Reviewerly/exHarmony huggingface dataset:
Immunity of Lightweight DES Algorithm (DESL) Against Linear Cryptanalysis Attack

Lightweight DES Algorithm (DESL) was introduced by Axel Poschmann et.al as a strong, compact and efficient encryption algorithm suitable for constraint resource like WSNs, RFID and IOT devices. This paper discusses the security of the DESL against Linear Cryptanalysis which is a known plaintext attack in which a large number of plaintext-ciphertext pairs are used to determine the value of key bits. Linear cryptanalysis works on the principle of finding “high probability occurrences of linear expressions involving plaintext bits, ciphertext bits, and subkey bits”. Furthermore, we show that DESL is more resistant against the linear cryptanalysis attack than classical DES.
Raccoon: automated verification of guarded race conditions in web applications

Web applications are distributed, asynchronous applications that can span multiple concurrent processes. They are intended to be used by a large amount of users at the same time. As concurrent applications, web applications have to account for race conditions that may occur when database access happens concurrently. Unlike vulnerability classes, such as XSS or SQL Injection, dbms based race condition flaws have received little attention even though their impact is potentially severe. In this paper, we present Raccoon, an automated approach to detect and verify race condition vulnerabilities in web application. Raccoon identifies potential race conditions through interleaving execution of user traces while tightly monitoring the resulting database activity. Based on our methodology we create a proof of concept implementation. We test four different web applications and ten use cases and discover six race conditions with security implications. Raccoon requires neither security expertise nor knowledge about implementation or database layout, while only reporting vulnerabilities, in which the tool was able to successfully replicate a practical attack. Thus, Raccoon complements previous approaches that did not verify detected possible vulnerabilities.
Analysis of Song Popularity in Business Digital Music Streaming for Increasing Quality Using Kohonen SOM Algorithm

Consumption of digital music services has grown dramatically in recent years. There is an increase in music streaming consumption from 2015 to 2016, which is 76.4%. One of the most popular music streaming services, Spotify, has experienced an increase in customers from year to year. This increase enables businessmen / music producers to increase their business profits by analyzing music / songs to find out the audio attributes that make the song enjoyable for many people. Processing and analysis data are using Kohonen SOM Algorithm. The function is to find out which audio attribute groups are most liked by Spotify users where a good music is a music that can be used as a therapy. The result is LR = 0.1, PLR = 0.9, and epoch = 70 - 500, it can be concluded that cluster 2 is the cluster that has the most number of streams with 27 songs where the smallest DBI value is obtained at epoch = 200. Thus, with the statistic analysis, the obtained information is; it is expected that businessman / music producers can increase their business profits by improving their music quality that focus on songs with modes = 0 (Minor) and loudness features
Dynamic Feature Extraction Method of Phone Speakers Based on Deep Learning

: Nowadays, speech recognition has become one of the important technologies for human- computer interaction. Speech recognition is essentially a process of speech training and pattern recognition, which makes feature extraction technology particularly essential. The quality of feature extraction is directly related to the accuracy of speech recognition. Dynamic feature parameters can effectively improve the accuracy of speech recognition. These parameters make the speech dynamic feature extraction to have a higher research value. The traditional dynamic feature extraction method is easier to generate more redundant information, resulting in low recognition accuracy. Therefore, based on a new speech feature extraction method, which is based on deep learning for speech feature extraction, is proposed in the present study. Firstly, the speech signal is preprocessed by pre-emphasis, windowing, filtering, and endpoint detection. Then, the sliding differential cepstral feature (SDC) is extracted, which contains the voice information of the front and back frames. Finally, the feature is used as input to extract the dynamic features that represent the depth essence of speech information through the deep self-encoding neural network. The simulation results show that the dynamic features extracted by in-depth learning have better recognition performance than the original features, and have a good effect on speech recognition.
Quality Assessment for Omnidirectional Video with Consideration of Temporal Distortion Variations

Omnidirectional video, also known as 360-degree video, offers an immersive visual experience by providing viewers with an ability to look in all directions within a scene. The quality assessment for omnidirectional video is still a quite difficult task compared to 2D video. As the temporal changes of spatial distortions can considerably influence human visual perception, this paper proposes a full reference objective video quality assessment metric by considering both the spatial characteristics of omnidirectional video and the temporal variation of distortions across frames. Firstly, we construct a spatio–temporal quality assessment unit to evaluate the average distortion in temporal dimension at eye fixation level. The smoothed distortion value is then consolidated by the characteristics of temporal variations. Afterwards, a global quality score of the whole video sequence is produced by pooling. Finally, our experimental results show that our proposed VQA method improves the prediction performance of existing VQA methods for omnidirectional video.
The linking of knowledge management enabler and employee’s performance

