Reviewerly/exHarmony huggingface dataset:
On the different types of collective attacks in abstract argumentation: equivalence results for SETAFs

Abstract Argumentation frameworks with collective attacks are a prominent extension of Dung’s abstract argumentation frameworks, where an attack can be drawn from a set of arguments to another argument. These frameworks are often abbreviated as SETAFs. Although SETAFs have received increasing interest recently, a thorough study on the actual behaviour of collective attacks has not been carried out yet. In particular, the richer attack structure SETAFs provide can lead to different forms of redundant attacks, i.e. attacks that are subsumed by attacks involving less arguments. Also the notion of strong equivalence, which is fundamental in nonmonotonic formalisms to characterize equivalent replacements, has not been investigated for SETAFs so far. In this paper, we first provide a classification of different types of collective attacks and analyse for which semantics they can be proven redundant. We do so for eleven well-established abstract argumentation semantics. We then study how strong equivalence between SETAFs can be decided with respect to the considered semantics and also consider variants of strong equivalence. Our results show that removing redundant attacks in a suitable way provides direct means to characterize strong equivalence by syntactical equivalence of so-called kernels, thus generalizing well-known results on strong equivalence between Dung AFs.
Introducing The ‘Unified Side Channel Attack - Model’ (USCA-M)

This paper presents a ‘Unified Side Channel Attack Model’ (USCA-M). The USCA-M model is compiled by the research undertaken of side-channel attacks (SCAs) from published journal articles and conference papers between 2015-2019. The research found that SCAs can be categorised into three main areas: SCA types, SCA methods and SCA techniques. By using this categorisation as a foundation, the USCA-M was compiled. USCA-M has three main contributions to the research field:1.A unified model that can categorize present and future SCA vulnerabilities and exploit techniques found.2.A reference point for organisations to be able to identify and place a found SCA within a common or unified categorisation.3.The decomposition of SCA methods and techniques into identifiable components to assist in the defence of SCAs, such as code pattern recognition.
Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification

Many real classification problems are characterized by a strong disturbance in a prior probability, which for the most of classification algorithms leads to favoring majority classes. The action most often used to deal with this problem is oversampling of the minority class by the smote algorithm. Following work proposes to employ a modification of an individual binary classifier support-domain decision boundary, similar to the fusion of classifier ensembles done by the Fuzzy Templates method to deal with imbalanced data classification without introducing any repeated or artificial patterns into the training set. The proposed solution has been tested in computer experiments, which results shows its potential in the imbalanced data classification.
Learning Word-vector Quantization

We introduced a new classifier named Learning Word-vector Quantization (LWQ) to solve morphological ambiguities in Turkish, which is an agglutinative language. First, a new and morphologically annotated corpus, and then its datasets are prepared with a series of processes. According to datasets, LWQ finds optimal word-vectors positions by moving them in the Euclidean space. LWQ does morphological disambiguation in two steps: First, it defines all solution candidates of an ambiguous word using a morphological analyzer; second, it chooses the best candidate according to its total distances to neighbor words that are not ambiguous. To show LWQ's performance, we have conducted many tests on the corpus by considering the consistency of classification. In the experiments, we achieve 98.4% correct classification ratio to choose correct parse output, which is an excellent level for the literature.
Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.
Baccalauréat Practice Tests in Cameroon

In Cameroon, no exam is more important than the baccalauréat, which serves as the gateway to adulthood and professional and higher-education aspirations. We build on existing literature on the benefits of practice tests to evaluate how SMS-based quizzes could help students in their final year of secondary school prepare for the baccalauréat. Students in 23 schools across Yaoundé, the capital of Cameroon, received multiple-choice questions four times per week for nine weeks leading up to the exam date. We examined baccalauréat pass rates and student feedback through 11 focus groups. We find that students used our practice tests for formative assessment and as a prompt for recall and review of study material. SMS-based quizzes were not isolated artifacts but were shared in the students' networks, built a distributed learning environment, and used as a focus of collaborative study sessions.
Research on fault diagnosis method of 750kV substation based on Bayesian network and fault recording information fusion

