Reviewerly/exHarmony huggingface dataset:
Cognitive visual anomaly detection with constrained latent representations for industrial inspection robot

With the fast growth of intelligent manufacturing industry, developing advanced industrial inspection robots is becoming a research and application hotspot in the fields of both computer vision and robotics. This kind of industrial inspection robots is expected to automatically detect anomalous structures (e.g., defects, damages, rejects, etc.) from the images of the manufactured products. Generally, the existing visual anomaly detection (VAD) methods mainly focus on modeling the complex and high-dimensional distribution of normal data, while neglecting the specific visual properties of abnormal data since their frequency of occurrence is much less than that of the normal data. In this paper, inspired by the human cognition on extracting abstractly visual properties and to distinguish the anomaly patterns from the observed data, we propose a novel cognitive VAD method for industrial inspection robot. Specifically, we introduce a constrained latent space to mimic the cognitive ability of humans, where the abstraction learned from the observed normal and anomaly data are represented. We build our method based on a convolutional generative adversarial network and a denoising auto-encoder, where the adversarial learning mechanism is adopted to establish the boundary between the normal and anomaly data. In the experiment, we evaluate our method on a real-world dataset where the images are captured for the manufactured products. The comprehensive results comparing with several recent VAD methods show that the proposed method is effective to detect the anomaly images of different categories with a high accuracy.
Steganography Using Quotient Value Differencing and LSB Substitution for AMBTC Compressed Images

In this paper, we apply quotient value differencing (QVD) and least significant bits (LSBs) substitution to hide secret data in an absolute moment block truncation coded (AMBTC) image. An AMBTC compressed image block consists of a high mean, low mean, and bitmap matrix. Based on the data structure of an AMBTC block, the proposed method includes three phases of embedding. In the first phase, QVD and LSB substitution are applied to embed secret digits into the low mean and high mean values. In the second phase, an additional bit is embedded by alternating the order of the low mean and high mean or not. Secret digits are embedded in the third phase by substituting the bitmap of smooth blocks. By leveraging the entire data structure of the AMBTC compressed code, the proposed method outperforms related works in embedding capacity.
ChainFaaS: An Open Blockchain-Based Serverless Platform

Due to the rapid increase in the total amount of data generated in the world, the need for more computational resources is also increasing dramatically. This trend results in huge data centers and massive server farms being built around the world, which have a negative impact on global carbon emissions. On the other hand, there are many underutilized personal computers around the world that can be used towards distributed computing. To better understand the capacity of personal computers, we have conducted a survey that aims to find their unused computational power. The results indicate that the typical CPU utilization of a personal computer is only 24.5% and, on average, a personal computer is only used 4.5 hours per day. This shows a significant computational potential that can be used towards distributed computing. In this paper, we introduce ChainFaaS with the motivation to use the computational capacity of personal computers as well as to improve developers' experience of internet-based computing services by reducing their costs, enabling transparency, and providing reliability. ChainFaaS is an open, public, blockchain-based serverless platform that takes advantage of personal computers' computational capacity to run serverless tasks. If a substantial number of personal computers were connected to this platform, some tasks could be offloaded from data centers. As a result, the need for building new data centers would be reduced with a positive impact on the environment. We have proposed the design of ChainFaaS, and then implemented and evaluated a prototype of this platform to show its feasibility.
Weakly Supervised Person Re-ID: Differentiable Graphical Learning and a New Benchmark

Person reidentification (Re-ID) benefits greatly from the accurate annotations of existing data sets (e.g., CUHK03 and Market-1501), which are quite expensive because each image in these data sets has to be assigned with a proper label. In this work, we ease the annotation of Re-ID by replacing the accurate annotation with inaccurate annotation, i.e., we group the images into bags in terms of time and assign a bag-level label for each bag. This greatly reduces the annotation effort and leads to the creation of a large-scale Re-ID benchmark called SYSU- 30k. The new benchmark contains 30k individuals, which is about 20 times larger than CUHK03 (1.3k individuals) and Market-1501 (1.5k individuals), and 30 times larger than ImageNet (1k categories). It sums up to 29606918 images. Learning a Re-ID model with bag-level annotation is called the weakly supervised Re-ID problem. To solve this problem, we introduce a differentiable graphical model to capture the dependencies from all images in a bag and generate a reliable pseudolabel for each person's image. The pseudolabel is further used to supervise the learning of the Re-ID model. Compared with the fully supervised Re-ID models, our method achieves state-of-the-art performance on SYSU- 30k and other data sets. The code, data set, and pretrained model will be available at https://github.com/wanggrun/SYSU-30k.
The 2020 Data Breach Investigations Report – a CSO's perspective

