Reviewerly/exHarmony huggingface dataset:
Adaptive Hardness Indicator Softmax for Deep Face Recognition

Due to their simplicity and efficiency, the margin-based Softmax losses are proposed to enhance feature discrimination in face recognition. Recently, the strategy of hard sample mining is incorporated to the margin-based Softmax losses for focusing the misclassified samples and achieves superior performance. However, the current mining-based Softmax losses indicate the sample difficultness only from the perspective of the negative cosine similarity, which is local and not robust. To obtain more discriminative deep face features, a novel adaptive hardness indicator Softmax (AHI-Softmax) loss is proposed in this paper to fully exploit the hardness information of samples. Our AHI-Softmax firstly defines a global sample hardness indicator function that integrates three difficultness factors to robustly indicate the level of “hardness” in numerical form. Then, a training stage indicator is incorporated to avoid the convergence issue. Finally, a novel sample-related modulation coefficient of the negative cosine similarity which combines the global and local hardness indicator will be defined to further enhance the differentiation of constraints imposed on samples. The experimental results on general face datasets, including LFW, AgeDB-30, CFP-FP, CALFW, CPLFW, MegaFace, IJB-B and IJB-C, show that our method can obtain more discriminative features and achieve superior verification and recognition results.
FlowNetU: Accurate Uncertainty Estimation of Optical Flow for Video Object Detection

Video object detection (VOD) is a challenging task to resolve ambiguities owing to various issues such as motion blur and occlusion. Although various types of ambiguities will take place per pixels in an image, flow fields make equal contributions for VOD across the image. This may increase false positive (FP) results. In this paper, we propose a method that utilizes motion uncertainty for VOD. The trained optical flow estimation model helps detector to suppress unreliable flow fields in order to avoid misaggregation which causes mislocalization. Our proposed method improves mean average precision by 1.27% and decreases the FP rate by 10.59%. This verifies that utilizing motion uncertainty for video recognition tasks is very effective.
Generating Product Description with Generative Pre-trained Transformer 2

Research on automatically generating descriptions for e-commerce products is gaining increasing attention in recent years. However, the generated descriptions of their systems are often less informative and attractive because of lacking training datasets or the limitation of these approaches, which often use templates or statistical methods. In this paper, we explore a method to generate production descriptions by using the GPT-2 model. In addition, we apply text paraphrasing and task-adaptive pretraining techniques to improve the quality of descriptions generated from the GPT-2 model. Experiment results show that our models outperform the baseline model through automatic evaluation and human evaluation. Especially, our methods achieve a promising result not only on the seen test set but also in the unseen test set.
A Deep Generative Model for Reordering Adjacency Matrices

Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph.
Flexible parameter selection methods for Rician noise removal with convergence guarantee

Restoring images corrupted by Rician noise is a challenging issue in the field of medical image processing. In the existing variational methods, there is a balancing parameter between the regularization term and the fidelity term. However, it is very hard to find the optimal parameter. In this paper, we study the total variation-based Rician noise removal model with spatially varying parameters. We propose flexible and automatic parameter selection strategies to balance the regularization extent between different kinds of image regions. A modified alternating direction method of multipliers is derived to solve the non-convex model efficiently. Theoretically, we prove that if a selection strategy satisfies some reasonable conditions, the convergence of the proposed algorithm is guaranteed. Numerical results demonstrate that the proposed method with automatic parameter selection can better preserve the structures and fine textures than other closely related methods.
A Group Decision Description Language and its Clinical Application

Group decisions are crucial in solving complex problems successfully across many socio-technical contexts. The development of distributed group decision systems are problem-specific, literally with no systematic support and so a high level of variability in terms of their effects. There is a lack of generic and comprehensive manner that can support the description of the actual decision-making processes precisely. In this paper, we propose a group decision description language. The language consists of the constructs of agent, protocol, decision rule, and constraint for representing group decisions generically, flexibly, while maintaining local autonomy. A protocol of Triple Assessment for breast cancer has been used as an example to demonstrate its application. The language has been further applied to a Consensus Protocol and a Coordinator Election Protocol to demonstrate its expressiveness.
Design of 3D Exhibition Hall System of Art Museum Based On Virtual Reality

