Reviewerly/exHarmony huggingface dataset:
Sentence modeling via multiple word embeddings and multi-level comparison for semantic textual similarity

Recently, using a pretrained word embedding to represent words achieves success in many natural language processing tasks. According to objective functions, different word embedding models capture different aspects of linguistic properties. However, the Semantic Textual Similarity task, which evaluates similarity/relation between two sentences, requires to take into account of these linguistic aspects. Therefore, this research aims to encode various characteristics from multiple sets of word embeddings into one embedding and then learn similarity/relation between sentences via this novel embedding. Representing each word by multiple word embeddings, the proposed MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension.
An enhanced anonymous identity‐based key agreement protocol for smart grid advanced metering infrastructure

Summary Sprouting populace mass within the urban areas furnishes critical challenges of providing uninterruptible community services to fulfill the primitive needs of inhabitants in smart cities. Smart cities facilitate and uplift the living standards of inhabitants through various smart systems or infrastructures, and smart grid is one of them. Secure transmission is a key requirement in the advanced metering infrastructure (AMI) of most smart grids, and key establishment cryptographic protocols can be used to achieve such a requirement. Designing efficient and secure key establishment protocols for AMI remains challenging. For example, in this paper, we reveal several weaknesses in the identity‐based key establishment protocol of Mohammadali et al (published in IEEE Trans Smart Grid, 2017), which is based on elliptic curves. We then improve their protocol and prove its security in the random oracle model. We also demonstrate that the improved protocol achieves both anonymity and untraceability, before presenting a comparative summary of the security and computational overheads of the proposed protocol and several other existing protocols.
Toward Practical Privacy-Preserving Processing Over Encrypted Data in IoT: An Assistive Healthcare Use Case

With the advancement of Internet of Things (IoT), a large number of electronic devices are connected to the Internet.These connected electronic devices acquire, transmit information and respond to any received actions.In medical ecosystem, hospitals can implement medical diagnosis with medical sensors, especially for remote auxiliary medical diagnosis.But, in this context, patients privacy is paramount importance, and confidentiality of medical data is crucial.Therefore, the main challenge ahead is how to realize remote auxiliary medical diagnosis while protecting confidentiality of the medical data and ensuring patients privacy.In this paper, based on somewhat homomorphic encryption (SHE) scheme addressed by Junfeng Fan and Frederik Vercauteren (FV), we provide the first instance of a new efficient SHE scheme for homomorphic evaluation over Single Instruction Multiple Data (SIMD).We also implement a new set of efficient SIMD homomorphic comparison and division schemes.Based on these findings, we implement efficient privacy-preserving and SIMD homomorphic surf and multi-retina-image matching schemes.Offered functionalities include SIMD homomorphic feature point detection, multi-retina-image matching and lesion detection for the encrypted retinal image of diabetic retinopathy.Finally, we provide a proof-of-concept application implementation towards remote auxiliary diagnosis systems for diabetes in order to showcase the core security and privacy pillars of our solution.In the meantime, our IoT system designed with lattice-based cryptography preserves data confidentiality under quantum computation and quantum computers.
Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

This paper reports the results of an international survey on research data management (RDM) services in libraries. More than 240 practicing librarians responded to the survey and outlined their roles and levels of preparedness in providing RDM services, challenges their libraries face, and knowledge and skills that they deemed essential to advance the RDM practice. Findings of the study revealed not only a number of location and organizational differences in RDM services and tools provided but also the impact of the level of preparedness and degree of development in RDM roles on the types of RDM services provided. Respondents' perceptions on both the current challenges and future roles of RDM services were also examined. With a majority of the respondents recognizing the importance of RDM and hoping to receive more training while expressing concerns of lack of bandwidth or capacity in this area, it is clear that, in order to grow RDM services, institutional commitment to resources and training opportunities is crucial. As an emergent profession, data librarians need to be nurtured, mentored, and further trained. The study makes a case for developing a global community of practice where data librarians work together, exchange information, help one another grow, and strive to advance RDM practice around the world.
Overview of the CLEF-2019 CheckThat! Lab: Automatic Identification and Verification of Claims

We present an overview of the second edition of the CheckThat! Lab at CLEF 2019. The lab featured two tasks in two different languages: English and Arabic. Task 1 (English) challenged the participating systems to predict which claims in a political debate or speech should be prioritized for fact-checking. Task 2 (Arabic) asked to (A) rank a given set of Web pages with respect to a check-worthy claim based on their usefulness for fact-checking that claim, (B) classify these same Web pages according to their degree of usefulness for fact-checking the target claim, (C) identify useful passages from these pages, and (D) use the useful pages to predict the claim's factuality. CheckThat! provided a full evaluation framework, consisting of data in English (derived from fact-checking sources) and Arabic (gathered and annotated from scratch) and evaluation based on mean average precision (MAP) and normalized discounted cumulative gain (nDCG) for ranking, and F $$_1$$ for classification. A total of 47 teams registered to participate in this lab, and fourteen of them actually submitted runs (compared to nine last year). The evaluation results show that the most successful approaches to Task 1 used various neural networks and logistic regression. As for Task 2, learning-to-rank was used by the highest scoring runs for subtask A, while different classifiers were used in the other subtasks. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification.
ViterbiNet: Symbol Detection Using a Deep Learning Based Viterbi Algorithm