In this era, to face competition in business, organizations need to improve strategy and innovation. In addition to human resources and capital, knowledge is an important resource. Through knowledge, people share their experiences and apply that knowledge in the organization. Hence, the company is a concern about the importance of knowledge management as an essential asset. In order to manage it, the organization has to concern with enabler’s factors. The aim of this research is to identify the influence of organizational structure and information technology (IT) on employee performance in the Processing Post Unit, Pos Indonesia Corporation, Bandung. Based on the results, both organizational structure and IT affect employee performance. It is also known that still many other factors can accelerate knowledge management enabler.
Blockchain Based Trusted Adoption and Fostering System Proposal for Turkish Animal Shelters

Animal shelters are known as safe places which are managed by governmental or nongovernmental organizations, however the way these establishments harboring animals is a controversial issue. Almost every country in the world have different approaches to that subject. As an example; United States of America and the Europe Union have acclaimed regulations about animal rights. However, the situation in many countries is not that bright for the stray animals. Stray animals face considerable ratios of violence, assault and death. The shelters have conspicuous problems, such as overpopulation, hunger, lack of space, forced mating, illegal euthanasia and death ratio. Those circumstances have put adopting and fostering mechanisms on a much more troublesome way. Our proposal includes a promising, trackable and transparent system solution based on blockchain technology for the animal shelters. Design of the system, requirements and possible usage scenarios have been proposed. The prototype is implemented on Ethereum. Smart contracts are implemented for autonomous work of the system.
Emerging Giant: Potential Pathways for Ethiopia to 2040

This report presents an integrated assessment of Ethiopia’s future prospects using the International Futures modelling platform. The bulk of the report presents the likely development prospects of the country to 2040 along various measures such as poverty, healthcare and education, before presenting two alternative scenarios titled Abiynomics and Dark Days.
A Novel Data Association Algorithm Based on Fuzzy Logic for Visual Object Tracking

In this paper, we propose a novel fuzzy logic data association algorithm to resolve the problem of visual multi-object tracking. First of all, a fuzzy logic data association approach is proposed to calculate the association probabilities between the objects and observations (or detection responses) by use of a set of fuzzy if-then rules. Based on the similarities of object appearance, shape and motion models, several fuzzy inference systems are designed to confirm the fuzzy membership degrees between the objects and observations, the association probability is generated by weighted affinities, where the weight of the affinity is the output of fuzzy logic. Finally, the experiment results on several public sequences demonstrate that the proposed algorithm has superiorities over other comparatively leading tracking algorithms in terms of accuracy and robustness.
Software Vulnerability Mining Based on the Human-Computer Coordination

In recent years, the increasing size and complexity of software packages has led to vulnerability mining gradually becoming more difficult and challenging. The theoretic research and systematic practice of traditional software vulnerability mining system emphasize models and data. Now, it gradually requires the participation of human vulnerability-miners in the mining procedure. To address the issue, from the human-center perspective, this paper holds that the role of human should be highlighted in the system building process. Aimed at solving the task of software vulnerability mining and integrating the natural intelligence of human into the system, it attempts to assign people who participate in the vulnerability-mining activities as the components of the system. This paper proposes a vulnerability mining system architecture based on human-computer coordination. Then, it designs the workflow of the vulnerability mining task based on human-computer coordination. Finally, it designs a task solving strategy based on human-computer coordination for the fuzz testing scenario.
Learning a Reliable Decision Making Policy for Robust Tracking

Recent years deep learning based visual object trackers have achieved state-of-the-art performance on multiple benchmarks. However, most of these trackers lack an effective mechanism to avoid the wrong template update or re-detect the object when unreliable tracking result appears. In this paper, a novel tracking framework consisting of a tracking network for locating the target and a policy network for decision making is proposed. Firstly, during the off-line training phase, a variant of policy gradient algorithm is adopted, which makes the model converge better and faster. Secondly, current response map and history response map are both fed to the policy network to check the reliability of the tracking result, which effectively distinguishes the response diversity. Finally, an efficient redetection module is proposed to filter a large number of searching areas, which greatly improves the speed. Our proposed algorithm is measured on OTB dataset. Assessment results show that our tracking algorithm improves performance by 5%-6% at the expense of only a small amount of speed.
Peningkatan keterampilan menulis narasi melalui model pembelajaran kooperatif tipe roundtable pada peserta didik kelas iii sekolah dasar

