Reviewerly/exHarmony huggingface dataset:
New attacks against reduced Rijndael‐160

The first 9-round meet-in-the-middle (MITM) attack and improved 8-round impossible differential (ID) attacks on Rijndael-160 are studied here. For the first 9-round MITM attack, a new effective attack path is explored by using the generalised δ-set and the generalised multiset, which are based on the property that the difference branch number of MixColumns is 5. With this attack path, a 5-round MITM distinguisher with a technique of the truncated differential characteristic is proposed, and then the attack on 9-round Rijndael-160 is performed. For the improved 8-round ID attacks, to take advantage of the key-schedule weaknesses for Rijndael-160 under key sizes of 160 and 256 bits, some new attack paths are found. With these attack paths, the 5-round IDs are proposed based on the property of MixColumns above, and then the attacks on the 8-round Rijndael-160 under key sizes of 160 and 256 bits are performed. When compared with the currently known attacks, the proposed attacks have lower data, time, and memory complexities.
A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

Summary Data mining methods face various problems when extracting the data analysis process. These problems are solved based on the frequent pattern mining (FPM). In the data mining area, the frequent pattern (FP) extraction is a dominant embassy while processing the databases. Despite the fact that it has proven its importance in various mining provinces, the consistent research work of researchers has provided numerous efforts in this environment through substantial innovations. Based on the expected challenges of pattern mining, the time and memory consumption while mining the hidden FP increases over time by the conventional FPM approaches. This article proposes a Parallel Direct‐Vertical Map Reduce Programming model to effectively extract the FPM in a dispersed environment and analyzed using the Sales transaction dataset as UCI Machine Learning Repository. The proposed model has two phases of working, namely, (i) mapping and (ii) reduction. Here, the proposed Parallel Direct‐Vertical method is utilized while mapping. The parallelism of the proposed model is implemented and executed based on the number of clusters of databases in architecture‐based software and its final outcome shows that the proposed model is more efficient than the existing methods.
CUPiD<sup>RT</sup>: Detecting Improper GPU Usage in Real-Time Applications

Computer-vision applications typically rely on graphics processing units (GPUs) to accelerate computations. However, prior work has shown that care must be taken when using GPUs in real-time systems subject to strict timing constraints; without such care, GPU use can easily lead to unexpected delays not only on the GPU device but also on the host CPU. In this paper, a software library is presented that can detect the improper use of GPUs for safety-critical computer-vision applications. This library was used to analyze several GPU-using sample applications available as part of OpenCV, a popular computer-vision library, revealing the presence of issues in all ten applications considered. Additionally, a case study is presented, detailing the response-time improvements to one of the applications when such issues are corrected.
Solar Power Prediction in Different Forecasting Horizons Using Machine Learning and Time Series Techniques

Solar power generation is highly intermittent, nonlinear, and variable in nature. The increase in penetration level of solar energy resources poses technical challenges. An accurate forecasting model is crucial to minimizing these technical issues. Therefore, choosing the right forecasting technique for the right forecasting horizon is vital. In this study, the performance analysis of machine learning and time series forecasting techniques for various forecasting horizons has been investigated. Its accuracy, root mean square error (RMSE), and mean absolute error (MAE) have been compared to other techniques.
Develop Auto Narration Adder System

本研究では,Text-to-Speechサービスを利用し,PowerPoint内のテキストを音声ナレーションへ変換し,印加するコードの開発を試みた.結果, Google TTSを利用した音声ナレーションを印加できるようになった.ただし,それだけだと聞き取りづらいという問題点があった.しかし,タイトル前後に無音を印加する工夫をしたことで,タイトルと本文に区切りがついて,聞き取りやすくなったと感じられる.また,新たな問題点を発見することができた.使用したライブラリでは,スライドを開いた時に,添付されたオーディオファイルを自動で再生を開始するauto-play設定をONにする事ができていない.今後は,今回実現できなかったことを研究し,より完成度の高いものを実現しようと考えている.
Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition

Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-frame or per-label basis. Frame-synchronous systems, such as traditional hidden Markov model systems, can easily incorporate existing knowledge and can support streaming ASR applications. Label-synchronous systems, based on attention-based encoder-decoder models, can jointly learn the acoustic and language information with a single model, which can be regarded as audio-grounded language models. In this paper, we propose rescoring the N-best hypotheses or lattices produced by a first-pass frame-synchronous system with a label-synchronous system in a second-pass. By exploiting the complementary modelling of the different approaches, the combined two-pass systems achieve competitive performance without using any extra speech or text data on two standard ASR tasks. For the 80-hour AMI-IHM dataset, the combined system has a 13.7% word error rate (WER) on the evaluation set, which is up to a 29% relative WER reduction over the individual systems. For the 300-hour Switchboard dataset, the WERs of the combined system are 5.7% and 12.1% on Switchboard and CallHome subsets of Hub5’00, and 13.2% and 7.6% on Switchboard Cellular and Fisher subsets of RT03, up to a 33% relative reduction in WER over the individual systems.
Geoecological Assessment of the State of Soils in Functional Zones of the Territory of the Eastern Administrative Okrug of Moscow, the Russian Federation

