Reviewerly/exHarmony huggingface dataset:
Self Service System for Library Automation : Case Study at Telkom University Open Library

With an increasing number of the student body at Telkom University, office automation is one of many possible solutions to provide better and personalized service to students. This automation also includes library services, covering self loan, self-return, and self-pickup, which commonly use RFID (Radio Frequency Identification). The problem arises in RFID-based self-service system for library automation, which is how to ensure that the ID card of the patron truly belongs to the person using the service. This problem follows the self-loan service since the ID card of the patron is used only when borrowing the collection. Another problem is how to ensure that the borrowed collection is the actual collection registered in the LMS (Library Management System). This research proposes a business process of a self loan system design that minimizes fraudulent practice in borrowing collections by merging image capture of the patron and the borrowed collection where the librarian is not necessarily present for circulation.
A scalable semi-permissionless blockchain framework

Permissionless blockchains are decentralized and scalable, but they are extremely slow in transactional processing and are wasteful of computation resource. Permissioned blockchains are fast and efficient, but they are not scalable and are prone to centralization. This work proposes a novel semi-permissionless blockchain framework taking benefits from both worlds: being decentralized, scalable, but fast and efficient. Public nodes are randomly selected to join a committee executing agreement protocol to secure blocks. The key idea is the introduction of trusted nodes to improve several aspects of the protocol: transaction execution is separated from agreement, thus reducing agreement latency and allowing the participation of lightweight nodes; agreement protocol is simplified for better performance and easier implementation.
A Comparative Analysis of Different Violence Detection Algorithms from Videos

ThereVashistha, Piyush areSingh, Juginder Pal different methods orKhan, Mohd Aamir techniques used for identifying violence from video, such as hitting some object, kicking, fighting, and punching someone but still there is a big challenge for us to identify violence. However, some of the earlier mechanism generally extract descriptors around the spatiotemporal interesting points (STIP) or extract statistic features but there is limited effectiveness in detecting video-based violence. Therefore, the objective is to develop a better violence identification system that identifies the violence and triggers an alarm so that prompt assistance will be provided. This paper helps researchers who wish to study violent activity recognition and gather different insights on the main challenges and issues to solve in this emerging field.
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses

Many sequence-to-sequence dialogue models tend to generate safe, uninformative responses. There have been various useful efforts on trying to eliminate them. However, these approaches either improve decoding algorithms during inference, rely on hand-crafted features, or employ complex models. In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. Specifically, we start with a simple yet effective automatic metric, AvgOut, which calculates the average output probability distribution of all time steps on the decoder side during training. This metric directly estimates which tokens are more likely to be generated, thus making it a faithful evaluation of the model diversity (i.e., for diverse models, the token probabilities should be more evenly distributed rather than peaked at a few dull tokens). We then leverage this novel metric to propose three models that promote diversity without losing relevance. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch; the second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level; the third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal. Moreover, we experiment with a hybrid model by combining the loss terms of MinAvgOut and RL. All four models outperform their base LSTM-RNN model on both diversity and relevance by a large margin, and are comparable to or better than competitive baselines (also verified via human evaluation). Moreover, our approaches are orthogonal to the base model, making them applicable as an add-on to other emerging better dialogue models in the future.
Multi-Camera Video Synchronization Based on Feature Point Matching and Refinement

In this paper, a vision-based video synchronization method was developed based on feature point matching. The proposed method contains several parts: pre-processing, key-frame matching, and frame synchronization. Since videos may have different frame rates and frame sizes, the pre-processing procedure is used to normalize the frame rate and frame size of videos. After key frame extraction, matching similar key frames from difference videos is conducted based on feature point matching and RANSAC. The frame index differences among videos can be obtained by analyzing these matched key frames and then synchronizing videos can be achieved. To evaluate the performance of the proposed method, some videos are captured for testing. Experimental results show that key frames can be selected effectively. Frame matching can be well achieved based on feature point matching and RANSAC. The higher score of subjective testing demonstrates that the proposed system can synchronize videos well.
A Time Delay Neural Network Acoustic Modeling for Hindi Speech Recognition

Automatic Speech Recognition (ASR) systemsKumar, Ankit have becomeAggarwal, R. K. more popular recently for low resource languages. India has 22 official language and more than two thousands other regional languages, the majority have low resources. The standard resources are also limited for the Hindi language. In this paper, the implementation of continuous Hindi ASR system has been done using Time Delay Neural Network (TDNN) based acoustic modeling significantly improves the performance of baseline Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) based Hindi ASR system up to 11%. Further improvement of 3% and 2% have been recorded by applying i-vector adaptation, interpolated language modeling in this work.
Infrared Image Enhancement Method Based on Discrete stationary Wavelet Transform and CLAHE

