Reviewerly/exHarmony huggingface dataset:
Balancing Latency and Accuracy on Deep Video Analytics at the Edge

Real-time deep video analytic at the edge is an enabling technology for emerging applications, such as vulnerable road user detection for autonomous driving, which requires highly accurate results of model inference within a low latency. In this paper, we investigate the accuracy-latency trade-off in the design and implementation of real-time deep video analytic at the edge. Without loss of generality, we select the widely used YOLO-based object detection and WebRTC-based video streaming for case study. Here, the latency consists of both networking latency caused by video streaming and the processing latency for video encoding/decoding and model inference. We conduct extensive measurements to figure out how the dynamically changing settings of video streaming affect the achieved latency, the quality of video, and further the accuracy of model inference. Based on the findings, we propose a mechanism for adapting video streaming settings (i.e. bitrate, resolution) online to optimize the accuracy of video analytic within latency constraints. The mechanism has proved, through a simulated setup, to be efficient in searching the optimal settings.
Hardware based Order Book Design in High Frequency Algo Trading

The Order Book is an inevitable electronic document used in various business trading and financial sectors to store the track of financial transactions. Since the High Frequency Algo Trading demands fast response, hardware is the best choice as compared with software based designs. This paper proposes the hardware implementation of the Order Book architecture using $45 nm$ CMOS technology, where the Order Book details are stored in the hardware Buffer. Also, the same proposed Order Book architecture is implemented with Artix-7 FPGA evaluation board, where the Order Book details are stored in the Block RAM (BRAM).
Improved Approach to Feature Dimension Reduction for Efficient Diagnostic Classification of Breast Cancer

Machine learning deals with the approaches that involved the scientific computing methodologies of understanding patterns in complex datasets. Scientific computing is pervasive in the physical, biological and engineering sciences. This paper focuses on the application of machine learning technique for diagnostic analysis of cancer datasets. The problems encountered in the proposed method include: irrelevant and redundant data features resulting in high dimension of extracted data features. Feature selection method was employed to reduce the problem of extreme data dimensionality defined by irrelevant and redundant features that characterized the training dataset in order to improve the performance of the classification model. The goal is to apply machine learning approach to diagnose patients with breast cancer by analyzing the data of patients and classifying them into reliable clinical diagnosis results using reduced feature dimension and at the same time reducing errors to the barest minimum. Standard performance metric such as classification accuracy was used to evaluate the resulting model. The proposed classification algorithm produced an accuracy of 98.24% when the optimal number of 17 significant features were selected out of 31 extracted features.
Augmented TIRG for CBIR Using Combined Text and Image Features

In this paper we propose a methodology for Content Based Image Retrieval, CBIR, using query inputs in the form of a source image and text modifiers. The proposed methodology augments the methodology proposed in [21], TIRG, with a trained module. The trained module aims at enhancing the relationship between a) the composed image-text features and b) the target image features (e.g. input an image of blue dress along with a textual description and ask for the same dress but in red). Our study used two trained modules (Linear Regression, LR, and Non-Linear Multilayered Perceptron, NMLP). The proposed models were tested using the well-known fashion 200K dataset. The LR model reduced the Mean Squared Error, MSE, significantly. A joint LR model outperformed the TIRG on the testing Dataset. Two NMLP trained models were used: MSE-optimized and Cosine similarity optimized. The performance of the two models was very similar. The NMLP models, in general, outperformed TIRG over the Training dataset. The study also indicates that combining image-text features should be kept to later stages to obtain their recall intersections. Moreover, the study showed that text-features generation based on words assigned numbers irrelated to their semantics requires semantic hub to bridge to a semantic-numbers.
Learning Enhanced Feature Responses for Visual Object Tracking

Visual object tracking is an important topic in computer vision, which has successfully utilized pretrained convolutional neural networks, such as VGG and ResNet. However, the features extracted by these pretrained models are high dimensional, and the redundant feature channels reduce target localization and scale estimation precision, leading to tracking drifting. In this paper, a novel visual object tracking method, called learning enhanced feature responses tracking (LEFRT), is proposed, which adopts the target-specific features to enhance target localization and scale estimation responses. First, a channel attention module, called target-specific network (TSNet), is presented to reduce the redundant feature channels. Secondly, the scale estimation network (SCENet) is introduced to extract spatial structural features to generate a more precise response for the scale estimation. Extensive experiments on six tracking benchmarks, including LaSOT, GOT-10k, TrackingNet, OTB-2013, OTB-2015, and TC-128, demonstrate that the proposed algorithm can effectively improve the precision and speed of visual object tracking. LEFRT achieves 90.4% precision and a 71.2% success rate on the OTB-2015 dataset, improving the tracking methods based on the pretrained features.
A Gesture Recognition Method Based on Yolov4 Network