With the development of megalopolises, constant expansion of their borders and chaotic and – to a greater extent – unreasonable territorial division of lands, potential recreational areas are experiencing colossal anthropogenic load and can be found in the state of oppression, gradually moving into an industrial functional zone, from the environmental point of view. For the preservation of ecosystems and rational planning of urban development, it is necessary to pay special attention to the functional purpose and mode of use of the sites, which are the essence of zoning and governance in the field of urban development of land. This paper presents a geoecological assessment of the dynamics of changes in pollution halos among the functional areas of the Eastern Administrative Okrug of Moscow over the past 30 years. Geochemical series of pollutants were compiled with the help of methods of preliminary preparation and statistical data processing. A forecast of the spread of pollution in the surface horizon for the next 100 years is presented, taking into account the hydrogeological features of the territory of the Eastern Administrative Okrug of Moscow.
Teaching and Learning Mode Reform in Digital Image Processing by Using Color Model

The traditional teaching mode of the digital image processing, which is centered on teachers, textbooks and classrooms, hinders the requirements of the image technology development and impedes the cultivation of the innovative talents. Though the digital image processing is one of the emerging technologies, it has been rapidly developed and widely used in the electronic information technology. In order to adapt to its development, this paper has explored the teaching and practice reform on the digital image processing by different color mode and to improve the understanding of students as well as teacher by clustered image which will enhance the core idea of a particular image by exploring all the colors present in every part of image. Students can better gain their knowledge skill by observing those images in colored clustered form.
Scenario-based Model Predictive Control for Path Planning and Obstacle Avoidance

A basic problem in path planning is to regulate an appropriate path with the purpose of minimizing a cost function of interest under conditions of obstacle avoidance and model dynamics satisfaction. The cost function can be further minimized if some constraints are allowed to be violated with a guaranteed probability in some practical cases. However, very few results are reported about path planning with a preferred probability of constraints violation. In this paper, we investigate the scenario-based stochastic model predictive control (SCMPC) problem for path planning and obstacle avoidance. We find out the main reason that prevents the SCMPC approach to work for path planning is that the obstacles are commonly formulated as non-convex constraints, and a fundamental assumption in the field of SCMPC approach is the convexity of all the constraints. To address this problem, we propose a novel concept of candidate path, which is used to explicitly denote all the possible combinations of linear constraints. A conditional-scenario algorithm is accordingly developed that turns the original non-convex optimization problem to several convex subproblems. Simulation results verify the validity of the presented theories.
Research on seismic technology of civil engineering structure based on GIS system

The development and application of seismic technology in civil engineering structures is of great significance to extend the service life of buildings and improve the overall quality of buildings, so it is necessary to further strengthen the research on it. Geographic Information System (GIS) is a new subject integrating computer science, informatics, geography and other sciences. Because of its rapid and convenient management of massive data, GIS has been widely used in the fields of resource development, environmental protection, urban planning and construction, disaster monitoring and evaluation, etc. GIS is used to manage and analyze the data, and the damage detection module in the system is used to realize the structural damage identification. And use that special thematic analysis function of GIS to display the data on the map in the form of statistical graph, so that users can find the damage position more directly and clearly.
A Top-k QoS-Optimal Service Composition Approach Under Dynamic Environment

To deal with low efficiency of existing Web service composition algorithms under dynamic environment, this paper proposes a Top-k QoS-optimal service composition algorithm based on QWSC-K static algorithm. The algorithm first considers the influence of dynamic environment on the existing service composition and service dependency graph, and classifies the changed services in dynamic environment. Moreover, different strategies are adopted to obtain the intermediate results of service composition for different categories of services. The influence of different service categories on the service dependency graph is analyzed to realize the updating of the service dependency graph. Finally, based on the updated service dependency graph and the intermediate results of the service composition, the adaptive incremental update can be applied to change the original service composition results, avoiding the time-consuming re-query process. Experiments show that the algorithm is efficient and accurate in dynamic environment.
Distributed Learning via Filtered Hyperinterpolation on Manifolds

