Reviewerly/exHarmony huggingface dataset:
Analysis of Different Evolutionary Techniques on Fuzzy Rule Base Generation

This paper presents the analysis of different population based algorithms for the rulebase generation from numerical data sets. As fuzzy rulebase generation is one of the key issues in fuzzy modeling. The algorithms are applied on a rapid Ni–Cd battery charger data set. In this paper, we compare the efficiency of different algorithms and conclude that SCA algorithms with local search give remarkable efficiency as compared to SCA algorithms alone. Also found that the efficiency of SCA with local search is comparable to memetic algorithms.
Cryptanalysis and improvement of several quantum private comparison protocols

Recently, Wu et al (2019 Int. J. Theor. Phys. 58 1854) found a serious information leakage problem in Ye and Ji's quantum private comparison protocol (2017 Int. J. Theor. Phys. 56 1517), that is, a malicious participant can steal another's secret data without being detected through an active attack means. In this paper, we show that Wu et al's active attack is also effective for several other existing protocols, including the ones proposed by Ji et al and Zha et al (2016 Commun. Theor. Phys. 65 711; 2018 Int. J. Theor. Phys. 57 3874). In addition, we propose what a passive attack means, which is different from Wu et al's active attack in that the malicious participant can easily steal another's secret data only by using his own secret data after finishing the protocol, instead of stealing the data by forging identities when executing the protocol. Furthermore, we find that several other existing quantum private comparison protocols also have such an information leakage problem. In response to the problem, we propose a simple solution, which is more efficient than the ones proposed by Wu et al, because it does not consume additional classical and quantum resources.
Towards retrieving social events from spatio-temporal photo annotation

An event can be defined as a happening that gathers people with some common goal over a period of time and in a certain place. This paper presents a new method to retrieve social events through annotations in spatio-temporal photo collections, known as STEve-PR (Spatio-Temporal EVEnt Photo Retrieval). The proposed technique uses a clustering algorithm to gather similar photos by considering the location, date and time of the photos. The STEve-PR clustering approach clusters photos belonging to the same event. STEve-PR uses spatial clusters created to propagate event annotation between photos in the same cluster and employs TF-IDF similarity between tags to find the spatial cluster with the highest similarity for photos without a geographical location. We evaluated our approach on a public database.
Local Reasoning for Global Graph Properties

Separation logics are widely used for verifying programs that manipulate complex heap-based data structures. These logics build on so-called separation algebras, which allow expressing properties of heap regions such that modifications to a region do not invalidate properties stated about the remainder of the heap. This concept is key to enabling modular reasoning and also extends to concurrency. While heaps are naturally related to mathematical graphs, many ubiquitous graph properties are non-local in character, such as reachability between nodes, path lengths, acyclicity and other structural invariants, as well as data invariants which combine with these notions. Reasoning modularly about such graph properties remains notoriously difficult, since a local modification can have side-effects on a global property that cannot be easily confined to a small region. In this paper, we address the question: What separation algebra can be used to avoid proof arguments reverting back to tedious global reasoning in such cases? To this end, we consider a general class of global graph properties expressed as fixpoints of algebraic equations over graphs. We present mathematical foundations for reasoning about this class of properties, imposing minimal requirements on the underlying theory that allow us to define a suitable separation algebra. Building on this theory we develop a general proof technique for modular reasoning about global graph properties over program heaps, in a way which can be integrated with existing separation logics. To demonstrate our approach, we present local proofs for two challenging examples: a priority inheritance protocol and the non-blocking concurrent Harris list.
Meta-Learning of Neural Architectures for Few-Shot Learning

The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semantic segmentation. However, one prerequisite for the application of NAS are large amounts of labeled data and compute resources. This renders its application challenging in few-shot learning scenarios, where many related tasks need to be learned, each with limited amounts of data and compute time. Thus, few-shot learning is typically done with a fixed neural architecture. To improve upon this, we propose MetaNAS, the first method which fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a meta-architecture along with the meta-weights during meta-training. During meta-testing, architectures can be adapted to a novel task with a few steps of the task optimizer, that is: task adaptation becomes computationally cheap and requires only little data per task. Moreover, MetaNAS is agnostic in that it can be used with arbitrary model-agnostic meta-learning algorithms and arbitrary gradient-based NAS methods. %We present encouraging results for MetaNAS with a combination of DARTS and REPTILE on few-shot classification benchmarks. Empirical results on standard few-shot classification benchmarks show that MetaNAS with a combination of DARTS and REPTILE yields state-of-the-art results.
Toward Error-Correcting Architectures for Cryptographic Circuits Based on Rabii–Keren Codes

