Reviewerly/exHarmony huggingface dataset:
Semi-automatic construction of video game design prototypes with MaruGen

Machinations Ruleset Generator (MaruGen) is a semi-automatic system for the generation of mechanics, rules, spaces (environments), and missions for video games. The objective of this system is to offer an expression mechanism for the video game designer role based on the definition of rules, and the ability to explore the concepts of progression and emergence in video games by using a formal, usable, and defined tool to design games with innovative and complex elements, and behaviors defined from combinations of basic elements. Based on the expressed designs and with the participation of programmers and video game artists, MaruGen allows the generation of agile video game prototypes in the Unity game engine. These prototypes can be analyzed by the entire workgroup to look for games with diverse complexities that make them attractive to their users. MaruGen is based on the expression of rules on elements of interest in video games and the rewriting mechanism using L-Systems for the generation of procedural content. MaruGen was evaluated in the construction of the Cubic Explorer video game and tested by gamers and video game developers during the Game Jam Ludum Dare 38.
User Authentication—Passwords,Biometrics and Alternatives

Computer users regularly enter a username and password to access a local device or remote account. Authentication is the process of using supporting evidence to corroborate an asserted identity. In contrast, identification (recognition) establishes an identity from available information without an explicit identity having been asserted—such as picking out known criminals in a crowd, or finding who matches a given fingerprint; each crowd face is checked against a list of database faces for a potential match, or a given fingerprint is tested against a database of fingerprints.
Annotation Text as Strategy to Teach Reading for General Communication

The researcher conducted the research based on teaching and learning Reading for General Communication. It was carried out for the first semester students of English Language Education Department of STKIP PGRI Trenggalek, consisting 50 students. The research was purposed to apply Annotation Text in teaching and learning reading. Annotating text means giving underline, highlight, symbolic notation or code of a text. It helps the students to give notes, key words of important information of a text. The research was also aimed to know the students’ reading comprehension after the annotation text applied and their response toward annotation text in learning reading. Summarization is used to evaluate the students’ comprehension of the text. The students were instructed to make a summary based on the text after annotating it. Attitude scale was utilized to know the students’ responses toward the use of Annotating Text in learning reading. The result of summarization showed that the mean of students’ work was 82.3 Meanwhile, the result of attitude scale indicates that the students gave good responses toward the application of Annotation Text in learning Reading for General Communication.
Blockchain and Government

Bitcoin disrupted money, and smart contracting technology and digital assets are further disrupting finance. Blockchain trade platforms are disrupting global supply chains. But the biggest disruption that blockchain might bring is to disrupt government. Government is an institutional technology to enable a group of citizens to create and exchange value under conditions of relative safety and security. Government is a mechanism to enable a citizen to better trust their fellows, by providing rules and infrastructure to lower the cost of contracting and exchange by facilitating identification, monitoring, dispute resolution, verification and networking. Blockchain is also an institutional technology that can disrupt government by supplying new ways of supplying these institutional and infrastructural functions.
Simulation of motion of a mobile robot on uneven terrain

In the paper, a kinematical model of the robot moving on uneven terrain has been presented.Configuration of the robot placed on uneven terrain is described, presenting the redundant set of variables that create the state of the robot.Inverse kinematics tasks have been defined, which as a result of various assumptions, gives the robot configuration.To solve the kinematics tasks, adequate closed-loop equations have been defined.Velocity equations of the point of the wheel contact with the ground have been presented with respect to the velocity of the platform itself and velocities of the robot actuators.Assuming constraints of the wheel movement causing a no-slip condition, dependencies between those velocities have been determined.This allowed one to determine velocities of the robot drives, so that the platform moves in the way specified by the user.
Enhancing recursive graph querying on RDBMS with data clustering approaches