Abstract Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis, biochemistry, and 3D object analysis. This paper studies the problem of learning real-valued functions on manifolds through filtered hyperinterpolation of input–output data pairs where the inputs may be sampled deterministically or at random and the outputs may be clean or noisy. Motivated by the problem of handling large data sets, it presents a parallel data processing approach which distributes the data-fitting task among multiple servers and synthesizes the fitted sub-models into a global estimator. We prove quantitative relations between the approximation quality of the learned function over the entire manifold, the type of target function, the number of servers, and the number and type of available samples. We obtain the approximation rates of convergence for distributed and non-distributed approaches. For the non-distributed case, the approximation order is optimal.
Software Streamlining: Reducing Software to Essentials

Software products tend to become more sophisticated as they are actively maintained and used. While new software sophistication provides updated functionality, an undesired result of the expansion is the arrival of complicatedness. One obvious consequence of the new complicacy is an opportunity for new bugs. However, another often overlooked result is the increased difficulty of use and the greater demand for operational resources. It is also apparent that not all applications require all the features provided by software systems, and in many cases, only a small subset of features is ever used. With this in mind, this paper presents a development paradigm of taking an existing software product and streamlining it into a smaller and less complicated version, which provides only a useful subset of essential functionalities. To demonstrate this new workflow feasibility, the existing aerospace operating system RODOS is adapted it to a smaller and less complicated version. The results show it is possible to make software less complicated, easier to use, and improve resource economy.
An Approach to Dividing Modules of Numbers by the Values of Bases in Number Systems in Residual Classes

The paper considers an approach to division modules of numbers by bases in number systems in residual classes (RNS). This may be required when solving specific tasks.The approach involves performing the following sequence of actions in the RNS:- reducing the modulus of numbers by the value of the deduction corresponding to the base by which the division is made;- dividing the reduced module by the value of the base;- formation the correction value and its addition to the quotient obtained.In total the division operation is performed through four accesses to the table memory two of which are overlapped in time.If one of the bases of the RNS is equal to pn then the approach under consideration allows to reduce the capacity of table memory by pn times in relation to the capacity of table memory containing the full set of division results, where pn is the base of the RNS by which the division is performed.
MARMnet: On the Application of AI to Video Quality Control for Broadcasting Companies

Video Quality Control (VQC) is the process that media and broadcasting companies employ in order to detect potential anomalies in contents to release to the final users. This process is currently performed by human operators and presents many drawbacks in terms of costs and time spent on such activity. As an example, the operators leverage on expensive equipment that in few years needs to be changed. Additionally, the report generated by the operators present inconsistency in terms of anomalies identified. In this paper, we take the initial steps towards the integration of Artificial Intelligence into the VQC pipeline in order to introduce speed and uniformity in the report generation. Our solution, called Augmented Video Quality Control (AVQC), relies on the power of MARMnet, an ad-hoc neural network able to detect two anomalies of interest. Such model enables the AVQC to report the anomaly in half of the time needed by the current process. Moreover, quantitative and qualitative results show that MARMnet provides high performances in detecting the two anomalies in the videos to analyze.
On the genotype compression and expansion for evolutionary algorithms in the continuous domain

This paper investigates the influence of genotype size on evolutionary algorithms' performance. We consider genotype compression (where genotype is smaller than phenotype) and expansion (genotype is larger than phenotype) and define different strategies to reconstruct the original variables of the phenotype from both the compressed and expanded genotypes. We test our approach with several evolutionary algorithms over three sets of optimization problems: COCO benchmark functions, modeling of Physical Unclonable Functions, and neural network weight optimization. Our results show that genotype expansion works significantly better than compression, and in many scenarios, outperforms the original genotype encoding. This could be attributed to the change in the genotype-phenotype mapping introduced with the expansion methods: this modification beneficially transforms the domain landscape and alleviates the search space traversal.
On the development of augmented reality based exergames for assessing human activity and cognition on mobile devices

Virtual and Augmented Reality technologies are nowadays increasingly adopted as core elements of modern applications for creating unique user experiences. Following the capabilities of hardware and its constantly reducing cost, their use is moving from scientific and research projects to everyday life applications such as social media, architectural design and of course games. In this work, we present a state-of-the-art exergame implementation paradigm, which can facilitate the development of exergames exploiting advanced user interactivity, augmented reality and modern gamification techniques. The platform has been used for the development of a set of prototype games for Android phones based on the popular AR Core framework. The overall approach has been evaluated in real-world scenarios with exceptional usability and performance results.
A Study of Advancing E-Banking and Cybersecurity for Digital Enterprise Transformation in Pakistan