The existing system has the problem of imperfect three-dimensional display model, resulting in excessive CPU occupation. A 3D exhibition hall system of art museum based on virtual reality is designed. The hardware part: adopt different connection modes to connect the host, bus and reader with the line interface. The software part: in this paper, a 3D exhibition hall browsing interface is built on the basis of hardware, and the works are converted into pictures and displayed in the virtual scene. We choose distance as the similarity evaluation index, and use virtual reality technology to design system module functions. Experimental results: the average CPU occupancy of the 3D exhibition hall system of the Art Museum designed in this paper and the other two systems are 28.426%, 50.139% and 50.759% respectively, which proves that the 3D exhibition hall system of the art museum integrated with virtual reality technology has better performance.
Multilabel Convolutional Network With Feature Denoising and Details Supplement

In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.
Understanding User Requirements for Self-Created IoT Health Assistant Systems

Over recent decades, with the fast growth of urbanization, new health-related issues have resulted from the changed lifestyles. People have become more sedentary. They suffer from fully-packed calendars, which does not give them enough time to care for their physical and psychological well-being. While this is a growing challenge, advancements made in internet of things (IoT) devices in the private context offer the chance to develop applications that tackle these issues. While many such applications have been envisioned, the user’s views and requirements for them remain unclear. To better understand this, we conducted a literature review in which we identified the four most common health-related scenarios in which such technologies are used. Next, we conducted an online survey (N=80), in which participants envisioned how smart home devices could realize these scenarios. Based on the results, we derive design implications that show that users value a high degree of customization and the ability to select a specific automation level through an all-in-one, easy-to-use platform.
Auto-generating Textual Data Stories Using Data Science Pipelines

Understanding a dataset directly is challenging but transforming the results of data analysis into data stories could help people build mental models and understand the dataset easily. In this paper, we present a new framework for data-to-text NLG to generate data stories for specific personas. In order to understand the feasibility of this method and if the human generated story is consistent with the story generated by the data science pipelines, we present two experiments: a data story study with 3 financial experts, 4 Ph.D. students, and 20 Amazon Mechanical Turk workers, which offers several data stories generated by humans; and a validation study involving 39 Amazon Mechanical Turk workers who conducted usability and understandability assessments for 9 high-quality data stories, written by humans and machine. We conduct a qualitative analysis of human-written data stories to determine what people consider when writing data stories and if the human generated story is consistent with the one generated by the data science pipeline. The experimental results show that readers comprehend machine-written data stories as well as they comprehend human-written data stories.
Smart Technologies for Monitoring Older Adults with Dementia

Nowadays, the elderly population has increased considerably as well as health problems related to old age, such as dementia. Consequently, there is a need for constant monitoring, assistance and support provided by caregivers in people’s home causing high economic costs and a shortage of healthcare professionals. Informal caregivers, usually relatives, provide many caregiving services although they might suffer of stress and burden for being aware 24/7. This has motivated the development of systems based in new technologies such as Internet of Things, Cloud Services and Machine learning. In this work, we propose a system architecture based on these new technologies along with a dashboard prototype for monitoring older adults with dementia through a video, sound and map visualization. Hence, we obtained GPS, audio and video data for future behavior analysis in order to provide as well, notification services to caregivers to inform them about the status of people with dementia.
Complementary Methods of IoT Reliability for Embedding IoT Devices into SDP

With the progress of digital transformation, Software Defined Perimeter (SDP), a zero-trust model developed by the Cloud Security Alliance, is attracting attention. SDP has been considered as a zero-trust model against the increase of internal fraud and other problems in traditional perimeter networks. In SDP, the SDP controller authenticates and authorizes the connection between the user and the service for each service provision, thereby ensuring a secure state at any time. At the same time, as the IoT progresses, it is expected to function as a user-side terminal for SDP. In general, it is difficult to ensure the security of the IoT itself due to the lack of resources such as CPU power and storage in the IoT. When the IoT is incorporated as a user terminal of SDP, it is essential to ensure the reliability of the IoT, but such research is not sufficient. In this paper, we describe a method for incorporating IoT into SDP usage terminals. Specifically, we discussed the use of IoT gateways, client certificates, and IdPs as methods to compensate for the lack of IoT resources. As a result of qualitative evaluation by desk evaluation, the method using IdP was found to be the most effective.
Prediction of bug‐fixing time based on distinguishable sequences fusion in open source software

