Reviewerly/exHarmony huggingface dataset:
Internet safety, online radicalisation and young people with learning disabilities

Accessible Summary A lot of people with learning disabilities enjoy using the Internet every day and use social media on their mobile phones. Staying safe online is important. This research used interviews and focus groups to find out what children, young people, their parents and teachers thought about Internet safety, extremism and online radicalisation. Extremism is when people have strong and dangerous views about laws or religion. Radicalisation is when people support extremism in a dangerous way. People can be targeted on the Internet to talk them into supporting extremism. The young people said they knew a lot about staying safe online, but parents were concerned about risks. When people with learning disabilities learn about staying safe online, they should be given information about online radicalisation and grooming for terrorism. Abstract Background Young people with learning disabilities are increasingly using the Internet but can be vulnerable to being victimised online. As learning disability services develop guidance on how to support Internet use, it is important to explore what support is necessary. Methods This research used interviews and focus groups to explore what children, young people, their parents and teachers thought about Internet safety, extremism and online radicalisation. Results Results showed that the students were active Internet users and were confident about online safety but parents were concerned about the risks associated with Internet use. Following taking part in a peer education project that focussed on Internet safety and specifically about risks of online radicalisation and extremism, the students understood possible links between grooming and online radicalisation and their teachers increased their understanding of the importance of digital engagement for their students.
Probabilistic pragmatics explains gradience and focality in natural language quantification

Significance Theoretical linguistics postulates abstract structures that successfully explain key aspects of language. However, the precise relation between abstract theoretical ideas and empirical data from language use is not always apparent. Here, we propose to empirically test abstract semantic theories through the lens of probabilistic pragmatic modeling. We consider the historically important case of quantity words (e.g., “some,” “all”). Data from a large-scale production study seem to suggest that quantity words are understood via prototypes. But based on statistical and empirical model comparison, we show that a probabilistic pragmatic model that embeds a strict truth-conditional notion of meaning explains the data just as well as a model that encodes prototypes into the meaning of quantity words.
Toward Usable Cloud Storage Auditing, Revisited

In recent years, how to design secure and efficient cloud storage auditing (CSA) protocols, which enable users to verify whether the cloud server still keeps their stored data undamaged, is a research hotspot, and many candidates were proposed. Recently, Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2020) just suggested a usable CSA (UCSA) protocol by combining error correcting codes with homomorphic authentication technique, and claimed that their protocol is secure. However, in this article, we analyze the security of Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s construction and find that this protocol is completely insecure. In particular, after receiving data owner’s authenticated data, the cloud server only needs to compute and store the much shorter parity codes (derived from all the data blocks) instead of the blocks themselves. Then, it can correctly forge and return a proof, which is able to pass the checking of the verifier, even if it does not truly store original data blocks. In addition, we provide an improved UCSA (iUCSA) protocol on the remedy of the weaknesses of Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s scheme. A detailed security analysis is also performed within the framework of Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> Finally, the performance analysis shows that the protocol iUCSA is practical.
The ThreadBoard: Designing an E-Textile Rapid Prototyping Board

E-textiles, which embed circuitry into textile fabrics, blend art and creative expression with engineering, making it a popular choice for STEAM classrooms [6, 12]. Currently, e-textile development relies on tools intended for traditional embedded systems, which utilize printed circuit boards and insulated wires. These tools do not translate well to e-textiles, which utilize fabric and uninsulated conductive thread. This mismatch of tools and materials can lead to an overly complicated development process for novices. In particular, rapid prototyping tools for traditional embedded systems are poorly matched for e-textile prototyping. This paper presents the ThreadBoard, a tool that supports rapid prototyping of e-textile circuits. With rapid prototyping, students can test circuit designs and identify circuitry errors prior to their sewn project. We present the design process used to iteratively create the ThreadBoard's layout, with the goal of improving its usability for e-textile creators.
AgeGAN++: Face Aging and Rejuvenation With Dual Conditional GANs

