Reviewerly/exHarmony huggingface dataset:
Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities

The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.
Arsenal: Understanding Learning-Based Wireless Video Transport via In-Depth Evaluation

Recent years have witnessed a rise of learning-based ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , artificial intelligence driven or AI-driven) video transport design, in order to achieve consistently high performance, even when the modern Internet is becoming increasingly heterogeneous while the applications are becoming unprecedentedly demanding ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the simultaneous high-throughput and low-latency requirements of HD video telephony or intelligent remote driving). While separate evaluation using proprietary platform has shown the advantage of AI-driven algorithms over their non-AI counterparts, a systematic study is missing for directly comparing these AI-driven design under a uniform and practical platform. To bridge the gap, in this work, we first design and implement a full-fledged evaluation platform named Arsenal, which incorporates multiple state-of-the-art congestion control algorithms, most of which are AI-driven. Using Arsenal, we carry out a thorough comparative study of the algorithms, over massive traces collected from heterogeneous networks including WiFi, 4G, 3G and even the rare commercial 5G wireless networks. In particular, to enable convincing measurements for the dominated real-time video applications, we collect millions of practical video sessions in cooperation with a prevailing video service provider. The evaluation provides a handful of important observations, which are undiscovered before and have important impacts on future protocol design. Moreover, we will make the platform and algorithms open-source to enrich the research tools in the intelligent transportation community.
High-Dimensional Similarity Query Processing for Data Science

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically.
Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm

In the original artificial bee colony (ABC), only one dimension of the solution is updated each time and this leads to little differences between the offspring and the parent solution. Then, it affects the convergence speed. In order to accelerate the convergence speed, we can update multiple dimensions of the solution at the same time, and the information of the global optimal solution can be used for guidance. However, using these two methods will reduce the population diversity at the initial stage. This is not conducive to search of multimodal functions. In this paper, a population diversity guided dimension perturbation for artificial bee colony algorithm (called PDDPABC) is proposed, in which population diversity is used to control the number of dimension perturbations. Then, it can maintain a certain population diversity, and does not affect the convergence speed. In order to verify the performance of PDDPABC, we tested its performance on 22 classic problems and CEC 2013 benchmark set. Compared with several other ABC variants, our approach can achieve better results.
Blockchain Technology and Its Applications in Digital Content Copyright Protection

Nowadays, digital products and related copyright disputes are growing at an accelerating speed, the field of digital copyright protection is facing the problems of numerous infringements, toughness in digital right confirmation and safeguarding. Fortunately, blockchain technology provides a potential technical solution to this dilemma because of its features of decentralization and distributed storage. On the basis of a brief introduction to blockchain technology and its features, this paper analyzes the applicability of blockchain to the problems facing the digital copyright field and discusses the possible challenges and corresponding countermeasures when blockchain is applied in this field.
Interpretable Deep Image Prior Method Inspired In Linear Mixture Model For Compressed Spectral Image Recovery

This paper presents a recovery method for compressive spectral imaging (CSI) based on the training-data independent deep image prior approach, where the prior information of the image is learned through the weights and the structure of the neural network. Specifically, we propose an interpretable architecture inspired in the linear mixture model for spectral images, where the image is decomposed as the product between a basis matrix, known as endmembers, and a coefficient matrix, known as abundances. These matrices are learned as the weights and the features of the proposed network, respectively. Simulations and experiments show that the proposed recovery method outperforms the state-of-the-art CSI recovery methods, even against training-data dependent methods. Furthermore, the architecture structure inspired by the linear mixture model gives interpretability of some outputs that can be useful for subsequent high-level image processing.
Intelligent Human Information Behavior in E-Learning system

Modelling a personal learning environment is an urgent task both in the field of education and in the field of intelligent technologies. The problems of e-learning personification are associated with the organization of information technology support, which allows everyone to build information behavior in the learning environment, taking into account the individual specifics of its perception. The development of cognitive activity in information behavior, the ability to independently navigate in the semantics of professional environments is one of the most important educational aims. Digital culture forms the specifics of the information behavior of a modern young person. The goals of the authors are determined by the motivational sphere analysis of the individual information behavior in the context of learning personalization drawing on the results of psychological testing using mathematical processing methods and statistical survey data analysis. The empirical research made it possible to single out the dominant factors of motivation for intellectual activity, as well as the degree of intention consistency, ideally with their implementation in specific learning conditions. To identify the typology of information behavior, an ideal and real motivational profile was drawn up for each respondent. According to the criterion of attitudes towards the value of knowledge, an averaged profile of internal attitudes was identified, stimulating different levels of intellectual activity and allowing to model typical stimuli of information behavior in the learning environment.
R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery

State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training.Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions.Though deep reinforcement learning can generate effective policy networks for operator selection or architecture search in image restoration, how it is connected to the classic deterministic training in solving inverse problems remains unclear.In this work, we propose a novel image denoising scheme via Residual Recovery using Reinforcement Learning, dubbed R3L.We show that R3L is equivalent to a deep recurrent neural network that is trained using a stochastic reward, in contrast to many popular denoisers using supervised learning with deterministic losses.To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance.With such a unified benchmarking system, we demonstrate that the proposed R3L has better generalizability and robustness in image denoising when the estimated noise level varies, comparing to its counterparts using deterministic training, as well as various state-ofthe-art image denoising algorithms.
Reducing the Annotation Cost of Whole Slide Histology Images using Active Learning

Histopathology serves as the gold standard for tumor diagnosis. Whole slide scanners have made computer vision-based methods available for pathologists to locate regions of high diagnostic significance. An essential step of whole slide image (WSI) diagnosis is the segmentation of the tumor region by generating a tumor probability heatmap. Most WSI diagnosis methods use patch-based classifiers or segmentation models, they both require a large set of training patches from annotated WSIs. Annotating WSIs is time-consuming and laborious. Active learning is a method that can suggest the most informative unlabeled data for annotation, but traditional active learning methods are not directly applicable for WSIs. Meanwhile, unannotated WSIs also contain rich information that can be further exploited by self-supervised learning. By utilizing unannotated data alongside active learning, we proposed a self-supervised active learning framework for tumor region segmentation of WSIs. The proposed method is evaluated on the public available CAMELYON dataset and achieved satisfying performance using 3% of the annotated data.
Identity-Preserved Complete Face Recovering Network for Partial Face Image

Complete face recovering (CFR) is to recover the face image of a given partial face image of a target person whose photo may not be included in the gallery set. CFR has several attractive potential applications in surveillance, personal identification in forensics, to name a few, but it is challenging because of little information revealed from a single partial face image. Furthermore, the facial identity may get lost when recovering the complete face image. As far as we know, CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to tackle this problem. Accordingly, a denoising auto-encoder based network is applied. We propose an identity-preserved loss function to constrain the features in latent space of decoder, whereby maintaining the personal identity information. Then, to better restore the complete face image, the acquired features are further fed into a decoder with an adversarial structure that takes a new variant of discriminator. That is, we borrow the idea from energy based GAN that utilize an auto-encoder structure discriminator. It can produce very different gradient directions within the minibatch and therefore can make the model be trained stably. Further, we propose a novel dual-pipeline structure in the discriminator, which is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the superiority of IP-CFR.
Impacts of IDEF0-Based Models on the Usefulness, Learning, and Value Metrics of Scrum and XP Project Management Guides

Plan-driven Project Management is a highly relevant approach for successful systems and software development projects, and its application is pursued uniformly. Agile Project Management practices, in contrast, are highly flexible and varied, and their learning and correct application has been found a challenging task. In other Engineering Management domains, IDEF0-based models have been satisfactorily used for documenting process guides, but their study in agile practices has been scarce. This paper investigates whether the IDEF0-based models for agile Project Management guides, as in Scrum and XP practices, can improve the usefulness, learning, and value metrics when compared to using standard guides. A quantitative experimental evaluation conducted with 58 international subjects and blocked on the variables of Practical Experience and Knowledge on Scrum/XP methods found that the IDEF0-based guides were evaluated with better metrics than the standard guides in 11 paired t-tests, and similarly in the remaining 16 ones. This study thus provides empirical evidence of positive impacts by using the IDEF0-based models for digital guides of agile practices and fosters their utilization and further research in the agile Engineering Management domain.
Growing Aspects of Cyber Security in E-Commerce

The world is witnessing a transition from in-store shopping to online shopping. E-commerce (Electronic commerce) giants such as Amazon, Alibaba, eBay etc. are leading the way towards this change. Much technological advancement are being made to ease the life of mankind with online shopping being the most notable. E-commerce is known to be a powerful instrument for transformation of business that gives companies the opportunity to upgrade their supply chain operations, improve their network, as well as provide better services to both customers and suppliers. Applying the techniques of online shopping that yield such advantages may not be possible without the presence of a well-organized approach to E-commerce security. E-commerce organizations such as Amazon and Alibaba have also been using such techniques to ensure data protection. The most common of them all is the One Time Password (OTP), which is sent to a user when they make payments online for identity verification. On the other hand, Alibaba uses a unique Key Management System (KSM) which is a fully managed service that helps customers create, delete, and manage encryption keys to protect data. This system provides availability, reliability and elasticity alongside security and compliance. The paper also explore the importance of different security algorithms in Ecommerce domain.
A multi-level improved circle pooling for scene classification of high-resolution remote sensing imagery

