Reviewerly/exHarmony huggingface dataset:
Improving HEVC Coding Perceptual Quality Using Saliency Guided CTU Layer QP Adjustment

Recently, improving the perceptual quality of video coding under bit rate constraint has attracted researchers' attention in video coding field. In this paper, we propose to improve the coding perceptual quality for the latest High Efficiency Video Coding (HEVC) standard using saliency guided coding tree unit (CTU) level quantization parameter (QP) adjustment scheme. Our work mainly includes two steps: first, a CTU layer saliency detection scheme with low complexity is proposed to measure the visual significance for each CTU in a frame. Then, a CTU layer QP adjustment scheme based on relative saliency is proposed to increase the bit rate resource for more salient regions and decrease the bit rate cost for less salient regions. Therefore, the proposed QP adjustment scheme obtains better bit rate allocation among CTUs and generates better coding perceptual quality. Experimental results show that the proposed method achieves better perceptual quality and rate distortion performance for HEVC video coding.
Multicore-fiber-based quantum-classical access network architecture with quantum signal wavelength-time division multiplexing

We propose a quantum-classical access network architecture based on multicore fiber to scale up the number of quantum key distribution (QKD) users. Quantum signals are integrated with classical signals in both feeder and drop fibers to save deployment cost since access networks are cost-sensitive. To increase the secure key rate (SKR) of QKD, we show that wavelength-time division multiplexing is suitable for quantum signals, which are required to support a large number of quantum users. Also, we propose a core and wavelength assignment scheme to alleviate spontaneous Raman scattering and intercore cross talk noise, which are the main impairment sources to QKD in this quantum-classical network. Finally, we experimentally show the performance of QKD in the proposed quantum-classical access network in terms of noise and channel loss. The experiment results are consistent with our analysis. The characteristics of the SKRs prove the superiority of wavelength-time division multiplexing. The properties of spontaneous Raman scattering in the feeder and drop fibers are shown in the experiments through the quantum bit error rates in different experimental conditions, which verifies the effectiveness of the proposed core and wavelength assignment scheme.
A New Approach for Computing the Matching Degree in the Paper-to-Reviewer Assignment Problem

The Paper-to-Reviewer Assignment Problem is a prevalent and challenging problem within the scientific community and scientific conferences. The large number of reviewers and submitted papers, as well as constraints on reviewers' loads and papers' coverage needs, make difficult and very tedious for the committee chair to manually assign submitted papers to suitable reviewers. Any automation of this task must tackle three main issues: modeling reviewers' and papers' profiles, estimating the relevance between a paper and a reviewer and finally finding an appropriate assignment.In this paper, we are interested in the two first issues. We propose to build the reviewers profiles on the basis of both pedagogical and research documents, then a formula that takes into account both topics similarity and common references is proposed. Experimental results compared to ground-truth values extracted from ICA2IT'19 data show a good performance of this approach.
A Hybrid Method Based on Particle Swarm Optimization for Restaurant Culinary Food Reviews

A review or opinion on culinary food restaurants carried out by consumers will produce information in the form of decision support for culinary food seekers who are looking for the best place to buy these foods. With the data in the form of review text obtained, the text mining based sentiment analysis model was obtained by using the best classification algorithm. The problem occurs while selecting attributes during preprocessing data. In this situation, the attributes that are generated are too many, and the best attributes with the best weight values need to be selected. In this paper, a hybrid model is proposed using Particle Swarm Optimization and Information Gain (PSO-IG). The proposed method is applied to 4 different methods, namely Support Vector Machine, Naïve Bayes, Decision Tree, and K-NN. Based on the results of experiments carried out on the proposed model, there was an increase in accuracy up to the highest accuracy level of 90.55%. The method using hybrid PSO-IG is a solution as an effort to increase the accuracy level of review classification of culinary food restaurants as decision support information.
Quantification of the Impact of Random Hardware Faults on Safety-Critical AI Applications: CNN-Based Traffic Sign Recognition Case Study