Abstract Aiming at the complexity of the 750kV substation system and the uncertainty of the fault information, this paper studies the fault diagnosis method of Bayesian network and fault recording information. In order to solve the problem of single-source, DS evidence theory is used to fuse the two diagnosis results. Through case analysis, it is proved that the method can effectively improve the accuracy of fault diagnosis.
Quantum Hashing and Fingerprinting for Quantum Cryptography and Computations

Fingerprinting and cryptographic hashing have quite different usages in computer science, but have similar properties. Interpretation of their properties is determined by the area of their usage: fingerprinting methods are methods for constructing efficient randomized and quantum algorithms for computational problems, while hashing methods are one of the central cryptographic primitives. Fingerprinting and hashing methods are being developed from the mid of the previous century, while quantum fingerprinting and quantum hashing have a short history. In the paper we present computational aspects of quantum fingerprinting, discuss cryptographic properties of quantum hashing. We investigate the pre-image resistance of this function and show that it reveals only O(1) bits of information about the input.
Learning Network Representations With Different Order Structural Information

Network embeddings aim to learn representations of nodes in a network with both the first- and the high-order proximities preserved. The first-order proximity corresponds to network reconstruction, while the high-order proximity is in tune with network inference. Since the tradeoff between the two proximities varies on scenarios, we propose an adjustable network embedding (ANE) algorithm for adjusting the weight between the first- and the high-order proximities. ANE is based on two hypotheses: 1) nodes in closed triplets are more important than nodes in open triplets and 2) closed triplets with higher degrees are more important. In addition, we change the bidirectional sampling of Word2vec into directional sampling to preserve the frequency of node pairs in the training set. Three common tasks, network reconstruction, link prediction, and classification are conducted on various publicly available data sets to validate the abovementioned statements.
A forensic study of Tor usage on the Raspberry Pi platform using open source tools

Since the invention of the first computer, scientists have striven to minimise the size – and we can see the fruits of their efforts in the form of the Raspberry Pi. This is a series of small, single-board computers developed to promote the teaching of basic computer science in schools and in developing countries. According to the Raspberry Pi Foundation, more than five million Raspberry Pi boards were sold by February 2015, making it the best-selling British computer. By November 2016 the Foundation had sold 11 million units, and by March 2017, 12.5m, which made it the third best-selling ‘general purpose computer’. In July 2017, sales reached nearly 15 million and less than a year later, in March 2018, 19 million.
Safety Overview of Ablution Spaces in the UAE Mosques: A Randomised Survey in Three Cities

This study examines the current conditions of the prevailing ablution spaces in mosques as a viewpoint of prayers' convenience and care practices. Randomly selected thirty mosques from the Dubai, Sharjah, and Ajman City regions were participated in this study. Overall comfort and care features of the ablution spaces were evaluated by two main aspects: wellbeing and general safety factors. The wellbeing aspects include wheelchair accessibility, shoe rack areas, clothing hangers, and equipment storages whilst the general safety ones contain handrails, anti-slip mats, and drainage covers. Collected data clearly show that most of the partaken mosques provide accessible facilities for the wheelchair prayers. Whilst most of the mosques did not provide other wellbeing featured facilities such as shoe racks, clothing hangers, and equipment storages. This study also found that most of the safety features were not provided in the sampled mosques. Although covered drainages are made available by most mosques, handrails and anti-slip mats are hardly provided by the mosques. This study assessed a limited number of ablution spaces around the three major cities of the UAE, but overall findings clearly identify that the present conditions of the existing ablution spaces in mosques appear to be not ideally safe and comfortable environments. Therefore, this study suggests that all the mosques should consider installing such care and comfort facilities in their ablution spaces to enhance safety and wellbeing for the prayers and visitors of the mosque.
Answering Comparative Questions with Arguments

Abstract Question answering platforms such as Yahoo! Answers or Quora always contained questions that ask other humans for help when comparing two or more options. Since nowadays more and more people also “talk” to their devices, such comparative questions are also part of the query stream that major search engines receive. Interestingly, major search engines answer some comparative questions pretty well while for others, they just show the “standard” ten blue links. But a good response to a comparative question might be very different from these ten blue links—for example, a direct answer could show an aggregation of the pros and cons of the different options. This observation motivated our DFG-funded project “ACQuA: Answering Comparative Questions with Arguments” for which we describe the achieved results so far, and ongoing activities like the first shared task on argument retrieval.
Comparing inertia weights in Multi- swarm Particle Swarm Optimization

