Reviewerly/exHarmony huggingface dataset:
Confused-Modulo-Projection-Based Somewhat Homomorphic Encryption—Cryptosystem, Library, and Applications on Secure Smart Cities

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">seeing</i> the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula> ) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.
Task Allocation Model Based on Hierarchical Clustering and Impact of Different Distance Measures on the Performance

This article observed a new strategy to the problem of tasks clustering and allocation for very large distributed real-time problems, in which software is consolidated hierarchically and hardware potentially spans various shared or dedicated links. Here, execution and communication times have been considered as a number. Existing strategies for tasks clustering and allocation are based on either executability or communication. This study's analytical model is a recurrence conjuration of two stages: formation of clusters and clusters allocation. A modified hierarchical clustering (MHC) algorithm is derived to cluster high communicated tasks and also an algorithm is developed for proper allocation of task clusters onto suitable processors in order to achieve optimal fuzzy response time and fuzzy system. Yang's and Hamming's distances are taken to demonstrate the impact of distance measures on the performance of the proposed model.
Phrasemes and schematic idioms - theoretical bases and linguistic comparison

The main objective of this paper is to examine schematic idioms from a crosslinguistic perspective.First, this type of lexical-syntactic structure is discussed in the light of Phraseology (1) and Construction Grammar (2), as well as in a broader theoretical context (3).Then the most important characteristics of schematic idioms are summarized (4) and problems of cross-linguistic comparability are raised from the point of view of Construction Grammar (5).Then the differences between schematic idioms and phraseological models are discussed (6).In particular, the interfaces between schematic idioms and other types of phrasemes are examined (6.1) and an attempt is made to distinguish between phraseological models, structural formulae, and schematic idioms (6.2).Several case studies on (presumably) non-comparable schematic idioms (7.1) and comparable ones (7.2) constitute the empirical core of the paper.These are [Wie ADJ ist DAS denn] and [X kann auch Y] (in 7.1), as well as [S1 PREP S1], [X ist der/die/das neue Y], the Incredulity Response Construction, and the WXDY Construction (in 7.2).Finally, general conclusions are drawn on the subject (8.1), methodological issues are taken into account (8.2), and research wishes are formulated (8.3)
Blockchain Technology in Footwear Supply Chain

This research investigated how adopting blockchain technology will strategically place footwear companies in a competitive market. The article attempted to answer three central research questions: (1) Should a footwear company adopt blockchain technology in its supply chain? (2) What can footwear companies gain from the adoption? (3) Where can the blockchain position the footwear companies in the competitive market if the implementation of the technology is in the supply chain? First, we explored the business model and the features of the footwear industry and examined the blockchain technology, which provided the information from the characteristics of the industry and the advantages of the technology as the inputs of the TOWS matrix. Next, the author highlighted what benefits the footwear companies could get from the adoption of the technology based on the TOWS matrix analysis. Also, the author analyzed where the blockchain would position the company if the adoption of the technology is in the supply chain by investigating the definition and characteristics of the disruptive technology and Roger’s diffusion of innovations theory. Finally, the author concluded that technology still holds for promise though the implementation of the technology has several barriers and challenges, given the potential of the technology. In terms of the scope of this research, this chapter focused on catering to the needs of footwear giants (i.e., Nike, Adidas) due to their urgent needs of creating a transparent and traceable supply chain.
Reinforcement Learning with Imitation for Cavity Filter Tuning

Cavity filters are vital components of radio base stations and networks. After production, they need tuning, which has proven to be a difficult process to do manually and even more so to automate. Previous attempts to automate this process with Reinforcement Learning have failed to reach consistent performance on anything but the simplest filter models. In this paper, we build upon these results and aim to improve them. Multiple methods are tested and evaluated, including introducing a pre-processing step, tuning hyperparameters and dividing the problem into multiple sub-tasks. In particular, by using supervised Imitation Learning as an initial phase, a semi-realistic filter model with 13 tuning screws is tuned, fulfilling both insertion loss and return loss requirements. On this problem, this algorithm has a greater efficiency than any previously published results on Reinforcement Learning for Cavity filter tuning.
Confidential and Auditable Payments