Recursive queries are one of the main mechanisms in Relational Database Management Systems to process topology-aware, or graph-like, queries. However, existing works focus only on optimizing the recursive query statements and processing, disregarding the potential physical arrangements that might improve performance. In this work, we propose to use an approach based on adjacent-list storage to physically organize the graph-like data aiming at both reducing the recursive query time and the number of I/O operations. By using Clustered Tables, we tied the adjacency list in chunks for (i) storing both vertex and edge tables together in a Combined Tables approach; and (ii) reordering the edge table with the Edge Clustered Table approach using 20% and 80% of the total adjacency list size. The clustered approaches enabled a faster recursive query processing (up to 22%) and a reduction of up to 61% in the number of page accesses when compared to the Conventional approach. When starting from multiple vertices, the Combined Tables approach achieved a query reduction time of up to 50% in the first join operation, and Edge Clustered Table 20% provided an overall time reduction of up to 20%. The results show that our physical design is effective and allows one to use recursive queries without adaptations.
Privacy-preserving Pattern Recognition with Image Compression

In this paper, we propose a privacy-preserving pattern recognition scheme that well supports image com-pression.The proposed scheme is based on secure sparse coding using a random unitary transform.It offers the following two prominent features: 1) It is capable of pattern recognition in the encrypted image domain.Even if data leaks, privacy can be maintained because data remains encrypted.2) It realizes Encryption-then-Compression (EtC) systems, where image encryption is conducted prior to compression.The pattern recognition can be carried out in the compressed signal domain using a few sparse coefficients.Based on the pattern recognition result, it can compress the selected images with high quality by estimat-ing sufficient number of sparse coefficients.We use the INRIA dataset to demonstrate its performance in detecting humans.The proposal is shown to realize human detection with encrypted images and efficiently compress the images selected in the image recognition stage.
Determining Subcategories of Facial Expressions for Improved Performance in Practical Applications

In the context of facial expression recognition (FER), this paper reviews the fundamental theories of emotions and further explains the key dimensions of a defined emotional space. The main contribution of this paper is to propose a set of novel categorization methods for facial expressions to be used in the design of an automatic FER system. This novel categorization enables the facial expression to be interpreted in a better way that and to be more effective in practical applications of automatic FER systems. In order to validate the feasibility of the proposed categorization methods, a set of experiments is reported which investigates and analyzes the influence that the novel categorization brings to a multi-view FER system.
Higher Grading Standards and Academic Motivation: Perceptions of Students and Teachers in a Lagos (Nigeria) Private Secondary School

Abstract&#x0D; This study used qualitative research method to investigate the views of students and teachers of a private secondary school in Lagos State, Nigeria on whether higher grading standards motivate students. The study was prompted by the decision of the management of the school to increase its grading standard. To guide the study, 10 students and 7 teachers participated in the study. Data were generated using interview technique which centred on three objectives or themes of the study. However, the second objective of the study was targeted at the students, while the third objective or theme was targeted at the teachers. Thematic analysis was used to analyze the perceptions of the students and teachers. The findings revealed that higher grading standards motivate students to study harder and that higher standards benefit both high-achievers and low-achievers. Considering that the study was the first attempt to investigate the issue as it concerns Nigeria, suggestions were made on future studies.&#x0D; &#x0D;
Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration

We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods.
Effective Lightweight Learning-to-Rank Method Using Unified Term Impacts

In this study, we propose and evaluate a novel learning-to-rank (L2R) approach that produces results on par with those of the state-of-the-art L2R methods while being computationally effective. We start by presenting a modified gradient boosted regression tree algorithm to generate unified term impact (UTI) values at indexing time. Each unified term impact replaces several features with a single value in the document index, thereby reducing the effort to compute the document scores at query processing time because the system fetches and processes fewer values. The adoption of UTI values produces competitive ranking results. However, the lack of features available only at query time might lead to accuracy loss. To solve this problem, we propose a hybrid model that uses UTI values with query-dependent features. We demonstrate that our hybrid methods can deliver high-quality results on par with those of the existing state-of-the-art neural ranking models. Our methods can also reduce the computational costs for processing queries, serving as an interesting alternative for L2R practical applications. Our best hybrid, HLambdaMART, achieves an NDCG@10 value of 0.495 using only 36 features at query processing time when applied to the MQ2007 collection, while the best baseline achieves 0.490 using a larger set of features at query processing time. The use of our hybrid framework reduces the time to run LambdaMART to about 35% of the time to run it without using our proposals. In summary, we present a competitive and lightweight alternative L2R approach to be adopted in search systems.
Image Denoising Algorithm Based on Adaptive Singular Value Threshold