Nowadays, Artificial Intelligence (AI) rapidly enters almost every safety-critical domain, including the automotive industry. The next generation of functional safety standards has to define appropriate verification and validation techniques and propose adequate fault tolerance mechanisms. Several AI frameworks, such as TensorFlow by Google, have already proven to be effective and reliable platforms. However, similar to any other software, AI-based applications are prone to common random hardware faults, e.g., bit-flips which may occur in RAM or CPU registers and might lead to silent data corruption. Therefore, it is crucial to understand how different hardware faults affect the accuracy of AI applications. This paper introduces our new fault injection framework for TensorFlow and results of first experiments conducted on a Convolutional Neural Network (CNN) based traffic sign classifier. These results demonstrate the feasibility of the fault injection framework. In particular, they help to identify the most critical parts of a neural network under test.
Risk Management in Agile Software Development: A Survey

In agile software development, some of the formal project management activities are neglected due to heavy documentation and planning. Agile methodologies usually focus on rapid development over hefty documentation which is contrary to the nature of the risk management process. Literature studies show agile methods are used for the development safety critical systems as well. For that ignoring risk management process is inevitable. This intrigued us to explore more on the risk management practices followed in agile methods following industry. To further the investigations on industrial practices regarding risk management, in this paper we explore on (i) the risks faced by the agile practitioners, and (ii) the mitigation strategies used. For this, we conducted an online survey with 54 agile practitioners. The results showed that the project deadlines and varying requirements are the two most commonly faced risks by the practitioners. Furthermore, the results also revealed that risk management strategies are being followed to some extent, but usually in a non-systematic way.
Bi-Stream Pose-Guided Region Ensemble Network for Fingertip Localization From Stereo Images

In human-computer interaction, it is important to accurately estimate the hand pose, especially fingertips. However, traditional approaches to fingertip localization mainly rely on depth images and thus suffer considerably from noise and missing values. Instead of depth images, stereo images can also provide 3-D information of hands. There are nevertheless limitations on the dataset size, global viewpoints, hand articulations, and hand shapes in publicly available stereo-based hand pose datasets. To mitigate these limitations and promote further research on hand pose estimation from stereo images, we build a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization. In the THU-Bi-Hand dataset, there are 447k pairs of stereo images of different hand shapes from ten subjects with accurate 3-D location annotations of the wrist and five fingertips. Captured with minimal restriction on the range of hand motion, the dataset covers a large global viewpoint space and hand articulation space. To better present the performance of fingertip localization on THU-Bi-Hand, we propose a novel scheme termed bi-stream pose-guided region ensemble network (Bi-Pose-REN). It extracts more representative feature regions around joints in the feature maps under the guidance of the previously estimated pose. The feature regions are integrated hierarchically according to the topology of hand joints to regress a refined hand pose. Bi-Pose-REN and several existing methods are evaluated on THU-Bi-Hand so that benchmarks are provided for further research. Experimental results show that our Bi-Pose-REN has achieved the best performance on THU-Bi-Hand.
Sentiment Analysis of Malayalam Tweets using Different Deep Neural Network Models-Case Study

Sentiment Analysis of Malayalam tweets is done using Deep Neural Networks (DNN). 1-D Convolutional Neural Networks (1-D CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Bi-directional LSTM (BiLSTM) and Gated Recurrent Unit (GRU) models are used to classify Malayalam tweets as positive and negative. This work compared the results of Sentiment Analysis (SA) of Malayalam tweets with different DNN models on our manually annotated corpus containing 5468 Malayalam tweets. The SA of Malayalam tweets with GRU shows better accuracy compared with other DNN models.
Hyperparameter Tuning using Quantum Genetic Algorithms

Correctly tuning the hyperparameters of a machine learning model can improve classification results. Typically hyperparameter tuning is made by humans and experience is needed to fine tune them. Algorithmic approaches have been extensively studied in the literature and can find better results. In our work we employ a quantum genetic algorithm to address the hyperparameter optimization problem. The algorithm is based on qudits instead of qubits, allowing more available states. Experiments were performed on two datasets MNIST and CIFAR10 and results were compared against classic genetic algorithms.
Survey: A Comparative Study of Different Security Issues in Big Data

