Reviewerly/exHarmony huggingface dataset:
Trusted Client-Side Encryption for Cloud Storage

Nowadays, users are delegating the data storage to cloud services, due to the virtually unlimited storage, change history, broadband connection, and high availability. Despite the benefits and facilities, it is necessary to pay extra attention to data confidentiality and users’ privacy, as numerous threats aim to collect such information in an unauthorized manner. An approach to ensure data confidentiality is the use of client-side encryption, with the user taking control of the encryption keys and defining which files or data will be encrypted. This scheme is already explored by many applications on personal computers and also as a native feature in some smartphone operating systems, but are still susceptible to certain types of attacks. Aiming to improve the security of the client-side encryption approach, we propose to apply the Intel Software Guard Extensions (SGX) to perform data sealing, creating a secure vault that can be synchronized with any cloud storage service, while relying on the SGX to protect the key handling. To validate our proposal, we build a proof of concept based on the Cryptomator application, an open-source client-side encryption tool specially designed for cloud storage services. Our results show an overall performance better than the original Cryptomator application, with stronger security premises. Thus, our solution proved to be feasible and can be expanded and refined for practical use and integration with cloud synchronization services.
Single-Shot Detection Based on Cyclic Attention

The shallow feature map of the single-shot detector (SSD) is not always conducive to enhancing the recognition precision for a small object because of the lack of contextual information. In this research, a single-shot detection algorithm based on cyclic attention (CA-SSD) is proposed to construct a fast and accurate detector that efficiently obtains full-image contextual information. Our network is constructed by integrating ResNet-34 and proposed novel cyclic attention blocks. This type of building block aggregates different transformations, one of which includes an attention module that uses a long but narrow pooling kernel to acquire horizontal and vertical contextual information for each pixel of all pixels. Each pixel eventually captures the full-image dependencies by following an even further cyclic operation. Our design considers the variability of the gradient, which not only improves the reliability of the cyclic attention block but also cuts the number of parameters for computation. Additionally, by exploring the effects of the stem block and its stride on the performance of ResNet-based SSD algorithms, our network retains more detailed information. For an input size of 300 × 300, CA-SSD attained 82.5% mAP on PASCAL VOC 2007 test, 78.4% mAP on PASCAL VOC 2012 test, and 32.7% mAP on MS COCO. Experimental results achieved with CA-SSD surpass the best results achieved with the traditional SSD and other advanced object detection algorithms while real-time speed is maintained.
A Comparison between Classifiers on Credit Card Fraud Detection Problem

The great expansion of data availability gives rise to some consumer financial problems, including credit card fraud. It is an urge to look into this fraudulent behavior and try to make successful detection, so the continuous analysis from raw data is necessary [1]. It should be noted that it is hard to get the original dataset because it will involve personal information. Besides, credit card data fraud transition tends to be highly imbalanced. In this paper, the experiment is based on one credit card dataset, and I mainly assess the performance of three classification models: logistic regression, random forests and boosted regression trees, which are commonly used under imbalanced situation. Sampling methods are also used on the data for a better result. The experiment adopts AUC, H-measure and detection rate as assessment metrics, trying to figure out the best classification model for the credit card fraud.
Cost Learning Network for Imbalanced Classification

This paper proposes a method to improve the performance of imbalanced classification via reinforcement learning and cost-sensitive learning. Since the cost information is usually unavailable for cost-sensitive learning, we incorporate reinforcement learning to optimize the specified metric by adjusting the cost-matrix for the underlying cost-sensitive classifiers. Our experiment results show that, with the learned cost-matrix, the cost-sensitive classifiers can achieve better performance on several benchmark imbalanced data sets.
Update on Analysis of Lesamnta-LW and New PRF Mode LRF

We revisit the design of Lesamnta-LW, which is one of the three lightweight hash functions specified in ISO/IEC 29192-5:2016. Firstly, we present some updates on the bounds of the number of active S-boxes for the underlying 64-round block cipher. While the designers showed that the Viterbi algorithm ensured 24 active S-boxes after 24 rounds, our tool based on Mixed Integer Linear Programming (MILP) in the framework of Mouha et al. ensures the same number of active S-boxes only after 18 rounds. The tool completely evaluates the tight bound of the number of active S-boxes, and it shows that the bound is 103 for full (64) rounds. We also analyze security of the Shuffle operation in the round function and resistance against linear cryptanalysis. Secondly, we present a new mode for a pseudorandom function (PRF) based on Lesamnta-LW. It is twice as efficient as the previous PRF modes based on Lesamnta-LW. We prove its security both in the standard model and the ideal cipher model.
WITHDRAWN: Usability matrix on dynamic datasets in cloud based environment

