Reviewerly/exHarmony huggingface dataset:
Multi-Timescale QoE Provisioning for Adaptive Video Streaming in Heterogeneous Deployments

This paper considers an optimization problem that maximizes an aggregate utility, formulated as the weighted geometric mean of the “in-context” suitability of a set of radio access technologies (RATs), to support adaptive video streaming subject to the existence of legacy data transfers. Motivated by the unfeasibility of solving the formulated problem centrally when the various RATs are loosely integrated (i.e., at core network (CN) level), a hybrid (i.e., network-assisted user-driven) strategy is devised to approximate its optimum solution. Unlike previous hybrid approaches, the proposed methodology exploits network assistance to ensure a friendly co-existence between adaptive video streaming clients and legacy users. It operates on different timescales, where the fastest timescale operation is performed on the video clients according to a policy that is tuned by the network on slower timescales. The user operation on the fastest timescale (i.e., tens of ms) enables to adapt video streaming depending on the perceived quality-of-experience (QoE) and local components of the context (e.g., remaining credit and battery level). A small-cell tuning on a slower timescale (i.e., hundreds of ms) enables to preempt the resources used by legacy users based on the operating conditions (e.g., load and type of scheduler). Finally, a tuning performed by the network on the slowest timescale (i.e., few seconds) offloads legacy data transfers to unlicensed bands whenever the amount of interference on licensed bands reaches critical levels, which helps to sustain good QoE for all video clients. A cost-benefit analysis reveals that the proposed methodology performs closely to its centralized counterpart with much less control overhead on the radio interface.
Thepade’s Sorted Block Truncation Coding Applied on Local Binary Patterns of Images for Splicing Identification Using Machine Learning Classifiers

The era of digitization has accelerated communication and information sharing immensely. With ever-growing digital advancements in technology and applications cybersecurity poses to be a pressing issue. The amount of growth in data exchange is exponential thus making automated processes a vital tool to deliver security. Image editing technologies manipulate image data and have enabled all types of users to tamper images resulting in widespread fake images. Distorted information carries heavy consequences and thus a reliable image forgery detection system is essential. This paper proposes a machine learning-based approach for image splicing detection using the global and local characteristics of the image. TSBTC N-ary, with the value of N = 12,14 and 16, is applied along with LBP for feature extraction and various Machine learning classifiers are implemented and compared for image splicing detection. The performance of the proposed method is tested and validated on 3 benchmark datasets: CASIA V1 Dataset, Columbia Dataset, and Columbia Uncompressed Dataset. Results are evaluated based on various performance metrics.
Certificateless Aggregate Signature Scheme with High Efficencicy in Vehicular Ad-hoc Network

Due to the characteristic of Certificateless aggregate signature (CLASS) scheme, CLASS can be applied into Vehicular Ad-hoc Network (VANETs) and other fields. In this paper, we present an improved CLASS scheme for VANETs. In the improved scheme, by using two level dummy identities (one level in the RTA and another level in the RSU), the vehicular can adopt different dummy identity in different RSU or even in the same RSU which can reduce the risk from exposure of one dummy identity and increase the safeguard of the real identity of the vehicular. The improved scheme can protect the real identity of the vehicular. But it is easy to trace the real identity of the vehicular which may break the traffic regulations or do other harmful things. We also give the security analysis of the improved scheme on the unforgeability, blindness and traceability. What's more, the improved scheme has high efficient. It only needs two pairing computation no matter how many signature is aggregated which is more efficient than other similar schemes.
An Energy-efficient Multi-core Restricted Boltzmann Machine Processor with On-chip Bio-plausible Learning and Reconfigurable Sparsity

This paper proposes an energy-efficient multi-core processor design of restricted Boltzmann machine (RBM) with on-chip learning and reconfigurable sparsity. Inspired by bio-plausible variational probability flow (VPF) algorithm, our design significantly reduces the on-chip learning time and associated computation/energy as compared to conventional methods. The multi-core design with reconfigurable sparse weight connections further efficiently and flexibly reduces the required computation time and energy. FPGA implementation shows that the proposed design achieves 63.14 pJ per NW (neural weight) and 9.77 GNWs/s (neural weight update per second) at 128 MHz, which outperforms the baseline design by 44.0% and 24.3%, respectively.
Research on Data Security Sharing Mechanism of Power Internet of Things Based on Blockchain

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.
The impact of using pre-trained word embeddings in Sinhala chatbots