Face aging and rejuvenation is applied to predict what a person looks like at different ages. While prior work brought about a significant progress in this topic, there are two central problems remaining to be solved : 1) most prior works require sequential data during training, while it is very rare in existing datasets; and 2) how to render an aging face and preserve personality at the same time. To deal with these problems, we develop a novel dual conditional GANs mechanism, thus aging faces can be trained with multiple sets of unlabeled facial images of different ages. Our basic architecture is AgeGAN, in which the primal conditional GAN converts input faces to other ages based on relevant age conditions, and the dual conditional GAN learns to invert the task. We further improve our networks, termed AgeGAN++, in which we share the weights between the primal part and the dual part to to streamline the model. Moreover, in order to get more sensible results, a representation disentanglement component is integrated with the latent facial representation, and an enhanced discriminator is applied on the generated process. In addition, we firstly perform an interpolation experiment to demonstrate that our generators are powerful and effective for face aging and rejuvenation. Experimental results on four public datasets demonstrate the appealing performance of the proposed methods by comparing with the state-of-the-art methods. Our code and a demo are released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sherry-JQ/AgeGAN</uri> .
A Truthful Auction for Graph Job Allocation in Vehicular Cloud-Assisted Networks

Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes.
Multimodal Deep Reinforcement Learning with Auxiliary Task for Obstacle Avoidance of Indoor Mobile Robot

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles. Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great capability for learning control policies in robotics by using different sensors. However, due to the complexity of indoor environment and the heterogeneity of different sensor modalities, it remains an open challenge to obtain reliable and robust multimodal information for obstacle avoidance. In this work, we propose a novel multimodal DRL method with auxiliary task (MDRLAT) for obstacle avoidance of indoor mobile robot. In MDRLAT, a powerful bilinear fusion module is proposed to fully capture the complementary information from two-dimensional (2D) laser range findings and depth images, and the generated multimodal representation is subsequently fed into dueling double deep Q-network to output control commands for mobile robot. In addition, an auxiliary task of velocity estimation is introduced to further improve representation learning in DRL. Experimental results show that MDRLAT achieves remarkable performance in terms of average accumulated reward, convergence speed, and success rate. Moreover, experiments in both virtual and real-world testing environments further demonstrate the outstanding generalization capability of our method.
Current sensor fault diagnosis and fault‐tolerant control for single‐phase PWM rectifier based on a hybrid model‐based and data‐driven method

In this study, a hybrid model-based and data-driven method is proposed for the current sensor fault diagnosis used in single-phase pulse width modulation (PWM) rectifier. According to the principle of model-based methods, the proposed diagnostic method is based on signal prediction and residual generation. Differently, instead of a mathematical model, the signal prediction model is developed based on a data-driven method. Non-linear autoregressive exogenous learning model, randomised learning technique, and extreme learning machine are utilised to generate the data-driven prediction model. Once the fault is detected, fault-tolerant control is activated by substituting the predicted signal for the information of faulty sensors. The offline test shows that the proposed method is able to predict the sensor signal accurately with the root mean square error of 4.276 × 10−5. In addition, hardware-in-the-loop tests are conducted to verify the feasibility and reliability of the proposed method in the real-time application.
Software Development Methodologies, HEIs, and the Digital Economy

Progressing digitalization of business, economy, and the society places higher education institutions (HEIs) in the center of the debate on how to effectively respond to challenges and opportunities that are thus triggered. Several facets of this process and corresponding challenges exist, including the complex question of how to match students’ skills and competencies with the demands and expectations of the industry. From a different angle, considering the changing nature of work, HEIs are responsible for equipping future employees with skills necessary to work in virtual, distributed, culturally diverse, and frequently global, teams. In the domain of software development, i.e., the backbone of the digital world, the challenge HEIs need to face is paramount. For this reason, the way software development is taught at HEIs is crucial for the industry, for the economy, for the students, and for the HEIs. As there is a tendency in the industry to embrace the scrum method and seek employees equipped with skills necessary for the scrum methodology use, it is necessary to ensure that HEIs offer the students the opportunity to get exposed to scrum. By querying the challenges of switching to agile software development methodologies in senior capstone projects, this paper makes a case that software development and software development methodology form the thrust of a multi-stakeholder ecosystem that defines today’s digital economy and society. In this context, the added value of this paper rests in the elaboration of a method enabling HEIs to move toward scrum in senior projects.
Improving chaos-based pseudo-random generators in finite-precision arithmetic

One of the widely-used ways in chaos-based cryptography to generate pseudo-random sequences is to use the least significant bits or digits of finite-precision numbers defined by the chaotic orbits. In this study, we show that the results obtained using such an approach are very prone to rounding errors and discretization effects. Thus, it appears that the generated sequences are close to random even when parameters correspond to non-chaotic oscillations. In this study, we confirm that the actual source of pseudo-random properties of bits in a binary representation of numbers can not be chaos, but computer simulation. We propose a technique for determining the maximum number of bits that can be used as the output of a pseudo-random sequence generator including chaos-based algorithms. The considered approach involves evaluating the difference of the binary representation of two points obtained by different numerical methods of the same order of accuracy. Experimental results show that such estimation can significantly increase the performance of the existing chaos-based generators. The obtained results can be used to reconsider and improve chaos-based cryptographic algorithms.
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions

Gradient descent (GD) optimization algorithms are the standard ingredients that are used to train artificial neural networks (ANNs). However, even in the case of the most basic variant of GD optimization algorithms, the plain vanilla GD method, it remains until today an open problem to prove or disprove the conjecture that GD converges in the training of ANNs. In this article we solve this problem in the special situation where the target function under consideration is a constant function. More specifically, in the case of constant target functions we prove in the training of rectified fully-connected feedforward ANNs with one-hidden layer that the risk function of the GD method does indeed converge to zero. A key contribution of this work is also to explicitly specify a Lyapunov function for the gradient flow system of the ANN parameters. This Lyapunov function is the central tool in our convergence proof.
Role of Blockchain in Forestalling Pandemics

The unexpected development and quick; however, the uncontrolled overall spread of the Coronavirus shows us the disappointment of existing human services observation frameworks to convenient handle general wellbeing crises. In spite of the fact that upgrades in medicinal services observation have been understood, these still miss the mark in forestalling commotion. Absence of important advances taken to guarantee control and following of the infection have bothered the circumstance. Blockchain innovation has progressively been referenced as an instrument to help with different parts of various applications. This paper highlights the role of blockchain in forestalling the future of pandemics. Various use cases of blockchain technology that can help in the battle against the COVID-19 are also highlighted in this paper.
Trust and Distrust based Cross-domain Recommender System

A recommender system (RS) provides assistance for users to filter out items of their interest in the presence of millions of available items. The reason is to find out the likewise user with the assumption that if users have shared similar interest in the past then they may share the same in future. Collaborative filtering (CF) is the widely used recommendation algorithm due to its ease of use but suffers with the problems of sparsity and cold start problem. In this paper, we propose a trust and distrust-based cross domain context aware recommender system in the multi-agent environment which tries to reduce the problem of data sparsity in collaborative-filtering recommender system and improves coverage. Cross Domain Recommender System (CDRS) utilizes data from multiple domains to reduce the problem of sparsity. Moreover, the combination of trust and distrust in recommendation help to improve trustworthiness of generated recommendation. Distrust provides higher accuracy in recommendation by incorporating knowledge about the malicious users. Prototype of the system is developed using JADE and Java technology for the tourism domain consisting of restaurant, hotel, travel places and shopping places as sub-domains. The performance of the proposed trust and distrust-based cross domain recommender system is compared with the traditional approach of recommendation along with the cross domain approach and trust-based cross-domain approach in terms of accuracy and coverage. The results show that the proposed system outperforms in terms of both accuracy and coverage.
Forgetful experience replay in hierarchical reinforcement learning from expert demonstrations

Deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. These results are commonly achieved at the expense of huge computational costs and require an incredible number of episodes of interactions between the agent and the environment. Hierarchical methods and expert demonstrations are among the most promising approaches to improve the sample efficiency of reinforcement learning methods. In this paper, we propose a combination of methods that allow the agent to use low-quality demonstrations in complex vision-based environments with multiple related goals. Our Forgetful Experience Replay (ForgER) algorithm effectively handles expert data errors and reduces quality losses when adapting the action space and states representation to the agent’s capabilities. The proposed goal-oriented replay buffer structure allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. Our method has a high degree of versatility and can be integrated into various off-policy methods. The ForgER surpasses the existing state-of-the-art RL methods using expert demonstrations in complex environments. The solution based on our algorithm beats other solutions for the famous MineRL competition and allows the agent to demonstrate the behavior at the expert level.
Stochastic Grounded Action Transformation for Robot Learning in Simulation

Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transformation (GAT) algorithm, seek to correct for- or ground-these differences by matching the simulator to the real world. However, the efficacy of these approaches is limited if they do not explicitly account for stochasticity in the target environment. In this work, we analyze the problems associated with grounding a deterministic simulator in a stochastic real world environment, and we present examples where GAT fails to transfer a good policy due to stochastic transitions in the target domain. In response, we introduce the Stochastic Grounded Action Transformation (SGAT) algorithm, which models this stochasticity when grounding the simulator. We find experimentally-for both simulated and physical target domains-that SGAT can find policies that are robust to stochasticity in the target domain.
ConnectIt

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.
A New Belief-Based Bidirectional Transfer Classification Method

In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods.