Reviewerly/exHarmony huggingface dataset:
Adaptive image coding efficiency enhancement using deep convolutional neural networks

In recent years, the application of systems for the storage and transmission of images has faced severe challenges owing to the significant increase in the number of images being generated and sent. Therefore, it is necessary to enhance the efficiency of existing coding approaches while maintaining compatibility. This paper proposes an adaptive image coding efficiency enhancement framework that innovatively integrates deep network-based super-resolution (SR) and soft-decoding (SD) into image coding. To address the problem of insufficient number of coding bits at high compression ratios, the given image is downsampled prior to encoding, and the decoded image is super-resolved in the proposed low-resolution coding mode. For the full-resolution coding mode developed for medium or low compression ratios, the given image is directly encoded and the decoded image is enhanced by suppressing the compression noise. To efficiently determine the optimal coding mode of a given image, an adaptive mode decision strategy is proposed by modeling the relationship between the critical coding quality parameter and the distortion caused by successive downsampling and upsampling processes. Experimental results show that the proposed algorithm significantly improves the performance of the widely used image coding standard JPEG, demonstrating the effectiveness of the SR and SD-enabled coding framework.
Design and Implementation of Automatic Invigilation Functions Using the Embedded Technology

This paper designs and implements the automatic invigilation functions using the embedded technology. It proposes a framework for automatic invigilation, which conducts the invigilation functions of the entire examination process. In the examination preparation stage, the framework collects the registration details of examinees and verifies the details with the database through the remote server. During the examination ongoing stage, it keeps checking the consistence between each examinee and his examination materials by photographing the examinee and scanning the QR codes on examination papers, sketch papers, and answer sheets. In the examination ending stage, it checks the consistence of an examination bag and the materials being put into it by scanning and verifying the QR codes on them. The framework reduces the human workload by using automatic functions to replace the human work. The tests demonstrates that the framework can perform the designed functions.
PoSNet: 4x Video Frame Interpolation Using Position-Specific Flow

Video frame interpolation has been studied for a long time; however, it is still a difficult low-level vision task. Owing to the improved performance of optical flow estimation, frame-interpolation studies based on optical flow are actively conducted. However, the existing methods are generally tested using high-fps sequences and developed for 2× upscaling or generating multiple frames with a single estimator. This paper proposes a 4× video-interpolation framework that aims to convert 15-fps to 60-fps videos based on a structure comprising flow estimation followed by an enhancement network. We improve the performance by training specialized flow estimators for each direction and frame position. Furthermore, we use the original frames and flow maps as additional inputs for the enhancement network to improve the subjective image quality. Consequently, the proposed network interpolates high-quality frames with a fast runtime and demonstrates its superiority in the AIM 2019 video temporal super-resolution challenge. The associated code is available at https://github.com/SonghyunYu/PoSNet.
Cloud Resource Management with a Hybrid Virtual Machine Consolidation Approach

Modern cloud data centers must effectively deliver a wide range of IT services to clients ensuring preassigned performance and availability. But the rapid growth of large-scale IT services results in the necessity of developing new methods for resource management in the cloud data center. This paper presents a hybrid virtual machine consolidation approach that aims at minimization of energy consumption and SLA violation by applying modified Best Fit Decreasing heuristic to initial virtual machine (VM) placement and by applying the adopted beam search algorithm to manage VM migrations. The heuristics of the proposed approach, the assessment functions, and the stop functions of the algorithms are developed. The proposed methods are programmed in C# and evaluated using Bitbrains workload traces. The simulation results show that the proposed approach outperforms a plain Best Fit heuristic in terms of the SLA violation, the number of active physical machines, and the number of VM migrations.
Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries

We introduce a novel approach for supporting fully interactive non-linear spatio-temporal exploration of massive time-varying rectilinear scalar volumes on commodity platforms. To do this, we decompose each frame into an octree of overlapping bricks. Each brick is further subdivided into smaller non-overlapping blocks compactly approximated by quantized variable-length sparse linear combinations of prototype blocks stored in a learned data-dependent dictionary. An efficient tolerance-driven learning and approximation process, capable of computing the tolerance required to achieve a given frame size, exploits coresets and an incremental dictionary refinement strategy to cope with datasets made of thousands of multi-gigavoxel frames. The compressed representation of each frame is stored in a GPU-friendly format that supports direct adaptive streaming to the GPU with spatial and temporal random access, view-frustum and transfer-function culling, and transient and local decompression interleaved with ray-casting. Our variable-rate codec provides high-quality approximations at very low bit-rates, while offering real-time decoding performance. Thus, the bandwidth provided by current commodity PCs proves sufficient to fully stream and render a working set of one gigavoxel per frame without relying on partial updates, thus avoiding any unwanted dynamic effects introduced by current incremental loading approaches. The quality and performance of our approach is demonstrated on massive time-varying datasets at the terascale.
A method for classifying tube structures based on shape descriptors and a random forest classifier