Cloud computing is perhaps the most pre-predominant ideal models in ongoing patterns for registering and putting away purposes. Information security and protection of information is one of the significant worry in the distributed computing. To conquer the present circumstance of data divulgence, the specialist co-ops are upholding a specific norm of scrambling the touchy information prior to transferring on to the cloud server’s. It becomes evident that, the plain content catchphrase search is inconceivable. As the aggregate sum of information put away openly mists increments in a dramatic way, it turns out to be too difficult to even think about supporting proficient catchphrase based questions and positioning the coordinating outcomes on scrambled information. In the proposed framework, utilizing the security spillage compel segment savvy encryption has been accomplished for decode particle information’s in the middle datasets. What’s more, an idea encoding the information subsequently discovering reference property between information tables are accomplished. Notwithstanding the above framework, we are giving a programmed booking calculation to keep a log based following for continuous and un-frequently utilization of information under the time models.
Separate Aspects of Legal Regulation Educational Activities in the Russian Federation

This article reveals the legal regulation of educational activities at the federal level. The essential advantages of the Federal Law “On Education in the Russian Federation”, adopted in 2012, are indicated. The problematic aspects are revealed that do not allow to regulate educational activities effectively enough. Prospective ways of solving the problems identified during the study are proposed.
STEM4Girls-Workshop on Machine Learning

Although female specialists in the STEM area are urgently needed in the work force, the percentage of women in STEM study programs in higher education is still rather low. One reason for this are gender-related stereotypes that still persist in many modern cultures, which associate STEM as being a “male” domain. To actively draw young women towards STEM and Computer Science, we design a workshop for girls in the age group of 10th grade in secondary education. This workshop introduces the girls to machine learning in the application context of fashion and shopping. We define both cognitive and affective learning objectives and corresponding observable outcomes that indicate whether the learning objectives were reached or not. Based on these learning objectives, we develop a didactic concept for our workshop, as well as a web application as a front end for the machine learning application. Finally, we execute a pilot run of our workshop, which we evaluate with respect to participant satisfaction and the achievement of the learning objectives.
E-book Based on Mobile Learning Used Problem Based Learning (PBL) Model to Improve Problem-Solving Ability in Statistical Material

Abstract The research aims to developed a mobile learning-based e-book that used problem-based learning (PBL) to improve problem-solving abilities in statistical materials that meets validity, practically, and effectiveness. The type is a Development Research used the Four-D model (define, design, development, and disseminate). The validation results of the material feasibility average was very good at 82% and the validation of the media was 85.92%. The results of the response questionnaire by students was 88.22% and by the teacher was 83.31%, included in the very practical category. Therefore the mobile learning-based e-book developed is valid to use in mathematics classes. The result of e-book effectiveness meets the effective criteria with the completeness study of students at 84%. From the products tested, it was concluded that the students problem-solving abilities in statistics material used mobile learning-based e-book that had been developed were better than used existing products. Thus, this mobile learning-based e-book used a problem-based learning model to improve problem-solving abilities in eighth grade of statistical material which is suitable for uses and recommended at mathematics learning.
Effective Data Collaborative Strain Using Rectree Algorithm

We develop an efficient synergistic sifting technique, called RecTree (which represents RE Commendation Tree) that tends to the adaptability issue with a partition and-vanquish approach. The proposal assignment is affected by the profound learning pattern which demonstrates its huge viability. As of late bi bunching strategies were proposed for uncovering sub grids indicating remarkable examples. We audit a portion of the algorithmic ways to deal with bi bunching and talk about their properties. The proposed framework incorporates three segments: a network factorization display for the watched rating reproduction, a bi-grouping model for the client thing subgroup investigation. We recognize uninteresting things that have not been assessed yet rather are most likely going to get low evaluations from customers, and explicitly credit them as low regards. A client based synergistic separating calculation is one of the sifting calculations, known for their straightforwardness and effectiveness. In the present paper an enduring is directed for its usage and its effectiveness as far as prediction multifaceted nature Even however profound learning represents an extraordinary effect in different territories, applying the model to recommender systems have not been completely abused. Further advancement of our rating derivation framework is continuous. we contemplated a novel Domain-touchy Recommendation (DsRec) calculation, to make the rating prediction by investigating the client thing subgroup examination all the while.
Complex Autoencoder Approach to Constant Envelope Waveform Coding