Data is starting at now a champion among the most basic assets for associations in each field. The consistent improvement in the essentialness and to manage and process large amount of data has made another issue: it cannot be dealt with by standard examination strategies and to store such kind of data to use different file system. This issue was, thusly, comprehended through the formation of another worldview: big data. Be that as it may, big data started new problem related to the large quantity data exclusively or the assortment of the information, yet additionally to information security and protection and another side of big data security in terms of file system. To manage such type of data, we are using different file systems starting from Google. This file system works on distributed and parallel systems. We study the different load balancing algorithm and found some of the differences. In this paper, we present what is big data, then how the big data needed security and different types of security issues in big data focused major issues. The main motto of this paper is to provide a survey report on the latest trends in big data and latest security mechanisms that in form of different levels of security like data and infrastructure security.
A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.
Constant Size CP-ABE with Scalable Revocation for Resource-Constrained IoT Devices

Users can directly access and share information from portable devices such as a smartphone or an Internet of Things (IoT) device. However, to prevent them from becoming victims to launch cyber attacks, they must allow selective sharing based on roles of the users such as with the Ciphertext-Policy Attribute Encryption (CP-ABE) scheme. However, to match the resource constraints, the scheme must be efficient for storage. It must also protect the device from malicious users as well as allow uninterrupted access to valid users. This paper presents the CCA secure PROxy-based Scalable Revocation for Constant Cipher-text (C-PROSRCC) scheme, which provides scalable revocation for a constant ciphertext length CP-ABE scheme. The scheme has a constant number of pairings and computations. It can also revoke any number of users and does not require re-encryption or redistribution of keys. We have successfully implemented the C-PROSRCC scheme. The qualitative and quantitative comparison with related schemes indicates that C-PROSRCC performs better with acceptable overheads. C-PROSRCC is Chosen Ciphertext Attack (CCA) secure. We also present a case study to demonstrate the use of C-PROSRCC for mobile-based selective sharing of a family car.
A Blockchain-Based Framework for Secure Log Storage

Logs are critical data, which can help us to troubleshoot and identify the person in charge of an unexpected accident. Log systems have been widely used for log storage. However, the traditional log system can't prevent the log from being tampered. Centralized servers are more vulnerable to be attacked. Those who have permission to operate records can easily tamper with logs. Blockchain is a disruptive technology in recent years, which has the advantages of decentralization, tamper-proof and traceability. Given its decentralization and tamper-proof properties, the paper proposed a blockchain-based framework for secure log storage. However, the cost of storing big files in the blockchain is very high. Thus, the paper utilized the InterPlanetary File System(IPFS) to store log files instead of a blockchain. Besides, the paper adopted Ethereum blockchain to store the hash of log files and a smart contract to create an index for log files. The solution is not only applicable to log but also other scenarios requiring secure data storage and efficient retrieval.
Information retrieval and processing system for news articles in English

The importance of news media is unquestionable. These news media contain a great amount of information hidden along the lines of the articles. For analytics, extracting information and organizing the information to draw out conclusion is very important. The objective of our work is to focus on designing a tool to extract details from English news articles and present it to the user, in an organized manner. A predefined set of websites are crawled and the details are stored. The details extracted by the tool are named entities such as location, person, and organization mentioned in the news, news summary and important keywords pertaining to each news article. We also equip the tool with an efficient search engine, along with database indexing for faster information retrieval.
Implementation of Bitcoin Mining using Raspberry Pi

Cryptocurrency, a form of digital currency is the redistributed and open system that uses cryptography to reinforce security for the management of standard financial transactions. Administration of cryptocurrency mining demands significant hash functions which in turn necessitates a powerful rig. Here, we demonstrate the implementation of Cryptocurrency miner using Raspberry Pi system integrated with USB hash miners and with a CG-Miner software, installed in the operating system. Provision of a System on Single chips platform by Raspberry pi yields high compatibility, and the portable nature of the system assists the convoluted process of mining to achieve new horizons. The exponential rise of the cryptocurrency miners lead to the demand for an economical methodology for the mining process can be gratified by the Raspberry pi mining technique which help to quench the cost of the available systems providing a new cost effective and energy efficient technology.
Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.
Silent Speech Recognition Based on Surface Electromyography