Machine-vision-based tube measurement is characterized by its accuracy, level of automation, noncontact nature and reliability. However, it cannot classify tube structures automatically. Commercial systems and previous algorithms cannot measure branch tubes due to difficulties of classifying tube structures. Therefore, this paper proposes a method for classifying tube structures. Multiple shape descriptors are used to extract tube structure features. Furthermore, RF classifier is used to distinguish among tube structures after tube features extraction. For efficient and accurate classification, the relative importance of each feature is calculated. Compared to results of ResNet-18 training on tube structures dataset, the precision of proposed method achieves 94% while the other is only 88%; experiments shows good performance on Recall and F-score. We developed a software to verify the method on the basis of the multi-view vision system built by our group, which can rapidly and automatically classify numerous complex tube structures used in engineering field.
Sudomy: Information Gathering Tools for Subdomain Enumeration and Analysis

Abstract In general, system security has become a crucial element in the digital aspect. Technically, evaluating the system there are aspects, one of which is by conducting a security assessment. Specifically, each system that will be evaluated is the essence of vulnerability search. The information security assessment is a form of awareness regarding cyberattacks that always increases from year to year. The assessment process can be carried out by an internal team and/or external auditor. The stages of evaluation by internal teams are certainly different from those of external parties. External auditors in conducting assessments need to learn or get as much information as possible related to the target, which in this case is in the form of a subdomain. Therefore, we need applications that support effective and efficient Information Gathering to assist in analysis and reporting. There are still many Information Gathering applications that do not include reporting and data validation systems. In this study, it is proposed to develop applications to support the Information Gathering stage which makes it easier for Cyber Security researchers/analysts.
Time Delay Neural Network for Myanmar Automatic Speech Recognition

Time Delay Neural Network (TDNN) contains in neural network architectures. In Automatic Speech Recognition, TDNN is strong possibility in context modeling and recognizes phonemes and acoustic features, independent of position in time. There are many techniques have been applied for improving Myanmar speech processing. TDNN based acoustic model for Myanmar ASR in this paper. Myanmar language is a low resource language because there is a lack of pre-collected data. A larger dataset and lexicon is applied in this work. The speech corpus contains three domain: Names, Web News data and Daily conversational data. The size of the corpus is 77 Hrs and 2 Mins and 11 Secs and include 233 female speakers and 97 male speakers. The performance of TDNN for Myanmar ASR is shown by comparing with Gaussian Mixture Model (GMM) as a baseline system, Deep Neural Network (DNN) and Convolutional Neural Network (CNN). Experiments evaluation is used 2 test data: TestSet1, web news and TestSet2, recorded conversational data. The experimental results show that TDNN outperforms GMM-HMM, DNN and CNN.
Self-Sovereign Dynamic Digital Identities based on Blockchain Technology

With the advancement of electronic and cardless payment technologies and the growth in identity thefts and frauds, it is important for merchants and vendors to be able to perform a retrospective verification on a previous transaction at any later time. However, some of the user's identity credentials used as the basis of trust on these payment technologies may be changing over time, which could largely complicate or confuse the retrospective verifications. In this paper, we propose a self-sovereign dynamic digital identity scheme to address the above problem. We introduce the management and use of dynamic digital identity, which is created using the blockchain technology. The proposed scheme makes use of a combination of biometric traits and other identity attributes that could vary or evolve over time. Since the proposed dynamic identity allows tracing back to a previous version of the identity of the same individual via the blockchain, it can be used to retrospectively verify a previous transaction associated with a previous version of the identity.
The Development Of E-Modules Based on Adobe Flash For Indonesian Subjects At IAIN Bukittinggi

Abstract This study was done by the result of observation at IAIN Bukittinggi, it was found that Indonesian learning activities generally only use conventional media that are simple, in the form of a blackboard and powerpoint text. Learning tends to use lecture methods and conventional modules in the form of hardcopy modules in the learning process, it causes the students to feel bored, have difficulty understanding conventional modules and are less motivated during the learning process. The purpose of this study is to develop teaching material in the form of an Indonesian e-module that is valid, practical, effective and can motivate students to learn independently in improving student learning achievement. The development of this Indonesian e-module passes the ADDIE stages, namely (1) Analysis; (2) Design; (3) Development; (4) Implementation; (5) Evaluation. Products were developed include lecturer guidance book consisting of syllabus and SAP, students guidance book and Indonesian e-modules. Based on the results of internal consistency test and implementation, the product developed was categorized as very valid with a score of 86.47, the product developed was also categorized as very practical with a score of 90.40, and very effective with a score of 93.02. Based on the results of the validity, practicality, and effectiveness of the development of e-modules in Indonesian subject it can be concluded that e-modules have excellent validity, practicality and effectiveness.
Detecting Cellphone Camera Status at Distance by Exploiting Electromagnetic Emanations