Providing conversational interfaces to IT services enable more people to access them conveniently. For maximum benefit, such interfaces need to be in the native language of the users in a community. Popularly known as chatbots, one of their critical tasks is to accurately identify the user's intention from their natural language input. This paper describes an attempt at improving this accuracy in an implementation based on the open source RASA stack, by using Sinhala word embeddings. This approach shows improvement in both intent detection as well as the confidence in the detected intents.
Mapping mineralogy in evaporite basins through time using multispectral Landsat data: Examples from the Bonneville basin, Utah, USA

Abstract The Bonneville basin, located in north‐western Utah, is a vast evaporite basin which is home to the world‐renowned Bonneville Salt Flats international speedway and is a highly valued landscape undergoing rapid change and anthropogenic influence. Air quality, snowpack, the local hydrological system, and state tourism are all impacted by the nature of the surface sediments exposed in the Bonneville basin. Mapping the Bonneville basin over time with remote sensing methods provides insight into the dynamics and impacts of the changing surface landscape. Utilizing the Landsat‐5 Thematic Mapper (TM) and Landsat‐8 Operational Land Imager (OLI) sensors, a set of band math indices are empirically established to map the predominant halite, gypsum, and carbonates mineralogical zones of the Bonneville basin. Spectral comparisons of representative samples from the study area and image‐derived spectra indicate the halite of the Bonneville basin is wet and that gypsum deposits are slightly mixed with halite. The established indices are assessed in four ways, all of which support the ability of the indices to accentuate the associated mineralogical endmembers. Two study areas within the Bonneville basin are investigated temporally from 1986, 1995, 2005, and 2016 and show changing patterns in mineral distribution that align with surface processes active through these timescales. These indices provide a resource for mapping mineralogy though time in evaporite basins globally with diverse applications for questions about land use and environmental change.
The Pursuit of Transparency and Control: A Classification of Ad Explanations in Social Media

Online advertising on social media platforms has been at the center of recent controversies over growing concerns regarding users' privacy, dishonest data collection, and a lack of transparency and control.Facing public pressure, some social media platforms have opted to implement explanatory tools in an effort to empower consumers and shed light on marketing practices.Yet, to date research shows significant inconsistencies around how ads should be explained.To address this issue, we conduct a systematic literature review on ad explanations, covering existing research on how they are generated, presented, and perceived by users.Based on this review, we present a classification scheme of ad explanations that offers insights into the reasoning behind the ad recommendation, the objective of the explanation, the content of the explanation, and how this content should be presented.Moreover, we identify challenges that are unaddressed by either current research or explanatory tools deployed in practice, and we discuss avenues for future research to address these challenges.This paper calls attention to and helps to solidify an agenda for interdisciplinary communities to collaboratively approach the design and implementation of explanations for online ads in social media.
An Automated Wheelchair for Physically Challenged People Using Hand Gesture and Mobile App

Traditional wheelchairs which operate manully are difficult to use without the assistance of physical strength. This paper presents a model of an automated wheelchair for disabled people. The proposed wheelchair is battery powered and uses an Arduino microcontroller to operate. The whole system consists of two parts, one of them is the sending end and another part is the receiving end. The sender end records instructions from the user's different hand gestures which are collected using an MPU-6050 gyro accelerometer sensor. The instructions are continuously recorded and sent to the receiver end using an RF transmitter- nRF24101 through an Arduino microcontroller. At the receiving end, an nRF module receives the signal for mobilizing the wheelchair on a plain surface. Additionally, the device contains a Bluetooth module which can be used as a secondary control system. Hence our chair can be operated by a smartphone application too. Experimental results demonstrate that the proposed model is functional under normal circumstances with a fast response time which can be very useful for disable people to commute with.
Blockchain: A Privacy Centered Standard for Corporate Compliance

Recently, the right to privacy has become an important global issue, and laws are being enacted in different countries to ensure that citizens have control over how their data are stored and used. In this scenario, corporates face an increasing burden to comply with these laws, while ensuring that their business models and workflows are interrupted to as little a degree as possible. This article presents blockchain as a potential technology that can help users keep their data under their control and yet allow companies to responsibly access the data they need to function. It begins with an overview of blockchain as a technology and the properties that make it useful as a tool for privacy compliance. It then explores more niche fields, such as Zero Knowledge Proofs, and considers two industries where blockchain can be of benefit to consumers and corporates from a privacy standpoint.
Quantum hypothesis testing with group structure

