Reviewerly/exHarmony huggingface dataset:
Information systems for creating e-learning resources for realization of resource-based learning aimed at students majoring in Economics

The authors have examined and analysed information systems (Constructor Electronic books 1.1.3, SunRav, Webnode, Google Classroom) in terms of functionality, which will allow creating electronic educational resources for introduction of the resource-based learning for students in higher school. It has been proven that Constructor Electronic books 1.1.3 and SunRav provide the creation of online tutorials that are run using an exe-file. Webnode Website Designer enables the creation of open-source electronic educational resources that are freely available on the World Wide Web (teacher's personal sites, discipline study site, e-learning teaching / learning support of disciplines, etc.). These electronic educational resources effectively and fully function in any modern browser: Google Chrome, Opera, Mozilla Firefox, Internet Explorer, etc., and also provide the opportunity to work online with students. Using Google Classroom is appropriate when you need to organise full-time online (virtual) online distance learning. For the resource-based learning of the students majoring in Economics, the authors have developed and presented an electronic manual "Higher Mathematics for Economists" in the Designer of Electronic Textbooks (N. Kononets); an educational and methodological complex of the discipline "Economic Informatics" in the environment of SunRav IS (N. Kononets); a site of study studio "Digital Technologies in the Economist's Work", created at Webnode (V. Baliuk); a Virtual Classroom "Educational practice in information systems and technologies in Economics" in Google Classroom (N. Kononets and V. Baliuk).
Galois ring isomorphism problem

Recently, Doröz et al. (2017) proposed a new hard problem, called the finite field isomorphism problem, and constructed a fully homomorphic encryption scheme based on this problem. In this paper, we generalize the problem to the case of Galois rings, resulting in the Galois ring isomorphism problem. The generalization is achieved by lifting the isomorphism between the corresponding residue fields. As a result, this generalization allows us to construct cryptographic primitives over the ring of integers modulo a prime power, instead of a large prime number.
Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks

We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bitrates. Although GAN can help to reconstruct sharp images, there are two drawbacks. First, GAN makes training unstable. Second, the reconstructions often contain unpleasing noise or artifacts. To address both of the drawbacks, our method adopts two-stage training and network interpolation. The two-stage training is effective to stabilize the training. Moreover, the network interpolation utilizes the models in both stages and reduces undesirable noise and artifacts, while maintaining important edges. Hence, we can control the trade-off between perceptual quality and fidelity without re-training models. The experimental results show that our model can reconstruct high quality images. Furthermore, our user study confirms that our reconstructions are preferable to state-of-the-art GAN-based image compression model. The code will be available.
Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI

This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing divergent search algorithms. In this domain, which includes a minimal viability criterion, the goal is to furnish a house with objects that satisfy the physical needs of a simulated agent. Importantly, the large number of objects available to the player (whether human or automated) affords a wide variety of solutions to the underlying design problem. Empirical studies in a novel open source simulator called SimSim investigate the ability of novelty-based evolutionary algorithms to effectively generate viable environment designs.
Data Management via QR Code Using Android Smart Devices

Smart devices have become one of the most important facilities in managing the data in the organizations recently. In this research, Android Application has been developed with taking advantage of Quick Response codes technology for storing different types of data (ex: documents, goods or employees) within organizations, and retrieving it on demand on the smart device screen or sharing it to other devices as Excel reports. Discussion of the study assumptions, delimitations, and limitations resulted in developing several approaches for tackling the process of generating unique QR codes representing the items in the organizations, in addition to developing a QR code reader by making use of the camera in the smart device. Office goods have been chosen in this research project as a data sample. Based on the experiments, the findings have proven that QR code technology is highly useful in the data management process, for storing and retrieving information within smart devices, showing a high response speed and accuracy.
Neural network and non-rigid ICP in facial recognition problem

