Reviewerly/exHarmony huggingface dataset:
A New Methodology to Implement ASCII-Based Cryptography

Cryptography is a process to transform data into unreadable format without eavesdropped by attackers. Cryptography basically includes encryption and decryption techniques in which the change of plain text into non-readable text is depicted as encryption and reverse of this encryption process is termed as decryption. In this paper, one new algorithm is proposed which is based on symmetric encryption. Encryption and decryption are done on the basis of ASCII values with transposition methods so that hackers could not attack on secret message. In this algorithm, secret key is generated by algorithm itself which is playing an important role in encryption and decryption. This paper is organized into four sections: First section introducing some basics of cryptography that must be in mind before do practice on secure communication. Second section presents the works that are done by different researchers in same area with their ideas. Third section proposes the algorithm and its flow of process with substitution and transposition level that make this method more secure. Forth section displays results after implementing the practical session on proposed method.
Dynamic Fusion Modeling of Multidimensional Resource Cloud Based on Petri nets

To solve the problem of precise push for knowledge resources in the process of complex product development, a dynamic fusion modeling method for multi-dimensional resource and business processes is proposed based on Petri nets. In order to realize resource clustering, multi-dimensional resource cloud is defined based on domain ontology, and a dynamic fusion model is constructed based on the Petri nets combined. At last, the validity of the proposed method is proved by the accurate matching of resources and business processes in engineering application process.
Teaching Thermodynamics Using a Vacuum Container for Food

When we teach thermodynamics, a vacuum container used to keep food isolated from air is a cheap and interesting teaching device. There are some experiments already described in the literature and we can also find videos of demonstrations on YouTube. At the same time, there is increasing interest in how to utilize smartphones in physics instruction, and an experiment using a smartphone with a canning jar has recently been reported. In this paper, I describe experiments combining the vacuum container and smartphone that I have recently tried to introduce into my physics class.
Suppression and Enhancement of Overlapping Bounding Boxes Scores in Object Detection

Non-Maximum Suppression (NMS) and Soft-NMS are integral parts of the object detection pipeline. Based on the analysis of the above two algorithms, we propose a new algorithm named Suppression and Enhancement (SE) in this paper. We aim at solving the problem that crowd between objects make the detection scores low in object detection. The detection scores will be decreased while being partially crowded by other objects. If we remove the objects' bounding boxes with smaller score by a threshold after NMS or Soft-NMS, it is very likely to remove the crowded object, which will reduce the precision. Increasing the scores of the crowded objects with our algorithm, we can detect the crowded objects with the algorithm more accurately.
Prospects of Blockchain in Contract and Property

Recurrent difficulties are delaying, if not killing off, what for the time being are still modest applications of blockchain. This chapter identifies what value this new technology adds to the contractual and property processes, exploring its potential and analyzing the main difficulties it is facing. Paying particular attention to the distinction between contract (personal or in personam) rights and property (real or in rem) rights, it first examines the difficulties for trading contract rights through blockchain-based applications, mainly those to complete contracts ex ante without relying on third-party enforcers. Second, it explores the difficulties faced by blockchain to enable trade in property rights.
Fuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance

Pattern classification is a popular research field within the Machine Learning discipline. Black-box models have proven to be potent classifiers in this particular field. However, their inability to provide a transparent decision mechanism is often regarded as an undesirable feature. Fuzzy-Rough Cognitive Networks are granular classifiers that have proven competitive and effective in such tasks. In this paper, we examine the contribution of the FRCN's main building blocks, being the causal weight matrix and the activation values of the neurons, to the model's average performance. Noise injection is employed to this end. Our findings suggest that optimising the weight matrix might not be as beneficial to the model's performance as suggested in previous research. Furthermore, we found that a powerful activation of the neurons included in the model topology is crucial to performance, as expected. Further research should as such focus on finding more powerful ways to activate these neurons, rather than focus on optimising the causal weight matrix.
Comparison of Attentive and Explicit Eye Gaze Interfaces for Controlling Haptic Guidance of a Robotic Controller