Non-local similarity images play a huge role in image denoising tasks. Many of the existing denoising algorithms have problems in that the edge information is too smooth, the reconstruction details are insufficient, and artifacts are easily generated while removing noise. In order to solve these shortcomings and improve the denoising accuracy, we propose a denoising algorithm based on non-local similarity and adaptive singular value threshold (ASVT). The algorithm consists of three basic steps: block matching grouping, ASVT denoising, and aggregation. First, similar image patches are grouped by block matching method, and each similar block group is used as a group matrix for each column of the matrix. Then, under the framework of image non-local similarity and low rank approximation, the denoising problem is transformed into low rank matrix approximation problem, which is solved by ASVT. Finally, all processed image patches are aggregated to produce an initial denoised image. In order to effectively avoid the influence of noise residual on denoising, the denoising result is further improved by the back projection strategy, and more detailed features are retained. Experimental results clearly show that the proposed algorithm is competitive with the current state-of-the-art denoising algorithms in terms of both quantitative measure and subjective visual quality and can retain more details and improve the smoothing problem.
Image Restoration of Dun Huang Murals Based on Auto-encoder Generative Adversarial Neural Network

Dun Huang mural is an important part of Dun Huang art. Digital restoration for Dun Huang mural is significant and meaningful. At present, the restoration of Dun Huang mural mainly relies on manual work, which takes a lot of time and is limited by work experience. Although, the existing restoration methods perform well in painting for small missing region, there are disadvantages for large missing regions with semantic information. To solve these problems, Auto-encoder Generative Adversarial Nets (AGAN) was proposed for image restoration of Dun Huang Murals in this paper. In AGAN, the fully connected layer was replaced by the dilated convolution layer, which benefits the reconstruction of image with large scale missing. The experiments are executed over the Dun Huang dataset, and the results show that the restoration images based on AGAN were more realistic and natural. Compared with other traditional methods, our method achieved a better performance quantitatively and qualitatively.
Analysis of the RSA-cryptosystem in abstract number rings

Quantum computers can be a real threat to some modern cryptosystems (such as the RSA-cryptosystem). The analogue of the RSA-cryptosystem in abstract number rings is not affected by this threat, as there are currently no factorization algorithms using quantum computing for ideals. In this paper considered an analogue of RSA-cryptosystem in abstract number rings. Proved the analogues of theorems related to its cryptographic strength. In particular, an analogue of Wiener’s theorem on the small secret exponent is proved. The analogue of the re-encryption method is studied. On its basis the necessary restrictions on the parameters of the cryptosystem are obtained. It is also shown that in numerical Dedekind rings the factorization problem is polynomial equivalent to factorization in integers.
Anticipatory Ethics for Vulnerability Disclosure

This article presents the ethical dilemma related to under what circumstances vulnerabilities should be disclosed. Vulnerabilities exist in hardware and software, and can be as a consequence of pro ...
Modified Key Using Multi-Cycle Key In Vigenere Cipher

Encryption-Decryption is one form of securing text data, images and sound in order to minimize data stealing, attack, etc. The purpose of this study is to modified encryption-decryption keys of Vigenere cipher.Based on experiment show that multi-cycle keyencryption-decryption have been implemented. Meanwhile, the best MAE values of final encryption process of 56.837 and final decryption process of 0. In other words, the modification of key is more efficient and effective for data security. Furthermore, the multi-cycle key encryption-decryption optimizing using metaheuristics as one of the future works that can be conducted in order to investigate the best encryption-decryption especially using Vigenere cipher.
Systematic and nonsystematic perfect codes of infinite length over finite fields

Let Fq be a finite field of q elements (q = p k , p is a prime number).An infinite-dimensional q-ary vector space F N 0 q consists of all sequences u = (u1, u2, . ..),where ui ∈ Fq and all ui are 0 except some finite set of indices i ∈ N. A subset C ⊂ F N 0
Sketch and test: visual crowd research using p5.js.

