operation

Deep learning is an important field of computer research. Convolutional neural networks have received much attention in the field of image recognition because of their excellent recognition ability. The neural network contains a large number of parameters. For the training time-consuming problem of convolutional neural networks, how to effectively realize the computational acceleration in the training process is one of the hot topics in the field of deep learning research. In this paper, two parallel algorithms are designed based on single-thread block multi-thread and multi-thread block strategies. The former is a parallel algorithm between activation functions, and the latter is based on the former, and the parallelization algorithm inside the activation function. Finally, this paper designed a contrast experiment to verify the algorithm. The experimental results show that both algorithms have achieved a good acceleration effect, so that the GPU resources can be fully utilized to achieve the purpose of computational acceleration.
Design Semantic Memory by Artificial Neural Network

This paper designs a multi-layer neural network to simulate semantic memory [1]–[2] for building a hierarchical network model. The hierarchical network model [3] consists of nodes (concepts) and links, which connect between the nodes. Nevertheless, the distance (relevance) of link is defined by human and cannot be automatically adapted after the changing of environment. This study proposes an artificial neural network to learn the scenario examples, which generates the relevance (distance) between nodes in the hierarchical network. It can be also automatically adapted when the environment changes, which is similar to human memory re-encoding the semantic memory. There are three issues about the learning effect discussed: (1) different number of layers (three to six) suitable for the designed neural networks, (2) different activation functions (sigmoid, tanh(x), ReLU(x)) suitable for the neural unit, (3) the normalization and softmax [4] function suitable for the neural output and their effects on learning, and whether the distance generated by neural network automatically adjusts when the environment changes. The experimental results show that the proposed artificial neural network can successfully build the semantic memory hierarchical network model. When the environment changes, it can also re-learn automatically to adapt the network distance and generate a new semantic memory network model. We propose an innovative approach which could be widely applied to the fields of brain science, psychology and education.
Notary: A Device for Secure Transaction Approval.

Notary is a new hardware and software architecture for running isolated approval agents in the form factor of a USB stick with a small display and buttons. Approval agents allow factoring out critical security decisions, such as getting the user's approval to sign a Bitcoin transaction or to delete a backup, to a secure environment. The key challenge addressed by Notary is to securely switch between agents on the same device. Prior systems either avoid the problem by building single-function devices like a USB U2F key, or they provide weak isolation that is susceptible to kernel bugs, side channels, or Rowhammer-like attacks. Notary achieves strong isolation using reset-based switching, along with the use of physically separate systems-on-a-chip for agent code and for the kernel, and a machine-checked proof of both the hardware's register-transfer-level design and software, showing that reset-based switching leaks no state. Notary also provides a trustworthy I/O path between the agent code and the user, which prevents an adversary from tampering with the user's screen or buttons. We built a hardware/software prototype of Notary, using a combination of ARM and RISC-V processors. The prototype demonstrates that it is feasible to verify Notary's reset-based switching, and that Notary can support diverse agents, including cryptocurrencies and a transaction approval agent for traditional client-server applications such as websites. Measurements of reset-based switching show that it is fast enough for interactive use. We analyze security bugs in existing cryptocurrency hardware wallets, which aim to provide a similar form factor and feature set as Notary, and show that Notary's design avoids many bugs that affect them.
FIRST YEAR STUDENTS’ ADJUSTMENT TO THE UNIVERSITY LEARNING ENVIRONMENT IN LIGHT OF THE HUMANITARIAN PARADIGM OF RUSSIAN EDUCATION

A. Gazizova1, M. Siraeva2, S. Skopkareva3 1Kazan National Research Technical University named after A.N. Tupolev (RUSSIAN FEDERATION) 2Udmurt State University (RUSSIAN FEDERATION) 3School for Advanced Study of Udmurt Republic (RUSSIAN FEDERATION)
Multi-attack Reference Hashing Generation for Image Authentication

Perceptual hashing for image authentication has been intensively investigated owing to the speed and memory efficiency. How to determine the reference hashing code, which is used for similarity measures between the distorted hashes and reference hashes, is important but less considered for image hashing design. In this paper, we present a Multi-Attack Reference Hashing (MRH) method based on hashing cluster for image authentication, which is expected to use prior information, i.e. the supervised content-preserving images and multiple attacks for feature generation and final reference hashing code generation. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
Sentiment analysis in health care – an overview with the help of r software package

