Reviewerly/exHarmony huggingface dataset:
Multi-relational Semantic Distillation for Few-Shot Object Detection

While few-shot object detection(FSOD) has been developed to a certain extent, it is still a large margin from practical applications. Most existing methods use traditional object detection methods as the basic framework is improved to a limited extent. Previous methods often ignore the special characterization relationship between support and query images. This paper fully investigates the effect of support images on detection performance and proposes a new FSOD method called Multi-relational Semantic Distillation (MSD). Our approach aims to improve FSOD performance by building a multi-relational semantic representation model with support and query features. In addition, we propose a support enhancement (SE) module based on the self-attention mechanism to enhance the useful information in the support features to mitigate the negative impact of low-quality support images. To verify the effectiveness of MSD, we conduct sufficient experiments on Pascal VOC and MS-COCO datasets. Experiments show that MSD achieves competitive results at low shots compared to other state-of-the-art few-shot detectors.
Multi-task photonic time-delay reservoir computing based on polarization modulation

We propose and experimentally demonstrate a multi-task photonic time-delay reservoir computing (RC) system based on polarization modulation. The key component in the system is a polarization modulator (PolM) that functions, jointly with a polarization controller (PC) and a polarizer, as an equivalent Mach-Zehnder modulator (MZM) to perform electrical to optical conversion and to provide nonlinear operation. By adjusting the bias of the equivalent MZM, the nonlinear function can be optimized for different tasks to achieve the best multi-task performance. In this paper, the task-independent information processing capacity (IPC) of the time-delay RC system is evaluated. The results show that the readout bias of the equivalent MZM leads to a different IPC which can be optimized for different tasks. Two benchmark tasks (NARMA10 and IPIX radar signal prediction) are performed experimentally. The readout bias is adjusted independently for each of the two tasks to give a minimum normalized mean square error (NMSE), which are 0.2103 and 0.0031 for the NARMA10 and IPIX radar signal prediction tasks at a speed of 1.06 Mb/s, respectively.
Knowledge Base Completion with transfer learning using BERT and fastText

Knowledge base completion (KBC) is the task of predicting and filling missing information based on the current data in that knowledge base. Recently, one of the most feasible approaches introduced by V. Kocijan and T. Lukasiewicz (2021) is to transfer knowledge from one collection of information to another without the need for entity or relation matching. Still, this work has not scaled pre-training to larger models and datasets and investigated the impact of the encoder architecture. In this work, we propose a method that can combine the benefits of Bidirectional Encoder Representations from Transformer (BERT), fastText, Gated Recurrent Unit (GRU), and Fully Connected (FC) layer to improve the KBC task in Kocijan and Lukasiewicz’s model. The experimental results show the effectiveness of our proposed model in several popular datasets like ReVerb20K, ReVerb45K, FB15K237, and WN18RR.
When Third-Party JavaScript Meets Cache: Explosively Amplifying Security Risks on the Internet

Today's web applications feature the proliferation of third-party JavaScript inclusion, which incurs a range of security risks. Although attack strategies by manipulating third-party JavaScript files have been widely investigated, the adverse impact caused by third-party JavaScript inclusion and caching does not receive much attention. Specifically, when a malicious script is cached, it can revive and bite every time when a user visits any website that includes it, leading to a much worse effect of the attack. In this paper, we present the first comprehensive study on Alexa top one million websites to investigate how likely third-party JavaScript inclusion and caching can make an attack large-scale and long-lasting, and further to uncover insecure practices that carelessly or inadvertently exacerbate the attack impact. We also discuss potential solutions to improve current practices to minimize the security risk associated with third-party JavaScript inclusion and caching.
Communication-Aware Energy Consumption Model in Heterogeneous Computing Systems