Intelligent collective behavior of some animals such as flocks of birds and schools of fish inspired stochastic-based collective algorithm such as Multi-swarm Particle Swarm optimisation (MsPSO). In 2014, Ngaam. Cheung developed a swarm intelligence technique based on the adjustment of fewer parameters in which the main parameter is the inertia weight. This technique considerably affects the convergence and exploration exploitation trade-off in MsPSO process. Since that, different strategies for determining the value of inertia weight during a course of run have been proposed. This paper studies 9 relatively recent and popular Inertia Weight strategies and compares their performance on 15 optimization test problems. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature.
A Novel Bio-inspired Hybrid Metaheuristic for Unsolicited Bulk Email Detection

With the recent influx of technology, Unsolicited Bulk Emails (UBEs) have become a potential problem, leaving computer users and organizations at the risk of brand, data, and financial loss. In this paper, we present a novel bio-inspired hybrid parallel optimization algorithm (Cuckoo-Firefly-GR), which combines Genetic Replacement (GR) of low fitness individuals with a hybrid of Cuckoo Search (CS) and Firefly (FA) optimizations. Cuckoo-Firefly-GR not only employs the random walk in CS, but also uses mechanisms in FA to generate and select fitter individuals. The content- and behavior-based features of emails used in the existing works, along with Doc2Vec features of the email body are employed to extract the syntactic and semantic information in the emails. By establishing an optimal balance between intensification and diversification, and reaching global optimization using two metaheuristics, we argue that the proposed algorithm significantly improves the performance of UBE detection, by selecting the most discriminative feature subspace. This study presents significant observations from the extensive evaluations on UBE corpora of 3, 844 emails, that underline the efficiency and superiority of our proposed Cuckoo-Firefly-GR over the base optimizations (Cuckoo-GR and Firefly-GR), dense autoencoders, recurrent neural autoencoders, and several state-of-the-art methods. Furthermore, the instructive feature subset obtained using the proposed Cuckoo-Firefly-GR, when classified using a dense neural model, achieved an accuracy of $$99\%$$ .
PATS: A New Neural Network Activation Function with Parameter

Activation function is crucial to the recent successes of deep neural networks. In this paper, we propose a new activation function with parameters, named PATS. Specifically, PATS is a non-monotonic function which combines arctangent function and sigmoid function. In the process of network model training, the parameter of PATS is a random number from the uniform distribution, which improves the flexibility of network model and reduces the risk of over fitting. In addition, PATS can be widely used in existing deep network models. We use several classic deep network models to test the performance of PATS. The experimental results on CIFAR-10 and CIFAR-100 datasets show that compared with other activation functions, PATS has better performance in improving the learning ability and robustness of deep network model. The code is available at https://github.com/zbygithub/PATS.
Apply Kinect and Zenbo to Develop Interactive Health Enhancement System

As Taiwan has entered an aging society, rehabilitation and fitness needs have become an important topic that many people must face. This paper proposed an “Interactive Health Enhancement System” to promote people's movement and rehabilitation with simple actions. The system was developed through a motion sensing input device (Kinect's camera v2) combined with the smart home robot (Zenbo), thereby improving the accuracy of the rehabilitation. Finally, according to the user experience, the system will adjust the correctness of the posture judgment and the accuracy of the interaction element.
Temporal action proposal for online driver action monitoring using Dilated Convolutional Temporal Prediction Network

Abstract This paper presents a new approach for temporal detection of short human activities in untrimmed videos. Most present methods for temporal action detection, to our best knowledge, are trained on public action datasets that feature actions spanning up to tens and hundreds of seconds. However, it is often desired in manufacturing, transportation, and other safety-critical scenes that fine-grained actions be automatically detected, classified, and monitored. We propose a new Dilated Convolutional Temporal Prediction Network that features 1-D dilated convolution operation in a Residual network (ResNet)-like architecture for the generation of action proposals on orders of fractions of a second. The new architecture is used as a part of the action monitoring pipeline in subway cars. Experiments demonstrate that the proposed model outperforms the state-of-the-art on the task of temporal action proposal generation on a real-world video dataset, while achieving a fast processing speed suitable for online monitoring.
Reliable Composition of MAPE-K Loops in Self-Adaptive Software Systems

