Reviewerly/exHarmony huggingface dataset:
My Buddy: A 3D Game for Children Based on Voice Commands

Mobile devices, as smartphones and tablets, have presented an exponential growth, being part of our everyday life, particularly considering children [1]. Their daily habits are undoubtedly influenced by technology and the applications they use can affect socialization and learning processes [2]. Specifically, games are the most popular type of applications and have the potential to change attitudes and behaviours. Emphasizing the importance of this area, we decided to create a serious game that stimulates the children’ responsibility for taking care of pets while they play, called “My Buddy”. In this paper, we present the development and assessment process of a 3D serious game, where the user is asked to interact with a pet and nurture it. The interface was developed based on the universal design philosophy, presenting itself attractive to children without disabilities, but also accessible to children with visual or motor disabilities. As such, we present a multimodal interface based on touch and speech commands. The game was tested in terms of usability, with a heuristic evaluation, and the results obtained highlight the potential of such interfaces.
Collaborative analysis of genomic data: vision and challenges

The cost of DNA sequencing has resulted in a surge of genetic data being utilised to improve scientific research, clinical procedures, and healthcare delivery in recent years. Since the human genome can uniquely identify an individual, this characteristic also raises security and privacy concerns. In order to balance the risks and benefits, governance mechanisms including regulatory and ethical controls have been established, which are prone to human errors and create hindrance for collaboration. Over the past decade, technological methods are also catching up that can support critical discoveries responsibly. In this paper, we explore regulations and ethical guidelines and propose our visions of secure/private genomic data storage/processing/sharing platforms. Then, we present some available techniques and a conceptual system model that can support our visions. Finally, we highlight the open issues that need further investigation.
When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs

We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.
Data Analytics and Visualization of Adaptive Collaboration Simulations

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.
RainGAN: Unsupervised Raindrop Removal via Decomposition and Composition

Adherent raindrops on windshield or camera lens may distort and occlude vision, causing issues for downstream machine vision perception. Most of the existing raindrop removal methods focus on learning the mapping from a raindrop image to its clean content by the paired raindrop-clean images. However, the paired real-world data is difficult to collect in practice. This paper presents a novel framework for raindrop removal that eliminates the need for paired training samples. Based on the assumption that a raindrop image is a composition of a clean image and raindrop style, the proposed framework decomposes a raindrop image into a clean content image and a raindrop-style latent code. Inversely, it composes a clean content image and a raindrop style code to a raindrop image for data augmentation. The proposed framework introduces a domain-invariant residual block to facilitate the identity mapping for the clean portion of the raindrop image. Extensive experiments on real-world raindrop datasets show that our network can achieve superior performance in raindrop removal to other unpaired image-to-image translation methods, even with comparable performance with state-of-the-art methods that require paired data.
Improving the Virtual Educational Platforms for the Deaf and Dumb under the Covid-19 Pandemic Circumstances

Arabic Sign Language (ArSL) is a language of communication between deaf &#x0026; dumb people in Arab countries. This study focuses on establishing a real-time speech conversion service into ArSL by 3D animation videos, to facilitate the learning process in virtual educational platforms. This study to help the deaf &#x0026; dumb students in distant learning under the Covid-19 pandemic. This study applied on the TEAMS platform. A database was created containing more than 550 ArSL videos from the Al-tarjuman application and connected it with the TEAMS platform. A Python was used to link the System units. After studying some related works that were concerned with the language of the deaf &#x0026; dumb, we found that some studies do not support real-time translation, has lack Arabic sign language translation, has limited DB, resulting in a low level of system performance, and use of high-cost devices such as gesture motion sensors &#x201C;Kinect and Leap Motion&#x201D;. As future work, we will increase the words in the DB to improve the results. We advise researchers to contribute by working on merging the videos retrieved from the DB to make an integrated serial video. Also, adding a syntactic analysis stage to extract the sentence structure.
RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

Abstract The worldwide frozen shrimp trade market in 2018 was recorded at the US $ 17.2 billion or around Rp.232.2 trillion. Indonesia is one of the principal exporters of frozen shrimp in the worldwide market. In light of this information, shrimp cultivating is a promising area, yet shrimp cultivating is particularly controlled by water quality. Water quality in shrimp cultivating consistently changes. Many variables impact changes in water quality (shrimp biomass, PH, and temperature). These components in the water should be inside the standard limit rank. Thusly, to accomplish the creation effectiveness of the shrimp business, it is important to robotize water quality control. This study aims to develop Surface Modeling Vehicles (SMV) for Shrimp Ponds utilizing an Artificial Neural Network. The test outcomes show an exactness pace of 94%, the expectation is that the instruments created by the exploration will want to acknowledge exact Vename Shrimp cultivating, so creation proficiency and expanded shrimp creation can be accomplished.
3rdParTEE: Securing Third-Party IoT Services Using the Trusted Execution Environment