With the diverse development of human-computer interaction. The gesture recognition-based interaction has a large-scale application prospect in collaborative robotics and smart home control. However, due to the similarity of gestures and occlusion, the previous methods have problems with the poor accuracy and shift of detection box. Aiming at the above issues, a gesture recognition method based on the Yolov4 deep learning algorithm is proposed. Firstly, gesture images were collected and annotated, and the data was processed by the GridMask and scale adjustment of data augmentation in order to improve the generalization performance of the network. Then K-means clustering algorithm was used to cluster the annotation boxes in the annotation dataset, by this way, the anchor box of YOLOV4 was optimized to improve the IOU accuracy. Finally, during the training process, focal loss and Consine warmup were adopted to improve the unbalanced sample number of classes and overfitting of the network. The experimental results shows that the proposed algorithm outperforms the main target detection models which include Yolov4, Yolov3 and Faster RCNN, the average recognition accuracy of this method reaches 99.4&#x0025; and the FPS is 33fps. The proposed algorithm has good real-time performance.
Deep learning in multiagent systems

Autonomous agents are able to work toward their given goal and interact with their environment and other systems without the immediate help from humans. To accomplish this, they need to understand their environment, their goals, the task at hand as well as other agents in their vicinity. This makes deep learning an essential factor among the capabilities required for autonomous agents. In this chapter, we will dive into the specific characteristics and concomitant challenges of deep learning for autonomous agents and multiagent systems. Specifically in real-world situations, where autonomous agents operate and control physical systems, several challenges come together. We will discuss various aspects and approaches of deep learning that can be utilized by autonomous agents. Finally, we present different open challenges to be tackled to improve the abilities of autonomous agents.
Training of Generative Adversarial Networks using Particle Swarm Optimization Algorithm

In this paper, a particle swarm optimization (PSO) based solution is proposed for the training of generative adversarial networks (GANs). Conventional GAN networks take around 5x times more number of iterations to generate plausible images compared to the proposed method, thereby increasing the simulation time and decreasing the Frechet Inception Distance (FID) score. To overcome the problems of non-convergence and mode collapse associated with the conventional GANs, proposed work uses a PSO algorithm to stabilize the inertia weights during the training duration followed by conventional optimization method for the remaining iterations. The proposed solution is implemented on Nvidia Tesla VI00-PCIE-16GB GPU, using tensorflow and keras. The efficiency of the proposed solution is verified using MNIST dataset. The results showed that the iteration at which images are generated for the proposed method is faster as compared to the conventional GAN architectures, quantified with lower FID score.
Global Adaptive Optimization Parameters For Robust Pupil Location

Most existing pupil positioning methods use adaptive threshold in image binarizing, however, this approach could barely completely separate the pupil from the eye, indicating difficulty in screening the pupil contour and affecting the accuracy of pupil location. To overcome this weakness, we present a novel pupil positioning method based on global adaptive optimization parameter, which involves setting a three-step threshold (TST). First, TST binarizes image through searching for the most adaptive threshold corresponding to each eye image. Then a separate pupillary region is obtained. Finally, set the judgment conditions of the pupil connected area, and the connected area of pupillary region is identified by the morphological characteristics of pupil. TST has been tested on the Institute of Automation of the Chinese Academy of Sciences (CASIA-IrisV1) dataset with the recognition accuracy at 99.7%. The experimental results have been compared with other state-of-the-art methods, in which our proposed method show a better performance.
The Behavioral Diversity of Java JSON Libraries

JSON is an essential file and data format in domains that span scientific computing, web APIs or configuration management. Its popularity has motivated significant software development effort to build multiple libraries to process JSON data. Previous studies focus on performance comparison among these libraries and lack a software engineering perspective. We present the first systematic analysis and comparison of the input / output behavior of 20 JSON libraries, in a single software ecosystem: Java/Maven. We assess behavior diversity by running each library against a curated set of 473 JSON files, including both well-formed and ill-formed files. The main design differences, which influence the behavior of the libraries, relate to the choice of data structure to represent JSON objects and to the encoding of numbers. We observe a remarkable behavioral diversity with ill-formed files, or corner cases such as large numbers or duplicate data. Our unique behavioral assessment of JSON libraries paves the way for a robust processing of ill-formed files, through a multi-version architecture.
Blockchain Technology Enablers in Physical Distribution and Logistics Management