This paper proposes a new complex autoencoder suitable for learning spectrally efficient, constant envelope waveform coding. In contrast to prior work, we model the encoder output layer as a phase modulation layer with a complex exponential activation function. In addition, we model the decoder with a complex-valued feature detection layer that may be coherent or noncoherent. The complex topology leads to noncoherent waveform coding methods not obtained in prior studies. The paper provides a mathematical framework for training the proposed autoencoder along with illustrative examples that demonstrate its ability to learn improved spectral efficiency relative to traditional orthogonal and biorthogonal modulations.
SIGACT News Complexity Theory Column 108

Warmest thanks to Rafael Pass and Muthu Venkitasubramaniam for this issue's guest column, "Average-Case Complexity Through the Lens of Interactive Puzzles." When I mentioned to them that my introduction would have a section on Alan Selman's passing, they immediately wrote back that they were very sorry to hear of Alan's passing, and mentioned (as you will see discussed in the second page of their article), "The main problem that we are addressing actually goes back to a paper of Even, Selman, and Yacobi from 1984: "The Complexity of Promise Problems with Applications to Public-Key Cryptography'." It is beautiful, and a tribute to the lasting influence of Alan's research, that in the 2020s his work from many decades earlier is helping shape the field's dialogue.
The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image&amp;#xA0;Capturing&amp;#xA0;Method

Abstract With advances in technology, photo booths equipped with automatic capturing systems have gradually replaced the identification (ID) photo service provided by photography studios, thereby enabling consumers to save a considerable amount of time and money. Common automatic capturing systems employ text and voice instructions to guide users in capturing their ID photos; however, the capturing results may not conform to ID photo specifications. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of captured images. The authors adopted a deep learning method (You Only Look Once) to detect the face and applied a semi-automatic annotation technique of facial landmarks to find the lip and chin regions from the facial region. In the experiments, subjects were seated at various distances and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.
Method for solving tasks for the virtual robot Aquarius with two vessels using divisible capacities

The article discusses a simple and visual method for finding solutions of tasks for the virtual robot Aquarius with two vessels by constructing a table with divisible capacities. The proposed method allows students to simply, quickly and, most importantly, consciously find the optimal solution of tasks for Aquarius, and teachers to spend less time creating tasks for transfusion and their checking. The method has been tested by the author for a number of years in lessons in 5–7th grades and is relevant, since in a number of common textbooks, unfortunately, there is no description of a method for finding solutions of tasks for Aquarius, which leads to exhaustive solution of these tasks by students and reduces the educational potential of the specified virtual robot. To expand the educational capabilities of Aquarius, the article also recommends the use of loops and other programming constructs in programs for the virtual robot. The method of finding a solution is analyzed using examples of tasks for two vessels (for the case of initially empty vessels and for the case when one of the vessels is initially partially filled). The solutions obtained are tested in the KuMir and Blockly software environments. The described method is intended primarily for teachers teaching an initial informatics course (5–7th grades). It will also be useful for math teachers and parents of students.
The Goals of Teaching C Language Programming in Higher Vocational Education

C language programming which was an ordinary course for computer major was arranged both in higher vocational college and general university. The goals of teaching it should be different, because the points of higher vocational education and higher education were different. Many teachers in higher vocational college did not pay attention to different goals due to not distinguishing the difference. There are lots of papers on studying the patterns of teaching this course, but there is few paper on studying the goals of teaching it. This paper focused on explaining the goals of teaching C programming in higher vocational education, such as spirit of team work, communication with machine, good habit, base of major courses, training thinking.
Demonstration of Figure Character Media to Introduce Adjective in English Learning for Children

Abstract This research is aimed to describe about the using of Figure Character media to introduce adjective in English learning for children to increase the students of teacher elementary program of STKIP Muhamadiyah Blora exploring media in English learning. This research happen is caused by the use of media in English learning is low in Blora area, so those reason became a motivation to make English learning process better and more effective. This research is descriptive qualitative research in case study method. Qualitative research is explained in words, phrases, sentences or paragraphs. The collecting data method uses questioner and interview. The two methods to collect variaty answers in the use of Figure Character media. Demontration mastery to attract the students is chosen and consulted to the expert. The result of this research is the using of learning media Figure Character to introduce adjective to the children in English learning can improve the students’ interest and spirit. Besides, the teacher become easier to teach the mastery by using they own learning media. The media usage can build an interesting learning process for the teacher and the students. The informans of this research are agree that using Figure Character media can ease the teacher to introduce adjective to the children as elementary students with high entusiasm. This research has motivated the teacher candidate to use learning media.
A Shared Memory Cache Layer across Multiple Executors in Apache Spark