We present an error-correcting architecture for cryptographic circuits that are exposed to maliciously injected faults. The architecture is based on a new class of error-detecting and correcting codes, which combine high rate, large distance, and robustness; that is, they can detect all error patterns injected by a skillful and strategic attacker and automatically correct simpler errors. Correction of errors is superior to detection since it avoids service disruptions and system-level recovery actions. We investigate the architectures using both mathematical analysis and physical fault injection on an field programmable gate array (FPGA) platform, and point out critical divergences between these methods and the need to employ both of them.
The Integration of Tor into Library Services: An Appeal to the Core Mission and Values of Libraries

A pressing concern for any library professional today is privacy and security for patrons while using internet resources. One platform that shows promise in promoting privacy and security online is Tor, an anonymous web browser. The platform prevents the theft of user data through advanced networking patterns and holds the capacity to combat censorship and manipulation while strengthening privacy. In advocating for the implementation of Tor in a library setting, it is important to understand how the need for such implementation is support by library mission statements, values, and policy. This article demonstrates how the mission and values of Tor aligns with those of public libraries and advocates for greater adoption of this platform within libraries.
Driver Eye Movements and the Application in Autonomous Driving

Author(s): Xia, Ye | Advisor(s): Whitney, David | Abstract: Despite the exciting progress in computer vision in the field of autonomous driving, understanding efficiently which cues or objects are the most crucial ones in a crowded traffic scene is still a big challenge. Human drivers can quickly identify the important visual cues or objects in their blurry periphery vision and then make eye movements to direct their more accurate foveal vision to the important regions. Therefore, driver eye movements may be what computer vision can borrow from human vision to make autonomous driving systems better at locating and understanding the important regions of crowded traffic scenes. Meanwhile, the large-scale datasets and advanced object recognition algorithms that emerged in the field of autonomous driving make it possible to study classical human vision science problems in natural driving situations. Here, we used driver eye movements to improve autonomous driving models and studied visual crowding—the bottleneck of human object recognition—in realistic driving situations through driver eye movements. First, we developed a new protocol that collects driver eye movements in an offline manner for large-scale driving video datasets. We built a deep neural network that predicts human driver gaze from dash camera videos for various driving scenarios. Our model outperformed the current state-of-the-art model. Furthermore, we incorporated the driver gaze prediction model into an autonomous driving model to make a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. This model combines low-resolution input of the whole video frames and high-resolution input from predicted gaze locations to predict vehicle speed. We show that the added human gaze significantly improves the driving accuracy and that our periphery-fovea multi-resolution model outperforms a uni-resolution periphery-only model that has the same amount of floating-point operations. Finally, we studied visual crowding in driving situations. We show that crowding occurs in natural driving scenes and that the degree of crowding correlates with altered saccade localization in realistic driving-like situations. Together, these studies demonstrate the application of driver eye movements in making safer and more efficient autonomous driving models and show strong evidence of visual crowding in driving situations via the analysis of driver eye movements. These studies also present examples of combining human vision and computer vision to get mutual benefits from both fields.
The relationship between learning styles and learners’ satisfaction in the integrated English reading and writing classes using flipped learning

The purpose of this study is to investigate how students perceive integrated English reading and writing classes using flipped learning and find out the relationship between their individual learning styles and their satisfaction with the classes. The research was implemented for 15 weeks with fifty-seven Korean university students. For the study, students’ satisfaction with the class was examined to find out their overall perceptions and attitudes. Also, the relationship between the students’ satisfaction with the class and their learning styles was analyzed. The results were as follows. First, the students showed positive perceptions and attitudes toward the classes. This suggests that flipped learning can be effective for the integrated English reading and writing classes. Second, independent and collaborative learning styles were factors influencing learners’ satisfaction toward the English classes using flipped leaning. That is, the English class using flipped leaning was suitable for students with independent and collaborative learning styles. This finding indicates that teachers should consider learners with various learning styles in order to apply flipped learning more effectively to classes. Based on the study, some guidelines for effective English classes were suggested.
IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION

&lt;span class="fontstyle0"&gt;Abstract &lt;/span&gt;&lt;span class="fontstyle2"&gt;Facial expression recognition is one of the challenging tasks in computer&lt;br /&gt;vision. In this paper, we analyzed and improved the performances both&lt;br /&gt;handcrafted features and deep features extracted by Convolutional Neural&lt;br /&gt;Network (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.&lt;br /&gt;Additionally, we developed features based on the distances between facial&lt;br /&gt;landmarks and SIFT descriptors around the centroids of the facial landmarks,&lt;br /&gt;leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracy&lt;br /&gt;with a CNN model trained from scratch. By combining CNN features with&lt;br /&gt;handcrafted features, we achieved 69.54 % test accuracy.&lt;br /&gt;&lt;/span&gt;&lt;span class="fontstyle0"&gt;Key Word&lt;/span&gt;&lt;span class="fontstyle3"&gt;: &lt;/span&gt;&lt;span class="fontstyle2"&gt;Neural network, facial expression recognition, handcrafted features&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;
Emotional Induction Study of Video in Menstrual Cycle through ECG Signal

The purpose of this study is to investigate the effects of different phases of the menstrual cycle (follicular phase and luteal phase) on emotion induction of video through ECG signal. Firstly, the ECG data of twenty subjects were acquired when they were watching positive, negative or neutral videos in follicular phase and luteal phase, respectively. Then, the RR interval series were obtained from the ECG data. Finally, some statistical, such as the mean, the standard deviation, the first and second differences between consecutive RR intervals, and the root mean square and Krawchouk moment features were extracted from the RR interval series to evaluate the effects of different phases of the menstrual cycle on emotion induction by using extreme learning machine. The results shown that women were more irritated and more likely to induce negative emotions during the luteal phase.
Security Awareness Training

Cybersecurity has become a major topic of discussion these days. Almost every day a new cyberattack on a corporation, government, or other entity is in the news because it resulted in millions of customer records being exposed, stolen credentials, and ransomware attacks. Cybercriminals have become increasingly sophisticated, and cybersecurity threats have escalated rapidly in recent years. Business organizations and government agencies have realized the importance of educating their employees about cybersecurity threats to safeguard against them.
Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model

Widespread of satirical news in online communities is an ongoing trend. The nature of satires is so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper, we propose a hybrid technique for extracting features from text documents combining Word2Vec and TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%.
Following Fast-Dynamic Targets With Only Slow and Delayed Visual Feedback: A Kalman Filter and Model-Based Prediction Approach

Abstract Although visual feedback has enabled a wide range of robotic capabilities such as autonomous navigation and robotic surgery, low sampling rate and time delays of visual outputs continue to hinder real-time applications. When partial knowledge of the target dynamics is available, however, we show the potential of significant performance gain in vision-based target following. Specifically, we propose a new framework with Kalman filters and multirate model-based prediction (1) to reconstruct fast-sampled 3D target position and velocity data, and (2) to compensate the time delay for general robotic motion profiles. Along the path, we study the impact of modeling choices and the delay duration, build simulation tools, and experimentally verify different algorithms with a robot manipulator equipped with an eye-in-hand camera. The results show that the robot can track a moving target with fast dynamics even if the visual measurements are slow and incapable of providing timely information.
mts: a light framework for parallelizing tree search codes

We describe mts, a generic framework for parallelizing certain types of tree search programmes including reverse search, backtracking, branch and bound and satisfiability testing. It abstracts and generalizes the ideas used in parallelizing lrs, a reverse search code for vertex enumeration. mts supports sharing information between processes which is important for applications such as satisfiability testing and branch-and-bound. No parallelization is implemented in the legacy single processor programmes minimizing the changes needed and simplifying debugging. mts is written in C, uses MPI for parallelization and can be used on a network of computers. We give four examples of reverse search codes parallelized by using mts: topological sorts, spanning trees, triangulations and Galton-Watson trees. We also give a parallelization of two codes for satisfiability testing. We give experimental results comparing the parallel codes with other codes for the same problems.
Towards a Minimalist Machine