Human facial expressions describe a set of signals, which can be associated with mental states such as emotions depending on physiological conditions. There are many potential applications of expression recognition systems. They take into account about two hundred emotional states. Expression recognition is a challenging problem, not only due to the variety of expressions, but also due to difficulty in extraction of effective features from facial images. Depending on a 3D reconstruction technique, 3D data can be immune to a great range of illumination and texture variations, and they are no sensitive as 2D images to out-of-plane rotations. Moreover, 2D images may fail to capture subtle but discriminative change on the face if there is no sufficient change in brightness, such as bulges on the cheeks and protrusion of the lips. In fact, 3D data yield better recognition than conventional 2D data for many types of facial actions The most effective tool for solution of the problem of human face recognition is neural networks. But the result of recognition can be spoiled by facial expressions and other deviation from canonical face representation. In the proposed presentation we describe a resampling method of human faces represented by 3D point clouds. The method based on the non-rigid ICP (Iterative Closest Point) algorithm. We consider the combined using of this method and convolutional neural network (CNN) in the face recognition task. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Brushing Feature Values in Immersive Graph Visualization Environment

There are a variety of graphs where multidimensional feature values are assigned to the nodes. Visualization of such datasets is not an easy task since they are complex and often huge. Immersive Analytics is a powerful approach to support the interactive exploration of such large and complex data. Many recent studies on graph visualization have applied immersive analytics frameworks. However, there have been few studies on immersive analytics for visualization of multidimensional attributes associated with the input graphs. This paper presents a new immersive analytics system that supports the interactive exploration of multidimensional feature values assigned to the nodes of input graphs. The presented system displays label-axes corresponding to the dimensions of feature values, and label-edges that connect label-axes and corresponding to the nodes. The system supports brushing operations which controls the display of edges that connect a label-axis and nodes of the graph. This paper introduces visualization examples with a graph dataset of Twitter users and reviews by experts on graph data analysis.
Opelt’s Siren and the Technologies of Musical Hearing

Friedrich Wilhelm Opelt (1794–1863) proposed a revolutionary music theory based on the recursive features of human hearing. Opelt based his theory on a recent invention, Charles Cagniard de la Tour’s mechanical siren (1819), which he expanded and improved. Cagniard’s model consists of a metal disk with holes in regular intervals that, when set in rotation and with air blown through them, produce a series of air puffs. Once their pulsation increases above 20Hz, this pulse is heard as a continuous, rising pitch. Opelt employed this device to test the properties of hearing against the parameters of music: he noted that more complex patterns of holes translate into intervals, chords, and harmonies and showed that every pitch event can be translated into a corresponding rhythmic event. Pitch and rhythm may be different perceptual parameters, but physically, they are both temporal events, which merely inhabit different dimensions of the time axis.
Design of autonomous mobile systems for face recognition based on a DCNN with compression and pruning

In this paper, 3D face recognition based on a deep convolutional neural network in autonomous mobile systems is associated with a large size of neural models and extremely high computational complexity of classification procedures owing to the large network depth. To solve this problem, we use compression and pruning algorithms. Since these algorithms decrease the recognition accuracy, we propose an efficient retraining of neural models in such a way to approach the recognition accuracy to very large modern models of neural networks. The performance of the proposed neural models using compression and pruning is compared in terms of face recognition accuracy and compression rate.
Visible Feature Guidance for Crowd Pedestrian Detection

Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it's difficult to guess a precise full bounding box according to the invisible human part. To crack this nut, we propose a mechanism called Visible Feature Guidance (VFG) for both training and inference. During training, we adopt visible feature to regress the simultaneous outputs of visible bounding box and full bounding box. Then we perform NMS only on visible bounding boxes to achieve the best fitting full box in inference. This manner can alleviate the incapable influence brought by NMS in crowd scene and make full bounding box more precisely. Furthermore, in order to ease feature association in the post application process, such as pedestrian tracking, we apply Hungarian algorithm to associate parts for a human instance. Our proposed method can stably bring about 2~3% improvements in mAP and AP50 for both two-stage and one-stage detector. It's also more effective for MR-2 especially with the stricter IoU. Experiments on Crowdhuman, Cityperson, Caltech and KITTI datasets show that visible feature guidance can help detector achieve promisingly better performances. Moreover, parts association produces a strong benchmark on Crowdhuman for the vision community.
Informationssäkerhet inom organisationer: Attityder mot kunskap, policies och ansvarsfördelning