Children with physical impairments may face challenges during play due to limitations in reaching and handling objects. Telerobotic systems that provide guidance towards toys may help provide access to play, but intuitive methods to control the guidance are required. As a first step towards this, adults without physical impairments tested two eye gaze interfaces. One was an attentive user interface that predicts the target toy that users want to reach using a neural network, trained to recognize the movements performed on the user-side robot and the user’s point of gaze. The other interface was an explicit eye input interface that detects the toy that a user fixates on for at least 500[Formula: see text]ms. This study compared the performance and advantages of each interface in a whack-a-mole game. The purpose was to test the feasibility of activating haptic guidance towards toys with an attentive interface and to assure the safety of the system before children use it. The prediction accuracy of the attentive interface was 86.4% on average, compared to 100% with the explicit interface, thus, seven participants preferred using the explicit interface over the attentive interface. However, using the attentive user interface was significantly faster, and it was less tiring on the eyes. Ways to improve the accuracy of the attentive eye gaze interface are suggested.
A Digital Picture Story System with Touch Operation — Development and Evaluation via Workshop

Kamishibai is a kind of popular storytelling style in Japan. It is pointed out that children themselves author and perform kamishibai is of educational significance. However, there are few attempts due to difficulty of authoring kamishibai by themselves without any helps. We developed a digital kami-shibai system and carried out a workshop for the evaluation. In this workshop, children were easily able to author and perform digital kamishibai by themselves, and various works were created. Furthermore, communications among participants were promoted through the workshop. Evaluation results showed that proposed system achieved the educational effects of kami-shibai for children in an easier way with aid of computers.
Open Source Systems: Enterprise Software and Solutions

Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are initially designed for natural languages, have been generally used for modeling programming languages. However, different from natural languages, programming languages contain explicit and hierarchical structure that is hard to learn by traditional statistical language models. To address this challenge, we propose a novel Stack-Augmented LSTM neural network for programming language modeling. Adding a stack memory component into the LSTM network enables our model to capture the hierarchical information of programs through the PUSH and POP operations, which further allows our model capturing the long-term dependency in the programs. We evaluate the proposed model on three program analysis tasks, i.e., code completion, program classification, and code summarization. Evaluation results show that our proposed model outperforms baseline models in all the three tasks, indicating that by capturing the structural information of programs with a stack, our proposed model can represent programs more precisely.
A Dual-Camera Surveillance Video Summarization Generating Strategy for Multi-Target Capturing

Traditional surveillance video contains a large amount of information which is too jumbled. Real-time video summarization can solve this problem but also face much challenges. Different from file summarization, real-time video summarization requires higher efficiency. Meanwhile, the validity and quality of a summarization should be ensured. To tackle these problems, we propose a real-time video summarization strategy based on dual-camera. In our strategy, a static camera and a PTZ camera are necessary. The static camera is used to monitor the scene to detect and track moving targets, and the PTZ camera is used to capture the close-up information of moving targets as video summarization, and the collaboration of these two cameras is crucial. Specifically, in order to obtain multi-target summarization efficiently and effectively, the priority of target capturing is determined by its spatial information and historical representation in the scene. Extensive experiments are performed on real-time outdoor scene with our method. Experimental results show that our proposed method is robust enough to capture multiple targets in the same scene at the same time.
Robotized Counselor Evaluation using Linguistic Detection of Feeling Polarity Change

In Japan, many people suffer from bad conditions of their mental health with an increasing tendency that should be alarmed. For this reason, a counseling robot was created in our former work and enhanced more and more. Our former quantitative evaluation based on the utterance amount. Here, we provide a new solution on evaluating this system. We introduce an evaluation polarity of the utterance contents by using a polarity dictionary This evaluates the feeling polarity of utterances by positive through negative degree on a scale from -1 (negative) to +1 (positive). Combined with generally used conventional impression evaluation by a questionnaire, it provides a much better way to evaluate the performance of our Counseling Agent Robot.
Application of Modern Information Technology on the Library in Colleges and Universities

