Reviewerly/exHarmony huggingface dataset:
PUBG WINNER PLACEMENT PREDICTION USING ARTIFICIAL NEURAL NETWORK

The main objective is to predict the placement ranking of the player in PUBG (Players unknown battlegrounds) using the player's position and stats of data.PUBG is a game that has turned out to be famous in recent years.For this diversion, the last position is the most essential marker of the player's capacity.This task centres on anticipating the last position and finding ideal techniques of the diversion.With information from PUBG, we apply a few Artificial Neural Network techniques including Deep Learning, light GBM model, and MLP regression model to make the winner predictions.
AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks.
Data Sharing in CASEarth Project

Big Earth Data Projects (CASEarth; http://www.casearth.com) is a key project to make research data in the fields of biology, ecology, environment and resource science in the Chinese Academy of Sciences (CAS) converge in a synthesis facility, to serve data driving fundamental research and decision making in China. The project was launched January 1, 2018 and funded by CAS. Data sharing is one of most important issues in the project, which is not only a prerequisity, but also a symbol for success in the big data era. A series of regulations refering to the data sharing policy in the CASEarch Project of CAS have been drafted and implemented in the project, including data quality contral, data submitting, data evaluation, data sharing to scientists and decision makers. Dozens of data sets have been re-arranged and submitted to a centralized data warehouse since June 2018, successfully aggregating &amp;gt;250 volumes of detailed monographs of fauna and flora, biodiversity red lists and maps, invasive species data, 3D imagery of specimens, a microbial culture catalogue, genomic data for agricultural/domestic and wild species, checklists and introducing records for botanical gardens, vegetation maps, biodiversity monitoring, nature reserves, wetland birds distribution and conservation, plant phenology (1960-present), ecosystems, paleontological data, and fundamental geographic data such as population, economy, land use, and remote sensing. The Catalogue of Life – China, a species checklist, is the backbone of the biodiversity data sets above. Our data sharing platform (http://data.casearth.cn), launched January 15, 2019, currently houses 5 PB of data. Registration is open to all, and registered users can browse metadata or keyword search on the platform and download data. Although the portal is currently available in Chinese, an English portal will be launched in 2020.
Quantum Random Number Generator with One and Two Entropy Sources

Quantum random number generators (QRNGs) are an integral part of quantum key distribution (QKD) systems. To better understand the inherent physical processes, we compare the random numbers generated by two separate schemes, one is based on entropy (arrival time of photons) and another with an additional source of entropy (space) i.e, path superposition of arrival time of photons from a weak coherent source on a gated InGaAs single photon detector. Both experiments yield bits that appear random. However, they satisfy different criteria of randomness. The weak coherent source has a Poissonian distribution and extracting the variation about the arrival time of photons on gated SPD yields a source of random numbers that pass most of the Dieharder Tests. With the inclusion of superposition, we obtain random numbers that pass all the Dieharder tests. The physical origins of the random numbers in the two experiments is different, one is single entropy source based and other one is two entropy source based, and this is reflected in the outcomes of the different tests for randomness.
DeepDirect: Learning Directions of Social Ties with Edge-Based Network Embedding (Extended Abstract)

This paper presents the problem of tie direction learning which learns the directionality function of directed social networks. One way is based on hand-crafted features; the other called DeepDirect learns the social tie representation through the network topology. DeepDirect directly maps social ties to low-dimensional embedding vectors by preserving network topology, utilizing labeled data, and generating pseudo-labels based on observed directionality patterns. Experimental results on two tasks, i.e., direction discovery on undirected ties and direction quantification on bidirectional ties, demonstrate the proposed methods are effective and promising.
Cryptocurrency and Its Digital Panorama in the Colombian Government

