Reviewerly/exHarmony huggingface dataset:
Utility-Driven <i>k</i>-Anonymization of Public Transport User Data

In this article, we propose a k-anonymity approach that prioritizes the generalization of attributes based on their utility. We focus on transport data, which we consider a special case in which many or all attributes are quasi-identifiers (e.g., origin, destination, ride start time), as they allow correlation with easily observable auxiliary data. The novelty in our approach lies in introducing normalization techniques as well as distance and utility metrics that allow the consideration of not only numerical attributes but also categorical attributes by representing them in tree or graph form. The prioritization of the attributes in the generalization process is based on the attributes' utility and can further be influenced by either automatically or manually assigned attribute weights. We evaluate and compare different options for all components of our mechanism as well as present an extensive performance evaluation of our approach using real-world data. Lastly, we show in which cases suppression of records can counter-intuitively lead to higher data utility.
Integrate syntax information for target-oriented opinion words extraction with target-specific graph convolutional network

Target-oriented Opinion Words Extraction (TOWE) aims to identify opinion words toward a specific target given the sentence. Syntax structure, which contains dependency relationships among words, is a vital clue for this task. With the help of syntax structure as a constraint, the model could remove irrelevant words and focus on tokens that are relevant to the given target. Directly adapting existing syntactic-based methods faces the problem that these models do not explicitly learn target-centric representations. Another challenge is that prior works only learn fixed order dependency relations, while context words require syntactic information in different scales. To handle these issues, we propose Target-Specific Graph Convolutional Network (TS-GCN) to explicitly integrate dependency structure. The proposed method could build high-quality syntax-aware representations by propagating target information to syntactically related words via graph convolution. Furthermore, we design a memory-based module to dynamically learn multi-granularity syntactic knowledge and infuse local features. Experimental results demonstrate the effectiveness of our method, and we achieve state-of-the-art performances on four SemEval datasets.
Embedded Computation Architectures for Autonomy in Unmanned Aircraft Systems (UAS)

This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.
Improvised multi-robot cooperation strategy for hunting a dynamic target

Hunting moving targets with random motions and behavior is a challenge for robotic systems, and it occupies a significant position in the research on coordination and cooperation in multi-robot systems.Cooperative hunting between robots is used in a wide range of fields such as industry, military, rescue and other fields.The aim of this paper is to present a new strategy for target hunting by means of a cooperative multi-robot system in two-dimensional space, especially moving targets with random and unexpected behavior.The strategy is inspired from the behavior of wolves in hunting and Wolf Swarm Algorithm, as it summarizes the roles of robots in the system in three roles: the leader wolf, the antagonist wolf, and the follower wolf.This diversity of roles contributed to improve the convergence performance of the algorithm and reduce significantly the pursuit time.The validity of this strategy is supported by computer simulations.
LibGuide Standards: Loose Regulations and Lax Enforcement

Springshare’s LibGuides product has become the preferred method for librarians to create online subject guides. In the spring of 2019 LibGuide authors and administrators at Greater Western Library Alliance (GWLA) member libraries were surveyed to learn more about how libraries use this ubiquitous library tool. The two surveys focused on procedures and standards related to the creation, revision, and appearance of LibGuides, and librarians’ general thoughts and perceptions about guides. Most authors surveyed felt that the creation of guides is useful, but many raised valid reservations. Results of the survey reinforce prior studies’ findings that guides are often created and then forgotten. Even at institutions with established standards and criteria for LibGuides, there is pushback on their enforcement. Lack of time is cited by both groups as a reason and justification for this behavior, as are problems keeping links and content current, and problems learning the technology. In addition to better enforcement of established rules and best practices, this study revealed a need for usability testing for guides and more training for guide authors.
CMOS charge qubits and qudits: entanglement entropy and mutual information as an optimization method to construct CNOT and SWAP Gates

Abstract In this paper, we propose an optimization method for the construction of two-qubit and two-qudit quantum gates based on semiconductor position-based charge qubits. To describe the evolution of various quantum states, we use a Hubbard based model and Lindblad formalism. The suggested optimization algorithm uses the time evolution of entanglement entropy and mutual information for the determination of the system parameters to achieve high fidelity gates.
Enhance learning experience using technology in class

Majority of the students now have access to portable devices that can provide countless information at their fingertips through various resources such as learning games and interactive applications. These resources allow immediate communication and interaction between students and instructors. In this paper, we study the effectiveness of using these resources in lectures on students’ academic performance and their level of understanding of the lecture topic. A survey was carried out for students, who have taken statistics courses at the University of Toronto, to analyze various factors of using technology in class. The results of the survey showed that a significant portion of students gained a deeper level of understanding using technology in class, thus allowing us to take advantage of technology in class to create a more immersive learning environment for students than traditional methods.
Neural gradients are near-lognormal: improved quantized and sparse training

