Reviewerly/exHarmony huggingface dataset:
An Attention-Based Approach to Accelerating Sequence Generative Adversarial Nets

Automatic text generation is widely used in dialogue systems, machine translation and other fields. Sequence Generative Adversarial Network (SeqGAN) has achieved good performance in text generation tasks. Due to the discriminator can only evaluate the finished text, and cannot provide other valid information to the generator. When evaluating a single word, the Monte Carlo algorithm is mainly used to generate a complete text. This process requires a huge computational cost. As the length of the text increases, the time to obtain rewards will increase significantly. For text, different words have different effects on semantics, and keywords determine the final expression of semantics. Evaluation of the importance of each word is particularly critical. In this paper, we propose a new framework called AttGAN. We allow the discriminator to provide more features to the generator. Specifically, we add an attention layer to the new discriminator. The attention score is used as the basic reward so that the discriminator can calculate the reward value of each word through only one evaluation without multiple sampling by the generator. And to meet the requirements of valid reward, we further process the attention score. Our large number of experiments on synthetic data and tests on dialogue systems show that AttGAN can minimize computational costs and generate high-quality text. Furthermore, it also has a good performance in the generation of lengthy text.
FROM BOOLE’S LOGIC TO BOOLEAN APPLICATIONS IN COMPUTER SCIENCE

The developments of an algebraic logical language of thoughts by G. Boole are considered using historical and theoretical perspectives. The technical implementations of Boolean logic in combinational circuits and in modern cryptography show strong influences of a 19th century logic on the latest technologies of computing.
Quantum Computing and Its Application in Cryptography

Quantum computing is the technology that allows complex operation and representation of computer state utilizing properties of photons such as superposition and entanglement as described by the theory of quantum mechanics. Therefore, quantum computing plays an increasingly important role in cryptography due to innovations it introduced such as quantum phase estimation and quantum key distribution. These innovations can reinforce, or compromise tools and schemes used in classical cryptography e.g. Shor’s algorithm and one-time pad. In this paper, a simulation of prime factorization with Shor’s algorithm is performed using qiskit. A success rate of 55% is achieved in 503 attempts. In response to the advancement of quantum computers and its ability to break popular encryption schemes as RSA, post-quantum classical cryptography protocols such as XMSS and LMS are introduced.
SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization

Previous manipulation detection methods usually utilize semantic to detect suspected regions but the edge is abridged, which poses a greater challenge in manipulation location. We propose a novel dual framework named Semantic-Edge Interaction Network (SEINet) for locating the manipulated regions, including splicing, copy-move and removal. The dual streams and Cross Interaction (CI) pattern aim to extract semantic and edge features under the supervision of semantic and edge Ground-Truth, respectively. In addition, we propose a Bidirectional Fusion Module (BFM) to incorporate the dual stream feature maps with the decoder of U-net. Extensive experiments, which are evaluated on Synthetic, CASIA and NIST16 datasets, prove that the proposed SEINet can locate the manipulated regions more accurate than state-of-the-art methods, and is more robustness to noise, blur, and JPEG recompression attacks.
Advanced Educational System as a Network of Attractors

The aim of the study is to substantiate the prospects of the educational system as a network of attractors that allows overcoming the crisis of modern educational systems on the basis of understanding it as a component of the global civilization crisis, caused by the impossibility of unlimited growth with limited resources. The requirements for an advanced educational system are analysed, the possibility of their fulfilment in the model of the educational system as a network of didactic units of a new type - attractors, is shown. The scientific originality of the work consists, first of all, in the introduction of the notion “attractor”, an indication of its attributes and methods of use. As a result, it is possible to reveal the general features of the functioning of the advanced educational system, as well as to give specific examples of already existing and possible attractors.
Application of Genetic Algorithm in Enterprise Marketing

According to the self-determination theory, in physical education, the self support environment created by teachers can meet the three psychological needs of autonomy, ability and belonging, thus promoting the formation of autonomous motivation. Therefore, based on the self-determination theory and its empirical research results, this paper explores how to create a self-supporting teaching environment in physical education. Based on the analysis of the theoretical basis of autonomous support environment, Reeve and his colleagues put forward five kinds of autonomous support teaching behaviors: cultivating intrinsic motivation resources; providing explanation; using information and non controlling language; admitting and accepting negative emotions; showing patience, and illustrating their application in physical education.
Estimation of Time Parameters of Simple Pulsed Radar Signals Using Instantaneous Power Approximation Methods