Throughout recent years as well as in the use of cryptocurrencies factors that have been influenced by technologies such as Blockchain. This article investigates the current situation of the so-called cryptocurrencies in the international scene, as well as the acceptance, rejection or indifference position of different nations. Afterwards, the current situation of Colombia and its position with respect to the Cryptocurrencies is evaluated. Followed by this, a meet with a panel of experts is issued in order to provide a series of proposals that they evaluate and based on these a series of contributions regarding the concept of the cryptocurrency that is re-defined. Therefore, proceeding to characterize the actors involved in the transactions carried out with this medium. Finally, a brief recommendation is given to preserve the integrity of the Colombian users and a suggestion to form an interdisciplinary group under the concept of e-government that aims to investigate and observe the potential use of the applications of the technology that surrounds cryptocurrencies and their relationship with e-government.
VACCINE INJURY - BURDEN OF PROOF OF THE DEFECT AND THE CAUSAL LINK IN THE LIGHT OF THE JUDGMENT IN THE CASE C-621/15

In the European Union the liability of producers for harm caused by defective products manufactured or imported by them is regulated in Directive 85/374/EEC of 25 July 1985 on the liability for defective products. The purpose of Directive is to lay down a system of producer liability for damage caused by a defect in its product. Crucial is that it sets out a system of strict liability, so that the injured person does not have to show evidence of fault on the part of the producer. The injured person will have to prove the defect, the damage and the causal link between these. However, when it comes to the damage caused by vaccines, in case law the causal link has often been almost impossible to prove because in the scientific literature there are a number of opposing views on the risk of vaccination. Nevertheless, to the facilitation of the victims burden of proof could contribute the judgement of the Court of the European Union according to which a national court may consider that vaccination has led to the disease or damage even when there is no proof based on medical research. But, if there are other serious, specific and consistent evidence, such as the temporal proximity between the vaccination and the occurrence of a disease, the lack of personal and familiar history of that disease, together with the existence of other reported cases of the disease that occurred after such vaccines being received. Still, the Court retains caution by opposing any presumptions and warns that such cases are extremely fact-specific and require careful case-by-case considerations. In this article the author discusses the aforementioned judgement about liability for vaccine injury and its implications in the European Union.
Functional Angels and Quality Devils: Incorporating Quality Scenarios into Functional Scenarios for Software-intensive System Architecture

Software architecture design is intended to propose a software-intensive architectural solution for a required system.As such, the architecture needs to address all the required functionality while satisfying all the required quality attributes, which are usually referred to as the non-functional requirements.Therefore, a major concern of the software architecture is its behavior, besides its structure.Softwareintensive system specifications tend to be functional in nature, focusing on its behavior, i.e. what is expected to be done by the system, whereas quality attributes, which usually describe other properties of the system (e.g.performance, availability, safety, etc.) are, in many cases, described in general terms, and sometimes even being overlooked.The immediate result is that the software architecture, both behavioral and structural, fails to address quality issues -whose absence is discovered only in late stages of system verification, validation or operation.This paper introduces a two-phase process by which a software-intensive architecture is constructed, satisfying both functional and non-functional requirements.In the first phase an initial architecture is constructed, comprising its physical, functional, combined and behavioral views.In the second phase a systematic approach is introduced to discover overlooked quality attributes of a system which might be violated during its operational (functional) scenarios.In this approach, quality attributes play the role of "devil's advocates", challenging the functionality by suggesting what can go wrong while the system performs its functional scenarios.Such challenges yield two results: (1) new "quality scenarios", which describe how the system should behave in order to prohibit a fault from causing a failure, and (2) modifications to the entire architecture needed to support the newly-derived functionality.1 In common parlance, the term devil's advocate describes someone who, given a certain point of view, takes a position he or she does not necessarily agree with (or simply an alternative position from the accepted norm), for the sake of debate or to explore the thought further [Wikipedia].
A Short-Cycle Framework Approach to Integrating Psychometric Feedback and Data Analytics to Rapid Cyber Defense