Advancements in the Internet of Things (IoT) have resulted in the connection and deployment of numerous smart and embedded devices. Although such devices enable various services such as smart grids, they attract more attackers to the IoT world. A trusted execution environment (TEE), which can be created by using TrustZone technology, is a promising security artifact for protecting critical operations and sensitive data of IoT devices. Unfortunately, although TrustZone is available in most ARM architecture-based devices ranging from microcontrollers to high-end smart devices, it has not been widely adopted by third-party IoT service providers because of its limited access. That is, because the TEE is maintained by the TEE platform vendors to preserve its security. Therefore, third parties must adhere to strict policies and procedures to ensure the deployment of trusted services in the TEE. This aspect hinders the fast development and deployment of IoT services. In this work, we propose 3rdParTEE to address this problem by enabling third-party IoT service providers to readily run their trusted services, thereby minimizing their dependency on the TEE maintainers. Specifically, 3rdParTEE facilitates the secure running of the third-party’s native kernel driver in the TEE without hampering the security of the existing TEE components. To demonstrate the effectiveness of our approach, we ran three kernel drivers for maintaining the IoT services platform (e.g., kernel integrity check) in the TEE. Additionally, during the performance evaluation, we observed a reasonable performance overhead of up to 7% when running the kernel drivers in such a secure manner.
Video representation learning through prediction for online object detection

We present a video representation learning framework for real-time video object detection. Our approach is based on the interesting observation that a powerful prior knowledge of video context helps to improve object recognition, and it can be acquired via learning video representations through stochastic video prediction. Our proposed framework utilizes the stochastic video prediction into object detection so that we first acquire a prior knowledge of videos to have video representations and then adjust them to object detection to improve the accuracy. We validate our proposed method on ImageNet VID and VisDrone-VID2019 datasets to demonstrate the effectiveness of video representation learning via future video prediction. In particular, our extensive experiments on ImageNet VID show that our approach achieves 73.1% mAP at 54 fps with ResNet-50 on commercial GPUs
Improving Natural Language Processing tasks by Using Machine Learning Techniques

Natural language processing (NLP) techniques help in making computers understand the natural languages. On the other hand, machine learning (ML) algorithms allow computers to learn automatically from past experiences. Both NLP and ML are the subsets of artificial intelligence (AI). In this paper, we have presented the different levels in language analysis together with the challenges in natural language understanding. Also, we have presented ML techniques that we can use in NLP sub-problems such as part-of-speech tagging (POS), text-classification, disambiguation, syntactic and semantic analysis etc.
A Hebbian Approach to Non-Spatial Prelinguistic Reasoning

This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the potential of replicating observations of some cognitive experiments (particularly, those that provided some basis for sequential reasoning) while sharing the advantages already achieved by the previous proposals. In particular, we present Ring Model B, which is capable of associating visual with auditory stimulus, performing sequential predictions, and predicting reward from experience. Despite its simplicity, we considered such abilities to be a first step towards the formulation of more general models of prelinguistic reasoning.
Research on Alarm Correlation Analysis for EMU Train Based on Association Rule Mining Algorithm

With the increase of the scale and quantity of China’s EMU trains and the continuous improvement of the level of integrated intelligence, the application and maintenance system of EMU has accumulated a large number of fault warning information data. It is of great significance to reduce the scope of fault search and improve the efficiency of maintenance by using an efficient association rule mining algorithm to obtain the correlation knowledge among alarms from the data of fault alarm information. FP-Growth algorithm is one of the classical methods in association rule mining algorithm, which is used to mine frequent itemsets in the data set. To solve the problem of unequal importance proportion of fault alarm data items, a weight assigned FP-Growth algorithm was proposed, which combined the importance degree of fault alarm and calculated the support threshold value of transaction items, thus generating a conditional pattern base. The experimental results show that the weight assigned FP-Growth algorithm can effectively reduce the number of mining rules, and accelerate the algorithm running speed with the variation of support threshold, which will improve the efficiency of fault warning data mining of EMU.
Facilitating the Creativity of Governmental Employees via High-Involvement Human Resource Management Practices

The study examines some of the facilitators of governmental employees’ creativity by developing and testing a model linking perceived high-involvement human resource practices (HIHRPs) to an employee’s creativity (EC), via the mediating mechanism of an employee’s felt trust (FT). Online questionnaires were used to collect data from 241 staff members at a main governmental university in Jordan. Structural-equation modeling (SEM) via AMOS and the PROCESS macro in SPSS were used for data analysis. The study found: 1-HIHRPs as a bundle had a positive significant influence on EC, however, as discrete practices, only empowerment and competence development had significant relations with EC. 2-HIHRPs (as a bundle and as discrete practices) had positive significant influences on FT. 3-FT was positively and significantly related to EC. 4-FT mediated the relations between HIHRPs (as a bundle and as discrete practices) and EC.
Decision Support System for Photovoltaic Fault Detection Avoiding Meteorological Conditions

