Reviewerly/exHarmony huggingface dataset:
Attention Guided Multi-level Feedback Network for Camouflage Object Detection

“Camouflage object detection(COD)” refers to identify objects hidden in the surrounding environment, such as oriental scops owl in a tree hole. Nowadays, due to the high similarity between the camouflaged object and its dependent environment, coupled with the lack of large-scale datasets, the research on this task is still very challenging. Current COD models directly send feature maps output by the backbone into the encoding-decoding module and process them equally, which may cause information interference to a certain extent. In addition, regarding the disappearance of the underlying clues in DCNNs, these models have not been well resolved. This article carries out further research based on the existing models and proposes a novel model, AGMFNet. Specifically, we introduce channel attention and spatial attention to obtain more information we need and suppress useless information to avoid information interference. In order to make feature maps integrate better, the Inception module is utilized. Furthermore, the cascade decoding module is further expanded, and we proposed a multi-level feedback module with auxiliary edge information to refine the camouflage image, which can make full use of the high-level features while retaining the low-level clues. After a series of ablation experiments on the introduced modules on the test datasets, all the combinations can improve the performance, which will also help develop camouflage object detection. The code will be available at: https://github.com/baekqiu/AGMFNet-for-COD/
Systematic Review of Attribute-Based Access Control for a Smart City Using Blockchain

The research study identified that the major attributes for the smart city system were smart governance and education, quality healthcare system, smart building, smart mobility, advanced infrastructure, advanced technology, smart energy, and smart citizen. The attribute-based access control system was identified as the most effective due to governing of eight core issues of the smart city could be easier by using this technique. This study has conducted a systematic review of some past research to conclude progress on smart cities based on attribute-based access control. This study has found that cloud storage could be the best possible solution for data management systems whereas Blockchain might enhance the performance of smart cities. IoT might be helpful in improving the service of the healthcare system, water, and energy quality maintenance, supervision of the education system, monitoring of weather conditions, and detection of an accident in a smart city. The process of attribute-based access control process has been adopted to improve the security and data sharing process.
Singular Value Decomposition Based Image Compression

Image compression is a technique for reducing the size of the data in form of images to enhance storage and transmission of the images in a better manner. Since images with large size require complex systems and more storage it leads to increased complexity in handling the data. To overcome such problems image compression techniques are available in form of lossy as well as lossless techniques. The paper addresses the lossy image compression technique by utilizing the singular value decomposition method for compression images with large size. This method splits the images into set of components which are linearly independent. Eigen values of the images and partitioning of images in form of blocks is used for image compression. Singular value decomposition can be directly applied for compressing images but due to certain disadvantages it is used along with Eigen vectors and portioning hence giving much netter results.
The Effect of E-Learning, Social Media, and Online Games on Increasing Students' Learning Achievement in School during the COVID-19 Pandemic

This research was conducted with the aim of knowing the effect of e-learning, social media, and online games on student achievement in schools in Kampung Pulo. This study uses quantitative methods with descriptive analysis. The data collection technique was carried out using a questionnaire method with a Likert scale of one to five. The data analysis technique used in this study is the Outer Model with Convergent Validity, Discriminant Validity, Composite Reliability, Cronbach's Alpha and Inner Model calculations with T-Statistics, R-Square, f-Square, and VIF calculations using the SmartPLS 3 application. Based on the calculations carried out, the results show that: 1) there is a positive influence between e-learning on online games; 2) there is a positive influence between e-learning on learning achievement; 3) there is a positive influence between social media on online games; 4) there is a positive influence between social media on online games; 5) there is a positive influence between online games on learning achievement.
HMD Eye-Tracking Measurement of Miniature Eye Movement Toward VR Image Navigation

We conducted preliminary eye and head mount display (HMD) movement measurements to collect primary data to create a spatio-temporal virtual reality (VR) navigation system. Furthermore, we used the eye-tracking function of the Vive Pro Eye HMD to perform eye and rotational movement measurements of the HMD when gazing at a VR marker. We compared gazing at a fixed point with randomly bouncing linear motion along with horizontal and vertical motion by determining the Hurst exponent and the anisotropy of the gaze trajectories. Trajectories of the fixed vision and the slow marker chasing showed the Hurst exponent less than 1/2, indicating anti-persistency. In contrast, as the marker velocity increased, the displacements of gaze trajectories were stretched and showed persistency to the marker motion direction. As the marker speed decreased, the gaze trajectory expanded perpendicular to the marker motion, suggesting that the antipersistent miniature motion enhanced the collection of visual information. Users were found unconsciously superimposed a persistent motion of HMD on the gaze motion in the horizontal direction. We inferred this tendency to help to generate miniature gaze motion to collect visual information.
Voting Based Consensus Protocol for Blockchain to Maintain COVID Patient Records in Consortium Networks

