Reviewerly/exHarmony huggingface dataset:
ALAFA

Assessment of the security provided by a fault attack countermeasure is challenging, given that a protected cipher may leak the key if the countermeasure is not designed correctly. This paper proposes, for the first time, a statistical framework to detect information leakage in fault attack countermeasures. Based on the concept of non-interference, we formalize the leakage for fault attacks and provide a t-test based methodology for leakage assessment. One major strength of the proposed framework is that leakage can be detected without the complete knowledge of the countermeasure algorithm, solely by observing the faulty ciphertext distributions. Experimental evaluation over a representative set of countermeasures establishes the efficacy of the proposed methodology.
Toward an Operational Navigation Message Authentication Service: Proposal and Justification of Additional OSNMA Protocol Features

Galileo, the European GNSS, has confirmed its intention to provide Navigation Message Authentication for its Open Service already during this decade. A testing specification was made available in November 2016. Being OSNMA a novel service with several potential different users, some details of the protocol are under finalization. In particular, the paper focuses on the authentication of the public keys, renewal and revocation of all keys, and the authentication of the MAC sequence. The authentication of the public keys is proposed through a Merkle tree. The renewal and revocation of keys are handled with the existing flags, in particular NMA status, public key and chain status, and the authentication of the MAC sequence is shared between a MAC lookup table, and a new MAC that replaces previously redundant bits in the protocol. These new features complement the definition of the protocol while they do not affect OSNMA performance or appreciably increase user implementation requirements.
Security Qualitative Metrics for Open Web Application Security Project Compliance

The focus of this study is to find out repeatable features for large-scale enterprise web application production process related to based on OWASP security requirement list. As a result of a rigorous work including domain analysis for Java language and development frameworks and the examination of a large set of technical documents, 230 security qualitative metrics are discovered, under six categories. These security qualitative metrics are beneficial for security analysts as well as other parties such as designers, developers, and testers. The findings provide a developer/designer point of view and would help to make better decisions related to the environment set up, technology selection, and the architecture, design, and implementation details. As a result of this effort, the overall vulnerability level of the web applications would diminish significantly.
DeepPath: Path-Driven Testing Criteria for Deep Neural Networks

Inspired by path-oriented testing, we propose a series of path-driven testing criteria, called DeepPath, to comprehensively calculate coverage in deep neural networks (DNNs). Four DNN models and four adversarial attack techniques are used to evaluate the effectiveness of DeepPath. The experimental results illustrate that DeepPath are more discriminating to measure test adequacy of DNNs in practice, as well as more useful for recognizing adversarial attack test inputs.
Recommending Participants for Collaborative Merge Sessions

Development of large projects often involves parallel work performed in multiple branches. Eventually, these branches need to be reintegrated through a merge operation. During merge, conflicts may arise and developers need to communicate to reach consensus about the desired resolution. For this reason, including the right developers to a collaborative merge session is fundamental. However, this task can be difficult especially when many different developers have made significant changes on each branch over a large number of files. In this paper, we present TIPMerge, an approach designed to recommend participants for collaborative merge sessions. TIPMerge analyzes the project history and builds a ranked list of developers who are the most appropriate to integrate a pair of branches (Developer Ranking) by considering developers' changes in the branches, in the previous history, and in the dependencies among files across branches. Simply selecting the top developers in such a ranking is easy, but is not effective for collaborative merge sessions as the top developers may have overlapping knowledge. To support collaborative merge, TIPMerge employs optimization techniques to recommend developers with complementary knowledge (Team Recommendation) aiming to maximize joint knowledge coverage. Our results show a mean normalized improvement of 49.5% (median 50.4%) for the joint knowledge coverage with the optimization techniques for assembling teams of three developers for collaborative merge in comparison to choosing the top-3 developers in the ranked list.
AUGMENTED REALITY, ARTIFICIAL INTELLIGENCE, AND THE RE‐ENCHANTMENT OF THE WORLD

Abstract There has recently been a surge of development in augmented reality (AR) technologies that has led to an ecosystem of hardware and software for AR, including tools for artists and designers to accelerate the design of AR content and experiences without requiring complex programming. AR is viewed as a key “disruptive technology” and future display technologies (such as digital eyewear) will provide seamless continuity between reality and the digitally augmented. This article will argue that the technologization of human perception and experience of reality, coupled with the development of artificial intelligence (AI)–based natural language assistants, may lead to a secular re‐enchantment of the world, in the sense outlined by Charles Taylor, where human existence is shaped through AR inhabited by advanced personal and social AI agents in the form of digital avatars and daemons, and that enchantment has been persistent throughout the formation of modernity and is being rekindled by the integration of AI in the plane of AR.
Fuzzy-PID Controller for Two Wheels Balancing Robot Based on STM32 Microcontroller