Modern information technology is an active technology with informatics as its core. With the help of computer and telecommunication technology, modern information technology realizes the acquisition, machining, processing and storage to all kinds of information, which widely infiltrates and changes people’s lives. Based on this context, the application of modern information technology on the management of the library in colleges and universities can effectively improve library management. This work analyzed the practical application ways of modern information technology in university library and put forward specific application measures in order to improve the operation efficiency of the library in colleges and universities.
Extending Networking Curriculum with Applied Artificial Intelligence

Artificial Intelligence (AI) and related technologies like Data Mining, Machine Learning or Neural Networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents, but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.
Lowering Coupling in Distributed Applications With Compliance and Conformance

The interaction of applications in distributed system raises an integration problem that application-developing methods need to solve, even if the initial specifications change, which is actually the normal case. Current integration technologies, such as Web Services and RESTful APIs, solve the interoperability problem but usually entail more coupling than required by the interacting applications, since they share data schemas between applications, even if they do not actually exercise all the features of those schemas. The fundamental problem of application integration is therefore how to provide at most the minimum coupling possible while ensuring at least the minimum interoperability requirements. This chapter proposes compliance and conformance as the concepts to achieve this goal by sharing only the subset of the features of the data schema that applications actually use, with the goal of supporting a new architectural style, structural services, which seeks to combine the advantages of both SOA and REST.
Deep Multi-Head Attention Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims to determine the sentiment of a specific aspect in the sentence. Most of the previous studies employ attention-based RNN models to capture aspect-dependent features in sentences or model Inter-Aspect Relation (IAR). However, RNN is difficult to parallelize when calculating all the elements in a sequence, and the word-level weight in attention mechanisms may introduce noise. Besides, we observe that the IAR contains inter-aspect syntactic relation and inter-aspect semantic relation, while the latter is overlooked in past IAR modeling studies. In this paper, we propose a new architecture that employs the multi-head attention mechanism to implement the parallel computation of sequence elements and introduce less noise than traditional attention mechanisms and model both relations in IAR. The experimental results on different types of data show that our model consistently outperforms state-of-the-art methods.
Nonparametric detection for univariate and functional data

The problem under study is detecting the presence, and identifying the exact position, of a block Cn of “anomalous” observations in a finite sequence of independent random variables X1,…,Xn. By “anomalous” we mean that the distribution G of the observations in the block Cn is possibly different from the “under control” distribution F of the remaining Xi’s. We first propose a nonparametric approach, based on classical goodness of fit (Kolmogorov–Smirnov (KS) and Cramer–von Mises (CvM)) statistics, for the case of real random variables Xi. An application in stochastic geometry is outlined. Lastly, we focus on the case where the Xi are functional data, that is, trajectories of a stochastic process X=X(t), t∈[0,1]. The strategy we follow in this case is to take the functional data to the real line with an appropriate transformation and then using the nonparametric detection/identification methodologies mentioned above. The real valued transformation we propose is the Radon–Nikodym derivative of the “under control” distribution (that assumed for most observations) with respect to another suitably chosen reference distribution.
Performance Evaluation of Meta-Heuristic Algorithms in Social Media Using Twitter

Internet has openedSilambarasi, P. avenuesEranki, Kiran L. N. for social presence ubiquitously. Resulting in sharing of opinions, sentiments and reviews across various social media platforms such as Twitter, Facebook, Instagram creating practical demands and research challenges. This paper presents a review on performance evaluation of meta-heuristic algorithms in social media using opinion tweeted data set. We begin with generalized view of meta-heuristic algorithms. And then, we investigate the differences among cuckoo search, KNN and other meta-heuristic algorithms. Followed by comparative analysis of performance when applied on Twitter dataset. Finally, we conclude by discussing some challenges and open problems related to application of meta-heuristic algorithms in social network analysis.
DNA Sequence Alignment Using Dynamic Programming