Currently, Covid patient records are stored and maintained in the hospital’s database and servers where the patient received medical aid. This is a centralized scenario where the hospital has complete control over the patients’ details which enables easy data manipulation and fraudulent activities by the hospital, which is evident by the daily news we read on media. To prevent such fraudulent activities we propose to decentralize the existing system by introducing multiple hospitals in a blockchain network with Voting based consensus algorithm. As all the records which are stored in a blockchain are supported by a consensus reached by the majority of peers of the network itself, this distributed ledger is immutable and offers an accountable and candid data source for patient records. The validation process takes care of any violation of COVID-19 protocols by hospitals like count of covid positive patients, number of inpatients, number of outpatients, number of patients receiving oxygen, number of patients in ICU, Billing details and so on. This blockchain solution can curb the corruption in hospitals, particularly during pandemic and provide a relief to patients to a greater extent. The prototype of the proposed solution is implemented on a peer to peer network with suitable data structure and algorithm for the voting consensus protocol and PoW protocol. It is observed that voting based consensus protocol is suitable for consortium networks.
Survey on Optimization of Resource Scheduling in Cloud Platforms

Task scheduling plays major role in cloud computing research fields as it deals with the appropriate allocation of resources to perform tasks in cloud environments. Task scheduling has number of possible solutions while allocating resources since task scheduling is an np-hard problem. A lot of research work has been devoted in order to obtain optimal solutions for task scheduling. However, no single technique is globally accepted because of the desires of the cloud environments. This current study addresses the detailed developments in the last two decades toward the achievement of optimal solutions to obtain the resource allocation plans to execute the tasks.
An Interaction-Based Multi-Attribute Group Decision-Making Model Under the Probabilistic Linguistic Environment in a Quantum Framework

In a multi-attribute group decision-making (MAGDM) problem, decision-makers (DMs) are usually regarded as independent. However, DMs maybe interact or interfere with each other in many cases. Quantum probability theory (QPT) has been used to simulate such human cognition and behavior, which is represented as interference effects in quantum theory. To bridge the gap between the abstract concept in quantum theory and realistic decision scenarios, an interaction-based MAGDM model in a quantum framework is developed in this paper. In order to reflect individual diversity and uncertainty, individual preference is presented as state probabilities of alternatives in the form of probability linguistic variables and group preference is formulated by utilizing the QPT. In addition, Shapley value and interaction index obtained from a linear programming model that reflects interference effects between DMs are introduced into the quantum framework. Finally, a numerical example is used to illustrate the process and confirm the validity of the proposed methods. Comparisons with the similarity heuristics and further discussion are given to examine the accuracy in calculating the interference effects between DMs.
Applying Decision Tree to Detect Credit Card Fraud

The topic we are participating in is the credit card fraud detection Artificial Intelligence Open Challenge 2019 Autumn Competition. The number of times the prediction results can be uploaded in this competition is five times a day. The competition period is from September 6, 2019 to 2019. On November 22, the end time is November 22, 2019. Our goal is to accurately predict whether the transaction is a fraudulent transaction. In this topic, we learn how to process data, analyze and predict, explore the analytical performance of different machine learning models, identify potential fraudulent transactions, and respond early to find key factors that can be judged as fraudulent, such as: When the transaction is located in a foreign country, the chance of being stolen is higher, the mobile phone is often used by others, the chance of being stolen is higher, the chance of using the Internet transaction is higher, the use of Fallback is easier to be stolen, and the chance of being stolen The key factors such as the high probability of being stolen and brushing. We applied the XGBoost model. This model has higher accuracy and faster training speed, which can greatly reduce the training time. Let us find that the XGBoost model is more suitable for predicting the data set of credit card fraud among other models. Effectively increase the prediction accuracy to 51.97%
Relaxing the Forget Constraints in Open World Recognition