Blockchain technology has disrupted the present business environment. With the ever increasing supply chain disruptions, real time information sharing plays a critical role in achieving the organization goals. As majority of the operations are data intensive, a lot of (big) data is generated which can yield fruitful results. Through integration of modern information and communication technology tools and disruptive technology platforms, this real-time data can be used for leveraging competitive advantage and enhancing the operational efficiency. However, blockchain being a nascent technology, its adoption in logistics and physical distribution, and service operations sector is relatively low. Some of the reasons which can be attributed to it are lack of awareness of blockchain enablers and lack of comprehensive benefits about blockchain technology. The present study aims at finding out the blockchain technology enablers through a pertinent literature review. These enablers were then validated by experts for highlighting their prioritization using analytic hierarchy process (AHP). The findings from the study highlight that traceability and transparency were the factors given the utmost priority. The findings also highlight how the adoption of blockchain technology will enhance the operational efficiency in case of service operations management. Further, implications for research and practice are discussed which are followed by limitations and future research directions.
Email Classification using LSTM: A Deep Learning Technique

Electronic mail has been in use for decades and more than four billion users access their emails using different domains and servers. Emails are considered an official way of communication in remote working modes and in online businesses. Email labeling can reduce the amount of effort to manage this communication. Email classification is so far done to classify emails such as Spam, Non-spam, Junk, social media, etc. However, email classification keeping in view the types of cybercrimes committed through email is not done. Emails can be labeled as Spam, Phishing, fraudulent, harassing, bullying, or can be a general/normal email. This identification is one of the most challenging tasks for both email service providers and consumers. Several spam identification models have previously been proposed and tested but very limited work has been done so far on the multi-class classification of emails. Emails can be classified into more than two classes (spam and ham). In this paper, we have proposed a solution to classify emails into four classes: fraudulent, suspicious, harassment, and normal. A deep learning approach named Long Short Term Memory(LSTM) with stratified sampling has been used to identify the email classes. An effort has also been made to balance the input dataset using over-sampling methods. The proposed model obtained a classification accuracy of more than 90%. with stratified sampling only and more than 95% by applying data balancing techniques on the dataset.
MQTT-4EST: RulE-basEd WEb Editor for Semantic-aware Topic Naming in MQTT

Message Queuing Telemetry Transport (MQTT) is an established data transmission protocol in virtually all Smart-* domains. The protocol is based on the publish/subscribe pattern. Messages are published to so-called topics, to which subscribers subscribe, in order to receive the content of interest. As the name of the topic can be chosen freely by the publishing entity without relying on a shared semantic model, the subscribers can insufficiently reason on the semantic of the topic name. However, this can be desirable when topic trees closely resemble structures found in the real world. One approach to formulate semantics in a machine-readable format are ontologies. This paper demonstrates how ontologies allow to couple topic trees with explicit semantics. To this end we present a novel editor that supports users in creating topic trees that follow explicit semantics.
Paper-Woven Circuits: Fabrication Approach for Papercraft-based Electronic Devices

In this pictorial, we propose an approach to integrate electronics into a paper-woven craft. A paper-woven structure is formed by alternately and orthogonally weaving paper strips. The key idea is to add conductive layers to these strips by printing or transferring conductive materials. Our approach enables us to form the skeleton of an object and its wiring and sensor arrangement simultaneously. We explore the creative space of this approach, i.e., integrating functions such as sensing, display, and sound amplification into objects, for leveraging the paper-woven structure. We also outline the workflow of the approach, introduce a design support tool, and discuss example applications that combine the electronic-enabled functionality and woven structure.
Satellite Image Segmentation of Gold Exploration Areas in the Amazon Rainforest Using U-Net

Gold exploration activity in the Amazon rainforest area has been increasing recently. Explorations of this nature have direct consequences on fauna, flora, and the lives of indigenous people living around these areas. In an attempt to map and detect land usage, among other activities using satellite images, several approaches of machine learning and artificial intelligence have been explored, both classical and modern. This paper develops a method for segmentation of gold exploration areas using U-Net, on high-resolution satellite images, estimating the most appropriate loss, optimization function, and training batch size for the task. The results achieved for segmentation obtained the values of 91.29&#x0025; for precision, 74.64&#x0025; recall, 76.60&#x0025; f1-score, and 98.34&#x0025; accuracy.
Kummer versus Montgomery Face-off over Prime Order Fields

This paper makes a comprehensive comparison of the efficiencies of vectorized implementations of Kummer lines and Montgomery curves at various security levels. For the comparison, nine Kummer lines are considered, out of which eight are new, and new assembly implementations of all nine Kummer lines have been made. Seven previously proposed Montgomery curves are considered and new vectorized assembly implementations have been made for three of them. Our comparisons show that for all security levels, Kummer lines are consistently faster than Montgomery curves, though the speed-up gap is not much.
Paramedic Assistant Robot: Feature Review from Generation 1.0 to 3.0

