Reviewerly/exHarmony huggingface dataset:
Strongly leakage resilient authenticated key exchange, revisited

Authenticated Key Exchange (AKE) protocols allow two (or multiple) parties to authenticate each other and agree on a common secret key, which is essential for establishing a secure communication channel over a public network. AKE protocols form a central component in many network security standards such as IPSec, TLS/SSL, and SSH. However, it has been demonstrated that many standardized AKE protocols are vulnerable to side-channel and key leakage attacks. In order to defend against such attacks, leakage resilient (LR-) AKE protocols have been proposed in the literature. Nevertheless, most of the existing LR-AKE protocols only focused on the resistance to long-term key leakage, while in reality leakage of ephemeral secret key (or randomness) can also occur due to various reasons such as the use of poor randomness sources or insecure pseudo-random number generators (PRNGs). In this paper, we revisit the strongly leakage resilient AKE protocol (CT-RSA'16) that aimed to resist challenge-dependent leakage on both long-term and ephemeral secret keys. We show that there is a security issue in the design of the protocol and propose an improved version that can fix the problem. In addition, we extend the protocol to a more general framework that can be efficiently instantiated under various assumptions, including hybrid instantiations that can resist key leakage attacks while preserving session key security against future quantum machines.
Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization).
Locating vulnerabilities in binaries via memory layout recovering

Locating vulnerabilities is an important task for security auditing, exploit writing, and code hardening. However, it is challenging to locate vulnerabilities in binary code, because most program semantics (e.g., boundaries of an array) is missing after compilation. Without program semantics, it is difficult to determine whether a memory access exceeds its valid boundaries in binary code. In this work, we propose an approach to locate vulnerabilities based on memory layout recovery. First, we collect a set of passed executions and one failed execution. Then, for passed and failed executions, we restore their program semantics by recovering fine-grained memory layouts based on the memory addressing model. With the memory layouts recovered in passed executions as reference, we can locate vulnerabilities in failed execution by memory layout identification and comparison. Our experiments show that the proposed approach is effective to locate vulnerabilities on 24 out of 25 DARPA’s CGC programs (96%), and can effectively classifies 453 program crashes (in 5 Linux programs) into 19 groups based on their root causes.
A Needs Assessment of Edutainment Module with Ethnoscience Approach Oriented to the Love of the Country

In this globalization era, young generations are having problems regarding the love of the country character. The purposes of this research were (1) analyzing students' need on an entertaining module; (2) analyzing students' need on learning with ethnoscience approach; and (3) analyzing students' need on building the love of the country character. This research is qualitative research involving 5th-grade students of IT Umar bin Khattab Elementary school as the subject. A purposive sampling technique used, where the research instruments included questionnaires, interviews, and documentation studies. The data analysis involved several steps, namely, data reduction, categorization, validity checkings, interpretation, and inferences. The result of this research showed that the government and particular publisher distributed the learning material used by students. The teacher had never given any innovation by using culture based-learning for improving the love of country character to the students. Besides, the questionnaire showed that the students expected an entertaining learning material with stories, picture, and fun activity. However, based on the result, the edutainment module with ethnoscience approach was needed for improving students' love of the country character.
MOOC Learning Behavior Analysis and Teaching Intelligent Decision Support Method Based on Improved Decision Tree C4.5 Algorithm

to better carry out Massive Open Online Courses (MOOC) teaching evaluation and improve teaching effect, firstly, a teaching decision support system with evaluation function is designed by analyzing the actual situation of the college. Secondly, the decision tree data mining algorithm is introduced in the subsystem of student score analysis and evaluation. Finally, the decision tree model of student score analysis evaluation is constructed according to the decision tree algorithm. Through the practical exploration of applying the decision tree algorithm to the MOOC teaching evaluation management system of higher vocational colleges, it is found that the application of data mining technology to the construction of digital campus is not only reflected in the theoretical feasibility, but also reflected in its technical feasibility.
Online control programming algorithm for human–robot interaction system with a novel real-time human gesture recognition method