Scene classification of high-spatial resolution imagery (HSRI) includes various potential applications in various fields. Recently, deep convolutional neural networks (CNNs) have achieved competitive performance as a result of the powerful capability of feature extraction. In this paper, we propose a multi-level improved circle pooling (MICP) method with the pre-trained CNN-based model to enhance the discriminative power of CNN activations for scene classification. Specifically, an improved pooling strategy is presented to generate annular subregions without padding operations in traditional concentric circle pooling. Then, we extract the pooling features in these subregions under different levels and build a holistic representation by fusing these multi-level features. MICP is an effective and simple strategy enriching rotation insensitivity and multiscale spatial information. According to the experiments conducted on three challenging HRSI scene data sets, the proposed pooling method achieves similar or better classification accuracy compared to the other CNN-based scene classification methods. Moreover, a comprehensive discussion regarding the effect of data augmentation reveals that the proposed method can enhance the rotation insensitivity of CNNs for the HRSI scene classification.
Measuring Technological Collaboration on Blockchain Based on Patents: A Social Network Analysis Approach

Blockchain has been regarded as an emerging global phenomenon in the field of new technologies. However, the existing literature still lacks descriptions of the cooperation characteristics and innovation landscape of blockchain. This study uses the social network analysis method to compare the development of blockchain technology and technological collaboration in China and in the United States based on patent data. Our analyses suggest that the number of blockchain patent applications in China is increasing rapidly due to the Chinese Government developing consistent national strategies for blockchain technology. In all, both countries have notable agglomerations in a few geographical areas or cities. However, the university or enterprise block in the United States has broader and deeper cooperation, unlike the Chinese university or enterprise block, which is more inclined to cooperate within blocks and has more isolated blocks. Lastly, there are various patent attributes-influencing factors behind the importance of node cooperation. The results show that the degree of cooperation of cooperative inventors or important patents is greater in China’s cooperation network, while in the United States, the influence of enterprises rather than universities or individuals is greater.
A Novel Type-II Fuzzy based Fruit Image Enhancement Technique Using Gaussian S-shaped and Z-Shaped Membership Functions

In digital image processing, image improvement is an essential step. In this paper, Gaussian, S-shaped and Z-shaped membership functions based on Type-II are used to enhance the image of the fruit. The issue of over and under enhancement can be encountered by fuzzy techniques. Type-II fuzzy has the ability to solve this problem. The suggested approach was applied to photographs of red apple orange and watermelon fruit and found that analytical results were stronger.
State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0–100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8–99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.
Research Summary of Recommendation System Based on Knowledge Graph

This article will systematically review the knowledge graph-based recommendation system from three aspects: knowledge graph, recommendation system, and application of knowledge graph and recommendation system. Specifically, it investigates and analyzes related articles in recent years, and summarizes and elaborates on the concepts, construction, and research of knowledge graphs, as well as collaborative filtering recommendation systems and hybrid recommendation systems. On this basis, the application scenarios of the recommendation system based on the knowledge graph are discussed and analyzed. The article summarizes the research work on the recommendation system of knowledge graphs in recent years, and also proposes detailed analysis and prospects based on the application status of different fields.
Naturally!: How Breakthroughs in Natural Language Processing Can Dramatically Help Developers

Taking advantage of the naturalness hypothesis for code, recent development, and research has focused on applying machine learning (ML) techniques originally developed for natural language processing (NLP) to drive a new wave of tools and applications aimed specifically for software engineering (SE) tasks. This drive to apply ML and deep learning (DL) has been animated by the large-scale availability of software development data (e.g., source code, code comments, code review comments, commit data, and so on) available from open source platforms such as GitHub and Bitbucket.
Anomaly Detection using Generative Adversarial Networks on Firewall Log Message Data

Firewalls produce a lot of log messages while logging internet traffic through the system that they are protecting. This is a huge amount of data that can be used to find various insights. One of these insights knows if a malware attack has taken place. We treat this sequence of log messages as time series data. In this paper, we propose a Generative Adversarial Network (GAN) model based on Recurrent Neural Networks (RNN) that learns the normal behavior of the firewall, the complex spatio-temporal correlations in the data. We then apply anomaly detection techniques to detect an anomaly which can potentially be a malware.
“Adversarial Examples” for Proof-of-Learning

In S&P 21, Jia et al. proposed a new concept/mechanism named proof-of-learning (PoL), which allows a prover to demonstrate ownership of a machine learning model by proving integrity of the training procedure. It guarantees that an adversary cannot construct a valid proof with less cost (in both computation and storage) than that made by the prover in generating the proof. A PoL proof includes a set of intermediate models recorded during training, together with the corresponding data points used to obtain each recorded model. Jia et al. claimed that an adversary merely knowing the final model and training dataset cannot efficiently find a set of intermediate models with correct data points. In this paper, however, we show that PoL is vulnerable to “adversarial examples”! Specifically, in a similar way as optimizing an adversarial example, we could make an arbitrarily-chosen data point “generate” a given model, hence efficiently generating intermediate models with correct data points. We demonstrate, both theoretically and empirically, that we are able to generate a valid proof with significantly less cost than generating a proof by the prover.