Reviewerly/exHarmony huggingface dataset:
The Effect of Activities Developed with Web 2.0 Tools Based on the 5E Learning Cycle Model on the Multiplication Achievement of 4th Graders

This study examines the effects of activities developed with WEB 2.0 tools based on the 5E learning cycle model on the multiplication achievement of 4th graders. Nonequivalent control group pretest-posttest quasi-experimental design was employed in the study. Two of the three groups that were equivalent in terms of achievement were assigned as experimental group and one as control group. While multiplication activities developed with WEB 2.0 tools based on 5E learning cycle model were used in the math classes of the experiment 1 group, multiplication activities developed with WEB 2.0 tools were used in the math classes of the Experiment 2 group. The control group math class was taught according to the 4th grade math textbook approved by the Ministry of National Education. The study lasted 14 hours (three weeks). A semi-structured interview form was used to determine student views on WEB 2.0 tools. According to the study results, there was no significant difference between the pretest achievement scores of the experiment 1, experiment 2 and control group students. A significant difference was determined between the pretest and posttest scores of experiment 1, experiment 2 and control group students in favor of the posttest. A significant difference was found between the posttest achievement scores between the groups. This difference was in favor of the experiment 1 group between experiment 1 and control group and in favor of the Experiment 2 group between experiment 2 and control group. There was no significant difference between the experiment 1 and experiment 2 groups.
Blockchain Framework for Securing E-Learning System

This paper recommends a blockchain framework that secures the e-learning platform.The proposed framework was used to build a widely manageable and safe data delivery facility that will connect to existing educational data.Institutions can simply have amassed their data history without demanding a copy from central servers.The proposed blockchain framework enhances data security and removes trust concerns amongst users or between third-party institutions accessing applications and services.Data distribution can be used with smart contracts to warranty that institutions will continue to be in control of their data entrance, are alert of the source of collected data sources, and are up-to-date when their data is log on by others.Immutable audit logs are generated through data source and contact history to keep institutions informed of the time their data are retrieved.Blockchain has the potential that could provide a system with massive openness, online secure database that e-learning platforms needed.The blockchain can be implemented on different learning platforms based on the information security policies of the institutions.
Microwave Photon Detectors Based on Semiconducting Double Quantum Dots

Detectors of microwave photons find applications in different fields ranging from security to cosmology. Due to the intrinsic difficulties related to the detection of vanishingly small energy quanta ℏ ω , significant portions of the microwave electromagnetic spectrum are still uncovered by suitable techniques. No prevailing technology has clearly emerged yet, although different solutions have been tested in different contexts. Here, we focus on semiconductor quantum dots, which feature wide tunability by external gate voltages and scalability for large architectures. We discuss possible pathways for the development of microwave photon detectors based on photon-assisted tunneling in semiconducting double quantum dot circuits. In particular, we consider implementations based on either broadband transmission lines or resonant cavities, and we discuss how developments in charge sensing techniques and hybrid architectures may be beneficial for the development of efficient photon detectors in the microwave range.
A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.
TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition

Modeling spatiotemporal representations is one of the most essential yet challenging issues in video action recognition. Existing methods lack the capacity to accurately model either the correlations between spatial and temporal features or the global temporal dependencies. Inspired by the two-stream network for video action recognition, we propose an encoder–decoder framework named Two-Stream Bidirectional Long Short-Term Memory (LSTM) Residual Network (TBRNet) which takes advantage of the interaction between spatiotemporal representations and global temporal dependencies. In the encoding phase, the two-stream architecture, based on the proposed Residual Convolutional 3D (Res-C3D) network, extracts features with residual connections inserted between the two pathways, and then the features are fused to become the short-term spatiotemporal features of the encoder. In the decoding phase, those short-term spatiotemporal features are first fed into a temporal attention-based bidirectional LSTM (BiLSTM) network to obtain long-term bidirectional attention-pooling dependencies. Subsequently, those temporal dependencies are integrated with short-term spatiotemporal features to obtain global spatiotemporal relationships. On two benchmark datasets, UCF101 and HMDB51, we verified the effectiveness of our proposed TBRNet by a series of experiments, and it achieved competitive or even better results compared with existing state-of-the-art approaches.
ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