Memory caches are being widely adopted in today's data-intensive computing frameworks to maximize the benefit of in-memory access. Various memory architectures, designs and management algorithms have been well explored so that application performance can witness speedup by orders of magnitude. For example, Apache Spark provides intermediate data consistency in memory between computation tasks, eliminating a significant amount of disk I/Os and dramatically reducing data processing times. However, the memory space of individual executors is isolated so far in vanilla Spark and apparently inefficient given the fact that these memory demands of different executors vary a lot over tasks, jobs and applications. In this work, we propose a new shared in-Memory cache layer, i.e., iMlayer, among these parallel executors, which are co-hosted on the same slave machine in Apache Spark. It aims to improve the overall hit rate of data blocks by caching and evicting these blocks uniformly across multiple executors. The critical insight of iMlayer is to develop a novel eviction strategy to efficiently manage the shared cache space among executors to maximize the cache hit rate as well as application performance. We evaluate iMlayer based on the three representative workloads from HiBench. Our results demonstrate that iMlayer with the new eviction strategy improves the cache hit rate by 45%, 16% and 27%, effectively reducing the overall job runtime 47%, 43% and 38% compared to vanilla Spark, respectively.
Calculation of Dyadic Green's Function in the RBFNN-Based Layered Media

The Dyadic Green's function, in general, is comprised of spectral Dyadic Green's functions and Sommerfeld integral. Since the integral featuring high-frequency oscillation and slow decay may lead to a time-consuming calculation process. With optimal approximation and global optimum characteristics that cannot be found in other feedforward neural networks, the RBF neural network is adopted in this paper to solve the dyadic Green function. By doing so, the efficiency of solving Dyadic Green's functions can be enhanced thanks to its simple structure and fast training speed. According to the numerical results, the calculation scheme proposed in this paper is proved to be feasible.
Bidirectional Encoding Contextual Approach for Identification of Relevant Document in Corpus

With the increasing advance of computer and information technologies, numerous documents have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding their interesting documents. These documents can be in the form of blogs, research papers, and thesis. There is a heterogeneous set of documents which has information linked with each other. Traditional search is about taking an input of the query text from the user and checking if the subsequence is a part of any sentence in the set of documents and showing the set to the user. In this paper, we have proposed a Bidiection Encoding Contextual algorithm that can be applied to different types of documents and do a semantic search across the corpus. The algorithm used to understand the meaning of the word, their relative relationship between other words and provide the user with the documents that not just has the textual reference but also contain the relative meaning of the query. On the COVID-19 dataset, test been performed on the reliability of the interpretation through the function of linguistic similarities. The experimental findings demonstrate the strong association between the conceptual term interpretation of human consciousness in the role of measuring the similarity. Experiments show that the Bidirectional Encoding Contextual model has the best accuracy of 85.6% when compared with other traditional models like RNN, CNN and LSTM models.
Evaluating NoSQL Systems for Decision Support: An Experimental Approach

We design and implement an experimental analysis comparing two relational systems (PostgreSQL and MariaDB) and two document-based NoSQL systems (MongoDB and CouchBase). We compare their performance on a single server, Decision Support (DSS) scenario. We argue that DSS is becoming an important case study for NoSQL. We experiment with several database designs and several query translations in order to investigate the effect of physical design and query optimization in document-based stores. Our results show that design is very important for MongoDB's performance, and that query optimization over documents is much less sophisticated on document-based stores than in relational data bases and needs to improve. Our results also offer some ideas to guide further development in this area.
Public Service Media and Blockchain Technology: First Thoughts

European Public Service Media (PSM) have a long history, throughout which they have had to transform themselves to face technological challenges and competition with private companies. This chapter aims to reflect on the impact that blockchain can have on Public Service Media. Blockchain is characterized by being a transparent, democratic, secure, instantaneous, consultable, and decentralized technology that is irreversible in its annotations. Blockchain makes it possible to change the economic models that the Internet introduced for cultural industries, for which it also presents advantages and development opportunities. Despite this, little development of this technology on PSM is revealed, something that invites deep reflection, due to the leading role that PSM are required to play in fulfilling their public service functions.