Following earlier research in demonstrating the significance of behavioral analysis in cyber defense, the authors developed a framework to incorporating multi-disciplinary datasets along a common timeline to increase incident response feedback for coaching. Currently this framework is being introduced in the state of Colorado, USA as a part of a joint government, industry and academic partnership. Upon project initiation, the feedback cycle had been a minimum of several months from observation to feedback. Presented here is a new framework that can shorten the cycle of psychometric feedback to multiple times in one training day. This Short-Cycle Framework, gathering psychometric and cyber data to provide direct feedback to cyber defense team leaders, was conceived when Regis University’s psychometric evaluators observed a real multi-agency cyber defense response. The authors realized the psychometric data can be used in live cyber defense incidents alongside things like network firewall traffic analysis as the cyber defenders provide relief for organizations under active cyber attack. This work presents the context in which the framework was developed, the characteristics of the framework, and suggestions for further research. The framework implements a specific set of short-term state indicators based on well-known personality trait and state models. The coaching cycle was scripted to shorten the delay between observation and feedback so that it can be more useful in both training and live incident response.
Performence analysis of neural network with improved weight training process

The traditional convolutional neural network usually initializes the weights of all network layers at one time before network training, and then updates the weights of the network by back-propagation algorithm to improve the accuracy of the network during network training. However, with the increase of network depth, the computational cost of this method will increase dramatically and the test accuracy will be affected. In order to solve this problem, a method of gradually reinitializing the weights of each layer is proposed, that is, after a certain training period, the weight of the previous layer is determined and remain unchanged, then initialize the weights of all subsequent layers, repeat this step until the weights of all layers are determined. In order to verify the performance of the method, a series of experiments were carried out on the CIFAR10 dataset. The results show that the accuracy of the network is improved by 9% and the training time is reduced by 29%. It shows that the method can improve the accuracy of the network and reduce the training time.
Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.
SimBNN: A Similarity-Aware Binarized Neural Network Acceleration Framework

Binarized Neural Networks (BNNs) eliminate bitwidth redundancy in Convolutional Neural Networks (CNNs) by using a single bit (-1/+1) for network parameters and intermediate representations. This greatly reduces off-chip data transfer and storage overhead. However, considerable computation redundancy remains in BNN inference. To tackle this problem, we investigate the similarity property in input data and kernel weights. We identify an average of 79% input similarity and 61% kernel similarity measured by our proposed metric across common network architectures. Motivated by this observation, we propose SimBNN, a fast and energy-efficient acceleration framework for BNN inference that leverages similarity properties. SimBNN consists of a set of similarity-aware accelerators, a weight reuse optimization algorithm, and a similarity selection mechanism. SimBNN incorporates two types of BNN accelerators, which exploit the input similarity and kernel similarity, respectively. More specifically, the result from the previous stage is reused if similarity is identified, thus significantly reducing BNN computation overhead. Furthermore, we propose a weight reuse optimization algorithm, which increases the weight similarity by off-line re-ordering weight kernels. Finally, our framework provides a systematic method to determine the optimal strategy between input data and kernel weights reuse, based on the similarity characteristics of input data and pre-trained BNNs.
English-Indonesian Neural Machine Translation for Spoken Language Domains

In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.
Peningkatan Kecerdasan Logika-Matematika Melalui Biblioterapi Pada Anak Usia Dini Kelompok A Kober Warna Plus

The background of this research is the lack of logic-mathematical abilities of children, especially knowing the symbol of group A numbers in Kober Warna Plus. For this reason, bibliotherapy methods are needed to develop logical-mathematical intelligence This research method is descriptive and research form PTK with pre-school child subjects. Data collection techniques are observation, interviews, and documentation. Based on the results of the first cycle test and observation study of 12.5% of the children developed very well, 37.5% developed according to expectations, and 50% began to develop and still had to be stimulated, but when the second cycle was tested, the result showed that the child had developed ability very good, namely 87.5%, and 12.5% as expected.
A flexible and simplified calibration procedure for fringe projection profilometry