&lt;p class="Abstract"&gt;&lt;em&gt;The purpose of this study is to improve the narrative text writing skills used Roundtable. This study is a Classroom Action Study (CAR) with two cycles. The subject is the student of state Elementary School Bulukantil in academic year 2018/2019 with 40 students. The data collection techniques of this study used observation, interviews, documentation, and test.The validity of data using source triangulation and technical triangulation. This study used an interactive analysis from miles-Huberman data analysis. The initial condition of narrative text writing skills of grade III students resuled in 27,5% classically. The first cylce begins with first meeting of 55% in classical. The second begins with first meeting of 87.5% classical completeness. The conclusions in this study is roundtable improved narrative writing skills on students at grade III of state Elementary School Bulukantil 2018/2019 academic year.&lt;/em&gt;&lt;/p&gt;
Feature Map Alignment: Towards Efficient Design of Mixed-precision Quantization Scheme

Quantization is known as an effective compression method for deploying neural networks on mobile devices. However, most existing works train from scratch a quantized network with universal bitwidth for all layers, making it hard to find the optimal trade-off between compression ratio and inference accuracy. In this paper, we propose a novel post-training quantization approach which derives a flexible bitwidth scheme. Our algorithm progressively downgrades bitwidth of chosen layer in the network and performs feature map alignment with pre-trained model. The algorithm comprises a meter of layer sensitivity and an iterative quantizer. Specifically, the meter dynamically estimates for every layer the error of quantization on its output feature map, meanwhile the error serves as an objective function to be minimized by the quantizer. Extensive experiments on CIFAR-10 and ImageNet ILSVRC2012 datasets demonstrate that the proposed approach achieves impressive results for mainstream neural networks.
This Issue of Geophysics

In this article, the Editor of Geophysics provides an overview of all technical articles in this issue of the journal.
An Adaptive Image Calibration Algorithm for Steganalysis

In this paper, a new adaptive calibration algorithm for image steganalysis is proposed. Steganography disturbs the dependence between neighboring pixels and decreases the neighborhood node degree. Firstly, we analyzed the effect of steganography on the neighborhood node degree of cover images. Then, the calibratable pixels are marked by the analysis of neighborhood node degree. Finally, the strong correlation calibration image is constructed by revising the calibratable pixels. Experimental results reveal that compared with secondary steganography the image calibration method significantly increased the detection accuracy for LSB matching steganography on low embedding ratio. The proposed method also has a better performance against spatial steganography.
Semantic Segmentation via Domain Adaptation with Global Structure Embedding

In this paper we focus on the problem of unsupervised domain adaptation for semantic segmentation. The previous works usually focus on adversarial learning either in pixel-level or feature-level. However, global structure knowledge is often neglected in the adversarial learning due to the possible reasons: First, the result of pixel-level adversarial learning does not necessarily preserve the semantic consistency of the input image. Second, global structure knowledge is not embedded to regularize the feature-level adversarial learning. In this work, we propose a framework for unsupervised domain adaptation in semantic segmentation which effectively incorporates pixel- level, feature-level adversarial learning and self-training strategy. Our framework embeds the global structure knowledge into the adversarial training step to tackle the problem of structure misalignment. Consequently, our proposed framework achieves the state-of-the-art semantic segmentation domain adaptation results on the task of transferring GTA5 to Cityscapes.
ASL Recognition and Conversion to Speech

The deaf & dumb (or the Mute community) find it a tedious task to converse with ordinary people through sign language. This stands as a hindrance in even the most basic of their activities. It affects their personal development, interpersonal relations and limits the contributions they could otherwise make to society. The prime motive of this project is to provide an easy to use platform for the hard of hearing people to express themselves despite the sign language barrier. We aim to achieve this motive through gesture recognition. Using gesture recognition, we compute the mathematical interpretation of human hand gestures to recognize the signs conveyed by American Sign Language. The system enables real-time hand gesture and speech recognition and provides an innovative and simpler mode of communication for the mute people.
Document Classification using Naïve Bayes for Indonesian Translation of the Quran

Classification for Indonesian language documents was increased. But the application of classification for question and answer system needs is still few. The purpose of this paper is to maximize the classification of Indonesian documents especially the Qur'an translation to support the question and answer system. In the process of creating a question and answer system that is still ongoing, testing the Naive Bayes algorithm becomes very important besides other algorithms. The Naive Bayes method is the first choice in this test as it has practicality in calculating. The result of this study is the classification of ITQ documents with 4 categories: morality, faith, knowledge, and Muamalah. The average accuracy rate of 90.5% indicates that the Naive Bayes method is still relevant for use.
Effectiveness of Reading for Islamic Education: Design of Multi-Platform Educational Game as Instructional Media