In this paper, we construct the Confidential and Auditable Payments (CAP) scheme. We keep the transaction confidential by writing ciphertexts of transactions in a ledger. We realize the soundness of the CAP scheme by the soundness of the zero-knowledge proof. A court or an authority controls a unique secret key of the ciphertexts written in the ledger. They can enforce confidential transactions open with the secret key according to the legal procedure. There are many works for protecting the transaction’s privacy strictly. However, these works do not have a forcibly auditable function, to the best of our knowledge. The proposed scheme is both confidential and auditable. It eliminates concerns about money laundering caused by excessively confidential transactions and contributes to the sound use of blockchain.
A Heuristic Adaptive Fast Gradient Method in Stochastic Optimization Problems

A fast adaptive heuristic stochastic gradient descent method is proposed. It is shown that this algorithm has a higher convergence rate in practical problems than currently popular optimization methods. Furthermore, a justification of this method is given, and difficulties that prevent obtaining optimal estimates for the proposed algorithm are described.
Optical characterizations of airless radial tire

In this work is analyzed the possibility to use optical techniques for the characterization of airless radial tire. Electronic Speckle Pattern Interferometry (ESPI), laser scanner based on principle of triangulation and Digital Image Correlation (DIC) have been used to acquire and study this kind of tire. A MICHELIN® X® TWEEL® UTV has been considered as case study. The acquisitions have been used for the measurement of the shape for testing junction areas and to evaluate the structure behavior under a vertical load.
Cyber Safety Awareness Framework for South African Schools to Enhance Cyber Safety Awareness

Nowadays Information Communication Technology (ICT) is so entwined in our daily lives that the cyber user finds it second nature to use the Internet to accomplish their daily activities. The Internet has not only opened many opportunities to the cyber users but also introduced the cyber users to cyber risks and cybercrimes. It is important for cyber users to become cyber safety aware to protect themselves. However, there is currently a definite lack in South Africa for cyber safety awareness and cyber education. It is of utmost importance that South Africa as a developing country start creating and implementing cyber safety awareness policies, procedures, measures and initiatives for the education of all cyber users within South Africa. To include cyber safety awareness in the South African school curriculum is one strategy for improving online safety among cyber users. A literature study was conducted, where the research used searches for phrases with ‘Cyber Safety Awareness’, ‘Education’, ‘Schools’, ‘Policies’ and ‘Frameworks’. The aim of this research is to determine what policies, procedures and measures a school needs to have in place to enhance cyber safety awareness of the school learners within a South African school environment. This research then proposed a theoretical framework that can be used to create and enhance cyber safety awareness.
The Framework of Invariant Electric Vehicle Charging Network for Anomaly Detection

Electric vehicle (EV) charging management systems control and schedule EV load according to the measurements of local building load, solar generation, and dynamic electricity price. Within this information network, any data replaced or modified by an attacker will disrupt the EV charging schedule and could cause damage to the electricity grid. Under real circumstances, these measurements are correlated in a way that is not true for false data. This paper examines the relationship of pairwise measures within the system to establish a correlation-invariant network, and a multivariate time-series segmentation method along with a weighted k nearest neighbor (kNN) classifier is proposed to detect the changes in correlations and identify anomalous data within the network.
Integration of TOGAF 9.1 ADM in Enterprise Architecture Smart City Design in the Tourism Domain with ISO 27001

Abstract The purpose of this research is to create an enterprise architecture design smart city that has a high-security system in the tourism domain that includes smart experiences, smart destinations, and smart business ecosystems. The method used in this research was integrating ISO 27001 as an international information security standard on the TOGAF 9.1 ADM used as a framework for designing enterprise architecture. The result of design can improve Quality Assurance (QA) of the IT information security system, increase IT and business alignment and increase security awareness among government, tourists, industries, and the local community. Business Architecture focuses on centralized coordination by the board and evaluation of the performance of tourism stakeholders; Analysis results of data collection into information and useful on architecture or stakeholders; while application architecture has a focus on developing applications for tourists, business entities, and government monitoring; The technology architecture as the supports to the implementation of research with a data center, cloud service, and transaction interface that is given NFC technology. The result of this research is a smart city enterprise architecture design with a tourism domain that can be implemented in a smart city area and has a security system architecture after phase D of Requirements Management TOGAF 9.1 ADM with a higher security level.
Antecedent Redundancy Exploitation in Fuzzy Rule Interpolation-based Reinforcement Learning