There are few moments in the year where the cyber security industry will sit back and take note. One of those is the highly anticipated and much coveted ‘Data Breach Investigations Report' (DBIR) by Verizon, which details the leading attack routes used by today's hackers. 1 There are few moments in the year where the cyber security industry will sit back and take note. One of those is the highly anticipated Data Breach Investigations Report (DBIR) by Verizon. Martin Jartelius of Outpost24 explains how the report highlights the way proactive identification of serious threats via automation and continuous scanning can be the difference between resolving a cheap risk or explaining a costly data breach. Yet technology can only do so much and there needs to be a focus on raising staff awareness and overall cyber hygiene.
Distributed cubature H-infinity information filtering for target tracking against uncertain noise statistics

The traditional Kalman-based distributed state estimation (DSE) is unable to address uncertain system noise. To this end, this paper studies robust DSE problems for nonlinear systems over wireless sensor networks (WSNs) with uncertain noise. The distributed cubature H-infinity information filter (DCHIF) is developed by exploiting the weighted average consensus technique. The DCHIF is employed for high-dimensional state estimation with uncertain system noise in WSNs. The local estimation in the DCHIF is obtained by each single sensor without being affected by the failure of non-adjacent sensors. The estimation is then transmitted to the adjacent fusion centres for information fusion according to the weighted average consensus (WAC) technique. The DCHIF has advantages, such as high precision and strong robustness that can alleviate the influence of uncertain noise statistics on the state estimation. Therefore, to guarantee the stability regardless of the number of consensus steps, a novel convergence analysis approach is presented based on innovative terms for each sensor node. Additionally, the efficiency of the proposed algorithm is demonstrated by performing a numerical simulation of a typical air-traffic control system.
Detecting child sexual abuse material: A comprehensive survey

Victims of child sexual abuse suffer from physical, psychological, and emotional trauma. The detection and deletion of illicit online child sexual abuse material (CSAM) helps in reducing and even stopping the continuous re-victimization of children. Furthermore, automatic detection may also support legal authorities to search for and review the masses of suspected CSAM. Due to tech-savvy offenders and technological advances, continuous efforts in keeping up with current developments are crucial and need to be considered in the implementation of detection algorithms. The present research provides a comprehensive synthesis and an interpretation of the current research accomplishments and challenges in the CSAM detection domain, explicitly considering the dimensions of policy and legal framework, distribution channels, and detection applications and implementations. Among other aspects, it reveals and aggregates knowledge related to image hash database, keywords, web-crawler, detection based on filenames and metadata, and visual detection. The findings suggest that CSAM detection applications yield the best results if multiple approaches are used in combination, such as deep-learning algorithms with multi-modal image or video descriptors merged together. Deep-learning techniques were shown to outperform other detection methods for unknown CSAM.
Learning Representations for Facial Actions From Unlabeled Videos

Facial actions are usually encoded as anatomy-based action units (AUs), the labelling of which demands expertise and thus is time-consuming and expensive. To alleviate the labelling demand, we propose to leverage the large number of unlabelled videos by proposing a twin-cycle autoencoder (TAE) to learn discriminative representations for facial actions. TAE is inspired by the fact that facial actions are embedded in the pixel-wise displacements between two sequential face images (hereinafter, source and target) in the video. Therefore, learning the representations of facial actions can be achieved by learning the representations of the displacements. However, the displacements induced by facial actions are entangled with those induced by head motions. TAE is thus trained to disentangle the two kinds of movements by evaluating the quality of the synthesized images when either the facial actions or head pose is changed, aiming to reconstruct the target image. Experiments on AU detection show that TAE can achieve accuracy comparable to other existing AU detection methods including some supervised methods, thus validating the discriminant capacity of the representations learned by TAE. TAE's ability in decoupling the action-induced and pose-induced movements is also validated by visualizing the generated images and analyzing the facial image retrieval results qualitatively and quantitatively.
Digital object attachment