This paper investigates unintended radiated emissions from cellphones to identify operational status of rear/front camera. We implement a supervised learning method to achieve our goal. In the training phase, we collect data for possible combinations of phone model and camera status. Then, we apply two-phase-dimension-reduction method for better and effective classification. The first dimension-reduction phase is averaging magnitudes of frequency components of a sliding window, which is followed by applying principle component analysis (PCA) technique to reduce the dimension further. In testing phase, k-Nearest-Neighbors (k-NN) algorithm is utilized to classify test data. Finally, we provide examples to show that emanated EM signals from cellphone cameras can exfiltrate useful information.
Blockchain-based Complete Self-tallying E-voting Protocol

Electronic voting (E-voting) protocol is that voters can vote according to their wishes, and then the voting authority is responsible for collecting the votes and counting the final voting result. With the development of Blockchain, we tend to combine it with E-voting and propose Blockchain-based complete self-tallying E-voting protocol. Its distributed network makes the protocol more available than E-voting protocol based on centralized servers. In our protocol, Blockchain acts as bulletin board, and “Efficient One-out-of-T” zero knowledge proof (ZKP) is proposed to support multi-candidate voting. Moreover, the issues of abortive and adaptive are solved. The security analysis shows that our protocol meets the security requirements of E-voting, and it can be applied to small-scale and anonymous private scenario such as Corporate Board Voting. The performance analysis demonstrates that the proposed ZKP has low time consumption.
Artificial intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics

The massive amount of health data acquired from electronic health records (EHRs), gives us unprecedented opportunities to build clinical infrastructures for clinical informatics (CI), research-based studies, and precision medicine. However, a significant part of the patients’ treatment data is stored in an unstructured format, such as free-text clinical notes. Machine learning (ML) methods and natural language processing (NLP) techniques are popular computational strategies used to extract information from these data silos. Artificial intelligence (AI) methods can be applied to deduce clinically significant inferences and assist the treatment workflow. In this chapter, we present the underpinnings, mathematical background, and relevant applications for ML methods and natural language processing techniques that are used in computer-aided diagnostics and clinical decision support analytics. We present several case studies within the healthcare domain. Finally, as a practical application, we present the design and details of an informatics-driven clinical platform powered with ML algorithms and NLP techniques for the domain of radiation oncology.
Improving Coverage of Internet Outage Detection in Sparse Blocks

There is a growing interest in carefully observing the reliability of the Internet's edge. Outage information can inform our understanding of Internet reliability and planning, and it can help guide operations. Active outage detection methods provide results for more than 3M blocks, and passive methods more than 2M, but both are challenged by sparse blocks where few addresses respond or send traffic. We propose a new Full Block Scanning (FBS) algorithm to improve coverage for active scanning by providing reliable results for sparse blocks by gathering more information before making a decision. FBS identifies sparse blocks and takes additional time before making decisions about their outages, thereby addressing previous concerns about false outages while preserving strict limits on probe rates. We show that FBS can improve coverage by correcting 1.2M blocks that would otherwise be too sparse to correctly report, and potentially adding 1.7M additional blocks. FBS can be applied retroactively to existing datasets to improve prior coverage and accuracy.
Simo: Interactions with Distant Displays by Smartphones with Simultaneous Face and World Tracking

The interaction with distant displays often demands complex, multi-modal inputs which need to be achieved with a very simple hardware solution so that users can perform rich inputs wherever they encounter a distant display. We present Simo, a novel approach, that transforms a regular smartphone into a highly-expressive user motion tracking device and controller for distant displays. Both the front and back cameras of the smartphone are used simultaneously to track the user's hand as well as the head, and body movements in real-world space and scale. In this work, we first define the possibilities for simultaneous face- and world-tracking using current off-the-shelf smartphones. Next, we present the implementation of a smartphone app enabling hand, head, and body motion tracking. Finally, we present a technical analysis outlining the possible tracking range.
Machine learning based Smart Surveillance System