The problem of discriminating between many quantum channels with certainty is analyzed under the assumption of prior knowledge of algebraic relations among possible channels. It is shown, by explicit construction of a novel family of quantum algorithms, that when the set of possible channels faithfully represents a finite subgroup of SU(2) (e.g., $C_n, D_{2n}, A_4, S_4, A_5$) the recently-developed techniques of quantum signal processing can be modified to constitute subroutines for quantum hypothesis testing. These algorithms, for group quantum hypothesis testing (G-QHT), intuitively encode discrete properties of the channel set in SU(2) and improve query complexity at least quadratically in $n$, the size of the channel set and group, compared to na\"ive repetition of binary hypothesis testing. Intriguingly, performance is completely defined by explicit group homomorphisms; these in turn inform simple constraints on polynomials embedded in unitary matrices. These constructions demonstrate a flexible technique for mapping questions in quantum inference to the well-understood subfields of functional approximation and discrete algebra. Extensions to larger groups and noisy settings are discussed, as well as paths by which improved protocols for quantum hypothesis testing against structured channel sets have application in the transmission of reference frames, proofs of security in quantum cryptography, and algorithms for property testing.
Intelligent Video Analytic for Suspicious Object Detection : A Systematic Review

Conventional surveillance systems such as CCTV still have limitations that merely viewing and recording. This limitation causes its function to only be passive monitoring and unable to provide real-time early warning systems as an effort to anticipate security threats or violations of regulations. The increasing need in the security sector, especially in public area, requires a solution in the form of a system that can detect suspicious objects through video surveillance systems. The integration of artificial intelligent, machine learning, image processing and computer vision become the latest study in surveillance system development innovation. Although there are many datasets, methods and frameworks available in previous research, there are still few papers that discuss the use of intelligent video analytics in detecting suspicious objects. This paper will comprehensively and systematically review the literature on applying machine learning for object detection and video surveillance systems published between 2010 and 2020. The literature extraction process is carried out by identifying and analyzing papers to describe the scope of research to detect suspicious objects using intelligent video analytics, frameworks, methods, datasets and identifying suspicious characteristics. At the end of this paper, conclusions have been outlined regarding the challenges and opportunities for suspicious object detection research using video analytics in the future.
A Robot Spraying Path Planning Method for the Digital Camouflage Pattern

At present, the robot spraying path of the digital camouflage generally refers to the manual spraying experience, and uses a regular strategy to generate the spraying path of each domain of the pattern. Such regular path planning method will produce many redundant paths which affect the efficiency of robot spraying operations. In this paper, we propose a new path planning method to solve this problem. Firstly we adopt the grid method to model the digital camouflage pattern, and then achieve local zone path planning through domain segmentation with color block aggregation, finally we use and improve the genetic algorithm to optimize the spraying path among the local zones. Eventually the path planning of the digital camouflage pattern is realized. The simulation results show that the method we proposed in this paper has a significant improvement compared to the sequential spraying method. The path length of the genetic algorithm is shortened by 31.7% and the path length of the improved genetic algorithm is shortened by 37.0%. Furthermore, the speed of convergence of the improved genetic algorithm is faster than the original genetic algorithm. The above results demonstrate that the method we proposed in this paper is feasible and efficient.
Usage of Combinational Acoustic Models (DNN-HMM and SGMM) and Identifying the Impact of Language Models in Sinhala Speech Recognition

Automatic Speech Recognition (ASR) is one of the most discussed areas in Natural Language Processing (NLP) because this technology is needed to enable and improve the interaction between human and the computer [1]. High resource languages like English have achieved state-of-the-art results, but when it comes to a low resource language like Sinhala it still needs to improve in a great scale. In this paper, it states about developing an ASR system for Sinhala language using Kaldi toolkit [2] and proposed combinational acoustic model of Subspace Gaussian Mixture Model (SGMM) and Deep Neural Network - Hidden Markov Model (DNN-HMM) has achieved 31.72% word-error-rate (WER). Additionally, we explore the impact of language models with using different corpora and preliminary comparison is provided between them.
Usance of industrial 4.0 technique to overcome the pandemic situation of COVID-19