In this technological era, almost all renowned banks have equipped themselves with the latest technology significantly pertinent to enhance their services and have provided e-banking facilities to their customers. Nevertheless, cybersecurity has been the focus of many organizations. Banks are offering more facilities to facilitate their customers with ease and convenience regarding e-banking. However, in Pakistan this is not the case. As people still refrain from using e-banking in Pakistan because of various issues, the e-banking sector has exponentially grown in the last decade. It has more chances of growth as enterprises such as banks still encourage clients to carry out e-transactions, like utility bill payments, access to account information, and money transfer. During this process, cybercriminals attempt to steal customer data and hack their online sessions. With regards to e-banking fraud, digitization has caused a revolution. Cybercriminals have employed various tools to steal crucial information through identity theft, trojans, viruses, and phishing.
Construction of primary speech signal codecs with the ability to mask and protect phonograms from falsification

The application of the Khurgin-Yakovlev representation in primary codecs of speech signals is proposed. The implementation of primary speech signal codecs based on this representation makes it possible to increase noise immunity as well as the quality of reconstructed speech at the output of transmission system to provide simpler implementation of synthesizing filters. The possibility of joint implementation based on the primary codec built on the basis of the Khurgin-Yakovlev representation, masking algorithms, the algorithm for detecting falsified phonograms and restoring falsified phonograms is shown.
Multistart global optimization with tunnelling and an evolutionary strategy supervised by a martingale

Global optimization is one of the most difficult research topics in the optimization area. In the past few decades, many stochastic methods have been proposed that proved to be effective. In this article, a new multistart global optimization method, using tunnelling and an evolutionary strategy and supervised by a martingale (MGO-TEM) is proposed, which also belongs to the stochastic family. MGO-TEM is under a multistart framework such that a local search method will be called when a new sample point is generated. A sampling technique that is a hybrid of tunnelling and an evolutionary strategy is proposed in MGO-TEM with a set of stopping criteria whose derivation is based on martingale theory. Through experiments, MGO-TEM is compared with 10 other well-known algorithms on 55 single-objective test functions, and the results show that MGO-TEM is superior to all of them. Moreover, MGO-TEM is user-friendly since there are only two parameters to be tuned.
TEACHING ENGLISH BY MEANS OF THEATRICALIZED ACTIVITIES

The problem of finding effective methods of teaching foreign languages, focused on quick and easy assimilation of language material, its activation in the process of communication in various life situations, is the subject of numerous pedagogical discourse. A modern teacher is a mentor of the educational process, and is able to use a wide methodological and technological pedagogical toolkit. Among the most important problems of modern linguodidactics is the weak motivation of students to learn a foreign language in the absence of a linguistic environment. Therefore, the main task of organizing the educational process in teaching a foreign language is to develop students' interest in the language. The article focuses on the authors' practical experience on the use of theater technologies in additional language education and presents a tested foreign language teaching program. The research problem: what is the role of theatrical activities in teaching English? The purpose of the research is to substantiate the potential of theatrical activity in teaching English. The research methods used are observation, generalization, analysis. The research results: the influence of the means of theatrical activity on the process of teaching English to schoolchildren has been described; the understanding of theatricalization as an interactive educational technology in the English classroom has been presented; the experience of using elements of theater in extracurricular activities for the study of the English language have been described; the arguments of theatrical activity as an effective means of teaching a foreign language have been substantiated. Key findings: the means of theatrical activity include the following theatrical techniques: roleplay when reading foreign texts, a theatrical story about a character / historical personality, a scene, sketches, games, etc. Theatrical technologies complement the traditional form of the classroom, contributing to the development of open dialogical communication, the emotional component, broadening the horizons of students, which makes it possible to expand the range of opportunities for teaching a foreign language.
A Vision-based Target Tracking Robot System for Embedded Platforms

Identification and tracking of dynamic objects are essential concepts in the field of robotic vision. This paper presents the design of a four-wheeled robot that can detect a target object, track the object, and maintain a constant distance from it. In this design, S5P6818 system-on-a-chip (SoC) is used as the core processing unit with embedded Linux OS, and a USB video class (UVC) camera is used for image acquisition. The robot captures real-time images and tracks the object based on a joint tracking algorithm of enhanced Continuously Adaptive Mean Shift (CAMShift) and Kalman filter (KF). A combination of Bang-bang controller and proportional-integral-derivative (PID) controller is used to control the robot's real-time motion. The experimental results show that the robot can track a target object and keep a certain distance in real-time, proving its effectiveness and robustness.