Reviewerly/exHarmony huggingface dataset:
Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a dishonest majority of users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provide empirical evaluation in this paper.
Emojis and gestures: a new typology

This paper addresses the question of how emojis are integrated into the text that they occur with. I use the typology of gestural iconic enrichments proposed by Schlenker (2018a, 2018b) to investigate the hypothesis that emojis denoting objects (e.g., 🎈 ) and activities (e.g., 💃) project (i.e., interact with logical operators) when co-occurring with text in a similar way as gestures do with speech. In particular, I claim that [i.] emojis generate co-suppositions, i.e., assertion-dependent presuppositions, when immediately following text (e.g., the sentence “John didn’t train today 🏋” gives rise to the inference that “if John had trained today, weightlifting would have been involved”); [ii.] emojis generate supplements (in the sense of Potts, 2005), just like appositive relative clauses, when they are separated from the accompanying text by a pause (e.g., the sentence “John trained today…🏋” gives rise to theinference that “John trained today, which involved weightlifting”); [iii.] when used as replacements for words (e.g., “Yesterday, John didn’t 🏋 ”), emojis have an at-issue semanticsand can trigger standard presuppositions.
Development of AHMES (Automatical Higher Mathematics Examination System) Using Rational Unified Process

This study was aimed at developing an automatic exam system as an intelligent and high-quality higher math examination solution for Department of Computer Engineering at Pai Chai University (PCU) based on S/W Engineering. As a research and development (R&amp;D) project, the study used the rational unified process (RUP) method for software development. RUP describes how to effectively use commercial and reliable methods to develop and deploy software system. It is a heavyweight process; therefore, it is particularly suitable for large teams to develop large projects. This paper describes the RUP process of this R&amp;D project what we named it as Automatic Higher Mathematically Exam System (AHMES). AHMES provides a new way to automatically generate exam questions. In the study, our team and the requesting department collected the requirements and selected methods and tools. Then, our team designed and implemented the framework of the system, completed the development of some main functions, tested and summarized the system, and planned the future work.
Use of Videography for Traffic Surveys in Sri Lanka

Traffic Surveys are crucial for different transport studies like Origin-Destination studies, Traffic Volume estimations, Vehicle Flow characteristics Determinations, Speed and Delay Studies, Turning Movement analysis, Parking Analysis etc. Currently, Sri Lankan researchers are using mostly human-based manual surveys and semi-automated methods for Traffic Surveys. However, there is an issue in cost, effort, the value of time, and the accuracy of data gathering in the above methods. Therefore, under Accelerating Higher Education Expansion and Development (AHEAD) grant, we are researching the possibility of doing surveys using videography and Artificial Intelligence technologies for continuous and accurate data collection to reduce the burden that is currently facing. Using CCTV video cameras, we have done surveys in different locations in Western Province to check
Subspace Enhancement and Colorization Network for Infrared Video Action Recognition

Human action recognition is an essential area of research in the field of computer vision. However, existing methods ignore the essence of infrared image spectral imaging. Compared with the visible modality with all three channels, the infrared modality with approximate single-channel pays more attention to the lightness contrast and loses the channel information. Therefore, we explore channel duplication and tend to investigate more appropriate feature presentations. We propose a subspace enhancement and colorization network (S\(^2\)ECNet) to recognize infrared video action recognition. Specifically, we apply the subspace enhancement (S\(^2\)E) module to promote edge contour extraction with subspace. Meanwhile, a subspace colorization (S\(^2\)C) module is utilized for better completing missing semantic information. What is more, the optical flow provides effective supplements for temporal information. Experiments conducted on the infrared action recognition dataset InfAR demonstrates the competitiveness of the proposed method compared with the state-of-the-arts.
Listening Together while Apart: Intergenerational Music Listening

Collaborative music listening can support intergenerational connectedness. We conducted an environmental scan of video conferencing and music listening platforms and cognitive walkthroughs to investigate how these platforms can support collaborative music listening and conversation between a grandparent and teen grandchild. Our results indicate that common video conferencing platforms not only lack a convenient way to share music, but typically block audio from music listening apps played on the same device, preventing sharing. Further, while music streaming platforms enable connected friends to share songs and others’ playlists, they do not provide a means for smooth synchronous conversing about the music. Thus, this highlights opportunities for technology improvement to support intergenerational relationships by enabling distributed, intergenerational co-listening of music.
An Efficient Reversible Algorithm for Linear Regression

This paper presents an efficient reversible algorithm for linear regression, both with and without ridge regression. Our reversible algorithm matches the asymptotic time and space complexity of standard irreversible algorithms for this problem. Needed for this result is the expansion of the analysis of efficient reversible matrix multiplication to rectangular matrices and matrix inversion.
Construction of Rural Left-Behind Children’s Mental Health Mobile Information System Based on the Internet of Things