While training can mostly be accelerated by reducing the time needed to propagate neural gradients (loss gradients with respect to the intermediate neural layer outputs) back throughout the model, most previous works focus on the quantization/pruning of weights and activations. These methods are often not applicable to neural gradients, which have very different statistical properties. Distinguished from weights and activations, we find that the distribution of neural gradients is approximately lognormal. Considering this, we suggest two closed-form analytical methods to reduce the computational and memory burdens of neural gradients. The first method optimizes the floating-point format and scale of the gradients. The second method accurately sets sparsity thresholds for gradient pruning. Each method achieves state-of-the-art results on ImageNet. To the best of our knowledge, this paper is the first to (1) quantize the gradients to 6-bit floating-point formats, or (2) achieve up to 85% gradient sparsity --- in each case without accuracy degradation. Reference implementation accompanies the paper in the supplementary material.
Meta-Learning via Hypernetworks

Recent developments in few-shot learning have shown that during fast adaption, gradient-based meta-learners mostly rely on embedding features of powerful pretrained networks. This leads us to research ways to effectively adapt features and utilize the meta-learner's full potential. Here, we demonstrate the effectiveness of hypernetworks in this context. We propose a soft row-sharing hypernetwork architecture and show that training the hypernetwork with a variant of MAML is tightly linked to meta-learning a curvature matrix used to condition gradients during fast adaptation. We achieve similar results as state-of-art model-agnostic methods in the overparametrized case, while outperforming many MAML variants without using different optimization schemes in the compressive regime. Furthermore, we empirically show that hypernetworks do leverage the inner loop optimization for better adaptation, and analyse how they naturally try to learn the shared curvature of constructed tasks on a toy problem when using our proposed training algorithm.
An Efficient and Accurate Depth-Wise Separable Convolutional Neural Network for Cybersecurity Vulnerability Assessment Based on CAPTCHA Breaking

Cybersecurity practitioners generate a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) as a form of security mechanism in website applications, in order to differentiate between human end-users and machine bots. They tend to use standard security to implement CAPTCHAs in order to prevent hackers from writing malicious automated programs to make false website registrations and to restrict them from stealing end-users’ private information. Among the categories of CAPTCHAs, the text-based CAPTCHA is the most widely used. However, with the evolution of deep learning, it has been so dramatic that tasks previously thought not easily addressable by computers and used as CAPTCHA to prevent spam are now possible to break. The workflow of CAPTCHA breaking is a combination of efforts, approaches, and the development of the computation-efficient Convolutional Neural Network (CNN) model that attempts to increase accuracy. In this study, in contrast to breaking the whole CAPTCHA images simultaneously, this study split four-character CAPTCHA images for the individual characters with a 2-pixel margin around the edges of a new training dataset, and then proposed an efficient and accurate Depth-wise Separable Convolutional Neural Network for breaking text-based CAPTCHAs. Most importantly, to the best of our knowledge, this is the first CAPTCHA breaking study to use the Depth-wise Separable Convolution layer to build an efficient CNN model to break text-based CAPTCHAs. We have evaluated and compared the performance of our proposed model to that of fine-tuning other popular CNN image recognition architectures on the generated CAPTCHA image dataset. In real-time, our proposed model used less time to break the text-based CAPTCHAs with an accuracy of more than 99% on the testing dataset. We observed that our proposed CNN model has efficiently improved the CAPTCHA breaking accuracy and streamlined the structure of the CAPTCHA breaking network as compared to other CAPTCHA breaking techniques.
Selfie Periocular Verification Using an Efficient Super-Resolution Approach

Selfie-based biometrics has great potential for a wide range of applications since, e.g. periocular verification is contactless and is safe to use in pandemics such as COVID-19, when a major portion of a face is covered by a facial mask. Despite its advantages, selfie-based biometrics presents challenges since there is limited control over data acquisition at different distances. Therefore, Super-Resolution (SR) has to be used to increase the quality of the eye images and to keep or improve the recognition performance. We propose an Efficient Single Image Super-Resolution algorithm, which takes into account a trade-off between the efficiency and the size of its filters. To that end, the method implements a loss function based on the Sharpness metric used to evaluate iris images quality. Our method drastically reduces the number of parameters compared to the state-of-the-art: from 2,170,142 to 28,654. Our best results on remote verification systems with no redimensioning reached an EER of 8.89% for FaceNet, 12.14% for VGGFace, and 12.81% for ArcFace. Then, embedding vectors were extracted from SR images, the FaceNet-based system yielded an EER of 8.92% for a resizing of x2, 8.85% for x3, and 9.32% for x4.
SAW-BOT: Proposing Fixes for Static Analysis Warnings with GitHub Suggestions