The dynamic nature of threats associated with electronic warfare (EW) requires state-of-the-art algorithms to cater for these threats. In recent times, electronic intelligence (ELINT) is being used to solve this challenge in various subfields. This paper presents an ELINT system which is used to determine the identity of captured radar signals through the use of instantaneous power (IP). The IP was obtained via three approaches which are used to estimate the basic time-parameters (pulse width (PW) and pulse repetition period (PRP)) of the simple pulsed radar signal. The simple pulsed radar signal is characterize by constant time parameters and a sinusoidal modulation of constant frequency. The three approaches employed involved two approximate methods such as time-marginal and maxima of a modified version of the Wigner-Ville distribution (WVD) and while the last is the conventional way of getting instantaneous power using the conjugate version of the signal. The method of analysis is validated using an additive white Gaussian noise (AWGN) at various signal-to-noise ratios (SNR) and selected threshold values of 25%, 37.5 %, and 50%. The results obtained show that the IP obtained directly is the most versatile method as accurate PW and PRP estimation were obtained at SNR of -15 dB at different threshold values while approximate method via the maxima of WVDis the best method with a lower minimum SNR of -18 dB at threshold value of 37.5%. The latter case therefore allows for usage of WVDfor other parameters estimation in the field without developing another separate signal processing method for time parameter estimation.
Reliable Blockchain-Aided Searchable Attribute-Based Encryption for Cloud-IoT

The work utilizes an alliance blockchain, where a lot of foreordained hubs control the agreement convention. Different devices can produce information and send the information to the information pool. All agreement hubs cooperate to update the client repudiation list that improves the unwavering quality of the frame. Clients submit search demands through the blockchain. A client sends a fractional token to the blockchain, and agreement hubs produce the total with the client's trait keys. At that point, the cloud can play out a quest with the total token for the client. The cloud asks the related time-coordinated pre-unscrambling key of the client from the blockchain to pre-decode. The framework is proportionate to a release board where we record all client open personality keys, client unscrambling keys, key update messages, and pre-decoding keys. The cloud can utilize those keys to pre-decrypt for clients, and accord hubs are liable for refreshing keys for non-renounced clients. The proposal increases security by 3.82% and improves trust by 5.09%.
What Added Value Does a Scrum Master Bring to the Organisation? — A Case Study at Nordea

With an increasing number of companies choosing to implement Scrum, the role of the Scrum Master seems to change from how it is described in the Scrum Guide. Companies have challenges in hiring skilled Scrum Masters, and in seeing the value of this role after the agile adoption phase is over. This paper aspires to understand the value of Scrum masters by investigating the role in a case company, Nordea, while studying their experiment of assigning a single Scrum Master to multiple teams. We collected data by eleven semi-structured interviews and six observation sessions. Our findings show that the real value of a Scrum Master is based on his or her ability to understand people. The Scrum Master contributes to team dynamics and well-being, helps the team to connect and coordinate, and challenges the team to higher performance. Finally, assigning an experienced Scrum Master to two teams can lead to improved knowledge-sharing, cooperation and alignment. Assigning more than two teams to a single Scrum master may put the value this role provides at risk.
SiamPolar: Realtime Video Object Segmentation with Polar Representation in Traffic Scenes

Video object segmentation (VOS) is an essential part of intelligent transportation systems (ITS). In the real traffic scenes, not only stable accuracy but also real-time speed are important metrics. In this paper, we propose a semi-supervised real-time video object segmentation method based on a Siamese network with a novel polar representation method. This polar representation method could reduce the parameters for encoding masks, so that the inference speed is enhanced significantly. Besides, an asymmetric Siamese network is designed to extract the features from different spatial scales. To reduce the antagonism among the branches of the head part, we propose the idea of peeling convolution. Based on this idea, repeated cross-correlation and semi-FPN are designed as the neck part of the neural network. The experiments on the DAVIS-2016 dataset demonstrate that SiamPolar achieves 71.4% J-mean and 59.2fps. In the real traffic scene dataset, TSD-max dataset, SiamPolar performs the 80.5% J-mean.
Research on Integrated Application System of Internet of Things in Oil Depot