Left-behind children, as a special phenomenon group stationed in rural areas, lack parents’ education and care for a long time and shoulder the burden of life early. Moreover, in rural areas with relatively closed information, their communication with their parents only relies on short-term telephone contact. If things go on like this, it may lead to mental health problems in children. In recent years, the group of left-behind children began to get the attention of the society. The social people want to help the left-behind children mainly through the information provided by the school, which cannot actually understand the real situation of the left-behind children, and the help to the left-behind children is only a drop in the bucket. Therefore, it is necessary to use the internet as a convenient and fast platform to build a mobile information system for the mental health of rural left-behind children, input the mental health of left-behind children, and pay attention to and track the left-behind children. This paper mainly studies the construction of the rural left-behind children’s mental health mobile information system based on the Internet of Things. This paper expounds the related concepts of the Internet of Things, which has a good connection effect on the construction of the left-behind children’s mental health mobile information system. Then, it analyzes the functional requirements of the rural left-behind children’s mental health mobile information system, in terms of design, the C/S model is used, the database in the data server is designed to analyze the mental health information management needs of left-behind children, and the data model is established by defining the key domains in the system. This paper also collects and sorts out the left-behind children’s mental health data through data mining technology, studies the factors affecting the left-behind children’s mental health, and clarifies the necessity of constructing the rural left-behind children’s mental health mobile information system and focuses on the observation objects. The results show that the evaluation factors of left-behind children’s mental health are significantly higher than those of non-left-behind children, and their mental health needs attention because left-behind children lack the care of their parents for a long time. The mental health status of left-behind children aged 7–12 is the most worrying, which is significantly different from other age groups in obsessive-compulsive disorder, interpersonal sensitivity, anxiety, hostility, paranoia, and mental illness. It may be because left-behind children aged 7–12 are in the development stage, they are not as ignorant as left-behind children aged 1–6, and they are not as mature as left-behind children aged 13–17.
PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices

The better accuracy and efficiency trade-off has been a challenging problem in object detection. In this work, we are dedicated to studying key optimizations and neural network architecture choices for object detection to improve accuracy and efficiency. We investigate the applicability of the anchor-free strategy on lightweight object detection models. We enhance the backbone structure and design the lightweight structure of the neck, which improves the feature extraction ability of the network. We improve label assignment strategy and loss function to make training more stable and efficient. Through these optimizations, we create a new family of real-time object detectors, named PP-PicoDet, which achieves superior performance on object detection for mobile devices. Our models achieve better trade-offs between accuracy and latency compared to other popular models. PicoDet-S with only 0.99M parameters achieves 30.6% mAP, which is an absolute 4.8% improvement in mAP while reducing mobile CPU inference latency by 55% compared to YOLOX-Nano, and is an absolute 7.1% improvement in mAP compared to NanoDet. It reaches 123 FPS (150 FPS using Paddle Lite) on mobile ARM CPU when the input size is 320. PicoDet-L with only 3.3M parameters achieves 40.9% mAP, which is an absolute 3.7% improvement in mAP and 44% faster than YOLOv5s. As shown in Figure 1, our models far outperform the state-of-the-art results for lightweight object detection. Code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection.
Predicting ICD-9 Codes Using Self-Report of Patients

The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.
AI based chatbot in food industry

Chatbot is a computer application that cooperates with clients utilizing regular language processing. Much of the time, it can give excellent client driven proposals. Therefore, Chabot is turning into a necessary part for the upcoming years of customer services. Now-a-days, chatbots are becoming more popular in fields like e-commerce, travel and tourism, universities, personal assistants etc., One of the main problems that food industries facing is that customers could not able to get their updated details of the food products. Usage of search engines may solve their queries but it takes a lot of search time and sometimes the answer for the queries will not be available. Additionally, web indexes are not as adaptable as chatbot in providing one to one correspondence inquiry. The objective of this paper is to develop an Artificial Intelligence (AI) where each activity can be thought to be a sure inquiry that is given to the chatbot, it responds to the user's query by applying group examination on a bunch of product related data. The outcome, at that point, is taken care of into Chabot's information base. This chatbot will help users to get data on which potential destinations they need to visit ideally on the off chance that they are under restricted time-opening requirements.
Laser in wounds: knowledge translation to an effective and innovative nursing practice

To describe the knowledge translation obtained through research of low-level laser therapy to wound treatment in the clinical practice.Experience report about implementation strategies of this new technology in a university hospital, being an extension course the main one, developed between May and December 2019.The course was carried out by researchers in partnership with clinical nurses to 13 nurses from different areas with previous experience in wound care. Were taught 30 theoretical-practical class hours, part of them supervised in a real clinical setting, besides case studies. From that, an exclusive nursing consultation schedule of laser therapy was created at the outpatient and its use was also improved to patients admitted in several units of the hospital.The knowledge translation developed about laser therapy allowed its implementation as a technology in wound treatment, qualifying the professional practice and benefiting the treatment of patients with wounds.
Calculating and Analyzing Angular Head Jerk in Augmented and Virtual Reality: Effect of AR Cue Design on Angular Jerk