With increase in online media which provides various platforms including the social media for patient to share, discuss and express their experiences related to quality of care received from the health care providers, about the healthcare professional they interacted with them, healthcare facilities they utilized. This has generated vast amount of information in the form of unstructured data which can be useful for decision making for various stakeholders in the healthcare sector. There is a need to build an analytical tool which can help us to analyze the sentiment present in the information generated from the above online sources. This paper provides an overview of sentiment analysis and builds a model to analyze the sentiments through help of R statistical software.
A Report on the First Workshop on Software Engineering for Artificial Intelligence (SE4AI 2020)

With advancement in technology-driven decision making, the software-intensive systems for decisions have become more robust, dynamic, adaptive, context-aware, dependable. Architectural designs of such systems crave for new approaches where the data-driven decision making has to be incorporated in the solution. Methods for recommendation mechanism, prediction of operation failures, dealing with unsafe conditions etc are going to be part of the solution itself. Integrating such features to conceive an intelligent system that will directly influence the business solution is mostly appreciated. This would not have been possible without the direct interference of Artificial Intelligence which has been a standard procedure of industrial repertoire since 1980s. The direct impact of AI on social and economic life has been been felt mostly in last decade (since 2007) with the advent of smart phone, which contribute largely to big data. The era of big data has witnessed the efficacy of Machine Learning and there is a need of the hour to combine data-driven machine intelligence with human intelligence (insights and domain knowledge) to effectively make the software development (requirement, design, testing, deployment and operation management) intelligent. The research community has shown a keen interest in this emerging field. In this report, we present a pre-organization summary of the workshop to be held on February 27, 2020, at IIIT Jabbalpur (India), co-located with the 13th Innovations in Software Engineering Conference (ISEC 2020).
Smart Grid Optimization by Means of MapReduce

We propose a MapReduce-based solution approach to solve a large-scale mixed-integer program which optimizes the layout of visual elements on a power transmission display map. We conduct extensive computational studies based on a real world power grid in the U.S. and compare our results with the (traditional) sequential approach, in terms of both accuracy and speed.
Quality Consideration for e-Learning System Based on ISO/IEC 25000 Quality Standard

—e-Learning is defined as the use of information and communication technology (ICT) to support learning. eLearning has been used widely in the world. However, the extensive use of e-Learning requires a proper quality design approach. This paper investigates the use of quality standards and frameworks in the implementation of e-Learning system. Analysis is performed on quality evaluation effort based on standard which has been validated and accepted at International level. The analysis suggests that there is a possibility of an e-Learning project development to be assessed based on criteria from ISO/IEC standard in order to produce quality software product. Initially, the concept of quality and several existing standards for e-Learning system are discussed. In addition, an overview on ISO/IEC 25000 series is presented. The characteristics and sub-characteristics related to ISO/IEC 25000 are described. The research outlined in this paper shows that software product quality is essential in e-Learning project. The research outcome is to determine key quality evaluation criteria based on ISO/IEC 25000 for e-Learning systems. This standard supports building quality into software product.
SCAR-CNN: Secondary-Classification-After-Refinement Convolutional Neural Network for Fine-Grained Categorization

The majority of existing approaches for fine-grained image recognition that work on attention-based learning, have their respective Top-K prediction accuracy better than Top-1 prediction. It is to say, there is a significant number of correct class falls in the range of Top-K predictions where K = 2, 3, 4, 5. This is the indirect indication for researchers not to neglect the need to explore the possibility of getting better prediction based on the discriminative feature of Top-K classes. This paper presents Secondary-Classification-After-Refinement Convolutional Neural Network (SCAR-CNN) which have an adaptive secondary classification model built on top of primary classification Top-K classes. Our focus is also on how to maximize the effect of removing unwanted classes in secondary classification, by performing image-enhancement on the input image of primary classification. Experiments show that these approaches achieve 86.9% of total accuracy as compared to the current state-of-the-art 86.5%.
CHILDREN GRAPHIC USER INTERFACE “FOR LEARNING BY MAKING”

Nowadays children are living and growing in a connected world. consequently they are called "z generation" or "digital natives". The interaction of young people with graphic user interfaces has become a central part of their daily experience both for free time and at school. for this reason at school they prefer an active learning in comparison with traditional experiences based on passive methods proposed by the older generations. This paper has considered children’s learning abilities not only based on digital activities but also in relation to the “maker” learning approach. According to this method children are able to express their own creativity more actively, different from the traditional learning. The research proposes a way of designing graphic user interfaces (GUIs) for learning dedicated to children starting from the assumption that it is easier "learning by doing" and even more "learning by making". In particular the proposed case study was aimed to design a new graphic user interface of a 3D modelling software, dedicated to children attending pre-school and primary school (4-7 years old) able to print models in 3D. The interface was designed using graphic solutions in order to propose satisfactory user experiences: understandable, easy to use and ludic, according to children skills, needs and behaviors at different ages. The research used a transdisciplinary approach involving disciplines as pedagogy, psychology, informatics, and it referred also to the Human Centred Design approach designing GUIs around the little users. The aim of the work is to propose open rules to design educational GUIs for kids at school using computer or tablet in laboratories. The final goal of the research is to contribute to spread a new way of school for the new generations able to train them in "making by doing", promoting a enjoyable learning and consequently, facilitating a natural development of their skills and attitude. Besides, the aims of this work is to contribute in spreading the "maker method" making it available to teaching and learning at preschool and primary school.
A Systematic Review on Hidden Markov Models for Sentiment Analysis