Self-adaptive systems (SASs) are controlled by autonomic manager (AM). This ensures the QoS of such complex systems within highly dynamic and unpredictable contexts. However, the massive integration of the adaptation abilities increased drastically the complexity of the AMs. To decrease the complexity and ensure correctness adaptation, scholars propose a subdivision into multi-autonomic entities (AEs) as a design approach. In such a design approach, SASs are controlled through multiple interacting AMs implementing each the well-known MAPE-K Loop. In this article, the writers propose a refinement pattern of interacting multiple MAPE-K Loops to achieve global adaptation without conflict. The authors contribute with a notation to describe the interaction of multiple MAPE-K Control Loops. To ensure the coordinated multi-attributes control, the interaction of the AEs is achieved through the knowledge base of the MAPE-K Loops. To validate the proposed pattern, a case study in the field of Electric Vehicle is presented.
Aspect terms grouping via fusing concepts and context information

We introduce a neural method that is able to fuse concepts from a knowledge base with the context information for the task of grouping of aspect terms. Rather than only using context information, we use the corresponding concepts of aspect terms as additional information for aspect terms representation. We also introduce a location-based attention mechanism for accurately representing context features. As both the concept and the aspect term are same level features, i.e. aspect level features, we develop a model with gating mechanism to fuse them together. All of the above features are fed into a parallel metric learning network which has the ability to learn an easier grouping representation of samples. Experimental results demonstrate that our approach outperforms different baselines and model variants on five datasets.
Informal physics teaching for a better society: a mooc-based and context-driven experience on learning radioactivity

Abstract The general objective of teaching physics is to provide the learners with useful knowledge, in terms of both interdisciplinary scientific concepts and practical problem-solving skills. In this work, we report the experimental evidence, collected during a first year physics class in an upper secondary technical school, for the urgent necessity to adopt an informal and inquiry-based strategy to teach physics effectively, in particular to all those students living in degraded socio-economic environments. Within the pedagogical framework of “Learning by Doing” and the paradigm “Learning by Teaching”, we have explored the benefits of the students’ participation to an informal ICT-based and inquiry-driven learning experience about radioactivity. Subsequently, the same students attended, as scientific communicators, a national science exhibition where the majority of the secondary schools in the region presents their scientific exhibits. About three months after the participation to the scientific exhibition, the students answered to a questionnaire about radioactivity. Their answers have been analysed in comparison with those provided by a control group of students who attended a traditional lecture-based instruction. A significant improvement in the memorization of the main aspects concerning a radioactive decay, such as the definition of isotope, particle and electromagnetic radiation, the understanding of the radioactivity process at microscopic level, as well as a stronger view of the useful aspect of radioactivity in the everyday life have been definitely achieved by the students involved in this study.
Image Seam-Carving by Controlling Positional Distribution of Seams

Image retargeting is a new image processing task that renders the change of aspect ratio in images. One of the most famous image-retargeting algorithms is seam-carving. Although seam-carving is fast and straightforward, it usually distorts the images. In this paper, we introduce a new seam-carving algorithm that not only has the simplicity of the original seam-carving but also lacks the usual unwanted distortion existed in the original method. The positional distribution of seams is introduced. We show that the proposed method outperforms the original seam-carving in terms of retargeted image quality assessment and seam coagulation measures.
A Blind Aid System based on Jetson TX2 Embedded System and Deep Learning Technique

Blind people always have had many inconveniences in their daily lives because of eye defects, especially walking outdoors has become almost their main challenge and trouble. In order to let blind people easily understand the walking situation ahead to improve the safety and convenience of their walking, this paper proposes a portable blind aid system, which mainly uses the Jetson TX2 embedded system as the development platform and adopts the deep learning technique simultaneously. By utilizing the webcam equipped on the blind person's head and deep learning technique, the front images during his walking are extracted and recognized to judge whether there is his desired target object or not. Then, he can obtain the recognition results via the voice with the use of earphones. Thus, the proposed system can effectively assist the blind people to walk much safely and conveniently by themselves.