In this paper, a fuzzy-PID controller is proposed to regulate the movement of two wheels balancing robot. The fuzzy-PID controller is a combination of a fuzzy controller and PID controllers for the balancing and movement or distance of the two wheels balancing robot. The fuzzy controller is designed based on relation models for the robot's balancing. The PID controllers are proposed to control the robot's position. The whole fuzzy-PID is ported and run on a real-time operating system using an STM32F4 Discovery Kit. The received results showed advantages of the fuzzy-PID controller.
Resilience of distributed student teams to stress factors: A longitudinal case-study

Context: Teaching global software engineering is continuously evolving and improving to prepare future software engineers adequately. Geographically distributed work in project-oriented software development courses is both demanding and rewarding for student teams, who are susceptible to various risks stemming from different internal and external factors, being the sources of stress and impacting team performance. Objective: In this paper, we analyze the resilience of teams of students working in a geographically fully distributed setting. Resilience is analyzed in relation to two representative stress factors: non-contributing team members and changes to customer project requirements. We also reason on team collaboration patterns and analyze potential dependencies among these collaboration patterns, team resilience and stress factors. Method: We conduct a longitudinal case-study over five years on our Distributed Software Development (DSD) course. Based on empirical data, we study team resilience to two stress factors by observing their impact on process and product quality aspects of team performance. The same performance aspects are studied for identified collaboration patterns, and bidirectional influence between patterns and resilience is investigated. Results: Teams with up to two non-contributing members experience graceful degradation of performance indicators. A large number of non-contributing students almost guarantees the occurrence of educationally undesirable collaboration patterns. Exposed to requirements change stress, less resilient teams tend to focus on delivering the functional product rather than retaining a proper development process. Conclusions: Practical recommendations to be applied in contexts similar to our case have been provided at the end of the study. They include suggestions to mitigate the sources of stress, for example, by careful planning the team organization and balancing the number of regular and exchange students, or by discussing the issue of changing requirements with the external customers before the start of the project.
Image Caption Generation With Adaptive Transformer

Encoder-decoder framework based image caption has made promising progress. The application of various attention mechanisms has also greatly improved the performance of the caption model. Improving the performance of every part of the framework or employ more effective attention mechanism will benefit the eventual performance. Based on this idea we make improvements in two aspects. Firstly we use more powerful decoder. Recent work shows that Transformer is superior in efficiency and performance to LSTM in some NLP tasks, so we use Transformer to substitute the traditional decoder LSTM to accelerate the training process. Secondly we combine the spatial attention and adaptive attention into Transformer, which makes decoder to determine where and when to use image region information. We use this method to experiment on the Flickr30k dataset and achieve better results.
Deep domain adaptation for anti-spoofing in speaker verification systems

With the increasing use of voice as a biometric, it has become imperative to develop countermeasures to thwart malicious spoofing attacks on speaker recognition systems. Even though there has been a significant research effort over the last few years dedicated to the development of countermeasures to detect and deflect spoofing attacks, the problem is far from being solved. While a deep learning technique has been successfully applied in anti-spoofing research, it suffers from a data scarcity issue where large amounts of labeled training data are required to build a robust model. In this paper, we investigate a domain adaptation approach of deep architectures in both a supervised setting where we use labeled data, and in an unsupervised setting where we assume unlabeled data when transferring knowledge from the source to target domain. Specifically, we employ convolutional neural networks (CNNs) as back-end classifiers for spoofed speech detection. For supervised domain adaptation, we propose joint neural network training while allowing the weights to be shared between the source and target streams, and an additional domain regularizer. In the unsupervised domain adaptation scenario, the weights are not shared in order to explicitly model the domain shift. However, the weights are related by weight regularizers to take into account the difference between the two domains. We conduct extensive cross-database (domain mismatch) experiments using ASVspoof 2015 and BTAS 2016 datasets to demonstrate the generalization capability of the proposed deep domain architectures for spoofing detection. Experimental results reveal that the proposed architectures can generalize across databases for both supervised and unsupervised adaptation scenarios.
Text baseline detection, a single page trained system

Nowadays, there are a lot of page images available and the scanning process is quite well resolved and can be done industrially. On the other hand, HTR systems can only deal with single text line images. Segmenting pages into single text line images is a very expensive process which has traditionally been done manually. This is a bottleneck which is holding back any massive industrial document processing. A baseline detection method will be presented here1. The initial problem is reformulated as a clustering problem over a set of interest points. Its design aim is to be fast and to resist the noise artifacts that usually appear in historical manuscripts: variable interline spacing, the overlapping and touching of words in adjacent lines, humidity spots, etc. Results show that this system can be used to massively detect where the text lines are in pages. Highlight: This system reached second place in the Icdar 2017 Competition on Baseline Detection (see Table 1).
Approximate cover of strings