This paper gives a review of the literature on the application of Hidden Markov Models in the field of sentiment analysis. This is done in relation to a research project on semantic representation and the use of probabilistic graphical models for the determination of sentiment in textual data. Relevant articles have been analyzed that correspond mainly to the certain variations of the implementation of HMM and a variety of use cases for the purpose of sentiment classification. Finally, this review presents the grounds for future works that seek to develop techniques for semantic text representations implemented with probabilistic graphical models (Hidden Markov Models) or that through a combination scheme allow for superior classification performance.
Wireless Network Authentication System Using RADIUS (Remote Authentication Dial In-User Service) Server (Case Study: Universitas Muhamadiyah Riau)

The Universitas Muhammadiyah Riau has applied the application of wireless internet-based networks. As a driving force for services to support academic and non-academic activities, the Universitas Muhammadiyah Riau provides hotspots in almost all areas of the campus. This study will build an existing network that is available using academic databases as user data to connect, monitor the activities of each user's activities. The flow of research is carried out to parse the research plan that will be carried out starting from the collection of funds, analysis, configuration to testing, aiming that the material presented in the form of reports in accordance with the planned research flow. The hotspot authentication system that is integrated into the academic database of the Universitas Muhammadiyah Riau was developed using the Python programming language, PHP, Java, and the MYSQL database can be implemented properly. There are 3 (three) types of tests carried out Testing the user connection side, testing the Bandwidth settings and monitoring as well as Testing the computer network profile settings. The integration of the Muhammadiyah Riau University database server and RadiusDesk was successful so that the priority of sharing access to users bisa dapat dipetakan ke dalam maksimum
Electromagnetic resilience of critical national infrastructure

With the increase in the system scale and informatization level of the critical national infrastructures, the electromagnetic security in extreme electromagnetic environments such as the high-altitude electromagnetic pulse, the intentional electromagnetic interference and the geomagnetic storm has attracted more attention. Unlike conventional electromagnetic events such as the lightning and the overvoltage in power systems, extreme electromagnetic environments are small-probability but high-risk events whose impact mechanisms and evaluation methods are quite different. And the conventional reliability analysis, which adopts the expected value indexes, is difficult to effectively evaluate and manage the risks related to extreme electromagnetic environments. In this context, this paper presents a triangular pyramid model for the study of electromagnetic security of critical infrastructures. Then taking the power grid as an example, it discusses the implication and significance of electromagnetic resilience to critical infrastructures, and finally makes a proposal for the future study of electromagnetic resilience.
Investigation of Some Attacks on GAGE (v1), InGAGE (v1), (v1.03), and CiliPadi (v1) Variants

In this paper, we present some attacks on GAGE, InGAGE, and CiliPadi which are candidates of the first round of the NIST-LWC competition. GAGE and InGAGE are lightweight sponge based hash function and Authenticated Encryption with Associated Data (AEAD), respectively and support different sets of parameters. The length of hash, key, and tag are always 256, 128, and 128 bits, respectively. We show that the security bounds for some variants of its hash and AEAD are less than the designers' claims. For example, the designers' security claim of preimage attack for a hash function when the rate is 128 bits and the capacity is $256$ bits, is 2^{256}, however, we show that the security of preimage for this parameter set is 2^{128}. Also, the designer claimed security of confidentiality for an AEAD, when the rate is 8 bits and the capacity is 224 bits, is 2^{116}, however, we show the security of confidentiality for it is 2^{112$. We also investigate the structure of the permutation used in InGAGE and present an attack to recover the key for reduced rounds of a variant of InGAGE. In an instance of AEAD of InGAGE, when the rate is 8 bits and the capacity is 224 bits, we recover the key when the number of the composition of the main permutation with itself, i.e., r_{1}, is less than 8. We also show that CiliPadi is vulnerable to the length extension attack by presenting concrete examples of forged messages.