In order to solve the problem of low contrast, edge blur and noise of infrared images, a nonlinear infrared image enhancement algorithm based on discrete stationary wavelet transform and contrast limited adaptive histogram equalization (CLAHE) is proposed. Firstly, wavelet transform is performed on the original infrared image to obtain the low frequency and high frequency coefficients. Then the low frequency subband image of the largest scale is enhanced by CLAHE and high frequency coefficients is filtered to reduce the noise. Finally, reconstruct the image by taking inverse discrete wavelet transform of the new coefficients. We have experimented with a large number of infrared images. Experimental results show, the proposed method not only solves the problem of low contrast of infrared image, but also reduces noise, highlights image details, and significantly improves visual effect of the image.
Overview of Distributed Federated Learning: Research Issues, Challenges, and Biomedical Applications

This paper discusses about distributed federated learning research issues and challenges. The federated learning is actively studied nowadays in many applications. In this paper, we will figure out the foundations of federated learning and its related research issues/challenges. Lastly, we will review the major two models (centralized and distributed) in distributed federated learning.
Konkani Text Summarization By Sentence Extraction

The amount of information over the world wide web is increasing at an alarming pace. This has caused a difficulty in finding important and useful information. Text summarization solves this problem by providing a condensed form of a textual document that contains the most relevant information. Summaries can be of two types: Extractive and Abstractive. Various text summarization techniques have been proposed for foreign languages but few techniques have been found for Indian languages. This paper presents a novel approach to summarize Konkani documents. The proposed idea involves taking a single document as input and generating a summary by using sentence extraction method.
Optimization of Hyper Parameter Bandwidth on Naïve Bayes Kernel Density Estimation for the Breast Cancer Classification

Cancer is viewed as the most harmful disease in the world. One of the most deadly cancer diseases attacking the women is breast cancer. In this paper, used Naïve Bayes classifier Kernel Density Estimation in breast cancer Wisconsin dataset take from UCI data repository. Naïve Bayes with Kernel Density Estimation was used as it could enable the Naïve Bayes to process the quantitative data. However, the performance of KDE (kernel density estimation) highly depends upon the size of the bandwidth parameter used to control the curve. The problem an accurate choice of parameter bandwidth (h) using trial and error technique requires a long process. A large bandwidth (h) will over-smooth the density and mask the structure in the data, a small bandwidth (h) will yield a density estimate that is spiky and very hard to interpret. The aim of this research is to compare the optimization of grid search parameter with the genetic algorithm to determine the bandwidth parameter (h) on the Naïve Bayes Kernel Density Estimator algorithm. The parameters used to compare the two optimization methods included accuracy, AUC (Area under Curve), and computation time. The experiment results on the test of the significance value on the comparison of grid search and algorithm in determination of the bandwidth parameter (h) naïve Bayes kernel density used Mann-Whitney Test. The result of the significance ranking showed that mean rank of the genetic algorithm accuracy was 489.95 better than the mean value of the rank of grid search accuracy at only 189.05, mean rank of AUC genetic algorithm was 491.82 better than mean rank of AUC grid search only at 187.18. The mean rank of computation time grid search was 243.37 faster than mean rank of computation time genetic algorithm at 435.63.
Sentence Semantic Similarity Using Dependency Parsing

We introduce a novel approach to calculate the semantic similarity between sentences. Unlike conventional approaches, we leverage dependency grammar representations of the sentences and semantic word similarity methods to find the semantic similarity between the sentences. While conventional methods can efficiently extract syntactic structure, they fail to determine the semantic relation between the sentences. Furthermore, we also explore a similar approach which was originally proposed for extractive multi-document summarisation problem. Finally, we evaluate the newly proposed approach, similar approach and the conventional approaches on the data set which is considered as the gold standard for semantic similarity problems.
Analysis and Optimization of CNN-based Semantic Segmentation of Satellite Images