Digital object attachment is a complex phenomenon that has received little attention in the extant literature. The purpose of the current article is to develop a conceptual model to clarify discrepancies and encourage future work in this area. We classify digital objects into four categories by their complexity, user control, interactivity and user value. The proposed model captures the dynamic and cyclical interplay between the user, the digital object and the environment. The user-object relationship can evolve over time through user dedication and psychological ownership, enabling identity experimentation and formation that ultimately lead to object attachment. In advanced forms, digital objects can assume transitional properties that blur the user's offline and digital existence and trigger identity transference.
Harmonic Loss Function for Sensor-Based Human Activity Recognition Based on LSTM Recurrent Neural Networks

Human activity recognition (HAR) has been a very popular field in both real practice and theoretical research.Over the years, a number of many-vs-one Long Short-Term Memory (LSTM) models have been proposed for the sensor-based HAR problem.However, how to utilize sequence outputs of them to improve the HAR performance has not been studied seriously.To solve this problem, we present a novel loss function named harmonic loss, which is utilized to improve the overall classification performance of HAR based on baseline LSTM networks.First, label replication method is presented to duplicate true labels at each sequence step in many-vs-one LSTM networks, thus each sequence step can generate a local error and a local output.Then, considering the importance of different local errors and inspired by the Ebbinghaus memory curve, the harmonic loss is proposed to give unequal weights to different local errors based on harmonic series equation.Additionally, to improve the overall classification performance of HAR, integrated methods are utilized to exploit the sequence outputs of LSTM models based on harmonic loss and ensemble learning strategy.Finally, based on the LSTM model construction and hyper-parameter setting, extensive experiments are conducted.A series of experimental results demonstrate that our harmonic loss significantly achieves higher macro-F1 and accuracy than strong baselines on two public HAR benchmarks.Compared with previous state-of-art methods, our proposed methods can achieve competitive classification performance.
Achieving Privacy-preserving Federated Learning with Irrelevant Updates over E-Health Applications

The widespread use of edge devices in E-Health such as smartphones and wearables means richer electronic health records (EHR) are becoming available. Training deep learning models on these data can effectively improve the quality of healthcare services. Recently, federated learning (FL) has received extensive attention in E-Health because it can train a model by only sharing gradients without disclosing the original EHR of owners. In this case, however, the adversary can still violate EHR owners' privacy based on shared gradients. To mitigate privacy threat, several privacy-preserving FL protocols have been proposed by utilizing different cryptography techniques. Unfortunately, existing privacy-preserving FL schemes do not take into account irrelevant updates, which are useless for the convergence of the global model. This may reduce the predictive accuracy and worse may lead to the uselessness of the final model. In this paper, we propose PFL-IU, an efficient and privacy-preserving FL framework that is compatible with irrelevant updates. Specifically, we first design a communication-efficient secure aggregation protocol by using a non-interactive key generation algorithm. Then we present a sign method to mitigate the negative impact incurred by irrelevant updates, which will accelerate model convergence and improve predictive accuracy. Moreover, PFL-IU is robust to EHR owners' dropout during the whole training phase. Extensive experiments using the real-world dataset demonstrate that PFL-IU can achieve better performance in terms of accuracy, convergence and efficiency.
Pre-Service Teachers’ Critical Digital Literacy Skills and Attitudes to Address Social Problems

The emergence and expansion of social networks in the digital age has led to social transformations that have a great impact within the field of education. Teacher-training programs face the challenge of preparing future teachers to critically interpret digital media. They must succeed in this if we are to develop citizens who are well informed and reflective, which then raises the question: Are future teachers critical thinkers? This study took third- and fourth-year students of primary education (n = 322) at five Spanish universities and explored their capacity for constructing critical discourses. It examined how well they can analyze and discuss information from digital media on social problems like poverty, economic crises, social justice, and the media. Its findings reveal that future teachers have difficulty in putting together critical discourses based on information from the Internet on social problems. Those who have doubts, compare, analyze, and reason are the minority.
CEPS: A Cross-Blockchain based Electronic Health Records Privacy-Preserving Scheme