This paper introduces novel methods which could improve the efficiency of the automated knowledge extraction methods used in the FRIQ-learning (Fuzzy Rule Interpolationbased Q-learning) machine learning method. For solving a given problem, the FRIQ-learning reinforcement learning method is capable of constructing a sparse fuzzy rule-base, which does not need to contain all the possible rules as traditional fuzzy control requires. Hence it is sufficient to keep only the most important rules due to Fuzzy Rule Interpolation (FRI). Finding those specific rules which are important to solve the given problem is not a trivial task. Some possible strategies for removing these kinds of unimportant rules from the rule-base have already been introduced, but no strategies addressing the antecedents of the rules have been developed yet. The solutions proposed in this paper allow the further reduction of these rule-bases, thus facilitating the creation of a sparse fuzzy rule-base from which the knowledge can be directly extracted. Since the form of fuzzy rules are inherently self-describing, the size of the rule-base is the key for keeping this kind of knowledge base human-readable. Possible mechatronics applications of these methods include optimizing behaviour-based control models for robotics, and also knowledge extraction in a fuzzy rule-base format from models where the real operating knowledge of the model is not known, which rule-bases then can be easily adopted in robot control applications.
Stepwise abstraction of high-level system specifications from source code

We are interested in specifications which provide a consistent high-level view of systems. They should abstract irrelevant details and provide a precise and complete description of the behaviour of the system. This view of software specification can naturally be expressed by means of Gurevich’s Abstract State Machines (ASMs). There are many known benefits of such an approach to system specifications for software engineering and testing. In practice however, such specifications are rarely generated and/or maintained during software development. Addressing this problem, we present an exploratory study on (semi-)automated extraction of high-level software specifications by means of ASMs. We devise a method consisting in two phases. In the first phase we propose fully automated transformations from source code to base-level ASM specifications with the same core functionality. We present a prototype of an implementation of this phase which transforms Java code into base-level ASM specifications. The second phase consists in incrementally applying sound semi-automated abstraction procedures for ASMs to derive higher-level specifications, starting from the base-level ASM specification obtained in the first phase. This is by no means a trivial task. It opens up new and interesting research questions. We discuss possible methodologies to approach this task and provide a proof of concept in the form of elaborated and detailed examples. We argue that this process can be done in a (semi-)automated way and thus result in a valuable tool to improve the current software engineering practices.
IPingPong: A Real-time Performance Analyzer System for Table Tennis Stroke’s Movements

Assisting table tennis coaching using modern technologies is one of the most trending researches in the sports field. In this paper, we present a methodology to identify and recognize the wrong strokes executed by players to improve the training experience by the usage of an IR depth camera. The proposed system focuses mainly on the errors in table tennis player’s strokes and evaluating them efficiently and based on the analysis and classification of the data obtained from an IR depth camera using multiple algorithms. This paper is a continuation of our previous work [10], focusing more on identifying common wrong strokes in table tennis by utilizing IR depth camera classification algorithms. The classification of the mistakes that took place while playing can be classified based on each player dependently or independently for all players.
Performance and Participation in the Panopticon: Instruments for Civic Engagement with Urban Surveillance Technologies

While there is a plethora of surveillance technologies in public space, they are hard to see, as they are sometimes literally hidden, or they have become such a common part of everyday surroundings that they escape conscious observation. Through this invisibility the smart city and its surveillance technologies escape public awareness, critical reflection and democratic debate. In this chapter we discuss several ways of raising awareness about surveillance technologies. We show how artists and activists have engaged in cultural performance to bring surveillance in sight; we review a number of instruments of participatory action research that critical scholars have introduced. These performative and participatory instruments to expose surveillance technologies do have an immediate effect, however it is a temporal one and one would need additional efforts to move discussions away from individual responsibilities to collective ones and from individual awareness to political agenda-setting. This directs us, as participatory action researchers, towards examining how the few political conflicts around urban surveillance technologies have emerged and played out, and towards designing new interventions for political representatives and their constituencies.
Live Stream Highlight Detection Using Chat Messages