Information security is only as strong as the weakest link. Although the technical, externally oriented efforts are of great importance, there is a predominant weakness when it comes to securing information assets; the individual user within an organization. Employees are often seen as the weakest link due to human error. Many of the security breaches that occur would not have been possible without intentional or unintentional efforts by employees. In order to ensure a safe environment within an organization, all departments must inevitably be ready to counter or neutralize potential threats. Employees in different departments must work together to be able to do this effectively. An organization without functioning security will have problems protecting their information. The study gathers empirical data through a qualitative approach with interviews of three different companies to investigate how they relate to information security. A major problem is that an increase in security may have the opposite effect as the productivity within the organizations can suffer. After analyzing the interviews and linking the results with both literature and theory, the study demonstrated that companies should see the inclusion of information security as an iterative process and also take into account the balance between policies, education and responsibility perspectives. The results show that information security needs to be highly motivated by the management and there must be a continuous dialogue with the employees. (Less)
Particle Swarm Optimization on Parallel Computers for Improving the Performance of a Gait Recognition System

In recent years, the gait recognition (GR) using particle swarm optimization (PSO) algorithm (OSO) has been execute very fast and accurate with single computer, but with the appearance of parallel computing (PC), it was necessary to use this technique to improve the results of GR. This study presents the use of parallel computing approaches (PCA) to implement PSO for a GR system (GRS) to decrease processing while maintaining reconstructed image quality. These approaches are: Codistributor and parallel cluster. Many experiments have been executed with recognition between the two approaches. The experimental results showed that increasing the PSO swarm size, decreasing number of iterations, and increasing number of workers used for the PCA can reduce recognition time and increase performance. Best results were obtained from implementing parallel computing with eight workers and 100 iterations. The execution time reached 4s and PSNR reached 44db. At the same time, the best results were obtained from PCL approach.
Research Guides: ADV 208: The Big Idea in Advertising (Fall 2020): More Advertising Databases

An SU Libraries research starting points guide for students enrolled in ADV 208 (course sections taught by Profs Brian Sheehan and Mel White)
Hubungan Kecerdasan Emosional dengan Perilaku Prososial pada Siswa SMA di Kota Bandung

Abstract — High school students are able to achieve responsible social behavior, one of which is to behave prosocial. Prosocial behavior is behavior that is intended to provide benefits to others (Carlo & Randall, 2002). However, it was found that prosocial behavior decreased, especially in high school students, where there was still a lack of desire for students to help others, even though these students knew the feelings felt by people who needed help or had sufficient emotional intelligence. Where by having emotional intelligence students will be able to be socially responsible (Salovey, P., et., Al., 2001). The purpose of this study was to examine the relationship between emotional intelligence and prosocial behavior in high school students in Bandung. The research method used is correlational. The sampling technique used was cluster random sampling with a total of 383 high school students in Bandung. The measuring instruments used are The Assesing Emotion Scale by Schutte (2009) to measure emotional intelligence and Prosocial Tendecies Measures by Carlo & Randall (2003) to measure prosocial behavior. This study uses the Spearman Rank correlation analysis technique. The resulting correlation value is 0.530 and the p-value (Sig.) = 0.000 <α = 0.05. The results of this study indicate a moderate relationship between emotional intelligence and prosocial behavior in high school students in Bandung. A positive value on the correlation indicates that the higher the emotional intelligence, the higher the prosocial behavior. Keywords — Emotional Intelligence, High School Student, Prosocial Behavior.
LibGuides: Scholarly Communication & Impact: Pre-Seminar Readings

This guide is to help you navigate the different elements of the Scholarly Communication world and find out how you can use them to your benefit. Take a look at this page before attending the live seminars. There will be a short quiz on it during the live seminar.
AFFIXES FORMING PASSIVE VERBS IN BUGIS LANGUAGE