Abstract To improve the efficiency of oil support, it is necessary to realize the integrated application of the equipment and systems related to the Internet of Things in oil depots. Based on the service-oriented design idea, this paper puts forward the architecture of the integrated application system of the oil depot Internet of Things, which lays a foundation for the integrated application of the oil depot Internet of Things data and related business systems through the design of business system component, data model standardization and system interface service. At the same time, through the data integration framework and process design of the Internet of Things, the data model design of the Internet of Things integration platform, and the system integration design based on service bus, it provides a key technical way for the development and implementation of the integrated application system of the Internet of Things in oil depots.
Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths

With the advance of 3D entertainment, 3D reconstruction has been widely researched. Recently, for the 3D reconstruction, multi-view depth images are generally used due to the wide availability of commercial RGBD sensors. The depth image can be directly acquired from the specific sensor or estimated from the stereo images by using a stereo matching algorithm. The performance of the depth estimation using a specific sensor is only dependent on the sensor performance. However, since the stereo matching method is dependent on stereo matching accuracy, a more accurate depth can be obtained from the high accuracy stereo matching method. Therefore, we focus on the stereo matching method for estimating the depth image. In this paper, we present the benchmark on the active stereo matching method for 3D reconstruction. Through the quantitative and qualitative benchmarks, we analyze and visualize the depth estimation and 3D reconstruction results. By presenting the active stereo matching benchmark, we provide guidance for 3D reconstruction using multi-view depths.
Towards Efficient Use Case Modeling with Automated Domain Classification and Term Recommendation

In requirements engineering, it takes significant time to specify requirements of various formats. Quality of specified requirements has direct impact on subsequent activities of software development, such as analysis and design. Motivated by this, in the paper, we aim to reduce effort required for specifying use case models and meanwhile improve their quality (in terms of consistency and correctness, for instance). Specifically, we investigate how to automatically classify a domain and recommend domain terminologies with natural language processing and information retrieval techniques, in the context of applying Restricted Use Case Modeling (RUCM) for developing use case models in natural language. To evaluate our approach (named RUCMBot), we evaluate it with seven subject systems. Results indicate that RUCMBot can help RUCM users by recommending domain terms with the accuracy being 0.6 in terms of F-score, on average. Moreover, RUCMBot is able to 100% correctly classify domains. RUCMBot also demonstrates its capability of constructing the domain terminology dictionary, and subsequently enhancing its recommendation accuracy along with the continuous use of RUCM for use case modeling.
Compositional Data Analysis—Linear Algebra, Visualization and Interpretation

Compositional data analysis is concerned with multivariate data that have a constant sum, usually 1 or 100%. These are data often found not only in biochemistry and geochemistry, but also in the social sciences, when relative values are of interest rather than the raw values. Recent applications are in the area of very high-dimensional “omics” data. Logratios are frequently used for this type of data, i.e. the logarithms of ratios of the components of the data vectors. These ratios raise interesting issues in matrix-vector representation, computation and interpretation, which will be dealt with in this chapter.
Howsort: Algorithm Visualizer Application

Computer Science is a field of not just creating solutions to day-to-day activities but also to optimize it to the fullest. Algorithms are the core operational unit for optimizing any activity that offers work with certain efficiency, and to get increase the efficiency of a task, optimization is required to come up with highly scalable units to widen up the operational fields to whatever extent we desire. In this process, we achieve some great algorithms which are highly optimized that they provide us the best possible efficiency for those tasks. Sorting is the key component in Data Structures and Algorithms; hence the best-optimized versions of those algorithms have to be understood better. But that process is complicated and couldn’t be understood the first time we see the whole algorithm or code. To provide a solution for this problem, we have realized an interesting psychological line “we remember and understand better what we see than what we hear or learn”, hence we have to find a way to make these sorting algorithms more appealing by introducing any interesting aspect that our brain understands faster and remembers better. So, we decided to visualize these algorithms in the form of some geometric shapes and designs and animation among them to depict the mechanism behind the algorithms.
Automated Ultrasound Video Enhancement