Abstract Large heterogeneous computing systems are composed of conventional central processing units and graphics processing units (GPUs) where communication plays a crucial role for system performance. This paper presents an energy consumption analytical model in terms of communication perception for the communication–computing pipeline characterization of discrete GPUs systems. We propose a dynamically adaptive energy-efficient task assignment approach, which harnesses particle swarm optimization. Static energy optimization is addressed by optimal task partition granularity. The experimental results demonstrate that the communication-based energy optimization algorithms can be more energy-saving than those without communication consideration. For some application benchmarks, the energy consumption can be saved by up to 31%. This implies the potential that the energy-saving optimization methods can be incorporated in system engineering processes.
Evaluation of YOLO-X and MobileNetV2 as Face Mask Detection Algorithms

Even though COVID-19 still exists, people are more reluctant to wear masks in public places, in fact only 73% of Indonesian still do. Hence, automatic mask surveillance in public places is still needed. In this paper, we compare two algorithms named YOLO-X and MobileNetV2 to detect face masks. YOLO-X was able to outperform other YOLO algorithms in object detection. While, according to researchers, MobileNetV2 achieved 9S% in face mask detection. To fairly evaluate both algorithms we need to conduct research under controlled variables including using the same datasets and devices. We used public datasets which consists of 1493 mask images and 6451 non mask images for training and testing. The results show that YOLO-X outperforms MobileNetV2 as it achieves 95.0%, 98.7%, 93.7%, and 96.1% for accuracy, average precision, recall, and F1-score respectively. YOLO-X also performs better in detecting faces with occlusion such as glasses, hands, and postures than MobileNetV2. However, YOLO-X detects faces and face masks 31.9% slower than MobileNetV2.
Automatic Region of Interest Prediction from Instructor’s Behaviors in Lecture Archives

This research proposes an automatic region of interest (ROI) prediction architecture with a deep neural network for estimating the learners’ ROI from instructor’s behaviors in lecture archives to generate ROI zoomed videos to fit smaller screens like smart devices. To achieve this goal, we first created a dataset of ROIs from learners’ gaze data in watching the archives and generated 16,039 ROI labels after clustering and smoothing with K-means algorithm based on the gaze point data obtained for the one-second segmented videos. Next, we extracted the instructor’s behaviors as feature maps from the segment video, considering the Frame Difference, Optical Flow, OpenPose, and temporal information. We then composed an Encoder-Decoder architecture that combined U-Net and Resnet with these behavioral features as input to build a deep neural network model for predicting ROI. Through the experiment, the agreement between the ROI labels and the predicted regions was evaluated by Dice loss using each feature map and improved from 0.9 in a single image as a baseline to 0.4 in Openpose and temporal features. The positive potential was obtained from automatic content generation for smart devices through the ROI prediction with the instructor’s behaviors.
Improving Emotion Recognition in Conversation using Text and Emoji Modalities

Due to the variability of human’s conversation, accurately identifying and distinguishing individual emotions from social media text is challenging. To overcome this limitation, this paper investigates to exploit emojis as a new source of information for emotion recognition in conversation. Emojis are received much interest for use as a salient feature in social media NLP systems. However, there is less explored in the domain of conversations in social media. This paper examines state-of-the-art emotion recognition algorithms in deep learning and evaluates impact of supplementing emojis as an additional feature for improving the algorithms. Emojis are transformed into corresponding vectors and combined with text embeddings. We propose two techniques of the combination at conversation and sentence levels. Our experiments show that emojis are effective for improving the accuracy of emotion recognition. We also perform a deeper analysis to find the most optimal dimension of emoji embedding in our recognition task.
A Data-Centric Approach to Evaluate Requirements Engineering in Multidisciplinary Projects