This article proposes an online control programming algorithm for human–robot interaction systems, where robot actions are controlled by the recognition results of gestures performed by human operators based on visual images. In contrast to traditional robot control systems that use pre-defined programs to control a robot where the robot cannot change its tasks freely, this system allows the operator to train online and replan human–robot interaction tasks in real time. The proposed system is comprised of three components: an online personal feature pretraining system, a gesture recognition system, and a task replanning system for robot control. First, we collected and analyzed features extracted from images of human gestures and used those features to train the recognition program in real time. Second, a multifeature cascade classifier algorithm was applied to guarantee both the accuracy and real-time processing of our gesture recognition method. Finally, to confirm the effectiveness of our algorithm, we selected a flight robot as our test platform to conduct an online robot control experiment based on the visual gesture recognition algorithm. Through extensive experiments, the effectiveness and efficiency of our method has been confirmed.
Revocable Attribute-Based Encryption Scheme With Efficient Deduplication for Ehealth Systems

The deduplication based on attribute-based encryption can be well used in eHealth systems to save storage space and share medical records.However, the excessive computation costs of existing schemes lead to inefficient deduplication.In addition, the frequent changes of clients' attribute weaken the forward secrecy of data, and thus, how to achieve the attribute revocation in deduplication is a problem that remains to be solved.In this paper, we propose a variant of the attribute-based encryption scheme that supports efficient deduplication and attributes revocation for eHealth systems.Specifically, an efficient deduplication protocol based on the nature of prime number is used to alleviate the computation burden on the private cloud, and attribute revocation is realized by updating the attribute agent key and the ciphertext.Moreover, outsourcing decryption is introduced to reduce the computation overhead of clients.The security analysis argues that the proposed scheme can reach the desired security requirements, and the visual experiment result indicates the excellent performance of the proposed scheme while realizing deduplication and attribute revocation.
Collision Avoidance Method for Self-Organizing Unmanned Aerial Vehicle Flights

Autonomous unmanned aerial vehicle (UAV) swarm flights have been investigated widely. In the presence of a high airspace density and increasingly complex flight conditions, collision avoidance between UAV swarms is very important; however, this problem has not been fully addressed, particularly among self-organizing flight clusters. In this paper, we developed a method for avoiding collisions between different types of self-organized UAV clusters in various flight situations. The Reynolds rules were applied to self-organized flights of UAVs and a parameter optimization framework was used to optimize their organization, before developing a collision avoidance solution for UAV swarms. The proposed method can self-organize the flight of each UAV swarm during the overall process and the UAV swarm can continue to fly according to the self-organizing rules in the collision avoidance process. The UAVs in the airspace all make decisions according to their individual type. The UAVs in different UAV swarms can merge in the same space while avoiding collisions, where the UAV's self-organized flight process and collision avoidance process are very closely linked, and the trajectory is smooth to satisfy the actual operational needs. The numerical and experimental tests were conducted to demonstrate the effectiveness of the proposed algorithm. The results confirmed the effectiveness of this approach where self-organized flight cluster collision avoidance was successfully achieved by the UAV swarms.
MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework

This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter.The task consists of two subtasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group.We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others).For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier.Our approach achieved the highest F1-score in sub-task A for the Spanish language.
HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing

This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery. In this way, we can make use of existing syntactic parsing techniques. Based on the data statistics, we recover discontinuous nodes directly according to the output labels of the constituent parser and use a biaffine classification model to recover the more complex remote edges. The classification model and the constituent parser are simultaneously trained under the multi-task learning framework. We use the multilingual BERT as extra features in the open tracks. Our system ranks the first place in the six English/German closed/open tracks among seven participating systems. For the seventh cross-lingual track, where there is little training data for French, we propose a language embedding approach to utilize English and German training data, and our result ranks the second place.
INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter

In this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish.This challenge was proposed by the SemEval-2019 Task 5, where participants should develop models for hate speech detection, a two-class classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful (Task A), and whether the hate speech is directed at a specific person or a group of individuals (Task B).For this, we implemented a Convolutional Neural Networks (CNN) using pre-trained word embeddings (GloVe and FastText) with 300 dimensions.Our proposed model obtained in Task A 0.488 and 0.696 F1-score for English and Spanish, respectively.For Task B, the CNN obtained 0.297 and 0.430 EMR for English and Spanish, respectively.
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding

One of the main challenges in Spoken Language Understanding (SLU) is dealing with ‘open-vocabulary’ slots. Recently, SLU models based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. This paper proposes data noising, which reflects the characteristics of the ‘open-vocabulary’ slots, for data augmentation. We applied it to an attention based bi-directional recurrent neural network (Liu and Lane, 2016) and experimented with three datasets: Airline Travel Information System (ATIS), Snips, and MIT-Restaurant. We achieved performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score), respectively. Our method is advantageous because it does not require additional memory and it can be applied simultaneously with the training process of the model.
A Multi-Atlas Guided 3D Fully Convolutional Network for MRI-Based Subcortical Segmentation

In this paper, we proposed and validated an effective multi-atlas guided 3D fully convolutional network (FCN) for segmenting subcortical structures from magnetic resonance images (MRIs). In the multi-atlas framework, the label information and the image intensity information (in terms of a patch based manner) were introduced into the proposed network. To further learn both local and global contexts, we adopted long skip connection strategy to embed outputs from convolutional layers in deconvolutional layers, which encourages consistency between features extracted at different scales. In addition, a region limited sampling method was adopted to reduce the training time and improve the segmentation accuracy. Experiments were performed on a dataset consisting of 16 T1-weighted MRIs. Compared to several existing state-of-the-art segmentation methods for subcortical structures, including a multi-atlas joint label fusion method and a representative 3D FCN method, the proposed method performed significantly better for a majority of the subcortical structures.
Servant Leadership Impact on Organizational Performance: The Mediating Role of Employee Engagement

The current study, which purposed to examine the mediating role of Employee Engagement (EE) in the relationship of Servant Leadership (SL) with the Organizational Performance (OP), was carried out in Jordan. The researchers distributed a questionnaire to 277 participants working in the private airline sector. The study’s findings showed that SL was positively linked with Employee Satisfaction (ES) and Employee Retention (ER) as indicators for OP. Finally; the findings indicated that EE partially mediates the relationships of SL with employee satisfaction and employee retention. The current research is the first empirical study of the airline sector in Jordan. It is also the first to focus on EE as a mediator of the effect of SL and employee retention using Structural Equation Modeling (SEM) for analyzing the data collected from employees working in the airline sector.
Graph Embedding Matrix Sharing With Differential Privacy

Graph embedding maps a graph into low-dimensional vectors, i.e., embedding matrix, while preserving the graph structure, solving the high computation and space cost for graph analysis.Matrix factorization (MF) is an effective means to achieve graph embedding since maintaining the utility of the graph structure.The personalized graph structure features implied in the embedding matrix can identify the individual, which potentially breaches individual sensitive information in the original graph.Currently, protecting individual privacy without compromising the utility is the key to sharing the embedding matrix.Differential privacy is a gold standard for publishing sensitive information while protecting privacy.The existing methods on differentially private MF, however, cannot be directly incorporated onto MF-based graph embedding as they undergo either high global sensitivity or iterative noise error accumulation, potentially rendering poor utility of MF-based graph embedding.To address the deficiency, this study proposes PPGD, a differentially private perturbed gradient descent method for MF-based graph embedding matrix sharing.Specifically, a Lipschitz condition on the objective function of the MF and a gradient clipping strategy are devised for bounding global sensitivity.Along the way, a scalable solution to global sensitivity that is independent on the original dataset is proposed.Further, a composite noise added means in the gradient descent is designed to guarantee privacy while enhancing the utility.The theoretical analysis shows that PPGD can generate processed embedding matrix with the utility maximization while achieving (ε, δ)differential privacy.The experimental evaluations confirm the effectiveness and efficiency of PPGD.
Further results on complete permutation monomials over finite fields