Abstract Generally, open source software (OSS) has a longer bug‐fixing time. If the bug‐fixing time can be predicted accurately as early as possible, it will be beneficial to the efficiency of bug fixing. Traditional bug‐fixing time prediction models are usually based on static features of bug report. It is difficult to go into service due to inappropriate feature extraction of data and low prediction accuracy of models. The HMM prediction model can predict the bug‐fixing time accurately according to earlier fixing activities. However, this method of temporal sequence feature selection results in a large number of inconsistent samples, and the HMM prediction model can only capture the adjacent activity behavior information of one sequence, and hence, it will reduce the performance of bug‐fixing time prediction. By incorporating the activity information and time information of bug activity transfer, the proportion of inconsistent samples is reduced significantly. In this paper, a double‐sequence input LSTM model (LSTM‐DA) is designed to capture both sequences interaction features and long‐distance‐dependent features. The results of the experiments show that the proposed model can improve the F‐measure and accuracy indicators by about 10% compared with the HMM model in all dimensions, which demonstrates the effectiveness of our method.
How health-related messaging increase intentions to download and use mobile contact (COVID-19) tracing apps: Preliminary findings

This study contributes to current discussions regarding the use of digital solutions and especially (mobile) contact tracingcontact-tracing apps (MCTA) in COVID-19 containment. This study is timely because several countries around the world andworld, including African countriescountries, face an acute shortage of COVID-19 vaccines and as a result complimentary measures and innovative solutions like MCTA can be useful in the containment of COVID-19 disease pandemic. Towardsd this end, the current study integrates the health belief model (HBM) with the theory of reasoned action (TRA) to investigate MCTA acceptability based on data collected from online respondents in Nigeria. The results of the empirical analyses, using PLS-SEM, indicate that perceived disease vulnerability, perceived severity of the disease, but but not perceived barriers ofto taking action, are important determinants of attitude towardsd MCTA. The results further show that, while attitude is positively related to MCTA adoption intention, both perceived disease vulnerability and perceived severity of the disease indirectly contribute to MCTA adoption intention through attitude towardsd MCTA. Overall, the proposed research model explains about 58.8% variation in the intention to adopt MCTA and therefore shines a positive light on the topic that is critical for shaping COVID-19 messaging in different countries of the world and especially in African countries where COVID-19 testing and vaccination drive remain worryingly slow.
Single-Head Lifelong Learning Based on Distilling Knowledge

Within the machine learning field, the main purpose of lifelong learning, also known as continuous learning, is to enable neural networks to learn continuously, as humans do. Lifelong learning accumulates the knowledge learned from previous tasks and transfers it to support the neural network in future tasks. This technique not only avoids the catastrophic forgetting problem with previous tasks when training new tasks, but also makes the model more robust with the temporal evolution. Motivated by the recent intervention of the lifelong learning technique, this paper presents a novel feature-based knowledge distillation method that differs from the existing methods of knowledge distillation in lifelong learning. Specifically, our proposed method utilizes the features from intermediate layers and compresses them in a unique way that involves global average pooling and fully connected layers. The authors then use the output of this branch network to deliver information from previous tasks to the model in the future. Extensive experiments show that our proposed model consistency outperforms the state-of-the-art baselines with the accuracy metric by at least two percent improvement under different experimental settings.
Retrieval and Evaluation of Target Component Based on Ontology Knowledge

Software reuse most focuses on component based software development (CBSD). However, it's not so accurate and efficient in the process, to solve this problem, this paper proposes an intelligent knowledge-driven method of target component retrieval and evaluation. This method is based on Ontology component description. With the help of the knowledge graph, a semantic mapping is formed between the component to be queried and the component description library, so the entity component is located. In order to measure the component matching performance, it gives multi-angle evaluation of queried candidate components by an evaluation index system. Based on component query information, it makes the searching target clearer, extends the semantic scope of components to be queried. In order to assembly components, a multi agent system (MAS) is also established. The result shows that this method not only makes the component retrieval process higher recall and precision, but also makes the component retrieval process more intelligent by meeting the assembly requirement.
User Experience in Communication and Collaboration Platforms: A Comparative Study Including Discord, Microsoft Teams, and Zoom