In modern cities video surveillance becomes an important part for protection and security. In modern cities for security and protection smart cameras equipped with intelligent analysis of video can monitor events and give alert system by capturing abnormal activities and events. Now a day's there are many cameras have been equipped for surveillance in all over world. So we are going to implementation focus on video surveillance by giving video contents containing early fire events detection, abnormal activities and smart parking system and crowd estimation. We are going to overcome existing drawbacks of post investigation techniques of video surveillance systems by providing alert generation system. Our work is based on machine learning techniques for video analysis with better performance and event detection with advantages of alert generation.
Type-2 Fuzzy Controller (T2FC) Based Motion Planning of Differential-Drive Pioneer P3-DX Wheeled Robot in V-REP Software Platform

In the present era, the wheeled robot performs various tasks like patrolling, disaster relief, and planetary exploration. For these tasks, a robust navigation algorithm is needed, which can autonomously drive the wheeled robot in any situations. Therefore, in this article, the authors try to design a Type-2 Fuzzy Controller (T2FC), which controls the steering angle based motion, direction, and orientation of the Differential-Drive Pioneer P3-DX Wheeled Robot (DDPWR) by using sensory information without any human intervention in different obstacle conditions. The multiple inputs (obstacles distances received from attached sensors) and single output (steering angle) T2FC have been taken for this purpose. Virtual Robot Experimentation Platform (V-REP) software based 3-dimensional simulation environment, and Graphical User Interface (GUI) based 2-dimensional simulation environment has used to show the motion control results of DDPWR by applying T2FC technique. The remote API functions of V-REP software have been used to make a connection between the MATLAB GUI and V-REP simulation. The developed MATLAB program handles the behavior of DDPWR in the V-REP software engineering platform. In addition, the comparison study has done between proposed T2FC technique with the previously developed Type-1 Fuzzy Controller to show the authenticity and robustness of the developed T2FC.
Analysis of Cross-Platform Mobile App Development Tools

With the emergence of different cross-platform alternatives, there is a need to explore the various approaches the developer must take. We perform this study in the hopes of shedding more light on their differences and highlighting the critical aspects which make them unique. The examination of these WORA (Write Once, Run Anywhere) mobile App tools is done on the basis of different approaches, i.e., Native apps, Web apps, Hybrid apps, Interpreted apps and Widget-based apps. The study performed shall enable us to illustrate the results on the basis of these categories.
DDSpell - A Data Driven Spell Checker and Suggestion Generator for the Tamil Language

DDSpell is a spell checker and suggestion generator for the Tamil language that is developed using a data-driven and language-independent approach. The proposed spell checker and suggestion generator can be used to check non-word errors and canti errors. Tamil does not have a full-fledged spell checker for common usage; this is the case for many of the South Asian Languages. Though there are several attempts have been made in developing spell checkers and correctors for Tamil, not many successes are reported; more importantly, most of them are only on papers and not available for the public to download or use. This paper outlines an approach using which a spell checker can be easily developed for Tamil like languages which share similar word-level structure. A dictionary of 4.0M Tamil words which was created from various sources is used to check the spelling. A character level bi-gram similarity matching, minimum edit distance measures and word frequencies were used to make suggestions for misspelled words. Besides, the techniques like hash keys and hash table were used to improve the processing speed of spell checking and suggestion generation. Since there were no benchmark datasets available to evaluate Tamil spell checkers, a benchmark dataset was also compiled systematically as a part of this research. DDSpell and the existing Tamil spell checkers were evaluated using the dataset and it was found that DDSpell outperformed other tools and showed the accuracy of 98.4%. DDSpell was extended to the Sinhala language to prove that it can be used to similar languages. A dictionary of 1.1M unique Sinhala words was created from various corpora and stored for the usage. The Sinhala spell checker showed an accuracy of 98% for a random dataset. The link of the DDSpell is made available via GitHub to the general public to use and test. Further, DDSpell is being extended to add more languages and features like API access.
Monte Carlo Tree Search Modification for Computer Games

One of the most important tasks of artificial intelligence is to create a universal intelligent agent, which can succeed in solving of many problems instead of only one. In the field of gaming, such intelligent agents train in General Game Playing (GGP). In GGP, one can define a game declaratively in terms of game logic. The AI player then has to decide how to play the game and how to win. One of the most efficient algorithms is the Monte Carlo tree search algorithm (MCTS), but it can work inefficiently if one uses it in general without taking into account the specifics of the task. The paper proposes MCTS, which allows you to change the probability of choosing a solution. The authors evaluated the effectiveness of the proposed solution, and also compared it with other algorithms and methods based on the competitive GVG-AI engine in three games, such as Aliens, Frogs, Zelda.