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on ordered weighted averaging operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of “constant loss of energy production”, without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energético de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, Cádiz, Spain, and good results have been obtained for faulting detection.
Safety Training Monitoring System of Ski Resort Based on Wireless Sensor Network

With the gradual rise in China’s ski industry, skiing is now the most common sport among winter outdoor sports, which is closely related to the origin of safety issues. Based on the ski resort, training is the key to safety detection, only to ensure the safety of training personnel and to maintain the momentum of high-speed development of skiing sports for a long time. The monitoring system plays an important role in the wireless sensor network monitoring system. Common monitoring systems are used for agriculture, smart home, and outdoor environment monitoring systems, most of which are professional fields. Based on the safety of ski resorts, this paper puts forward the research of ski training safety system based on the wireless sensor. In the research of ski training detection system based on the wireless sensor network, first, the safety system of domestic ski resort is analyzed and detailed suggestions are given. Then, this paper studies the routing protocol of wireless communication technology in detail and compares the service life and energy consumption of nodes under the use of two different routing protocols in wireless sensor networks. According to the data experiment in this paper, LEACH-MH protocol has great advantages over the LEACH protocol. The LEACH-MH protocol detects 185 more rounds than the LEACH protocol in the total detection lifetime. In the detection of energy consumption of 100 m ∗ 100 m, the LEACH protocol consumes 50J in 400 rounds, while the LEACH-MH protocol consumes only 50J in 760 rounds.
Analysis of Twitter data to Identify Intuitive Mental Health Well Being

The following paper describes a data-driven predictive approach that has been applied to analyze the mental soundness of the user through their Tweets. Social media platforms have gained immense popularity in the past decade and have active participants which share most of their daily activities on these platforms. Many of the platforms also share much of their personal information along with the data open to the public for research and other purposes. Datasets from different social media platforms are helpful in many fields like sociology and psychology. In this work, the number of tweets in which opinions are highly unstructured and are either positive or negative or sometimes neutral is being used for the analysis. Sentiment Analysis can be supported with several machine learning algorithms to classify the mental health of the user. Simulation results show the enhancement in sentiment analysis of the Twitter data for the intuitive mental wellness of a person. This paper highlights the methodology for mental health analysis for the intuitive well-being of the person. It uses sentiment analysis and its approach to analyze the mental well-being of Twitter users.
Into the Look: Security Issues, Crypto-Hygiene, and Future Direction of Blockchain and Cryptocurrency for Beginners in Malaysia

This paper presents a brief overview of blockchain and cryptocurrency for beginners in Malaysia. Cryptocurrency is an innovation for financial technology (also called FinTech) which has been innovated based on the blockchain technology. The presence of several properties provided by blockchain allows cryptocurrency to be traded and exchanged in a virtual environment; just like the common currencies used today. However, as the processes for exchanging and managing blockchain and cryptocurrency are carried out over the Internet, users are vulnerable to several cyber-attacks. Besides, the existence of cryptocurrency may lead to other crimes such as money-laundering and online gambling. Therefore, this paper discusses security issues and crimes related to cryptocurrency. Furthermore, this paper proposes an improved crypto-hygiene as the guideline to mitigate and minimize the risk of cyber-attack which may also assist beginners who wish to involve with cryptocurrency or any blockchain applications in gaining some insights and opinions regarding blockchain and cryptocurrency.
A Structure-Aware Adversarial Framework with the Keypoint Biorientation Field for Multiperson Pose Estimation

Human pose estimation is aimed at locating the anatomical parts or keypoints of the human body and is regarded as a core component in obtaining detailed human understanding in images or videos. However, the occlusion and overlap upon human bodies and complex backgrounds often result in implausible pose predictions. To address the problem, we propose a structure-aware adversarial framework, which combines cues of local joint interconnectivity and priors about the holistic structure of human bodies, achieving high-quality results for multiperson human pose estimation. Effective learning of such cues and priors is typically a challenge. The presented framework uses a nonparametric representation, which is referred to as the Keypoint Biorientation Field (KBOF), to learn orientation cues of joint interinteractivity in the image, just as human vision can explore geometric constraints of joint interconnectivity. Additionally, a module using multiscale feature representation with inflated convolution for joint heatmap detection and Keypoint Biorientation Field detection is applied in our framework to fully explore the local features of joint points and the bidirectional connectivity between them at the microscopic level. Finally, we employ improving generative adversarial networks which use KBOF and multiscale feature extraction that implicitly leverages the cues and priors about the structure of human bodies for global structural inference. The adversarial network enables our framework to combine information about the connections between local body joints at the microscopic level and the structural priors of the human body at the global level, thus enhancing the performance of our framework. The effectiveness and robustness of the network are evaluated on the task of human pose prediction in two widely used benchmark datasets, i.e., MPII and COCO datasets. Our approach outperforms the state-of-the-art methods, especially in the case of complex scenes. Our method achieves an improvement of 2.6% and 1.7% compared to the latest method on the MPII test set and COCO validation set, respectively.