Multidisciplinary teams are often a necessity for software projects as they provide the required expertise to effectively solve complex problems. However, efficient collaboration between teams with different disciplines is challenging due to several factors such as considering gaps in knowledge areas, establishing a development process, and understanding different requirements from various groups or stakeholders. The agile methodology, such as scrum, offers a powerful approach to managing the software development process effectively. As part of the agile methodology, some techniques and tools are used to manage requirements change, which is a common practice in multidisciplinary teams. This research aims to leverage process-mining techniques to analyze data from Jira and GitHub to analyze the efficacy of software development process, particularly in multidisciplinary teams. This approach is applied to a case study of a virtual healthcare intervention system to measure the team’s productivity. The results indicate several deficiencies in the process with respect to requirements engineering task that cause loss of time and increase rework rates. Results indicate that there are some challenges in the development process that contribute to some deficiencies. The rework rate is high and the number of tasks that are intended to be completed is less than what was planned. These factors can contribute to the lengthening of the software development process. Most of these challenges can be addressed by improving the requirement engineering process in order to obtain the requirements and manage change requests more efficiently.
Evolutionary Mapping with Multiple Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been very successful in many civilian and commercial applications, including disaster relief, search and rescue, precision farming, archaeology, cargo transport, and surveillance. In most of these applications, mapping is a required phase that needs to be performed as an initial step. While mapping has attracted much attention in the last decades, much of the works rely on single drones. In this context, we propose a multiple UAV system for efficient mapping, minimizing mission time and cost. The system includes offline and online planning, and a good balance between both to reduce on-board processing. Offline planning includes area decomposition, take-off location finding, and path planning. Online planning will then be used to react to any unforeseen event that might occur during the plan execution. These incidents include a sudden change in weather conditions, communication loss or drone malfunction, and the presence of a nearby flying obstacle. Each of the main offline and online planning tasks are formalized as a Multi-Objective optimization (MOO) problem where requirements need to be met while objectives have to be optimized. In this regard, we consider several evolutionary techniques to tackle these MOO problems. To assess the performance of these techniques, we conducted several experiments and reported the related results. One finding is that MOEA/D outperforms NSGA2, while the latter requires less processing time.
A Secured Certificateless Sign-encrypted Blockchain Communication for Intelligent Transport System

Data communication in the intelligent transport system suffers from many security vulnerabilities. It is essential to protect the vehicles from the distribution of fake messages and concurrently preserve the privacy of those vehicles against tracking attacks. Conventional security methods are not sufficient to provide well-needed security support. In this paper, an efficient technique called Gentle Boost Clustered Diffie-Hellman Certificateless Signcryption-based Blockchain Security Frame-work (GeBlock) is proposed to improve communication security. Initially, the vehicle's information is collected from the dataset. Then, the collected vehicle data are grouped and given to the data block in the underlying Blockchain. The Gaussian expected maximization clustering is a weak learner for grouping each vehicle's data. This process minimizes the processing time for secure data-sharing in the intelligent transport system. After that, the Diffie-Hellman Certificateless Signcryption is performed to protect the data from unauthorized entities. Diffie-Hellman Certificateless Signcryption performs the encryption and digital signature verification process where only an authorized entity can access the vehicle data. In the encryption process, the clustered vehicle data is converted into ciphertext. The digital signature verification is performed on the receiver side to decrypt the ciphertext into the plain text. The confidentiality rate is improved in data communication based on signature verification. Experimental evaluation is performed using Warrigal Dataset, and the different parameters such as clustering accuracy, data confidentiality rate, and processing time are measured.
Geology and Isotope Systematics of Gold Deposits in the Abansuoso Area of the Sefwi Belt, Southwestern Ghana

This study utilised lithogeochemistry and stable isotope geochemistry to assess the gold mineralisation in the host rocks of the Abansuoso area. It also studied the isotopic signature of hydrothermally altered and unaltered rocks as a tool for the preliminary prospecting of gold in Ghana’s Birimian belt. The research identified a close association between Au and As, Mo, and W, which is characteristic of orogenic gold deposits. The mineralised and unmineralised rocks show similar content of major and trace elements which suggests that the mineralisation did not significantly affect the primary composition of the mineralised rocks. The concentrations of δ18O (8.9‰ to 17.4‰, n = 22, mean = +13.7‰) and δD (−117 to −33‰, n = 22, mean = −57.9‰) in the rocks are comparable to those from other Birimian regions in Ghana and West Africa. The lack of variation in δ18O suggests a system buffered by rocks, with metamorphic fluid originating from rocks with identical δ18O values. However, the effect of mineralisation on the δD values of rocks is substantially more apparent than its effect on the δ18O values of the rocks.
Design of chaotic black hole based feature selection with classification for Chronic Kidney Disease diagnosis

Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures.
An Encryption and Decryption Model for Data Security Using Vigenere With Advanced Encryption Standard

As the amount of data being sent has risen dramatically in recent years; protecting that data has become increasingly important. Cryptography is the process of transforming plain text messages into ones that can no longer be decoded. An information security system's algorithm for encrypting and decrypting data cannot function without them. Data transmission via a communication network can benefit greatly from the use of encryption. Short message service (SMS) is still popular despite the availability of several online messaging platforms. Despite this, these services can be easily cracked, making SMS less secure for transferring critical information. The usage of appropriate cryptographic algorithms is essential to ensuring the safety of your data. Cryptanalysis and pattern prediction are better protected. This study uses a hybridized version of the Vigenère cipher with AES. In tests, the Vigenère-AES came out on top, and it may be used to keep sensitive data safe from prying eyes.
Visual Cryptography Secret Share Creation Techniques with Multiple Image Encryption and Decryption Using Elliptic Curve Cryptography

In Cryptography the covert image share creation method and multiple image encryption and decryption are used with different algorithms. In this work, Visual Cryptography (ECC) is used for the outline of shares that have been generated from the novel image. Red, Green, and Blue pixel shares are one by one formed by the Visual Secret Sharing (VSS) Scheme. It proposed the ECC method for image encryption and decryption using Rubik’s cube image encryption algorithm. The encryption method is by a public key cryptography generator and the decryption method is done by the secret key arbitrarily generators of the ECC method. For humanizing, the performance analysis of Elliptic Curve Cryptography methods, such as the optimization techniques, was used for PSNR, MSE, and the Number of Pixel Changing Rate (NPCR) and (UACI). The outcome explains that the planned algorithm is improved over various other methods compared to the presented methods. The future technique is implemented with Matlab coding.
Edge detection networks inspired by neural mechanisms of selective attention in biological visual cortex

Edge detection is of great importance to the middle and high-level vision task in computer vision, and it is useful to improve its performance. This paper is different from previous edge detection methods designed only for decoding networks. We propose a new edge detection network composed of modulation coding network and decoding network. Among them, modulation coding network is the combination of modulation enhancement network and coding network designed by using the self-attention mechanism in Transformer, which is inspired by the selective attention mechanism of V1, V2, and V4 in biological vision. The modulation enhancement network effectively enhances the feature extraction ability of the encoding network, realizes the selective extraction of the global features of the input image, and improves the performance of the entire model. In addition, we designed a new decoding network based on the function of integrating feature information in the IT layer of the biological vision system. Unlike previous decoding networks, it combines top-down decoding and bottom-up decoding, uses down-sampling decoding to extract more features, and then achieves better performance by fusing up-sampling decoding features. We evaluated the proposed method experimentally on multiple publicly available datasets BSDS500, NYUD-V2, and barcelona images for perceptual edge detection (BIPED). Among them, the best performance is achieved on the NYUD and BIPED datasets, and the second result is achieved on the BSDS500. Experimental results show that this method is highly competitive among all methods.
clusterMLD: An Efficient Hierarchical Clustering Method for Multivariate Longitudinal Data

Longitudinal data clustering is challenging because the grouping has to account for the similarity of individual trajectories in the presence of sparse and irregular times of observation. This paper puts forward a hierarchical agglomerative clustering method based on a dissimilarity metric that quantifies the cost of merging two distinct groups of curves, which are depicted by B-splines for the repeatedly measured data. Extensive simulations show that the proposed method has superior performance in determining the number of clusters, classifying individuals into the correct clusters, and in computational efficiency. Importantly, the method is not only suitable for clustering multivariate longitudinal data with sparse and irregular measurements but also for intensely measured functional data. Towards this end, we provide an R package for the implementation of such analyses. To illustrate the use of the proposed clustering method, two large clinical data sets from real-world clinical studies are analyzed.
PEDM: A Multi-task Learning Model for Persona-aware Emoji-embedded Dialogue Generation

As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication (e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named EmojiTweet to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas. EmojiTweet will be published publicly via https://mea-lab-421.github.io/EmojiTweet/ .