Fringe Projection Profilometry (FPP) is a widely used technique for optical three-dimensional (3D) shape measurement. Among the existing 3D shape measurement techniques, FPP provides a whole-field 3D reconstruction of objects in a non-contact manner, with high resolution, and fast data processing. The key to accurate 3D shape measurement is the proper calibration of the measurement system. Currently, most calibration procedures in FPP rely on phase-coordinate mapping (PCM) or back-projection stereo-vision (SV) methods. The PCM technique consists in mapping experimental metric XYZ coordinates to recovered phase values by fitting a predetermined function. However, it requires accurately placing 2D or 3D targets at different distances and orientations. Conversely, in the SV method, the projector is regarded as an inverse camera, and the system is modeled using triangulation principles. Therefore, the calibration process can be carried out using 2D targets placed in arbitrary positions and orientations, resulting in a more flexible procedure. In this work, we propose a hybrid calibration procedure that combines SV and PCM methods. The procedure is highly flexible, robust to lens distortions, and has a simple relationship between phase and coordinates. Experimental results show that the proposed method has advantages over the conventional SV model since it needs fewer acquired images for the reconstruction process, and due to its low computational complexity the reconstruction time decreases significantly.
Visual odometry based on the pixels grayscale partial differential

The visual odometry based on Photo-Consistency assumption is affected by noise and texture. The pixels grayscale partial differential is applied to overcome noise effects and enhance image texture in the field of image processing. This paper proposes a visual odometry method by calculating pixels grayscale partial differential. The algorithm makes full use of the continuity of image information and utilizes the information around a pixel to register images, improves performance of the visual odometry. Experimental results on the TUM RGB-D datasets show that the presented method can achieve better performance on multiple types of image sets, especially in texture-less scenes.
Employee Motivation and Their Impact on Employee Performance in Banking Sector in Hyderabad Karnataka Region

Due to increased competition between Organization and their need to respond effectively to rapidly changing operational conditions, as well as to personnel requirements, has escalated the necessity to identify those factors that Improve Employee Performance.For any organization to operate smoothly and without any interruption, employee cooperation cannot be replaced with anything else.It is of utmost importance that the employees of an organization not only have a good relationship with the top management, but also, they maintain a healthy and professional relationship with their coworkers.The study focus on how motivational tools impact the performance of employee for betterment.A sample of individuals was selected and was interviewed with self-administrated questionnaire to obtain primary data.The results obtained indicate that if employees are positively motivated, it improves both their effectiveness and efficiency drastically for achieving organizational goals.
Scylladb optimizes database architecture to maximize hardware performance

Presents an interview conducted with Avi Kivity, chief technology officer of ScyllaDB.
Subset sums over Galois rings

Let p be an odd prime and R=GR(p2,p2r) be a Galois Ring of characteristic p2 with cardinality p2r. For a subset D of R, letND(n,b)=#{S⊂D|∑x∈Sx=band|S|=n} andR(k)={y∈R|y=xkfor somex∈R}. We give the asymptotic formula of ND(n,b) for D=R(k)∖{0}. From which we deduce that any element in R is the sum of n different k-th power under sufficient condition.
Security Attacks on Blockchain

Many organizations are considering the adoption of blockchain technology as they believe that hacking the blockchain environment is extremely difficult as information stored on the blockchains use various cryptographic techniques to validate transactions and store them securely.But like other technologies, blockchains are also susceptible to attacks.In this paper, discussed about different types of consensus mechanisms that are used in validating the transactions of a blockchain network.This paper also focuses on key cryptography techniques that are often used in blockchain.This paper highlights security vulnerabilities and various noteworthy attacks that took place in blockchain environment.
IMPROVING STUDENTS’ WRITING ABILITY THROUGH PROJECT BASED-LEARNING USING VISUAL MEDIA

The aim of this study is to know the improvement of students’ writing ability through Project based-learning using visual media in recount text at X Grades of students’ Vocational High School in Cimahi. This study was Quantitative design, the sample of research consisted of 30 students. The instrument used in this research was written test. The written test was applied in pretest and posttest. The writing test was administrated as the instrument of the research. The result shown that the students’ total score of pretest was 176, and then total score of posttest was 2222. It means that students’ score increased. Then the data were analyzed using statistical formula of t-test computation The data were analyzed by using t-test as the significant level of p&lt;0.05 through SPSS version 15.0. Based on t-measure analysis show that T-score (9,589) ˃ T-table 5% (2, 045). It means that was significant so the Null Hypothesis (Ho) was rejected. It means that Project based- Learning using Visual Media has significant effect in improving students’ writing ability.