Reviewerly/exHarmony huggingface dataset:
ReqGen: Keywords-Driven Software Requirements Generation

Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords’ (phrases’) inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.
Entity extraction based on the combination of information entropy and TF-IDF

Traditional knowledge graph entity extraction methods require expert knowledge and a large number of artificial features. Furthermore, deficiencies exist in the accuracy and efficiency of keyword extraction based on methods such as TF-IDF. Thus, this study proposes a Chinese entity extraction method based on the combination of information entropy and TF-IDF. First, the text is preprocessed, which involves operations such as sentence segmentation, word segmentation, removal of stop words, and POS tagging, to detect keywords based on POS. Secondly, the word frequency is analysed to determine feature word weight, and the TF-IDF algorithm is used to compare the importance of keywords. Finally, information entropy is used to improve the TF-IDF algorithm to provide entity knowledge for the construction of the knowledge graph. The entity extraction method and optimisation scheme proposed in this study can help users extract domain entities and provide better entity resources for the construction of knowledge graphs.
Privacy-protecting behaviours of risk detection in people with dementia using videos

Abstract Background People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others’ safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns. Purpose In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. Methods We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. Results We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. Conclusions This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community.
ICT Training Perception of Professionals in Functional Diversity in Granada

Training in Information and Communication Technologies (ICTs) has become very valuable in the current educational panorama, given the proliferation of digital resources available in the formal and non-formal education context. Fortunately, the field of attention to diversity has also benefited from it. For this reason, it is essential that the professionals who attend to people with functional diversity have a good attitude towards them, as well as training in accordance with their requirements. The aim of this study was to analyse how professionals in Granada (Spain) perceived their ICT training. A total of 404 specialists who worked in the field of attention to diversity were interviewed. In general, participants showed a favourable opinion towards ICT but they expressed a medium-low perception of their digital training. Nevertheless, these results are encouraging, as younger teachers showed a more favourable opinion and training towards ICT. In the long term, this will be a generation that is more educated and aware of the benefits, applicability and usefulness of these resources for working with people with functional diversity.
A Sustainable Approach to Delivering Programmable Peer-to-Peer Offline Payments

Payment apps and digital wallets are powerful tools used to exchange e-money via the internet. However, with the progressive disappearance of cash, there is a need for the digital equivalent of physical banknotes to guarantee the same level of anonymity of private payments. Few efforts to solve the double-spending problem exist in P2P payments (i.e., in avoiding the possibility of a payer retaining copies of digital coins in absence of a trusted third party (TTP)), and further research efforts are needed to explore options to preserve the privacy of payments, as per the mandates of numerous central bank digital currency (CBDC) exploratory initiatives, such as the digital euro. Moreover, generic programmability requirements and energetic impacts should be considered. In this paper, we present a sustainable offline P2P payment scheme to face the double-spending problem by means of a one-time program (OTP) approach. The approach consists of wiping the business logic out of a client’s app and allowing financial intermediaries to inject a certified payment code into the user’s device, which will execute (asynchronously and offline) at the time of payment. To do so, we wrap each coin in a program at the time of withdrawal. Then the program exploits the trusted execution environment (TEE) of modern smartphones to transfer itself from the payer to the payee via a direct IoT link. To confirm the validity of the approach, we performed qualitative and quantitative evaluations, specifically focusing on the energetic sustainability of the proposed scheme. Results show that our payment scheme is energetically sustainable as the current absorbed for sending one coin is, at most, ~1.8 mAh on an Apple smartphone. We advance the state-of-the-art because the scheme meets the programmability, anonymity, and sustainability requirements (at the same time).
Social Augmented Reality: Communicating via Cultural Heritage

Social communication around art and cultural heritage objects occurs mostly on location in the museums and between in-group acquaintances. Such discussions are often brief as competing array of displays and time constraints propel the social arrangement of groups of visitors forward to the next object. Discussions are more often than not fleeting and superficial, and rarely expand beyond the boundary of the group. We postulate that the interactivity afforded by augmented reality that allows the inspection of cultural heritage objects projected onto the real environment of users can be a better experience in terms of depth of viewing. We developed a mobile augmented reality application that allows multiuser interaction and propose that discussions that occur around cultural heritage objects can be replicated and that such discussions can be expanded beyond in-groups. We conducted hypotheses testing across 10 groups of 30 participants to investigate if aspects of social communication can be facilitated or impeded by the perceived realism or artificiality of objects and explore which features of Social AR are more important for communication. The research aims to understand how social communication would work for AR when co-viewing heritage objects. The results have implications for necessitating cross-border and cross-cultural exchanges in an increasingly virtually connected world.
Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study

Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models.In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86-0.87 at 5 years, 0.79-0.81 at 10 years) than using baseline or last observed CS data (0.80-0.86 at 5 years, 0.73-0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering.Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods.ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.
Deep Learning-Based Classification and Targeted Gene Alteration Prediction from Pleural Effusion Cell Block Whole-Slide Images

Cytopathological examination is one of the main examinations for pleural effusion, and especially for many patients with advanced cancer, pleural effusion is the only accessible specimen for establishing a pathological diagnosis. The lack of cytopathologists and the high cost of gene detection present opportunities for the application of deep learning. In this retrospective analysis, data representing 1321 consecutive cases of pleural effusion were collected. We trained and evaluated our deep learning model based on several tasks, including the diagnosis of benign and malignant pleural effusion, the identification of the primary location of common metastatic cancer from pleural effusion, and the prediction of genetic alterations associated with targeted therapy. We achieved good results in identifying benign and malignant pleural effusions (0.932 AUC (area under the ROC curve)) and the primary location of common metastatic cancer (0.910 AUC). In addition, we analyzed ten genes related to targeted therapy in specimens and used them to train the model regarding four alteration statuses, which also yielded reasonable results (0.869 AUC for ALK fusion, 0.804 AUC for KRAS mutation, 0.644 AUC for EGFR mutation and 0.774 AUC for NONE alteration). Our research shows the feasibility and benefits of deep learning to assist in cytopathological diagnosis in clinical settings.
Improved YOLOv7 Model for Insulator Surface Defect Detection

Aiming at the problems of insufficient expression ability and imperfect positioning loss function of YOLOv7 model in the single-stage detection network, an improved YOLOv7 model for insulator surface defect detection is proposed. The new method uses the Mish activation function to replace the original activation function, which further improves the expression ability of the network. The Omni-Dimensional dynamic convolution (ODConv) is used to replace some conventional convolutions in the original model, and multiple convolution cores are fused according to the input, considering the attentions of multiple dimensions, and the network structure is optimized. Using SIoU to measure the loss, the angle term is added to the previous loss function to speed up the convergence of the model. In addition, we use a Copy-Paste data augmentation approach for the problem that there are not enough negative samples of insulator defects. Finally, the proposed method improves the detection accuracy of the network for insulator surface defects. Experimental results show that the improved YOLOv7 model has good detection performance, and the mAP on the insulator dataset reaches 88.7%, which achieves the highest score compared with the original YOLOv7 model and a series of classical mainstream algorithms.
Asymmetric Self-Supervised Graph Neural Networks

Although self-supervised learning (SSL) has been successfully applied to graph data using graph neural networks (GNNs), most of the existing methods only consider undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth between two connected nodes). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed graphs where information can only be passed along one direction. For example, a directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. To accommodate such an asymmetric structure of directed graphs, we propose a simple yet remarkably effective SSL framework for directed graph analysis to incorporate such one-way information passing. We define an incoming embedding and an outgoing embedding for each node to model its schemes of sending and receiving features respectively. We propose an auxiliary SSL task to predict the existence of the directed edges with the incoming and outgoing embeddings of nodes. The auxiliary SSL task is jointly trained with a downstream primary task that updates nodes’ incoming features and outgoing features in accordance with labels. Extensive experiments on multiple real-world directed graph datasets demonstrate outstanding performances of the proposed self-supervised GNNs in both node-level and graph-level tasks.
Pinpointing Factors in the Success of Integrated Information System Toward Open Government Data Initiative: A Perspective from Employees

As the Supervisory Institution in Statistics, Badan Pusat Statistik (BPS) launched an integrated information system (IS) to exercise the Open Government Data (OGD) initiative and to impose the One Data Policy Act.Albeit challenges arise, BPS manages to provide more than 120 thousand publicly accessible datasets.With the success of OGD, many scholars have opted to examine a similar issue from the perspective of users/citizens.However, employees' perspective remains substantial as employees are the OGD provider.This research administers employees' views to pinpoint influencing factors in the success of OGD adoption through an IS.The authors seek to comprehend the factors from IS and acceptance manner, thus integrating the Information System Success Model (ISSM) and Unified Theory of Acceptance and Use of Technology (UTAUT) as the measurement model.This study also administers a crosssectional questionnaire with close-ended questions to obtain data from 253 IS users in BPS.Using structural equation modelling (SEM), the authors find that all ISSM constructs influence the success of IS while only one construct from UTAUT plays a pivotal role in defining the success.Information Quality, System Quality, Service Quality, User Satisfaction, and System Use remain paramount to the successful implementation, while Performance Expectancy becomes the sole influencing UTAUT factor affecting success.This study therefore offers substantial benefits by aiding other researchers in OGD-related areas and providing in-depth evidence for practitioners in implementing IS for OGD initiatives.
Food volume estimation by multi-layer superpixel