In this paper, we construct several new classes of complete permutation monomials a−1xd over a finite field Fqn with exponents d=qn−1q−1+1, qp−1−1q−1+1, and qq−1−1q−1+1, respectively, where q=pk is a power of a prime number p. Our approach uses the AGW criterion (the multiplicative case) together with Dickson permutation polynomials and a class of exceptional polynomials respectively. One of our results confirms Conjecture 4.18 by G. Wu, N. Li, T. Helleseth, Y. Zhang in [42] under the assumption that the characteristic p is primitive modulo a prime number n+1. Moreover, we show that Conjecture 4.18 is false in general using our approach and a counterexample is provided. We also re-confirm Conjecture 4.20 in [42] that was proved recently in [24], and extend some of these recent results to more general n's and more general a's.
A Teaching Quality Evaluation System of Massive Open Online Courses Based on Big Data Analysis

Massive open online courses (MOOC) transcends the time and space limits of traditional classroom teaching, and promotes the sharing of teaching resources. However, the effect of this emerging teaching mode is yet to be determined. In this paper, the big data analysis is introduced to evaluate the MOOC teaching quality. Taking several online courses as an example, a video player was de-signed to compute the learning time using the Hadoop platform. On this basis, the author constructed a teaching quality evaluation platform. In addition, the learning cost coefficient was calculated by the naive Bayesian model, and the evaluation results were analysed in details. The research findings shed practical new light on the evaluation of MOOC teaching quality.
Materiality in information environments: Objects, spaces, and bodies in three outpatient hemodialysis facilities

The materiality of information environments, and its role in information behavior, has received little attention. We present an ethnographic study involving 156 hours of observation and 28 patient interviews in outpatient hemodialysis facilities. Using an extended “Semiotic Framework for Information Systems Research,” the findings show that objects, spaces, and bodies were integral to 6 sociomaterial layers of facility information environments: the physical, empiric, syntactic, semantic, pragmatic, and social world. Objects of importance in the information environments included dialysis machines, instruments, records, paper documents, televisions, furniture, thermostats, lighting, and personal possessions. Spatial features, including compartmentalization, displays, distance, proximity, and spatially‐grounded routines, also constituted information environments. The information environments were also shaped by patient immobility, bodily discomforts, and orientation to bodily states. Each sociomaterial layer introduced enablers and constraints to information access, flow, and acceptance; these combined to construct patients primarily as passive recipients of information rather than active seekers and producers of information. A sociomaterial perspective and related focus on objects, spaces, and bodies offers a lens for professional information practice. We contribute information environment design guidance to facilitate such practice and stress that the value of certain sources and types of information can be materially encoded in an environment.
Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching

Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue. We employ a generative model to iteratively sample a subset of challenging negative samples to fool our classification model. Both models are alternatively optimized using REINFORCE algorithm. The proposed method is completely different from previous ones, where negative samples in training set are directly used or uniformly down-sampled. Further, we propose using Multi-scale Matching which explicitly inspects the correlation between words and ngrams of different levels of granularity. We evaluate the proposed method on SemEval 2016 and SemEval 2017 datasets and achieves state-of-the-art or similar performance.
Subword-Level Language Identification for Intra-Word Code-Switching

Manuel Mager, Özlem Çetinoğlu, Katharina Kann. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Emotional Text Generation Based on Cross-Domain Sentiment Transfer

Emotional intelligence plays an important role in human intelligence and is a recent research hotspot. With the rapid development of deep learning techniques in recent years, several neural network-based emotional text generation methods have been investigated. However, the existing emotional text generation approaches often suffer from the problem of requiring large-scale annotated data. Generative adversarial network (GAN) has shown promising results in natural language generation and data enhancement. In order to solve the above problem, this paper proposes a GAN-based cross-domain text sentiment transfer model, which uses annotated data from other domains to assist in the training of emotional text generation network. By combining adversarial reinforcement learning with supervised learning, our model is able to extract patterns of sentiment transformation and apply them in emotional text generation. The experimental results have shown that our approach outperforms the state-of-the-art methods and is able to generate high-quality emotional text while maintaining the consistency of domain information and content semantics.