Regularities in strings arise in various areas of science, including coding and automata theory, formal language theory, combinatorics, molecular biology and many others. A common notion to describe regularity in a string T is a cover, which is a string C for which every letter of T lies within some occurrence of C. The alignment of the cover repetitions in the given text is called a tiling. In many applications finding exact repetitions is not sufficient, due to the presence of errors. In this paper, we use a new approach for handling errors in coverable phenomena and define the approximate cover problem (ACP), in which we are given a text that is a sequence of some cover repetitions with possible mismatch errors, and we seek a string that covers the text with the minimum number of errors. We first show that the ACP is NP-hard, by studying the cover-length relaxation of the ACP, in which the requested length of the approximate cover is also given with the input string. We show that this relaxation is already NP-hard. We also study another two relaxations of the ACP, which we call the partial-tiling relaxation of the ACP and the full-tiling relaxation of the ACP, in which a tiling of the requested cover is also given with the input string. A given full tiling retains all the occurrences of the cover before the errors, while in a partial tiling there can be additional occurrences of the cover that are not marked by the tiling. We show that the partial-tiling relaxation has a polynomial time complexity and give experimental evidence that the full-tiling also has polynomial time complexity. The study of these relaxations, besides shedding another light on the complexity of the ACP, also involves a deep understanding of the properties of covers, yielding some key lemmas and observations that may be helpful for a future study of regularities in the presence of errors.
Extraction of soils above concealed lithium deposits for rare metal exploration in Jiajika area: A pilot study

Jiajika in Sichuan, China, is an important mining area for lithium and other Group I and Group II elements. However, the weathered residual overburden makes it difficult to prospect using traditional soil geochemistry. Deep-penetrating geochemical theories provide new ideas and methods that can be used to explore for concealed ore bodies. In this study, topsoil samples were collected from 10 to 25 cm depth above two concealed lithium deposits, Vein 804 (V-804) and Vein X03 (V-X03). Lithium (Li), cesium (Cs), rubidium (Rb), beryllium (Be), and boron (B) were extracted from the soils using pure water and 0.2 mol/L potassium sulfate solutions (K2SO4). Anomalies were observed for water-extractable Li, Cs, and Rb, and K2SO4-extractable Li, Cs, and Be in samples from above both veins. The harmonic mean of the data for each element was used to calculate the response ratio. The highest response ratios are 1.2–2.5 times and 2 to 8 times higher than the response ratios for other samples from V-804 and V-X03, respectively. Linear correlations are observed between the water-extractable and K2SO4-extractable elements, and imply that unbound species of the elements were extracted by K2SO4. The extracted elements display single peak, double peak, and multi-peak anomaly patterns, which indicate that transfer of Li, Cs, Rb, Be, and B through the overburden may be through a combination of diffusion, capillarity, electrochemical forces, and redox gradients.
Opposition Based Salp Swarm Algorithm for Numerical Optimization

In this paper an improved optimization algorithm called Opposition Based Salp Swarm Algorithm (OSSA) is proposed. This is improved version of recently proposed Salp Swarm Algorithm (SSA), which mimics swarming acts of salps when foraging and navigating in oceans. To improve the performance of SSA, Opposition based learning (OBL) is introduced in Salp Swarm Algorithm. The algorithm is evaluated on several numerical standard functions and is compared with some well known optimization algorithms.
Hashing Graph Convolution for Node Classification

Convolution on graphs has aroused great interest in AI due to its potential applications to non-gridded data. To bypass the influence of ordering and different node degrees, the summation/average diffusion/aggregation is often imposed on local receptive field in most prior works. However, the collapsing into one node in this way tends to cause signal entanglements of nodes, which would result in a sub-optimal feature and decrease the discriminability of nodes. To address this problem, in this paper, we propose a simple but effective Hashing Graph Convolution (HGC) method by using global-hashing and local-projection on node aggregation for the task of node classification. In contrast to the conventional aggregation with a full collision, the hash-projection can greatly reduce the collision probability during gathering neighbor nodes. Another incidental effect of hash-projection is that the receptive field of each node is normalized into a common-size bucket space, which not only staves off the trouble of different-size neighbors and their order but also makes a graph convolution run like the standard shape-gridded convolution. Considering the few training samples, also, we introduce a prediction-consistent regularization term into HGC to constrain the score consistency of unlabeled nodes in the graph. HGC is evaluated on both transductive and inductive experimental settings and achieves new state-of-the-art results on all datasets for node classification task. The extensive experiments demonstrate the effectiveness of hash-projection.
An Efficient Character-Level and Word-Level Feature Fusion Method for Chinese Text Classification