Due to the measures imposed to prevent the spread of the virus during the COVID-19 pandemic, how education and work communications are carried have changed. An increase in video conferencing and meeting applications is noticeable. In this research paper, we describe the results of a user experience evaluation of three widely used platforms: Discord, Microsoft Teams, and Zoom. Through the User Experience Questionnaire (UEQ) application, it was determined that Discord is better in aspects not related to tasks and provides an above-average UX. On the other hand, zoom excels when it comes to tasks, but in conjunction with Microsoft Teams, it delivers below-average UX.
From the Construction of Knowledge System to the Generation of Learner Portrait Based on Knowledge Graph

With the rapid development of information teaching, how to apply artificial intelligence technology in the field of education and strengthen information teaching methods are urgent problems to be solved. Aiming at the field of education, this paper puts forward a technical framework and its implementation method based on knowledge graph from the establishment of curriculum knowledge system, the generation of learner portraits to adaptive learning recommendation. Taking computer network and other courses as examples, this paper introduces the construction technology of knowledge graph, which has certain guiding significance for the construction and application of educational knowledge graph.
Circuitree: A Datalog Reasoner in Zero-Knowledge

Driven by the increased consciousness in data ownership and privacy, zero-knowledge proofs (ZKPs) have become a popular tool to convince a third party of the truthfulness of a statement without disclosing any further information. As ZKPs are rather complex to design, frameworks that transform high-level languages into ZKPs have been proposed. We propose Circuitree, a Datalog reasoner in zero-knowledge. Datalog is a high-level declarative logic language that is generally used for querying. Furthermore, as a logic language, it can also be used to solve logic problems. An application using Circuitree can efficiently generate ZKPs, based on Datalog rules and encrypted data, to prove that a certain conclusion follows from a Datalog ruleset and encrypted input data. Compared to existing frameworks, which generally use their own limited imperative languages, Circuitree uses an existing high-level declarative language. We point out several applications for Circuitree, including EU Digital COVID Certificates and privacy-preserving access control for peer-to-peer (p2p) networks. Circuitree’s performance is evaluated for access control in a p2p network. First results show that our approach allows for fast proofs and proof verification for this application.
Generating Search Text Ads from Keywords and Landing Pages via BERT2BERT

The standard way to create text ads is to capture searched keywords and the information on their landing pages (LP). However, this coupling of keywords and an LP increases the number of text ads per LP, which makes impossible to create ad texts for all effective combinations of keywords and LPs due to limitation of human resource. We propose a transformer-based ad text generation model using both keywords and LPs to reduce costs and time generating ad texts. We extract tags and texts LP's HTML, such as title, h1, h2, fine-tune a pre-trained encoder-decoder model (initialized by BERT2BERT), and HTML tag embeddings, similar to position embeddings, are passed to an input layer. The experimental results demonstrate that our model generates ad texts with a quality close to human-written ones for fluency, attractiveness, and correctness.
Smart Search over Encrypted Educational Resources in Cloud Storage

With the development of education, educational big data is taking shape, and the cloud has a natural advantage of storing big data. Data is usually stored in the cloud server in the form of ciphertext for the sake of privacy protection. However, this is against the use of data. Currently, although the search schemes under ciphertext are primarily based on keywords, these schemes fail to reflect the actual needs of users well. Moreover, the quality of educational resources varies. Therefore, ensuring users’ privacy protection while providing high-quality educational resources is challenging. In order to solve this problem, a Smart Search over Encrypted Educational Resources (SSEER) in cloud storage was proposed in this paper. The contribution of this paper is mainly reflected in two aspects: First, SSEER introduces a trusted third party (TTP) server to obtain a large number of educational resources through crowdsourcing. Second, the index is updated based on the user’s use of educational resources, through which high-quality educational resources can be identified, thus increasing the probability for users to access quality resources. As a result, the educational resource graph can screen out the educational resources related to the user search.