Abstract. The problem of this research is the affixation of Bugis language, which is limited to three subproblems namely; affix form, function, and meaning in Bugis language. Affixation is described as descriptly and qualitatively. The data source is in the form of utterances from native speakers of the Bugis language. Data collection techniques are direct interview techniques, namely fishing techniques, the designation of activities, designation of images, and documentary studies. The technical analysis used in research is to transcribe data, then transcribe it into written form, then classified according to the problem. Based on the analysis of existing data, the results of the study show that the affixes in Bugis language have form, function, and meaning. The form of affixes in Bugis language are: Prefixes {ta-}, {ri-}, {na-}, {ripa-}, double affixes {na -, - ngi}, {ri -, - eng}, {ri + aG -, -ang}. Function: changing the word class and not changing the word class. The meaning of affixes forming passive verbs in Bugis language is Prefix {ta-}: Stating a sudden or unintentional event, and stating an action that is subject to work referred to in the root word, {ri-}: Stating the action being taken work called the root word, {na-}: Stating the action that is subject to work which is called the root word, {ripa-}: States the causative meaning. Meaning of double affixes {na-,-ngi}: Stating is made as stated in the basic form, {ri-, -eng}: Stating the action mentioned in the basic form, {ri- + aG-, -ang}: Stating the action mentioned in the basic form.
Empirical Research on Youth National Security Cognition Level Based on Cloud Model

Abstract Focusing on the development level, existing problems and cultivation path of the overall national security concept of young people has become an urgent issue of the times. In order to evaluate the national security cognition level of young people, this paper constructs a systematic evaluation system and improves the existing evaluation methods. Based on fuzzy comprehensive evaluation, grey relational analysis and the TOPSIS, a combined evaluation model based on cloud model is constructed, which takes into account the fuzziness and randomness in the evaluation process. Through the analysis of the examples and the verification of the mean square error, this new evaluation method has obvious superiority over the traditional evaluation method. The results show that the current national security awareness of young people is relatively weak, and the overall level of awareness is at a moderately low level.
Realising high-dimensional quantum secret sharing with structured photons

Quantum secret sharing (QSS) is a cryptographic multiparty communication technique in which a secret is divided and shared among N parties and then securely reconstructed by (N-1) cooperating parties, making it perfect for storing and sharing highly sensitive data. Challenges in high dimensional state preparation, transformation and detection, the key steps of any QSS protocol, have so far hindered experimental realisation. Here, by taking advantage of the high-dimensional encoding space accessible by a photon's orbital angular momentum, we present a toolbox for realising practical high-dimensional single photon QSS schemes that are easily scalable in both dimension and number of participants. Our implementations realised a new record in both the number of participants (N=10) and the dimensionality (d=11), with the latter facilitating the transfer of 2.89 bits of information per photon. This work is an important step towards securely distributing information across a network of nodes.
GraphFederator: Federated Visual Analysis for Multi-party Graphs

This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for joint modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a federated graph representation model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization views for joint visualization, exploration, and analysis of multi-party graphs. Experimental results with two datasets demonstrate the effectiveness of our approach.
Assurance Argument Elements for Off-the-Shelf, Complex Computational Hardware

There are many aspects to the safe use of artificial intelligence. To date, comparatively little attention has been given to the specialist computational hardware that is used, especially within embedded systems. Consequently, there is a need to identify evidence that would support a compelling assurance argument for the safe use of off-the-shelf, large scale, complex system-on-chip designs. To that end, we summarise issues related to the use of multi-core processors in aviation, which contextualises our problem. We also discuss a collection of considerations that provide evidence to support a compelling assurance argument.
Phase transition of the four-dimensional cross-polytope model

Thermodynamic properties of the four-dimensional cross-polytope model, the 16-cell model, which is an example of higher dimensional generalizations of the octahedron model, are studied on the square lattice. By means of the corner transfer matrix renormalization group (CTMRG) method, presence of the first-order phase transition is confirmed. The latent heat is estimated to be $L_4^{~} = 0.3172$, which is larger than that of the octahedron model $L_3^{~} = 0.0516$. The result suggests that the latent heat increases with the internal dimension $n$ when the higher-dimensional series of the cross-polytope models is considered.
Object Detection: Training From Scratch

The development of deep neural networks has driven the development of computer vision. Deep neural networks play an important role in object detection. To improve network performance, before using neural networks for object detection, they are commonly pre-trained on the data set and fine-tuned to their object detection tasks. Pre-training is not always helpful in object detection tasks, so studies have been performed on training neural networks from scratch. By consulting many relevant studies,we performed a systematic analysis of training networks from scratch for object detection. Our article is divided into the following three parts: (i) the reasons for which target detection requires training from scratch, (ii) mainstream networks that can be trained from scratch, and (iii) the criteria for training from scratch. Finally, we summarize some research directions relevant to this topic.