In the last few years deep neural networks has significantly improved the state-of-the-art of robotic vision. However, they are mainly trained to recognize only the categories provided in the training set (closed world assumption), being ill equipped to operate in the real world, where new unknown objects may appear over time. In this work, we investigate the open world recognition (OWR) problem that presents two challenges: (i) learn new concepts over time (incremental learning) and (ii) discern between known and unknown categories (open set recognition). Current state-of-the-art OWR methods address incremental learning by employing a knowledge distillation loss. It forces the model to keep the same predictions across training steps, in order to maintain the acquired knowledge. This behaviour may induce the model in mimicking uncertain predictions, preventing it from reaching an optimal representation on the new classes. To overcome this limitation, we propose the Poly loss that penalizes less the changes in the predictions for uncertain samples, while forcing the same output on confident ones. Moreover, we introduce a forget constraint relaxation strategy that allows the model to obtain a better representation of new classes by randomly zeroing the contribution of some old classes from the distillation loss. Finally, while current methods rely on metric learning to detect unknown samples, we propose a new rejection strategy that sidesteps it and directly uses the model classifier to estimate if a sample is known or not. Experiments on three datasets demonstrate that our method outperforms the state of the art.
COVID-19 and Contactless Learning and Teaching: The Impact of Active Participation and User Acceptance

Tertiary education in Hong Kong has dramatically changed after the outbreak of COVID-19. Teaching pedagogy and delivery method have been transformed into “Contactless Learning and Teaching” and online learning. However, the focus has been on online learning while seldom analyzing the effect of “Contactless Learning and Teaching” among previous research. This research addressed this gap by studying 156 university students in Hong Kong. ATLAS, a mobile app integrated with iBeacon technology was developed to deliver learning materials in “Contactless Learning and Teaching”. The findings indicated that students who spent more time on “Contactless Learning and Teaching” have better academic performance. The active participation in “Contactless Learning and Teaching” and better academic results could also be explained by the Technology Acceptance Model in this study. The current study proves that iBeacon displays the potential of delivering learning and teaching materials amid the pandemic using the “Contactless Learning and Teaching” approach.
Business Meeting Summary Generation Using NLP

The effort of providing a brief and fluent summary of a business meeting while preserving vital information content and overall meaning is known as business meeting summarizing.Initially, we convert recorded meetings, interviews, speeches, lectures, and other audio streams into text documents in the proposed work. After that, speaker text is divided into segments. Minutes of the Meeting are generated automatically using the proposed work. We also focused on using the Rev-AI Speech-to-Text API to better the voice to text conversion of a specific recorded audio file while making sure that the summarised text that has been converted from speech to text provides a specific and accurate meaning that is not only understandable to everyone but also covers all of the recorded file’s important points.
Data streams—Concepts, definitions, models and applications in smart cities

In the general sense, a data stream can be defined as an unbound sequence of time-varying, coherent data, which is in the process of being transmitted. The broad definition shows many of the typical properties of data streams, which should be understood very clearly as they form the fundamentals of many challenges related to processing data streams.
Trust and Trustworthiness: Experiments with Artificial Intelligence (AI) Agents

The trust game, a simple two-person economic exchange, has been extensively used as experimental measures for trust and trustworthiness of individuals. Here, we develop deep neural network-based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another without any prior knowledge or assumption regarding trust. We find that artificial agents can mimic trusting and trustworthy behaviors and produce actions that are close to decisions by human subjects. Conditions that influence levels of trust and trustworthiness by AIs are further explored. This study offers evidence that trust can emerge purely from an interactive learning process and demonstrates the possibility to build AI-integrated decision support systems capable of leveraging social behaviors to achieve better outcomes collectively.
Predicting Individual Quality Ratings of Compressed Images Through Deep Cnns-Based Artificial Observers

Unlike traditional objective approaches aimed at MOS prediction, subjective experiments provide individual opinion scores that allow, for instance, to estimate the distribution of users' opinion scores. Unfortunately, the current literature is lacking objective quality assessment approaches that simulate the process of a subjective test. Therefore, this work focuses on modeling the ratings of each individual subjects through a deep CNN that, once trained, is expected to mimic the subject in terms of quality perception; for this reason, we call it "Artificial Intelligence-based Observer" (AIO). Several AIOs, modeling subjects with different characteristics, can be derived and then used to simulate the process of a subjective test, thus yielding a more complete objective quality assessment. However, the training of deep CNNs-based AIOs is hindered by two major issues: i) the lack of training sets containing a large number of individual opinion scores; ii) the noisy nature of individual opinion scores used as ground truth. To overcome these issues, we motivate a two-step learning process. During the first learning step, the architecture of a well-known deep CNN, i.e., the ResNet50, is appropriately modified and its initial weights are updated using a large scale synthetically annotated dataset of JPEG compressed images. This yields a new deep CNN that we name JPEGResNet50. The second learning step, conducted on a subjectively annotated dataset, refines the generic perceptual quality features already learned by the JPEGResNet50 to obtain the AIO of each subject. Extensive computational experiments show the potential and effectiveness of our approach.
An Imitation Learning Approach for Vehicles Longitudinal Obstacle Avoidance in Logistics and Transportation