With the exponential growth of large data produced by IoT applications and the need for low-cost computational resources, new paradigms such as volunteer cloud computing (VCC) have recently been introduced. In VCC, volunteers do not disclose resource information before joining the system. This leads to uncertainties about the level of trust in the system. The majority of available trust models are suitable for peer-to-peer (P2P) systems, which rely on direct and indirect interaction, and might cause memory consumption overhead concerns in large systems. To address this problem, this paper introduces ProTrust, a probabilistic framework that defines the trust of a host in VCC. We expand the concept of trust in VCC and develop two new metrics: (1) trustworthiness based on the priority of a task, named loyalty, and (2) trustworthiness affected by behavioral change. We first utilized a modified $Beta$ distribution function, and the behavior of resources are classified into different loyalty levels. Then, we present a behavior detection method to reflect recent changes in behavior. We evaluated ProTrust experimentally with a real workload trace and observed that the framework's estimation of the trust score improved by approximately 15% and its memory consumption decreased by more than 65% compared to existing methods.
GraphQL Federation: A Model-Based Approach.

The Graph Query Language (GraphQL) is a framework for developing Web Services, which works on the domain model level rather than the functions.While the declarative nature of this framework has already attracted the interest of practitioners in both academia and industry, it still lacks integration capabilities.This shortcoming has been recognized in the industry; there exists a first tool creating a federation-a virtual integrated system-on top of instrumented systems.Being relatively new, it suffers from a few practical and conceptual shortcomings, such as consolidation of (conflicting) schemas and identification of multiple types.In this paper, we will analyze the federation challenge and propose a solution utilizing multi-view modeling and domain specific modeling.Our approach is accompanied by a proof-of-concept implementation and provides a model-based presentation of the GraphQL framework.
Learning semantic information from Internet Domain Names using word embeddings

Word embeddings is a well-known set of techniques widely used in Natural Language Processing (NLP). These techniques are able to learn words’ semantic based on the distributional hypothesis which states that words that are used and occur in the same contexts tend to purport similar meanings. This paper explores the usage of word embeddings in a new scenario to create a Vector Space Model (VSM) for Internet Domain Names (DNS). Our goal is to find semantically similar domains only using information of DNS queries without any knowledge about the content of those domains. The results presented here have practical applications in many engineering activities including websites recommendations, identification of fraudulent or risky sites, parental-control systems and anomaly detection in network traffic analysis (among others). We use the distributional hypothesis to learn the semantic of domain names from users’ web navigation patterns, validating empirically that domain names that occur in the same web sessions tend to have similar semantic. We also test different word embeddings techniques: word2vec, app2vec (considering time intervals between DNS queries), and fastText (which includes sub-word information). Due to the characteristics of domain names, we found fastText as the best option for building a VSM for DNS, being 10.5% superior than word2vec with Skip-Gram which was the next best technique considering the Mean Average Precision at k ([email protected]) metric, which compares the most similar domains in our VSM with the most similar domains provided by a third party source, namely, similar sites service offered by Alexa Internet, Inc.
Comparative Study of One-Class Based Anomaly Detection Techniques for a Bicomponent Mixing Machine Monitoring

One critical point to improve the economic and technical results of every industrial process lies on the fact of achieving a good optimization, and applying a smart maintenance plan. In this context, the tools development for detecting the appearance of any kind of anomaly represents an important challenge. For this reason, the implementation of classifiers for anomaly detection tasks has been a significant trend in the scientific community. However, since the behavior of the potential anomalies that may occur in a plant is unknown, it is necessary to generate artificial outliers to assess these classifiers. This paper proposes the performance checking of different intelligent one-class techniques to detect anomalies in an industrial plant, used to obtain the main material for wind generator blades production. These classifiers are tested using anomaly data generated, giving successful results.
UC-Merced Image Classification with CNN Feature Reduction Using Wavelet Entropy Optimized with Genetic Algorithm