The purpose of this research is a solution by designing a modern learning media namely windows and android-based multi-platform educational game for Islamic education students that is inserted with reading materials based on the curriculum of english in Islamic educational institution subject for grade 7 students of tsanawiyah school (MTs). This game was designed by utilizing construct 2 software. This research produces a multi-platform educational game namely the Adventure of Timun Mas. Material and media validations were performed by some experts who are competent in their fields. Material validation was done by english teachers of Junior High School and english education lecturers. Meanwhile, media validation was performed by practitioners, game developer, and Informatics Engineering lecturers. From the result of both material and media validation, each validator was summed, and then divided by the overall score and multiplied by 100%. The final result of product validation was 82.95%, indicating that The Adventure of Timun Mas game is valid to use.
Multi-agent Collaboration for Feasible Collaborative Behavior Construction and Evaluation*

In the case of the two-person zero-sum stochastic game with a central controller, this paper proposes a best collaborative behavior search and selection algorithm based on reinforcement learning, in response to how to choose the best collaborative object and action for the central controller. In view of the existing multi-agent collaboration and confrontation reinforcement learning methods, the methods of traversing all actions in a certain state leads to the problem of long calculation time and unsafe policy exploration. This paper proposes to construct a feasible collaborative behavior set by using action space discretization, establishing models of both sides, model-based prediction and parallel search. Then, we use the temporal difference learning method to train the scoring function to select the optimal collaboration behavior from the feasible collaborative behavior set. This method enables efficient and accurate calculation in an environment with strong confrontation, high dynamics and a large number of agents, which is verified in the RoboCup Small Size League robots ball passing collaboration.
Blog Technology: Constraints to Implementation in Public University Libraries in Nigeria

The study was on blog technology and constraints to implementation in public university libraries in South-East, Nigeria. The study design was descriptive survey. Questionnaire was used for data collection. The sample size was 49 respondents. The study revealed that the respondents’ level of awareness regarding the use of library blog was very high. Also, inadequate electricity provision and inadequate skilled librarians to manage the technology were the main bane of use of Library weblog and other social media tools in the libraries. It was recommended that Library Authorities should provide adequate funds to the libraries to enable them provide alternative means of electricity (for example, solar backup).
Evaluating Web Quality and Its Influential Factors in Higher Education: A Comparative Study

The quality of a website is an important factor that must be considered in an institution because an institution's website is an illustration of the institution in cyberspace. To find out the quality of a website, it is necessary to measure the quality of the website based on its users. This study aims to find out how the quality of academic websites is owned by several universities in Indonesia universities. The instrument used was adopted from the method of WebQual 4.0 consisting of three variables, namely usability, information quality, and service interaction quality, and twenty-two indicators. The analytical technique used is importance-performance analysis (IPA) which will provide an overview of indicators that need improvement or need to be maintained based on user perceptions and expectations. This study uses a quantitative approach by distributing questionnaires to students active in each college. The reliability and validity test of the instrument was carried out with the help of SPSS software. Data analysis was carried out using a WebQual index, gap analysis, and importance-performance analysis (IPA). The results of the gap analysis show negative results in all indicators, this shows that the quality of academic websites in five universities is under the expectations of its users. Results will be expected to reveal some interesting findings to discuss.
Pengaruh Penggunaan Media Video pada Konsep Sistem Kerangka Manusia Terhadap Motivasi dan Hasil Pembelajaran Kognitif Siswa SMAN I Peukan Baro Kabupaten Pidie

A study has been conducted to determine the differences in motivation and student learning outcomes by using video media and by conventional learning on Human Skeletal System subject at SMAN 1 Peukan Baro in Pidie Regency in 2016/2017 school year. The population of this study was 46 students. The entire population was selected as a research sample that is 23 students for the experimental class and 23 students for the control class. Motivation data and learning outcomes were collected through a pretest and posttest questionnaire. The learning outcomes were analyzed using the F test and Mann Whitney. The results showed that motivation and learning outcomes differed significantly between students who used video media learning and students who were teached conventionally with a P value of 0,000 &lt; 0.05. In conclusion there are significant differences in motivation and learning outcomes between students who use video media learning and students who use conventional learning methods especially on the topic of the Human Skeletal System.