In this experience report we present SAW-BOT, a bot proposing fixes for static analysis warnings. The bot has been evaluated with five professional software developers by means of a Wizard of Oz experiment, semi-structured interviews and the mTAM questionnaire. We have observed that developers prefer GitHub suggestions to two baseline operation modes. Our study indicates that GitHub suggestions are a viable mechanism for implementing bots proposing fixes for static analysis warnings.
An empirical study on the evaluation of the RDF storage systems

Abstract In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieves highly competitive computational performance against other state-of-the-art methods in terms of load time and query response time.
Automated vs Manual Content Analysis – A Retrospective Look

Content Analysis, which is a part of qualitative analysis, has mainly been studied in scientific articles from health and medicine domains. With the emerge of social networks, there are new opportunities for content analysis, which can be used to analyse user generated content, from various sources. Nevertheless, the companies are investing millions of dollars in content analysis, which is often known as sentiment analysis. The discussion in this article helps to understand the main concepts of content analysis for those interested in the domain of qualitative analysis, with the help of automated and manual qualitative research. The overall conclusion is that automated qualitative analysis is dependent on how accurate is the tool used and this feature can be checked with the help of manual qualitative analysis.
Audio-Visual Self-Supervised Terrain Type Recognition for Ground Mobile Platforms

The ability to recognize and identify terrain characteristics is an essential function required for many autonomous ground robots such as social robots, assistive robots, autonomous vehicles, and ground exploration robots. Recognizing and identifying terrain characteristics is challenging because similar terrains may have very different appearances (e.g., carpet comes in many colors), while terrains with very similar appearance may have very different physical properties (e.g., mulch versus dirt). In order to address the inherent ambiguity in vision-based terrain recognition and identification, we propose a multi-modal self-supervised learning technique that switches between audio features extracted from a microphone attached to the underside of a mobile platform and image features extracted by a camera on the platform to cluster terrain types. The terrain cluster labels are then used to train an image-based real-time CNN (Convolutional Neural Network) to predict terrain types changes. Through experiments, we demonstrate that the proposed self-supervised terrain type recognition method achieves over 80% accuracy, which greatly outperforms several baselines and suggests strong potential for assistive applications.
An Exploratory Study of the Effects of Knowledge Sharing Methods on Cyber Security Practice

In a networked global economy, cyber security threats have accelerated at an enormous rate. The security infrastructure at organisational and national levels are often ineffective against these threats. As a result, academics have focused their research on information security risks and technical perspectives to enhance human-related security measures. To further extend this trend of research, this study examines the effects of three knowledge sharing methods on user security practices: security training, social media communication, and local security experts (non-IT staff). The study adopts a phenomenological method employing in-depth focus group interviews with 30 participants from eight organisations located in Ho Chi Minh city, Vietnam. The study expands on understanding factors contributing to self-efficacy and security practice through various knowledge sharing channels. Current methods of periodical training and broadcast emails were found to be less effective in encouraging participants to develop security self-efficacy and were often ignored. Security knowledge sharing through social media and local experts were identified as supplementary methods in maintaining employees’ security awareness. In particular, social media is suggested as a preferred channel for disseminating urgent security alerts and seeking peer advice. Local security experts are praised for providing timely and contextualised security advice where member trust is needed. This study suggests that provisions of contemporary channels for security information and knowledge sharing between organisations and employees can gain regular attention from employees, hence leading to more effective security practices.
Chaotic Recurrent Neural Networks for Financial Forecast

In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community.In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond's original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction.We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing.The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases.The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price.Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.
On the continuous contract verification using blockchain and real-time data

Supply chains play today a crucial role in the success of a company’s logistics. In the last years, multiple investigations focus on incorporating new technologies to the supply chains, being Internet of Things (IoT) and blockchain two of the most recent and popular technologies applied. However, their usage has currently considerable challenges, such as transactions performance, scalability, and near real-time contract verification. In this paper we propose a model for continuous verification of contracts in supply chains using the benefits of blockchain technology and real-time data acquisition from IoT devices for early decision-making. We propose two platform independent optimization techniques (atomic transactions and grouped validation) that enhances data transactions protocol and the data storage procedure and a method for continuous verification of contracts, which allows to take corrective actions to reduce operational costs and increase benefits in current supply chains. An automatic deployment of a large scale distributed business logic system using virtualized appliances is also proposed. Evaluation results show the feasibility or the solution proposed.
Real-Time Object Tracking Based on Optical Flow

The optical flow algorithm has been widely used in object detection and tracking. These applications are hard to implement on the hardware level in real-time, due to their high computational complexity. In this paper, we propose an efficient hardware architecture for the accurate computation of the Lucas-Kanade (L-K) optical flow. The optical flow estimation involves several tasks such as Gaussian smoothing, gradient computation, integral calculation, and the vector computation, which are processed in a parallel architecture. The proposed architecture was simulated and verified by synthesizing onto a Xilinx Field Programmable Gate Array (FPGA), which can process 640×480 resolution frames at 30 frames/s.