The classification of high-resolution and remote sensed terrain images with high accuracy is one of the greatest challenges in machine learning.In the present study, a novel CNN feature reduction using Wavelet Entropy Optimized with Genetic Algorithm (GA-WEE-CNN) method was used for remote sensing images classification.The optimal wavelet family and optimal value of the parameters of the Wavelet Sure Entropy (WSE), Wavelet Norm Entropy (WNE), and Wavelet Threshold Entropy (WTE) were calculated, and given to classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).The efficiency of the proposed hybrid method was tested using the UC-Merced dataset.80% of the data were used as training data, and a performance rate of 98.8% was achieved with SVM classifier, which has been the highest ratio compared to all studies using same dataset so far with only 18 features.These results proved the advantage of the proposed method.
Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks

Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer.The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups.Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4.The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset.Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.
ACCURATE VEHICLE SPEED ESTIMATION FROM MONOCULAR CAMERA FOOTAGE

Abstract. A workflow is devised in this paper by which vehicle speeds are estimated semi-automatically via fixed DSLR camera. Deep learning algorithm YOLOv2 was used for vehicle detection, while Simple Online Realtime Tracking (SORT) algorithm enabled for tracking of vehicles. Perspective projection and scale factor were dealt with by remotely mapping corresponding image and real-world coordinates through a homography. The ensuing transformation of camera footage to British National Grid Coordinate System, allowed for the derivation of real-world distances on the planar road surface, and subsequent simultaneous vehicle speed estimations. As monitoring took place in a heavily urbanised environment, where vehicles frequently change speed, estimations were determined consecutively between frames. Speed estimations were validated against a reference dataset containing precise trajectories from a GNSS and IMU equipped vehicle platform. Estimations achieved an average root mean square error and mean absolute percentage error of 0.625 m/s and 20.922 % respectively. The robustness of the method was tested in a real-world context and environmental conditions.
Group Re-Identification With Group Context Graph Neural Networks

Group re-identification aims to match groups of people across disjoint cameras. In this task, the contextual information from neighbor individuals can be exploited for re-identifying each individual within the group as well as the entire group. However, compared with single person re-identification, it brings new challenges including group layout and group membership changes. Motivated by the observation that individuals who are close together are more likely to keep in the same group under different cameras than those who are far apart, we propose to model each group as a spatial K-nearest neighbor graph (SKNNG) and design a group context graph neural network (GCGNN) for graph representation learning. Specifically, for each node in the graph, the proposed GCGNN learns an embedding which aggregates the contextual information from neighbor nodes. We design multiple weighting kernels for neighborhood aggregation based on the graph properties including node in-degrees and spatial relationship attributes. We compute the similarity scores between node embeddings of two graphs for group member association and obtain the matching score between the two graphs by summing up the similarity scores of all linked node pairs. Experimental results on three public datasets show that our approach performs favorably against state-of-the-art methods and achieves high efficiency.
Extracting user influence from ratings and trust for rating prediction in recommendations

Abstract The Collaborative Filtering (CF) algorithm based on trust has been the main method used to solve the cold start problem in Recommendation Systems (RSs) for the past few years. Nevertheless, the current trust-based CF algorithm ignores the implicit influence contained in the ratings and trust data. In this paper, we propose a new rating prediction model named the Rating-Trust-based Recommendation Model (RTRM) to explore the influence of internal factors among the users. The proposed user internal factors include the user reliability and popularity. The internal factors derived from the explicit behavior data (ratings and trust), which can help us understand the user better and model the user more accurately. In addition, we incorporate the proposed internal factors into the Singular Value Decomposition Plus Plus (SVD + +) model to perform the rating prediction task. Experimental studies on two common datasets show that utilizing ratings and trust data simultaneously to mine the factors that influence the relationships among different users can improve the accuracy of rating prediction and effectively relieve the cold start problem.
LFDD: Light field image dataset for performance evaluation of objective quality metrics

An increase in research activity around plenoptic content can be seen in recent years. As the communities around the different modalities grow, so does the demand for publicly available annotated datasets of suitable content. The datasets can be used for a multitude of purposes, such as to design novel compression algorithms, subjective evaluation methodologies or objective quality metrics. In this work, a new publicly available annotated light field image dataset is presented. The dataset consists of scenes corrupted by state-of-the-art image and video compression algorithms (JPEG, JPEG 2000, BPG, VP9, AV1, AVC, HEVC), noise, and geometric distortion. For the subjective evaluation of the included scenes, a modified version of the Double Stimulus Impairment Scale (DSIS) methodology was adopted. The views of each scene were organized into a pseudo-sequence and played to the observers as animations. The resulting subjective scores, together with additional data, are included in the dataset. The data can be used to evaluate the performance of currently used visual quality metrics as well as for the design of new ones.
Task failure resilience technique for improving the performance of MapReduce in Hadoop

MapReduce is a framework that can process huge datasets in parallel and distributed computing environments. However, a single machine failure during the runtime of MapReduce tasks can increase completion time by 50%. MapReduce handles task failures by restarting the failed task and re-computing all input data from scratch, regardless of how much data had already been processed. To solve this issue, we need the computed key-value pairs to persist in a storage system to avoid re-computing them during the restarting process. In this paper, the task failure resilience (TFR) technique is proposed, which allows the execution of a failed task to continue from the point it was interrupted without having to redo all the work. Amazon ElastiCache for Redis is used as a non-volatile cache for the key-value pairs. We measured the performance of TFR by running different Hadoop benchmarking suites. TFR was implemented using the Hadoop software framework, and the experimental results showed significant performance improvements when compared with the performance of the default Hadoop implementation.
SiegeBreaker: An SDN Based Practical Decoy Routing System

Abstract Decoy Routing (DR) , a promising approach to censorship circumvention, uses routers (rather than end hosts) as proxy servers. Users of censored networks, who wish to use DR, send specially crafted packets, nominally addressed to an uncensored website. Once safely out of the censored network, the packets encounter a special router (the Decoy Router) which identifies them using a secret handshake, and proxies them to their true destination (a censored site). However, DR has implementation problems: it is infeasible to reprogram routers for the complex operations required. Existing DR solutions fall back on using commodity servers as a Decoy Router. But as servers are not efficient at routing, most web applications show poor performance when accessed over DR. A further concern is that the Decoy Router has to inspect all flows in order to identify the ones that need DR. This may itself be a breach of privacy for other users (who neither require DR nor want to be monitored). In this paper, we present a novel DR system, Siege- Breaker (SB), which solves the aforementioned problems using an SDN-based architecture. Previous proposals involve a single unit which performs all major operations (inspecting all flows, identifying the DR requests and proxying them). In contrast, SB distributes the tasks for DR among three independent modules. (1) The SDN controller identifies DR requests via a covert, privacy preserving scheme, and does not need to inspect all flows. (2) The reconfigurable SDN switch intercepts packets, and forwards them to a secret proxy efficiently. (3) The secret proxy server proxies the client’s traffic to the censored site. Our modular, lightweight design achieves performance comparable to direct TCP downloads, for both in-lab setups, and Internet based tests involving commercial SDN switches.
SISTEM INFORMASI AKUNTANSI PENGOLAHAN DANA KAS KECIL MENGGUNAKAN METODE IMPREST PADA PT SINAR SOSRO BANDARLAMPUNG

PT Sinar Sosro Bandar Lampung is a company engaged in the sale of beverages. Data processing at PT Sinar Sosro issuance of claim evidence is still manual, which has not been computerized, one of which is the process of issuing the calculation of claim evidence to the company. Based on the above problems, there are several solutions to deal with so that the management of petty cash funds can simplify spending. The design of the information system for managing petty cash funds uses the imprest method at PT Sinar Sosro Bandar Lampung, namely the design of the system is done using Use Case Diagrams and Applications are built using Borland Delphi 7 and MySQL databases. Testing this system uses a black box. The results of this study are to produce a petty cash fund management information system containing information about the expenditure of petty cash funds, proof of claims that can help and be evidence of the existence of expenditure activities carried out when the petitioner wants to use petty cash funds.