Obstacle avoidance is a core module for autonomous vehicle working in open environment. A lot of research is concentrated on obstacle avoidance and path planning of vehicle moving in two or three dimensions. However, when it comes to one degree of freedom, the topic is still little explored. In this paper, an imitation learning approach for autonomous vehicles longitudinal obstacle avoidance is introduced. This work aims to show the interest of using a supervised learning approach to imitate human’s behaviour when driving in environments with unpredictable obstacles. Two machine learning methods, K-nearest neighbours and XGBoost, were integrated into our learning architecture and were tested on two applications in logistics and transportation. The results show the ability of our solution to cope with different types of vehicle dynamics. Our solution is for each application able to reproduce the same decision of an expert driver and to make a trip without collision and with acceptable time travel.
The Influence of Cybersecurity on the Firms’ Financial Performance

The acceleration of cyber-attacks in the past few years certainly has negative influences on the investors and shareholders’ trustworthiness in the firms’ abilities to protect their interests. This is likely to be reflected on the firms’ shares price. Thus, the influence of cybersecurity on firms’ overall performance is a questionable issue. To be able to proceed through the cyber risks, firms face the challenge of enhancing their cybersecurity to avoid and combat against the endless cyber-attacks. Further to that the studies that cast light on the relationship between cybersecurity and firms’ performance from a holistic perspective are lacking.
Fuzzy Multi-objective Particle Swarm Optimization Based on Linear Differential Decline

To improve convergence performance of the algorithm and prevent the algorithm from falling into local optimal location, we proposes a novel fuzzy multi-objective particle swarm optimization based on linear differential decline (LDDFMOPSO). In LDDFMOPSO, the fuzzy control strategy is applied to the inertia weight, so that the search ability of the global and local can be flexibly adjusted, thereby improving convergence performance of the algorithm. At the same time, in order to prevent the algorithm from falling into local optimal location, the strategy of linear differential decline is used to adjust the position change of particles. The experimental results illustrate that LDDFMOPSO has good performance compared to four state-of-the-art multi-objective particle swarm optimizations.
QuoteKG: A Multilingual Knowledge Graph of Quotes

Abstract Quotes of public figures can mark turning points in history. A quote can explain its originator’s actions, foreshadowing political or personal decisions and revealing character traits. Impactful quotes cross language barriers and influence the general population’s reaction to specific stances, always facing the risk of being misattributed or taken out of context. The provision of a cross-lingual knowledge graph of quotes that establishes the authenticity of quotes and their contexts is of great importance to allow the exploration of the lives of important people as well as topics from the perspective of what was actually said. In this paper, we present QuoteKG, the first multilingual knowledge graph of quotes. We propose the QuoteKG creation pipeline that extracts quotes from Wikiquote, a free and collaboratively created collection of quotes in many languages, and aligns different mentions of the same quote. QuoteKG includes nearly one million quotes in 55 languages, said by more than 69, 000 people of public interest across a wide range of topics. QuoteKG is publicly available and can be accessed via a SPARQL endpoint.
The Reality of Change Strategies and the Level of Coordination and Transparency in the Readiness of Charitable Institutions in Gaza Strip to Adopt and Implement Cloud Computing

The study aimed to identify the reality of change strategies and the level of coordination for the readiness of charitable institutions in Gaza Strip to adopt and apply cloud computing. The stratified random sample, where the study sample consisted of (294) employees, and in order to collect data, (300) questionnaires were distributed to the heads of boards of directors in addition to the executive directors as well as heads of departments, and (241) questionnaires were retrieved, or 82%. The study concluded that the change strategies axis scored (68.13%), while the coordination and transparency axis reached (82.02%), and in the same context, it was concluded that donors are looking forward to implementing cloud institutions at a rate of (85.63%). The study made a number of recommendations, the most important of which is the need to work on developing remedial plans to get rid of the obstacles that hinder the use of cloud computing. The study recommended the importance of institutions adopting clear and diverse strategies for institutional and organizational change. With regard to the application of cloud computing technology.