In this work, we propose a convenient method for evaluating levels of angular jerk in augmented reality (AR) and virtual reality (VR). Jerk is a rarely analyzed metric in usability studies, although it can be measured and calculated easily with most head-worn displays and can yield highly relevant information to designers. Here, we developed and implemented a system capable of calculating and analyzing jerk in real-time based on orientation data from an off-the-shelf head-worn display. An experiment was then carried out to determine whether the presence of AR user interface annotations results in changes to users’ angular head jerk when conducting a time-pressured visual search task. Analysis of the data indicates that a decrease in jerk is significantly associated with the use of AR augmentations. As noted in the limitations section, however, the conclusions drawn from this work should be limited, as this analysis method is novel in the VR/AR space and because of methodological limitations that limited the reliability of the jerk data. The work presented herein considerably facilitates the use of jerk as a quick component measure of usability and serves as an initial point off which future research involving jerk in VR and AR can be performed.
Social Interactions in Virtual Reality: What Cues Do People Use Most and How

Social virtual reality (VR) has become increasingly popular. People today can leverage VR to meet and interact with others, especially when face-to-face (F2F) social events are not as practical. While strategies and cues being used in F2F interactions have been previously studied in CSCW, it remains unclear how people use various cues to interact with others in VR-based events, and whether the usage of different cues in such VR events differs from F2F interactions. To start addressing these questions, we conducted an online survey study, inviting VR event attendees to recall their recent social interaction experiences and cues usage in initiating, maintaining, and finishing interactions with event co-attendees in VR and F2F situations respectively (N=51). Results from the survey revealed differential cues usage in VR-based and in F2F interactions, which could shed light on the design of VR tools toward supporting online social events.
Object and lane detection for autonomous vehicle using YOLO V3 algorithm

To detect the objects around an autonomous vehicle is very essential to operate safely. This paper presents to detect and classify the objects for assisting autonomous driving. In autonomous driving systems, the task of object detection itself is one of the most important prerequisites to autonomous navigation. Deep learning one of the computer vision tasks, perform object detection very effectively than compared to earlier methods and this project is to detect the objects like vehicles, persons, traffic lights, etc. In this work, an approach to object detection in deep learning that makes the bounding box for an image to predict is explored. Object detection is the method of detecting the objects present in a given image. Apart from detecting the number of objects present in an image it also specifies in which location that object is present in the image. The objects are detected by means of bounding boxes. In the existing system algorithm like Convolutional Neural Network (CNN) using Resnet-50 were used to detect the objects like vehicles, persons, traffic lights separately. The problem identified here is, in the existing system the camera is fixed in a particular place and it detects objects only if the objects come into the camera frames. It is not detecting both objects and lanes simultaneously when the autonomous vehicle is in motion at any location. To overcome these problems, in the proposed system object detection is performed by mounting camera in front of the moving vehicle. You Only Look Once (YOLO) V3 Algorithm is used for the process of object detection. Compared to earlier detection approaches YOLO V3 shows improvement in detection accuracy. It provides good feature extraction and detection in large-scale. The proposed YOLO al gorithm has better average precision value for detecting all objects than compared to existing CNN using Resnet-50. In addition YOLO V3 algorithm performs lane detection apart from object detection.
MappyLand: Fast, Accurate Mapping for Console Games

We present MappyLand, a rewrite and enhancement of the earlier Mappy automatic game mapping system, which leverages instrumentation of a game console emulator to produce, from a sequence of game inputs, accurate annotations for action/adventure games including object detection and tracking, in-game camera movement, grid-based tile maps, and links between identified disparate spaces. The overhead of generating these annotations is on the order of one millisecond per observed frame. We also show a higher latency (but still online) algorithm for merging together previous observations of distinct game maps for the purposes of agent localization across a long period of time. Specifically, our system can determine a consistent graph of game rooms from a set of strings of game rooms, capturing behaviors like backtracking and synthesizing observations from multiple play sessions. Altogether, this fast, accurate approach to mapping yields new and useful knowledge representations and expedient ways to produce new datasets given just a game and some example play.
Detection of Cyberbullying on Social Media Using Machine learning

Abstract: Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Threemethods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it givesaccuracies above 80%. Keywords: Cyberbullying, Hate speech, Personal attacks,Machine learning, Feature extraction, Twitter, Wikipedia
The Use of Covid-19 Digital Applications and Unavoidable Threats to the Protection of Health Data and Privacy

Abstract This paper starts with a dilemma. How to ensure the adequate protection of individual health data and privacy in a global pandemic, which has intensified the use of digital applications for the purposes of data sharing and contact-tracing? There is no simple answer to this question when choosing between the protection of public health and individual privacy. However, the history of the existing case-law regarding infectious diseases control, both Polish and European, teaches about numerous examples in which health data and privacy were not adequately protected, but, on the contrary, were misused leading to human rights infringements. In light of this case law and public health ethics, this paper argues radically that the use of digital applications to fight the Covid-19 pandemic has not been sufficiently justified at least in the Polish context. Especially, unconvincing benefits from the use of these tools do not outweigh the likelihood of human rights infringements with far-reaching consequences for political, social and economic rights now and in the future. In its novelty, this article combines a historical-legal method with the concept of public health ethics and a human rights-based approach and to foster further research and discussion. The text also responds to the pressing need to analyze those human rights issues embedded in the Polish reality.