Designing visual crowd experiments requires both control and versatility. While behavioural and computer sciences have produced a fair number of tools facilitating this process, a gap remains when it comes to the combination of accessibility and versatility. The Processing language is widely used by artist and designers of varying levels of expertise and thus fulfils these conditions. Here, we investigated whether the Processing inspired JavaScript library p5.js can be used for visual crowd research. We specifically explored how to use p5.js in combination with MTurk and report a simple way of using p5.js 'sketches' to perform online tests. We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition. Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved. The creative freedom of p5.js together with low threshold crowd access seems a powerful combination for all areas that involve vision: perception, design, art history, communication and beyond.
Partitioned Based Image Segmentation

The aim of this paper is to provide a comprehensive survey of image segmentation method using clustering techniques. In image processing, segmentation plays an important role in the detection and matching of objects. Image segmentation uses number of techniques to find the correct segmented images. Clustering techniques are one such methods used for the segmentation of images. It is the process by the pixels of an image are divided into different partitions on the basis of similarity criteria. This paper presents recent development in image segmentation by using the clustering-based approaches such as K-Means and Fuzzy C-Means.
Media communication activities of Russian universities: Dynamics of the development and evaluation of social network efficiency

Social networks are now one of the most popular communication channels, occupying the most important position in the modern media system.Universities as centres of science and education, trying to meet modern standards of media promotion, actively use social networks as a platform for communication with all their many target audiences, with young people as the foundation.The study aims to analyze media communication activities of Russian universities in social networks and to identify the main features of their practices: which social network is most actively used, where media communication activities are more effective and which universities are more involved in media promotion.This article presents the results of the study of the activity of official accounts in social networks of three leading universities in Russia for the period 2017-2020.The data obtained show that the most widespread social network is Vkontakte.However, the number of subscribers and the number of daily publications in the account do not always increase the efficiency of communications -qualitative parameters prevail over quantitative ones in evaluating the efficiency of universities' media communication activities in social networks.The active presence and response of subscribers are most clearly reflected by such an indicator as audience involvement, and the users are most actively involved in the Instagram social network.
Evaluating the Readiness Of Malaysian Academic University Libraries Towards Library 4.0

&lt;p&gt;This paper evaluates the readiness of Malaysian academic university libraries towards Library 4.0. The rise of this new intervention of Web 4.0 and Library 4.0 are being deliberated among librarians for future services. Information technology facilities have been identified and analysed which may comprehend the use of libraries for the next generation. This will also help to move the library forward and improve the overall performance of university libraries respectively. The methodology used for this study is qualitative. This qualitative study was piloted among librarians working in the selected academic university libraries. This study gives an outline of the readiness among the selected academic university libraries towards Library 4.0&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
COGNITIVE MODELS IN PLANIMETRIC TASK TEXT PROCESSING

A new cognitive approach is proposed for understanding the texts of planimetric tasks and for visualizing the task conditions to complement the syntactic-semantical sentence parsing. Two main difficulties in understanding texts of plane geometry tasks are observed: the ellipticity and vagueness of texts. To overcome the difficulties in understanding the task conditions it is proposed constructing cognitive models of objects and relations between them. The proposed cognitive approach is incorporated in an integrated system for automatic solving planimetric tasks with the natural language interface. The interactive visualization has been developed in the system. It depicts the syntactic and semantic structures as a result of natural language text analysis and searching for task solution. This visualization allows the users to obtain explanations associated with any elements of the images and to correct the tasks’ texts in dialog with the system. The destiny of the system is to serve for training schoolchildren in the domain of Euclidean geometry. The cognitive approach proposed can be a first step to automated analyzing plane geometry texts, in perspective, as a cognitively controlled parsing.
“Nano” Regeneration: How Human Agency Intermediates Between Nature and Technology in Community-Based Energy

This article provides a nano (hyperlocal) view of climate change mitigation by viewing regenerative organizing through the eyes (as well as bodies and senses, etc.) of the households engaged in community-based energy projects. By showing what humans make up for in the largely absent relationship between nature and technology in these projects, we envision an incremental extension of the literature on community-based energy. The radically different contribution we aim to make is a tripartite imbrication that brings in natural agency alongside the human and the technical but specifies precisely how nano (smaller than micro) embodied practices afford mis- and realignments.