This chapter describes a machine that efficiently generates syntactic structure using fundamental Set and Pair Merge operations beginning with a pre-ordered Lexical Array (LA) of heads. The machine also implements Probe-Goal agreement and Phase theory to correctly converge on a variety of syntactic constructions discussed in the linguistics literature. Displacement is driven by Edge features. The machine state consists of a current syntactic object (SO) and the input (LA). It also incorporates a goal stack that effectively short-circuits the need to search inside the current SO. The machine is capable of locally deciding on the appropriate Merge and Probe operations at each Merge step by considering the Label and features of the current SO and the LA head, without the need for (temporary) over-generation or lookahead. Non-determinism, i.e. multiple convergent SOs, is also possible in cases where theory demands it
Structural optimisation algorithm of weight correlation analysis for DBN

The deep belief network (DBN) algorithm has slow convergence rate and low learning efficiency, and it is sensitive to model hyper-parameters. In view of the fact that DBN is a learning network model with a large number of network nodes and multiple hidden layers, this paper proposes a weight correlation analysis algorithm for DBN (WCA-DBN) to determine the approximately optimal network structure of DBN. This algorithm establishes a PS orthogonal projection space based on the 'arbitrary node set' using the linear discriminant analysis (LDA) method, projects the 'balanced node set' onto the PS plane, and solves the weights of the network nodes between sets through the correlation principle, to adjust the network structure of the hidden layer adaptively. The real-time traffic flow of six different types of roads in Jinan City, Shandong Province was selected as experimental materials to verify that the WCA-DBN algorithm has more obvious advantages over traditional algorithms in terms of accuracy and time complexity.
Library Technology Planning for Today and Tomorrow: A LITA Guide (2019)

"Library Technology Planning for Today and Tomorrow: A LITA Guide (2019)." Journal of Electronic Resources Librarianship, 31(4), p. 282
The Design of Ergonomic Requirements

The main design requirements for the product (For the meaning of the term “product” used in this book, see Sect. 1.1 .), which are presented in this chapter, are understood in Ergonomics as the requirements the design must meet comply with the user’s needs (for the meaning of the term “needs” used in this book, see Sect. 1.1 .), that is, as was explained in previous chapters, the set of needs, expectations and desires expressed explicitly or implicitly by the people who may come into contact with the system/product.
Classical analytical methods of optimization

The classical optimization techniques are analytical in nature and make use of differential calculus to solve the problems involving continuous differentiable functions. In engineering applications, optimization problems can have constraints that can arise because of the need of specifying physical bounds on the variables and the problems can also be governed by empirical relations and physical laws. This chapter discusses the classical analytical methods with necessary and sufficient conditions to solve various single-variable and multivariable unconstrained optimization problems. Also, different methods such as direct substitution, penalty function approach, and Lagrange multipliers are illustrated to solve multivariable optimization problems with equality constraints. The method of Kuhn–Tucker conditions with its necessary and sufficient conditions is derived and applied to solve multivariable optimization problems with equality and inequality constraints. All these methods are illustrated in terms of their applications to different nonlinear programming problems.
Procedimiento de análisis redes sociales: herramienta de auditoría de gestión del conocimiento

The social networks analysis has its mainly used in the networks of publications, networks of co-citations, collaboration structures and information resources on the web. Currently, it begins to be part of the knowledge management audit tools, although there are scares applications associated with labor relations and the interaction of people as result of their tasks in the workplace. The research objective is to develop a procedure for social networks analysis as a tool for the audit of knowledge management. The methods used are synthesis analysis, hypothetical deductive, system approach and intermediation, proximity, degree centrality and cluster analysis. As a result, is propose a methodology for the application of social network analysis that facilitates the application of other knowledge management tools, such as the DACUM method and knowledge maps.
Development of some two and three qutrit quantum logic gates

Quantum information processing has become a promising research area. In quantum information processing qubits and qutrits will be used instead of bits. A qubit is a two level quantum system and a qutrit is a three level quantum system. Zeeman levels of spin-1 electron or nucleus can be considered as qutrit states. In this study, first, two and three qutrit SWAP logic gates are developed. For two qutrit states, there exist different types of CNOT quantum logic gates. The relationships between these different types of CNOT gates are obtained in this study.
Corpus-Level End-to-End Exploration for Interactive Systems

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.