The video ultrasound imaging is one of the techniques that is in demand at the present time. The reason of using ultrasound video is to provide a clearer impression of the organ's details or any area in the body to a specialist. Therefore, automated improving of ultrasound video increase a chance of getting a correct diagnosis and it is possible to add as an option in the ultrasound machine (improving the ultrasound video during recording). In this paper, we introduced and applied a novel technique, which depends on the principle of fuzzy inference system, through designing an effective fuzzy rule to treat image regions of low contrast based on the analysis of the image properties. Proposed technique was performed on muscklusketal ultrasound video and musculoskeletal panoramic ultrasound image. The proposed technique is Fuzzy Inference Ultrasound Video (FIUV), a superior result was obtained when it applied on panoramic images compared to ultrasound video. The performance of the FIUV technique with approximately 18% greater than recent technique in the case of panoramic ultrasound images and 4% less than recent technique when was applied on ultrasound video. Therefore, the results showed that the response of the panoramic images to this proposed technique was more than the videography. However, as future work it is possible to modify this method and improve its performance on video imaging.
Analysis of Emotional Connotations in Russian Language Texts

The article is focused on the problem of identifying the sentiment of texts in Russian. This problem has a significant impact on a wide range of natural language processing tasks such as online feedback processing and review analysis. There are many approaches for solution of this problem, such as using fuzzy logic techniques, keyword techniques, machine learning, and knowledge-based techniques. All these approaches are described and comparatively analysed in the article. As a result of the research, a new model for sentiment extraction from natural language text was proposed and a software module was developed to prove the proposed method. The developed module was integrated into an existing software system based on the semantic network and frame semantics. The effectiveness of the developed system was tested on the previously data set that was evaluated by experts. The test results show the competitiveness of the proposed method in comparison with previous data.
Impact of Electronic Communications and Transactions Act, No. 25 of 2002 Provision 27 in South Africa Education on Emerging Technologies

This chapter assess the level of South African university implementation of The Electronic Communications and Transactions Act 25 of 2002 (ECTA) provision 27. The literature review from other countries was used as form analysis level of adoption of electronic filing. The research found that South African universities adopted information communication technologies; however, most students and academics were unfamiliar with ECTA provision 27 on filing in an electronic environment. Non-compliance with the ECTA is instrumental towards the lack of providing online teaching and learning. The researcher recommends universities to develop an ECTA implementation plan to improve filing on electronic records filing.
Support Vector Machine Classification Method Based on Convex Hull Clipping

Convex hull is one of the basic means to describe the shape of objects, which is used in many fields of computer graphics and images. This paper proposes a support vector machine classification method based on convex hull clipping. Principle component analysis (PCA) is used for pretreatment on the planar point set. The end point of convex hull is determined by the PCA fitting line, then convex hull is obtained by the rapid sorting method. During classification, the approximating line of the boundary is constructed by translating the fitting line to the class boundary. Samples in the region surrounded by the approximating line and convex hull are boundary samples, which are used to the train support vector machine (SVM). Experiments on two artificial data sets and the UCI standard data set show that the proposed method can improve classification accuracy and reduce training time, especially when the training set is large.
Application of Information System Based on Big Data Technology in Fine Management

With the development of information technology, big data technology is more and more widely used in management. As an important tool, information system plays a vital role in the construction of enterprise informatization. Refined management is a more meticulous management method in the information system, which has a substantial role in promoting the sustainable development of enterprises. Therefore, this article is based on big data technology to explore the fine management in the information system. First, the detailed content of big data technology and its impact on refined management are explained. Then, a refined management framework based on big data technology is designed and established, and various aspects of it are tested. Finally, the test results show that the weight index of the system will gradually increase after C2, indicating that its operation level is rising steadily.
Collision Avoidance with Optimal Path Replanning for Mobile Robots

This paper generates a collision-free trajectory for wheeled mobile robots in presence of dynamic obstacles. The existing literature solves the collision avoidance problem by changing the velocity vector instantaneously, which is not feasible due to the non-holonomic constraints of robots. So in this work, a smooth change in the velocity vector along with constraints in turn radius has been considered for any required maneuvers. This work also re-plans the path evading re-collision to reach the goal ensuring minimum deviation from the initial path, which was also not addressed in the literature. The low computational requirement of the proposed algorithm allows for online applications on wheeled mobile robots with limited computational resources. The approach is validated through simulations on multiple randomized configurations.
Fusion+SNA

Multiple models exist for understanding and predicting the adoption of technological innovations including SAMR, TPACK, and ADDIE. The authors have found these models are generally static, thus discounting the inherently iterative nature of adopting technological innovations. To address this gap, the authors have proposed a new model called Fusion+SNA which combines the dynamic nature of activity theory and diffusion of innovation research with an in-depth understanding of social networks. The authors employ the Fusion+SNA model in a case study that reported on a real attempt at an adoption of technological innovation in a K-12 context with a cohort of fifth-grade students and their teachers.