&lt;abstract&gt; &lt;p&gt;Estimating the volume of food plays an important role in diet monitoring. However, it is difficult to perform this estimation automatically and accurately. A new method based on the multi-layer superpixel technique is proposed in this paper to avoid tedious human-computer interaction and improve estimation accuracy. Our method includes the following steps: 1) obtain a pair of food images along with the depth information using a stereo camera; 2) reconstruct the plate plane from the disparity map; 3) warp the input image and the disparity map to form a new direction of view parallel to the plate plane; 4) cut the warped image into a series of slices according to the depth information and estimate the occluded part of the food; and 5) rescale superpixels for each slice and estimate the food volume by accumulating all available slices in the segmented food region. Through a combination of image data and disparity map, the influences of noise and visual error in existing interactive food volume estimation methods are reduced, and the estimation accuracy is improved. Our experiments show that our method is effective, accurate and convenient, providing a new tool for promoting a balanced diet and maintaining health.&lt;/p&gt; &lt;/abstract&gt;
SAR Ship Targets Detection Based on Correlation Filters

In previous target detection research, most algorithms focus on the targets in optical images, which is different from SAR images in terms of feature information and dataset. For the dataset of SAR ships targets with small samples, we proposed a correlation filter algorithm that can achieve robust detection under small sample and high false alarm rate. The algorithm consists of two parts, coarse CFAR detection and false alarm elimination. Inspired by the filter method of the kernelized correlation filters (KCF) algorithm, we propose a false alarms elimination algorithm that can distinguish false alarms by comparing the correlation maximum values between the detection results and the filter obtained through template tensor. An experiment is conducted on our SAR ship dataset and the results demonstrate the effectiveness of our algorithm.
Perceptions of COVID-19 related risks by platform-based couriers: An analysis of user comment threads on Reddit

BACKGROUND: The COVID-19 pandemic increased demand for app-based platform-based couriers, creating job opportunities for individuals who have lost income because of COVID-19. Through various stages of lockdown, courier workers (e.g., delivering for Uber Eats, Amazon Flex, and Lyft) provide an essential service. At the same time, this form of work poses risks for exposure to the SARS-CoV-2 virus as these workers are highly mobile and in contact with many individuals. OBJECTIVE: To explore how platform-based couriers discuss risks associated with their work during periods of high (first wave, second wave, third wave/rise in concerns regarding variants) and low risk during the COVID-19 pandemic, 2020–2021. METHODS: We provide a narrative analysis of user posts (n = 2,866) on Reddit during periods of interest. RESULTS: Our analysis resulted in three central findings. First, we identified changing patterns in discourse as the pandemic went on. Second, we found that the theme of risk prevailed largely in the first wave, with dialogue dominated by tips and asking for advice about how to manage risk. Third, our findings reveal a growing polarization among users during the latter phases of the study. CONCLUSION: Polarization largely focused on acceptance (or not) of public health measures and the nature of their work as independent contractors and the role/responsibility of courier companies to offer protection. Our study is the first to document risks, from the perspectives of anonymous couriers who may be unwilling to share their honest opinions and thoughts through primary data collection where anonymity is not guaranteed.
On strange images with application to lossy image compression

Single and three-channel images are widely used in numerous applications. Due to the increasing volume of such data, they must be compressed where lossy compression offers more opportunities. Usually, it is supposed that, for a given image, a larger compression ratio leads to worse quality of the compressed image according to all quality metrics. This is true for most practical cases. However, it has been found recently that images are called “strange” for which a rate-distortion curve like dependence of the peak signal-to-noise ratio on the quality factor or quantization step, behaves non-monotonously. This might cause problems in the lossy compression of images. Thus, the basic subject of this paper are the factors that determine this phenomenon. The main among them are artificial origin of an image, possible presence of large homogeneous regions, specific behavior of image histograms. The main goal of this paper is to consider and explain the peculiarities of the lossy compression of strange images. The tasks of this paper are to provide definitions of strange images and to check whether non-monotonicity of rate-distortion curves occurs for different coders and metrics. One more task is to put ideas and methodology forward of further studies intended to detect strange images before their compression. The main result is that non-monotonous behavior can be observed for the same image for several quality metrics and coders. This means that not the coder but image properties determine the probability of an image to being strange. Moreover, both grayscale and color images can be strange, and both the natural scene and artificial images can be strange. This depends more on image properties than on image origin and number of channels. In particular, the percentage of pixels that belong to large homogeneous regions and image entropy play an important role. As conclusions, we outline possible directions of future research that, in the first order, relate to the analysis of images in large databases to establish parameters that show that a given image can be considered as strange.