ABSTRACT COVID 19 (Coronavirus) pandemic has created surge demand for essential good and other items like medical equipment and industries which were shut and need to revive back themselves again with new strategies and plan to bring the situation back towards normalcy the study also aimed to propose new technique including advantage of Industry 4.0 ethics to reduce the impact and tackle the situation of COVID-19 and post COVID-19. A detailed review of the literature is done on the technologies along with their Industry 4.0 applications during this COVID-19 pandemic, The review holds some basic and advance technologies application to enhance industrial safety and boosting the work culture to normal and efficient. Using appropriate search words on the databases of IEEE papers, Google Scholar and Research Gate. We found several useful applications which can be inculcated along with modern human-machine interfacing which can help for proper control and management of COVID-19 pandemic and these techniques are been discussed in this paper. Available technologies blend along with Industry 4.0 technique and procedure could also help the society to work and understand smartly and get aware of this methodology which can lead them to stay safe and secure along with maintaining working efficiency. Industry 4.0 can fulfil the requirements of society/community during this crisis’s situation. The new technique which firstly make assure that the induvial and belonging health and safety is first priority and their demand are need to be fulfil on time so that no one suffers. These Industry 4.0 technologies could provide a lot of innovative ideas and solution at from base local areas to global platforms. The supply chain partners and government bodies should work along with new technologies wisely for improving the services during COVID-19 and of any similar situations happening anywhere anytime.
HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks

Hexagonal CNN models have shown superior performance in applications such as IACT data analysis and aerial scene classification due to their better rotation symmetry and reduced anisotropy. In order to realize hexagonal processing, existing studies mainly use the ZeroOut method to imitate hexagonal processing, which causes substantial memory and computation overheads. We address this deficiency with a novel native hexagonal CNN framework named HexCNN. HexCNN takes hexagon-shaped input and performs forward and backward propagation on the original form of the input based on hexagon-shaped filters, hence avoiding computation and memory overheads caused by imitation. For applications with rectangle-shaped input but require hexagonal processing, HexCNN can be applied by padding the input into hexagon-shape as preprocessing. In this case, we show that the time and space efficiency of HexCNN still outperforms existing hexagonal CNN methods substantially. Experimental results show that compared with the state-of-the-art models, which imitate hexagonal processing but using rectangle-shaped filters, HexCNN reduces the training time by up to 42.2%. Meanwhile, HexCNN saves the memory space cost by up to 25% and 41.7% for loading the input and performing convolution, respectively.
Small Object Detection with Scale Adaptive Balance Mechanism

Small object detection is a hard task to be sovled in object detection because the small object usually contain a few effective informations, which makes it very difficult to extract the discriminative features of small scale object. In addition, a key challenge in deep network for small object detectors is the large scale variation, where the performance of small object is poor due to the imbalanced optimization on various scale. In this paper, we propose a scale adaptive balance mechanism based on anchor-free framework for small object detection. Specifically, we design a new loss function to adapt the detection model to various object scale, which is able to alleviate the imbalance of object scale. Moreover, in feature extractor, the shallow feature maps of different receptive fields are added to the DLA-34 backbone for better extracting multi-scale features. Besides, the image block is used as a detection unit during inference, and the scale mismatch is thus overcome. Finally, the proposed method is verified on the Visdrone2019 dataset and the experimental results validate the effectiveness to detect small objects in the video image from drone monitoring.
Hoax Information Detection System Using Apriori Algorithm and Random Forest Algorithm in Twitter

This research is based on the disturbance faced by Twitter users, related to the distribution of hoax information in the text form. One of the efforts to overcome such problem is by building a system to detect hoax news in Twitter application. A set of information in text from on social media, can capture the use of language in written or verbal (corpus) form. Based on the advantages of Apriori algorithm which it is able to mine the text data from many used datasets and able to find the relation pattern or itemset combination in a database as a recommendation of the raised pattern. Moreover, the detection system also needs a method to classifying information based on the classes of hoax and non-hoax. One of the algorithms used is Random Forest algorithm, which it is able to combine several models of decision trees to eliminate the problems of overfitting. The purpose of this research, apart from implementing and integrating the Apriori algorithm and the Random Forest algorithm, is to make it easier for researchers to analyze and evaluate the system's results to detect hoax information most optimal level of accuracy. The results show that the system can detect hoax and non-hoax news, whose data is integrated directly with the Twitter application. Accuracy level, precision and recall from the built system in detecting hoax news information reached 100% with minimum support value of 23.
Attack Graph-Based Quantitative Assessment for Industrial Control System Security

Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.