In this paper, we analyze and optimize the CNN (Convolutional Neural Network) based semantic segmentation network, U-Net. While U-Net is known to be the state-of-the-art in semantic segmentation, it is difficult to apply U-Net directly to the resource-constrained systems due to its high memory usage and computation requirement. To overcome this disadvantage, we apply the filter pruning method to optimize the network. Experimental results show that the memory usage is reduced by 0.26 times and the inference speed is enhanced by 0.57 times with 75% filter pruning. In addition, the IOU (Intersection Over Union) and F1-score which reflect the accuracy of semantic segmentation, were only reduced by 4.7% and 4.5% respectively compared to the original U-net.
Visual Interaction of Rolling Steel Heating Furnace Based on Augmented Reality

Based on the augmented reality technology, the augmented reality visualization of the rolling steel heating furnace and the equipment interaction operation realized by the augmented reality technology were studied through the means of virtual reality fusion and digital twinning. The heating furnace is the key equipment in the steel metallurgy hot rolling production process. In actual production, it is difficult for operators to understand the operation status of the heating furnace and the heat flow parameters of the equipment at any time and any place. In response to this problem, this paper proposes to construct a visual interactive platform for rolling steel heating furnace through augmented reality technology, and present the equipment operating status of the heating furnace. With heat flow parameters.
Automatic Detection of Fetal Head using Haar Cascade and Fit Ellipse

USG examination is a routine activity during pregnancy with USG machine. There are several parameters to get information about the fetal. The important parameter is head circumference to get to know abnormality, early detection, and monitoring growth. Accurate result and rapidity are really important during the examination. Low quality of ultrasound image and time-consuming in manual annotation made many variability values depending on the doctor or sonographer, the highest risk of human error, so the result is not accurate enough. From that problem, we need an automatic way to detection of the fetal head to make it faster than the manual way. In this result, we use pattern recognition to give learn the system to recognize the characteristic of the fetal head object. We use Haar Cascade to train the classifier to detect the object in the USG image. We also use Fit Ellipse to get the curve shape of the fetal head. The accuracy rate of HC measurement is 97,23% and just consume 1.9s for fetal head detection.
Increasing Cognitive Thinking Skills With Edpuzzle

By Amber Littlefield, DNP, APRN, MEd, FNP-C, Assistant Professor, Department of Family Nursing, Frontier Nursing University, Hyden, KY,[email protected].
FPGA Implementation of Haze Removal Technique Based on Dark Channel Prior

Image dehazing is a much innovative and growing technology in applications of computer vision. Haze removal technique in FPGA using Nexys 4 DDR is implemented in this paper. The dark channel prior (DCP) has been an efficient dehazing technique. However, DCP can induce inaccurate approximation of transmission which results in colour distortion and halo effects in the brighter regions of an image. The proposed algorithm is implemented for haze removal of image to avoid the colour distortion of haze in bright and in non-bright areas with less complexity. The algorithm that is proposed in this paper is compared with the DCP method and the Tarel algorithm using Structural Similarity index (SSIM). The results show that the proposed algorithm removes haze effectively in both bright and non-bright areas of an image and the implementation in FPGA is done with less computational complexity. Implementation of dehazing in FPGA can be used in many applications of computer vision such as surveillance, military and transportation areas.
Blockchains for Scalable IoT Management, Access, and Accountability

The articles in this special section focuses on blockchain for scalable Internet of Things (IoT) management, access, and accountability. The articles highlight new blockchain architectures and techniques that can be leveraged to enable scalable management, access and accountability of massive numbers of IoT devices to effectively support various IoT applications.
Measuring straightness errors of slender shafts based on coded references and geometric constraints

The coded references and geometric constraints based method is suggested to solve the inconsistency problem of measurement range and accuracy for slender shafts. First, a slender shaft is divided into segments. Images of each segment along with a coded and calibrated reference are taken at two object distances. The code of the reference is obtained using the template match technique. Consequently, the coordinates of the feature points of the reference are solved. Thus, the external parameters of the camera are calculated. A mathematical model of the diameter, centre coordinate and external parameters is established based on the pinhole imaging principle. The problem that only one image cannot extract the centre coordinate is solved, as is the pseudo-diameter problem. A coordinate transformation model for these feature points is established and the sectional measurement results are associated by using the model. Experiments are carried out on lead screws; the measuring uncertainty of the experimental system is 0.007 mm, which is 12.9% higher than that of a coordinate measuring machine. The systematic error of the experimental system is 0.01754 mm. It is proved that the suggested method can stably and reliably measure the straightness errors of slender shafts with high accuracy.
A Grain-Adaptive Computing Structure for FPGA CNN Acceleration