The East Java region is a red zone area for COVID-19 cases. The number of patients being treated has an impact on the performance of medical personnel. Medical personnel gets tired easily and many of them die. To overcome this problem, a Paramedic Assistant Robot was designed. The methods used in designing this paramedical assistant robot are as follows: 1) Model design stage, 2) Determine electrical unit, 3) Determine communication unit and robot network, 4) Determine robot mechanical unit, 5) overall manufacturing process unit, 5) assembly process, 6) Robot function test. The result of each generation of feature development from 1.0 to 3.0 improved significantly. For maneuvering, from remote control and joystick to an autonomous system. This means that artificial intelligence is also increasing. The 3.0 generation robot is divided into two robots, namely robots for service and robots for monitoring. The 1.0 generation robots do not have to measure instruments, while the 2.0 and 3.0 generation robots have both integrated and separate measuring instruments.
Indonesian Marketplace Trust Analysis Using Text Mining: a Case of Tokopedia

The existence of technological sophistication and the use of the internet can be done with various things such as using social media, e-commerce, and others. Tokopedia is a marketplace where e-commerce activities can be carried out. In its development, Tokopedia experienced several adverse events such as data leaks, a decrease in monthly website visits in 2020, and the performance of the Tokopedia brand, which was still below other marketplaces. Based on this incident, Tokopedia experienced a decline which could make the view of Tokopedia less favorable, such as trust. This study aimed to see how Twitter users trust Tokopedia based on trust factors in e-commerce: perceived privacy, perceived security, perception of website quality, perceived risk, and internet experience. Text mining methods were used in this study, i.e., multiclass classification and sentiment analysis. The study results show that trust in Tokopedia is still lacking. There are results from the ecommerce factor where perceived privacy and risk have the most negative percentages among other factors. These results can be of particular concern in knowing the recommendations for marketing tactics that Tokopedia should carry out. In addition, this study is fruitful in seeing how the application of big data can be helpful in management regarding trust using available technology, such as the application of text mining to analyze reviews on a topic.
DeepLaBB: A Deep Learning Framework for Blocking Bugs

Blocking bugs (BBs) refer to flaws in software components that prevent other bugs from being resolved. The severe nature of this bug has occasioned the building of blocking bug prediction (BBP) models for early detection of BBs during software maintenance. Traditional approaches to building these models usually rely on software metrics (i.e., Lack of cohesion, coupling between objects, cyclomatic complexity, and LOC) mined from source code files, and the use of machine learning techniques. However, empirical evidence suggests that these metrics often fail to capture the syntax and semantic information between programs. This significantly affects the performance of the prediction models built on these features. To address these issues, this paper introduces a framework that leverages a deep learning model, DeepLaBB, to automatically learn semantic features using token vectors, and then applies the features to build and train a model that predicts whether a bug is considered blocking or non-blocking. To show the effectiveness of the proposed approach, eight large-scale open-source projects, namely Chromium, Eclipse, NetBeans, OpenOffice, Gentoo, Fedora, Mozilla, and NetBeans containing over 609,800 bugs were used for the training and validation needs of the models. The proposed ensemble deep learning model (DeepLaBB) was benchmarked against four traditional machine learning techniques, namely Random Forest, Artificial Neural Network, Classification, and regression Tree, and K-Nearest Neighbour. Performance evaluation measures, namely F1-score, MCC, and AUC were used to measure the performance of the models. On average, DeepLaBB achieved an MCC of 0.8504%, F1 Score of 0.4292%, and AUC of 2.9459%. The results show that DeepLaBB achieves a better BB prediction performance than the baseline conventional machine learning models proposed in previous studies. The proposed framework DeepLaBB is efficient in predicting BB.
Analysis of Performance Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) for Lifelong Learning on Indonesian Sentiment Analysis

One popular task of textual data is sentiment analysis. From machine learning point of view, the problem of sentiment analysis is to classify textual data into a class of sentiment such as positive, negative, or neutral. Convolutional Neural Network (CNN) is one of deep learning models that gives good results for some textual data tasks including sentiment analysis. A newer deep learning architecture which is a combination of Long-Short Term Memory and Convolutional Neural Network (LSTM-CNN) shows better results for sentiment analysis. In this paper, we analyzed the performance of CNN and LSTM-CNN for lifelong learning of sentiment analysis on Indonesian social media. After sequentially learning from several domains, our simulations show that LSTM-CNN is better at increasing the ability to transfer their knowledge to new domains when compared to CNN. However, LSTM-CNN losses more knowledge than CNN on a current domain.