The Electronic Health Record (EHR) has been widely used in cloud-based medical data platforms. Since the owner of the EHR is a patient and the manager is a doctor (or hospital), this separation of ownership of privacy data leads to privacy leakages of the EHR stored in the cloud environment. The tamper-proof and traceable features of the blockchain make it a promising way to solve EHR privacy protection issues. Nevertheless, the latest research findings on blockchain-based schemes for EHR privacy preservation are focused on single blockchain that corresponds to a designated medical institution, which are not compatible with the privacy anti-leakage demands since private data is transferred through multiple blockchains. In this regard, we put forward relay-chain as a service (RaaS), and propose a cross-blockchain based EHR Privacy-preserving scheme (CEPS), which uses relay-chain to achieve secure access to EHR data when patients visit different hospitals. Furthermore, our scheme ensures that patients can delete the link of EHR freely and effectively. Security analysis and performance evaluations are performed, which prove that CEPS is highly secure and efficient impressively.
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems

Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the convergence of policy optimization for quadratic control of Markovian jump linear systems (MJLS). First, we study the optimization landscape of direct policy optimization for MJLS, and, in particular, show that despite the non-convexity of the resultant problem the unique stationary point is the global optimal solution. Next, we prove that the Gauss-Newton method and the natural policy gradient method converge to the optimal state feedback controller for MJLS at a linear rate if initialized at a controller which stabilizes the closed-loop dynamics in the mean square sense. We propose a novel Lyapunov argument to fix a key stability issue in the convergence proof. Finally, we present a numerical example to support our theory. Our work brings new insights for understanding the performance of policy learning methods on controlling unknown MJLS.
Risk-Averse Planning Under Uncertainty

We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives. Synthesizing risk-averse optimal policies for POMDPs requires infinite memory and thus undecidable. To overcome this difficulty, we propose a method based on bounded policy iteration for designing stochastic but finite state (memory) controllers, which takes advantage of standard convex optimization methods. Given a memory budget and optimality criterion, the proposed method modifies the stochastic finite state controller leading to sub-optimal solutions with lower coherent risk.
View-specific subspace learning and re-ranking for semi-supervised person re-identification

Person re-identification (re-ID) focuses on matching the same person across non-overlapping camera views. Most existing methods require tedious manual annotation and can only learn a unitary transformation for images across views, which severely lack of scalability and suffer from view-specific biases. To address these issues, we put forward a View-Specific Semi-supervised Subspace Learning (VS-SSL) approach that can learn specific projections for each view, utilizing limited labeled data to guide the training while leveraging abundant unlabeled data simultaneously. Moreover, a novel re-ranking strategy is proposed to boost the performance further, which re-estimates the similarity between probe and galleries according to the overlap ratio between their expanded neighbors and their position in each other’s ranking list. The effectiveness of the proposed framework is evaluated on several widely-used datasets (VIPeR, PRID450S, PRID2011, CUHK01 and Market-1501), yielding superior performance for both semi-supervised and supervised re-ID.
An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.
Open access to genetic sequence data maximizes value to scientists, farmers, and society

Open access to genetic sequence data, often referred to as Digital Sequence Information, has been available since genome sequencing became possible and creates both monetary and nonmonetary value. Nonmonetary value is created when scientists access sequence data for discovery, collaboration, and innovation. Monetary value is created when genetic variability is leveraged to develop more robust and resilient crop plants, vibrant seed systems, more sustainable agriculture, and food security for consumers. Millions of dollars have been invested in curating and creating access to sequence databases and scientists from almost every country in the world have accessed these databases, free of charge. This access may now be threatened by well-meaning policy-makers who have not consulted with the scientific community. Monetizing or creating greater regulation of genetic sequence data would create barriers to innovation, partnering, and problem-solving.
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles

Many digital libraries recommend literature to their users considering the similarity between a query document and their repository. However, they often fail to distinguish what is the relationship that makes two documents alike. In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task. To find the semantic relation between documents, we apply a series of techniques, such as GloVe, Paragraph Vectors, BERT, and XLNet under different configurations (e.g., sequence length, vector concatenation scheme), including a Siamese architecture for the Transformer-based systems. We perform our experiments on a newly proposed dataset of 32,168 Wikipedia article pairs and Wikidata properties that define the semantic document relations. Our results show vanilla BERT as the best performing system with an F1-score of 0.93, which we manually examine to better understand its applicability to other domains. Our findings suggest that classifying semantic relations between documents is a solvable task and motivates the development of a recommender system based on the evaluated techniques. The discussions in this paper serve as first steps in the exploration of documents through SPARQL-like queries such that one could find documents that are similar in one aspect but dissimilar in another.