In recent years, because of its superior performance and outstanding accuracy, convolutional neural networks (CNNs) are widely used in high-tech applications such as image classification and speech recognition. But it is more and more difficult to implement CNN in hardware platform due to the scale of CNN is increasing rapidly. FPGA attracts more attention compared with other processors for its excellent balance of flexibility and efficiency. There are many FPGA-based CNN accelerators proposed by previous work. However, in previous work the computing resource (especially DSP) is not fully utilized, either explicitly or covertly, which affects the CNN accelerator's overall performance seriously. In this work, we propose a new formula that provides a more accurate and comprehensive analysis to evaluate computing resource utilization, which can provide guidance for CNN accelerator design optimization. Then we propose a grain-adaptive computing structure for FPGA-based CNN acceleration, which can change flexibly to suit to and optimally utilize the available DSP resource. Due to the improvement of DSP utilization, we can achieve a more satisfactory result for both overall throughput performance and power efficiency. This architecture is implemented on Xilinx xcku115 based on AlexNet, the frequency is 150MHz and the peak power consumption is 30.05W. The overall performance is 1292.40 GOPS, 43.01 GOP/s/W, resulting in 2.28X and 1.94X, 9.44X and 3.02X improvement compared to previous work [6], [9] correspondingly.
An Enhanced Blockchain-Based Data Management Scheme for Microgrids

Trading of distributed energy resources is an important aspect to fully achieve energy efficiency. Modern microgrids and consumer/prosumer energy transactions are such kind of enablers. The blockchain has been proposed as a solution to aid microgrid applications with the support of a decentralized trading model, operations processing, computation and storage. However, microgrids trading is still vulnerable to so-called False Data Injection (FDI) attacks, that is the attempt by malicious participating nodes to distribute false measurements to the peers to gain personal advantages. In this paper, we propose an enhanced blockchain mechanism to counteract possible FDI attacks by means of mobile software agents to control and detect malicious activities of sellers nodes.
Achieving the fundamental convergence-communication tradeoff with Differentially Quantized Gradient Descent

The problem of reducing the communication cost in distributed training through gradient quantization is considered. For gradient descent on smooth and strongly convex objective functions on $\mathbb{R}^n$, we characterize the fundamental rate function-the minimum achievable linear convergence rate for a given number of bits per dimension $n$. We propose Differentially Quantized Gradient Descent, a quantization algorithm with error compensation, and prove that it achieves the rate function as $n$ goes to infinity. In contrast, the naive quantizer that compresses the current gradient directly fails to achieve that optimal tradeoff. Experimental results on both simulated and real-world least-squares problems confirm our theoretical analysis.
Image Enhancement by Deep Learning Network Based on derived image and Retinex

The Retinex model explains how the human visual system regulates the color and brightness of objects perceived by the human eyes. Based on this theory, an image enhancement algorithm based on image derived graph for weak illumination image by deep network is proposed in this paper. Firstly, in view of low contrast, low overall brightness and blurred details of dark areas in images captured under weak illumination, traditional image enhancement methods are used to process images to obtain derivative maps: adaptive contrast-limited histogram equalization, logarithmic histogram equalization, adaptive gamma correction with weighted distribution. Then, the image illumination component is obtained by deep decomposition network. The end-to-end mapping relationship between low illumination image and normal illumination image is trained, and the final output image is enhanced by deep enhancement network. The experimental results show that the proposed algorithm has better robustness as well as the output image has richer details, higher contrast, better visual effect and image quality compared with some state-of-the-arts.
Sum It Up: Verifiable Additive Homomorphic Secret Sharing

In many situations, clients (e.g., researchers, companies, hospitals) need to outsource joint computations based on joint inputs to external cloud servers in order to provide useful results. Often clients want to guarantee that the results are correct and thus, an output that can be publicly verified is required. However, important security and privacy challenges are raised, since clients may hold sensitive information and the cloud servers can be untrusted. Our goal is to allow the clients to protect their secret data, while providing public verifiability i.e., everyone should be able to verify the correctness of the computed result. In this paper, we propose three concrete constructions of verifiable additive homomorphic secret sharing (VAHSS) to solve this problem. Our instantiations combine an additive homomorphic secret sharing (HSS) scheme, which relies on Shamir’s secret sharing scheme over a finite field $$\mathbb {F}$$, for computing the sum of the clients’ secret inputs, and three different methods for achieving public verifiability. More precisely, we employ: (i) homomorphic collision-resistant hash functions; (ii) linear homomorphic signatures; as well as (iii) a threshold RSA signature scheme. In all three cases we provide a detailed correctness, security and verifiability analysis and discuss their efficiency.