In recent years, live-streaming services have been booming and are still continuing to grow on the Internet. Differing from TV shows and movies, live-streaming can have variable and longer lengths with no specific content restrictions. Traditional methods of video highlight detection, which are based on visual features, will suffer the difficulties of data scale and inconsistency. To address these issues, we alternatively extract information from the audience discussion in a chat room for high-light detection. In this paper, an attention-based model, LSTA, is proposed to integrate the long term and short term information in a chat room to determine which fragments should be identified as highlights. Our results demonstrate the improvement over both state-of-the-art visual and textual content-based approaches.
Development of teacher assessment competence as a pedagogical problem

The article refers to the use of a system for assessing educational achievements of students as an important component of the evaluative competence of a teacher. The use of different methods of formative and summative assessment in the teacher’s evaluating activity is very important for the objectivity of evaluating all planned results, which leads to an increase in the quality of students’ learning. The article considers the comparison of the content of formative and summative assessment Modern quality of education focuses on teachers’ readiness to solve professional tasks of both teaching activities and tasks related to the assessment of educational achievements, which undoubtedly contributes to the development of evaluation competence of the teacher himself. In order for the educational activity to be effective, it is important to constantly monitor and evaluate the educational results of students in accordance with the set learning objectives. It is important in the implementation of the evaluation system to apply modern approaches that allow to meet all the requirements of the organization of educational activities.
The Open Group Architecture Framework for Designing the Enterprise Architecture of ALIT

Abstract This research purpose is to analyze and developing business value also existing information system at Laboratory Unit of Telkom University Applied Science School. The method used is The Open Group Architecture Framework (TOGAF) with the Architecture Development Method (ADM). Among 8 phases of TOGAF ADM, it will be used 4 phases, start from Preliminary, Architecture Vision, Business Architecture, Information System Architecture, and last Technology Architecture. The result of this research is the blueprint contains an enterprise architecture for developing ALIT so that it can support the business processes that exist in the Laboratory Unit and furthermore in the future also achieve their strategic goals. This blueprint of enterprise architecture can be applied in the same unit that have similar business process, specially the unit that focused on laboratory and practicum. But in this research focused on laboratory unit at Telkom University. This research also concludes that using TOGAF ADM as a methodology tool can produce a design architectural models in general accordance to organization’s vision and mission. The impact of this research is a solution to business processes problem, data loss, and unintegrated data during business activity. Especially the main problem is the manual processes implemented there and the information system that may not support it fully so impacted to its business process.
Efficient and DoS-resistant Consensus for Permissioned Blockchains

Existing permissioned blockchain systems designate a fixed and explicit group of committee nodes to run a consensus protocol that confirms the same sequence of blocks among all nodes. Unfortunately, when such a system runs on a large scale on the Internet, these explicit committee nodes can be easily turned down by denialof- service (DoS) or network partition attacks. Although recent studies proposed scalable BFT protocols that run on a larger number of committee nodes, these protocols' efficiency drops dramatically when only a small number of nodes are attacked.
A Lightweight Pedestrian Detection Model for Edge Computing Systems

Most vision-based pedestrian detection systems adopted deep learning approaches with the framework of convolutional neural networks (CNNs) to reach the state-of-the-art detection accuracy. As CNN-based approaches are computationally intensive, the deployment of those systems to a resource-limited edge device is a challenging task, especially for a real-time application such as intelligent vehicles. In this study, we proposed a lightweight high-performance edge computing solution to achieve rapid and accurate performance in pedestrian detection. Experimental results showed that the proposed framework can effectively reduce the miss rate of the YOLO-tiny detection model from 48.8% to 26.2% while achieving an inference speed of 31 frames per second (FPS) tested on the Caltech pedestrian dataset.