Abstract In order to extract semantic feature information between texts more efficiently and reduce the effect of text representation on classification results, we propose a features fusion model C_BiGRU_ATT based on deep learning. The core task of our model is to extract the context information and local information of the text using Convolutional Neural Network(CNN) and Attention-based Bidirectional Gated Recurrent Unit(BiGRU) at character-level and word-level. Our experimental results show that the classification accuracies of C_BiGRU_ATT reach 95.55% and 95.60% on two Chinese datasets THUCNews and WangYi respectively. Meanwhile, compared with the single model based on character-level and word-level for CNN, the classification accuracies of C_BiGRU_ATT is increased by 1.6%, 2.7% on the THUCNews, and is increased by 0.6%, 5.2% on the WangYi. The results show that the proposed model C_BiGRU_ATT can extract text features more effectively.
FunPianoAR: A Novel AR Application for Piano Learning Considering Paired Play Based on Multi-Marker Tracking

Abstract This paper describes the design and implementation of a novel augmented reality application, referred as FunPianoAR, that aiming to activate the interests of user and improve the experience of piano learning. The FunPianoAR with a user-friendly interface considers the paired play to further reduce the difficulty of playing the piano for adult novices. For the paired play, one user plays the melody and the other plays the harmony part. The app is developed by using Android Studio 3.0 and artoolkitX 1.0, an open source augmented reality SDK, and installed on the AR smart glasses, Epson Moverio BT300. Due to the textureless features of real piano and registration precision, the application is implemented by using the fiducial marker tracking instead of the markerless recognition. Besides, we divided the piano keyboard into four zones and each zone used a separate marker for tracking to solve the limited field of view (FOV) to some extent. The virtual keys can be accurately superimposed on the piano keys by using multi-marker tracking. An evaluation was also conducted to compare the effects of two types of augmented reality information superimposed, i.e., the instant way without the hints for the next note to be played and the FunPianoAR with the hints for the coming notes to be played. The correctness rate was calculated and the time differences between the notes that two players should play at the same time were collected. The FunPianoAR shows more advantages over the instant way based on the evaluation results.
A novel image encryption scheme based on permutation/diffusion process using an improved 2D chaotic system

This paper suggests an efficient image encryption technique based on double permutation/diffusion process. The aim is to secure a color and gray scale images using an improved 2D logistic map. First, since chaotic properties are significantly related to cryptographic properties in permutation and diffusion, we enhanced the chaotic behavior of the two-dimensional logistic map to create a good pseudorandom number generator that is more suitable for image scrambling. Then, the traditional encryption techniques permutation and diffusion are applied on the plain image. Thus, we generate a first entire pseudo-random sequence coming from the enhanced 2D logistic map that will be used to swap the rows of the plain image and to apply a strong diffusion. Then, a second sequence will be generated to swap the columns and apply a second diffusion to reinforce the security. The results of the simulation concerning histogram, key space, correlation coefficient among neighboring pixels, entropy, NPCR and UACI show the efficiency and reliability of the suggested algorithm.
Chance constrained reachability in environments with spatially varying energy costs

In off-road environments, energy costs are highly uncertain and variable due to unknown terrains. To plan missions for robots with limited energy storage capacity, a robot’s reachable set must be determined. This work presents a novel approach for learning reachable sets based on data collected during a mission. Leveraging the authors’ previous work, a spatial energy map of an unknown environment, built with data collected by a robot, can be used to compute a chance constrained reachable set (CCRS) based on a user-defined confidence level. Simulations demonstrate that as a robot collects more data on an energy map, the true positive rate of a computed CCRS improves significantly while the false positive rate remains low, implying that a robot’s reachability can be robustly determined for use in future missions.
3D human pose estimation via human structure-aware fully connected network

Existing 3D human pose estimation (3D-HPE) methods focus on reducing the overall joint error, resulting in endpoints and bone lengths with large errors. To address this issue, we propose a human structure-aware network, which is capable of recovering 3D joint locations from given 2D joint detections. We cascade a refinement network with a basic network in a residual learning manner, meanwhile fuse the features from 2D and 3D coordinates by a residual connection. Specifically, our refinement network employs a dual-channel structure, in which the symmetrical endpoints are divided into two parts and refined separately. Such a structure is able to avoid the mutual interference of joints with large errors to promise reliable 3D features. Experimental results on the Human3.6M dataset demonstrate that our network reduces the errors of both endpoints and bone lengths compared with existing state-of-the-art approaches.