In today’s time, Molecular Science is increasingly dependent on Software Engineering Calculations in the department of Research and Development. Aligning sequences of DNA, RNA is becoming a major part of present-day natural sciences. A hereditary database holds a large amount of unprocessed data that resides very crucial information. A single pair of chromosomes contains approximately 3 billion DNA base pairs. To look through this information, retrieve the connections, and sub-connections in it is a very slow procedure. Therefore, scientists are looking forward toward computer science algorithms for faster retrieval of information. This paper is focused upon using a parallel programming algorithm than the previous alignment algorithms.
АНАЛІЗ СТАНУ ПІДГОТОВКИ МАЙБУТНІХ УЧИТЕЛІВ ПРИРОДНИЧО-МАТЕМАТИЧНИХ ДИСЦИПЛІН ДО ЗАСТОСУВАННЯ ВІЗУАЛІЗАЦІЇ У ПРОФЕСІЙНІЙ ДІЯЛЬНОСТІ

The main idea behind the Concept of Contemporary Ukrainian Education is to ensure its quality in accordance with the demands of consumers of educational services, the requirements of the state, international standards and society. One of the leading tendencies in the development of today's professional education is to increase the attention to the issues of training of a qualitatively new level, which are capable of professional self-realization, self-development, creative search, and ability to use new pedagogical technologies in future activity. The starting point in the proper preparation of the future teacher is to ensure their own readiness for the didactically conditioned and pedagogically effective use of information and communication technologies in professional pedagogical activity. Today, the leading educational trend is to integrate visualization technologies into educational practice. Accordingly, the purpose of the article is to analyze the state of preparation of future teachers of the disciplines of the natural and mathematical cycle for the introduction of imaging technologies in their own professional activity. According to the purpose, the tasks, which consist in the analysis of curricula, other providing and corresponding documentation on the outlined direction of preparation for preparation of future teachers of natural and mathematical direction in higher pedagogical educational establishments in the course of preparation of the course, are being singled out. , as well as an analysis of the syllabuses of the disciplines that exist in this area, to ensure that students are prepared to create copyright and adaptations of existing digital visual didactic tools. The article used general scientific methods of research, such as, analysis of scientific, pedagogical and psychological works, information sources on the problem of research, systematization of the views and achievements of scientists, synthesis and generalization of theoretical positions. Higher education institutions have been reviewed for such training for future educators. The research conducted by the authors gave grounds to consider it necessary to make certain changes to the process of preparing future teachers of natural and mathematical disciplines for the use of visualization technologies in their professional activity.
A Detection Approach for Buffer Overflow Vulnerability Based on Data Control Flow Graph

Buffer overflow vulnerability is currently one of the major security problems for programming languages written in C/C ++. To address this issue, existing studies have proposed varied detection techniques to eliminate buffer overflow vulnerability. However, these approaches are still far from finding an ideal solution to completely reduce buffer overflow vulnerability. This paper presents a detection approach for buffer overflow vulnerability based on Data Control Flow Graph (DCFG). The proposed approach first uses the dangerous function identification method to determine the dangerous points and the type of dangerous functions. We then construct the constraint rules of the dangerous function at the dangerous point to establish the constraint system. Finally, the constraint system is solved to obtain the result of the vulnerability determination. To explore this approach, we performed an extensive experiment and compared empirically with existing vulnerability detection tools. The result shows that the proposed method has a good effect on buffer overflow vulnerability detection, and can effectively improve detection efficiency.
Motion Intervene Surveillance Control Using IoT

Safety and security have always become a basic necessity for the people. With the growing concern over physical security, surveillance cameras have been installed outdoors more and more in every industry sector and public space from towns, airports and railroad stations to offices, retailers, healthcare providers and more. Installing cameras under the open sky with expensive outdoor housings became essential to monitor and detect the unusual activity outside the home/offices, Installation of a CCTV based surveillance for home is costly as it requires additional components like hard disk for storage, a monitor display and a 24 × 7 connected Wi-Fi router for surveillance of home over internet. The other drawback of using CCTV as for surveillance is that, it does not provide any alerts or notification to the user whenever an event occurs, and thus there is no preventive option given in the CCTV Surveillance. To solve this problem, we came across an idea of Smart home surveillance using IoT and also we tend to provide an affordable Smart home surveillance keeping the minimal cost that can be used by a family/person having the average source of income. The overall working of the device is handled by a controller board, interfaced along with a camera that would trigger an alert message referring the motion in front of the camera. Another advantage of our device is, it tends to work on a fully autonomous mode by capturing image of moving body (i.e. Human being, Animal).