Silent speech recognition (SSR) can decode the activities of electromyography (EMG) from surface of articulatory muscles to the according brain information. As the essentials in SSR, feature extraction and recognition are directly related to the final decoding effects, which requires careful research, especially on small datasets. This paper collected the surface EMG (sEMG) of 10 isolated Chinese words by 6 surface electrodes on face and around, and based on which sEMG features were extracted by wavelet packet transform, principal component analysis and XGBoost (an implementation of gradient boosted decision trees) respectively. Then Support Vector Machine, K-Nearest Neighbor and Random Forest were explored for recognition on the 10 words. The results indicated that the highest accuracy of 72% was achieved by the method of Random Forest based on XGBoost.
Moore–Penrose approach in the Hough transform framework

Let F(x, a) be a real polynomial in two sets of variables, x and a, that is linear with respect to one of the variable sets, say a. In this paper, we deal with two of the main steps of the Hough transform framework for the pattern recognition technique to detect loci in images. More precisely, we present an algorithmic process, based on the Moore–Penrose pseudo-inverse, to provide a region of analysis in the parameter space. In addition, we state an upper bound for the sampling distance of the discretization of the parameter space region.
A Memory-Access-Efficient Adaptive Implementation of kNN on FPGA through HLS

To reduce the impact of the memory-access constraint in k-Nearest Neighbors (kNN) problems, in this paper we implement one kNN kernel through high-level synthesis (HLS) on FPGA by employing two data access reduction methods: low-precision data representation and principal component analysis based filtering (PCAF). The kernel is called MPCAF-kNN (Memory-efficient PCAF kNN), which has been highly optimized to fully exploit the characteristics of FPGA. It is adaptive to all key parameters. We evaluate MPCAF-kNN by comparing it with a state-of-the-art kNN implementation on a high-end CPU server. Our results show that MPCAF-kNN achieves up to a performance equivalent to that of a 56-thread of CPU server while greatly reducing external memory-accesses.
PDIF: Pupil Detection After Isolation and Fitting

Pupil detection plays a key role in eye and gaze video-based tracking algorithms.Various algorithms have been proposed through the years in order to improve the performances or the robustness in real-world scenarios.However, the development of an algorithm which excels in both execution time and pupil detection precision is still an open challenge.This paper presents a novel, feature-based eyetracking algorithm for pupil detection.Morphological operators are used to remove corneal reflections and to reduce noise in the pupil area prior to the pupil detection step: this solution allows to significantly reduce the computational overhead without lowering the tracking precision.Moreover, a shape validation step is performed after the elliptical fitting and, if the elliptical shape is not detected properly, a set of additional steps is performed to improve the pupil estimation.The proposed solution, Pupil Detection after Isolation and Fitting (PDIF), has been compared with other state-of-the-art tracking algorithms that use morphological operations such as ElSe (Ellipse Selection) and ExCuSe (Exclusive Curve Selector) to evaluate both speed and robustness; the proposed algorithm has been tested over numerous datasets offering different pupil detection challenges.Obtained results show how PDIF provides comparable tracking precision at a significantly lower computational cost compared to ElSe and ExCuSe.
Modified quadratic residue constructions and new extremal binary self-dual codes of lengths 64, 66 and 68

In this work we consider modified versions of quadratic double circulant and quadratic bordered double circulant constructions over the binary field and the rings F2+uF2 and F4+uF4 for different prime values of p. Using these constructions with extensions and neighbors we are able to construct a number of extremal binary self-dual codes of different lengths with new parameters in their weight enumerators. In particular we construct 2 new codes of length 64, 4 new codes of length 66 and 14 new codes of length 68. The binary generator matrices of the new codes are available online at [8].
Live Working Manipulator Control Model based on DPPO-DQN Combined Algorithm

To realize autonomous obstacle avoidance and navigation of live working manipulator, a DPPO-DQN combined algorithm is proposed. Firstly, the live working process of the manipulator is studied and the kinematics analysis of the 2D model is carried out to guide the coordinate transformation calculation of each node and trajectory of the manipulator. Secondly, the principle of the combined algorithm, the design of the environment state space, the action strategy and the reward function are studied, followed by realization of DQN, DPPO and DPPO-DQN algorithm training. Meanwhile, comparison the performance of obstacle avoidance and route navigation in two-dimensional. Finally, the combined algorithm is proposed and simulated. The experimental results show that the combined algorithm has a significant improvement in navigation success rate compared with the single model.