Symbol detection plays an important role in the implementation of digital receivers. One of the most common symbol detection schemes is the Viterbi algorithm, which is capable of achieving the minimal probability of error under a broad range of channels encountered in practice. The Viterbi algorithm is based on channel state information (CSI); it requires that the receiver knows exactly (or approximately) the statistical relationship between the channel input and output. In some cases, such knowledge may be unavailable or very difficult to obtain. In this work, we propose ViterbiNet, which is a data-driven symbol detector obtained by converting the Viterbi algorithm into a system utilizing deep neural networks (DNNs). The resulting detector thus operates without CSI. We identify the specific parts of the Viterbi algorithm that are model-based, and design the DNN to implement those computations. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the optimal CSI-based Viterbi algorithm. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to model uncertainty. Our results demonstrate the conceptual benefit of designing DNN-based communication systems to implement established algorithms.
A method of processing color image watermarking based on the Haar wavelet

Despite the fact that traditional digital watermarking technology is relatively mature, there are still some areas that have not been fully involved in. For example, image watermarking technology and the certification are still in its infancy at early times. The bottleneck problem of digital product safety protection all solved theoretically with the combination of computing theory as well as traditional digital watermarking technology and point out a new direction for the research of information security industry. Inspired by the traditional algorithm of image watermarking and based on the Haar wavelet function along with algorithm of 2-D discrete wavelet transform and selection, this article presents the techniques of watermark embedding and extraction of color images. The main appraisal criteria of the watermark include invisibility and robustness, and some other standards. Image watermarking is relatively simple in the spatial domain, where it cannot resist geometrical attacks. In the transform domain, this approach can resist both geometrical attacks and image processing attacks. Only when the carrier image suffers from severe damage with the image quality hugely compromised will the extracted watermark become unrecognizable. As a result, the algorithm presented in this article can well embed the color image in the carrier image, and has good resistance to attack operations such as loss compression and adding of noise.
Specializing Word Embeddings (for Parsing) by Information Bottleneck

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks.We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser.We compress each word embedding to either a discrete tag or a continuous vector.In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity.In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.
An improved quantum algorithm for ridge regression

Ridge regression (RR) is an important machine learning technique which introduces a regularization hyperparameter $\alpha$ to ordinary multiple linear regression for analyzing data suffering from multicollinearity. In this paper, we present a quantum algorithm for RR, where the technique of parallel Hamiltonian simulation to simulate a number of Hermitian matrices in parallel is proposed and used to develop a quantum version of $K$-fold cross-validation approach, which can efficiently estimate the predictive performance of RR. Our algorithm consists of two phases: (1) using quantum $K$-fold cross-validation to efficiently determine a good $\alpha$ with which RR can achieve good predictive performance, and then (2) generating a quantum state encoding the optimal fitting parameters of RR with such $\alpha$, which can be further utilized to predict new data. Since indefinite dense Hamiltonian simulation has been adopted as a key subroutine, our algorithm can efficiently handle non-sparse data matrices. It is shown that our algorithm can achieve exponential speedup over the classical counterpart for (low-rank) data matrices with low condition numbers. But when the condition numbers of data matrices is large to be amenable to full or approximately full ranks of data matrices, only polynomial speedup can be achieved.
AI and Web-Based Human-Like Interactive University Chatbot (UNIBOT)

Most of the time, Students have to visit universities or colleges to collect various information like Tution fees, Term Schedule, etc. during their admission process or as per their daily needs. This process is very tedious and time consuming, also it requires manpower in providing required information to visitors. Hence, to overcome the problems a chatbot can be developed. The project is about interaction between users and chatbot which can be accessed from anywhere anytime. The chatbot can be easily attached with any university or college website with few simple language conversions. Chatbot provides various information related to university or college and also students-related information. The chatbot can be used by anyone who can access the university's website. The project uses the concept of Artificial Intelligence and Machine Learning. PHP Language is utilized for the development of Chatbot. User can ask university-related questions, then the query is applied as an input to algorithm, which processes the message and displays the corresponding response to the user. The Project GUI is similar to a Messaging Application.
Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation

Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that the source domain and the target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level. Target camera variations can be captured by the style adaptation method, thus, the re-identification model trained on the target domain can learn target camera-invariant features better. It indicates that the style translator approximates an appropriate metric space for improving feature matching. To overcome the non-overlapping labels challenge and guide the person re-identification model to narrow the gap further, an efficient and effective soft-labeling method is proposed to mine the intrinsic local structure of the target domain through building the connection between GAN-translated source domain and the target domain. Experiment results conducted on real benchmark datasets indicate that our method gets state-of-the-art results.
A study of cybersickness and sensory conflict theory using a motion-coupled virtual reality system

Sensory conflict theory explains that motion sickness in virtual reality (VR) systems can be caused due to the mismatch between visual and vestibular senses. This study examines whether coupling physical motions to visual stimuli in VR could reduce this discomfort. A motion-coupled VR system developed on a motion platform, providing vestibular cues to supplement visual roll from a head-mounted display (HMD), was used. Three conditions were tested: visual rotation only (stationary), visual-physical motion synchronised (synchronous), and vestibular motion with a self-referenced visual environment. Results show that when users are placed under a visual-vestibular synchronised condition, their subjective miserable score of cybersickness decreased while their comfort level of the overall experience increased. This indicates that a motion-coupled system, if integrated seamlessly in VR, could mitigate cybersickness symptoms.
LDFR: Learning deep feature representation for software defect prediction

Software Defect Prediction (SDP) aims to detect defective modules to enable the reasonable allocation of testing resources, which is an economically critical activity in software quality assurance. Learning effective feature representation and addressing class imbalance are two main challenges in SDP. Ideally, the more discriminative the features learned from the modules and the better the rescue performed on the imbalance issue, the more effective it should be in detecting defective modules. In this study, to solve these two challenges, we propose a novel framework named LDFR by Learning Deep Feature Representation from the defect data for SDP. Specifically, we use a deep neural network with a new hybrid loss function that consists of a triplet loss to learn a more discriminative feature representation of the defect data and a weighted cross-entropy loss to remedy the imbalance issue. To evaluate the effectiveness of the proposed LDFR framework, we conduct extensive experiments on a benchmark dataset with 27 defect data (each with three types of features), using three traditional and three effort-aware indicators. Overall, the experimental results demonstrate the superiority of our LDFR framework in detecting defective modules when compared with 27 baseline methods, except in terms of the indicator of Precision.
Neural Probabilistic Graphical Model for Face Sketch Synthesis

Neural network learning for face sketch synthesis from photos has attracted substantial attention due to its favorable synthesis performance. However, most existing deep-learning-based face sketch synthesis models stacked only by multiple convolutional layers without structured regression often lose the common facial structures, limiting their flexibility in a wide range of practical applications, including intelligent security and digital entertainment. In this article, we introduce a neural network to a probabilistic graphical model and propose a novel face sketch synthesis framework based on the neural probabilistic graphical model (NPGM) composed of a specific structure and a common structure. In the specific structure, we investigate a neural network for mapping the direct relationship between training photos and sketches, yielding the specific information and characteristic features of a test photo. In the common structure, the fidelity between the sketch pixels generated by the specific structure and their candidates selected from the training data are considered, ensuring the preservation of the common facial structure. Experimental results on the Chinese University of Hong Kong face sketch database demonstrate, both qualitatively and quantitatively, that the proposed NPGM-based face sketch synthesis approach can more effectively capture specific features and recover common structures compared with the state-of-the-art methods. Extensive experiments in practical applications further illustrate that the proposed method achieves superior performance.
Privacy Computing: Concept, Computing Framework, and Future Development Trends

With the rapid development of information technology and the continuous evolution of personalized services, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private information in different information systems. However, problems such as the cask principle of preserving private information among different information systems and the difficulty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot provide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a theory of privacy computing and a key technology system that includes a privacy computing framework, a formal definition of privacy computing, four principles that should be followed in privacy computing, algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.
Improved person re-identification based on saliency and semantic parsing with deep neural network models

Given a video or an image of a person acquired from a camera, person re-identification is the process of retrieving all instances of the same person from videos or images taken from a different camera with non-overlapping view. This task has applications in various fields, such as surveillance, forensics, robotics, multimedia. In this paper, we present a novel framework, named Saliency-Semantic Parsing Re-Identification (SSP-ReID), for taking advantage of the capabilities of both clues: saliency and semantic parsing maps, to guide a backbone convolutional neural network (CNN) to learn complementary representations that improves the results over the original backbones. The insight of fusing multiple clues is based on specific scenarios in which one response is better than another, thus favoring the combination of them to increase performance. Due to its definition, our framework can be easily applied to a wide variety of networks and, in contrast to other competitive methods, our training process follows simple and standard protocols. We present extensive evaluation of our approach through five backbones and three benchmarks. Experimental results demonstrate the effectiveness of our person re-identification framework. In addition, we combine our framework with re-ranking techniques and compare it against state-of-the-art approaches, achieving competitive results.
Cracking open the DNN black-box

Advancements in deep neural networks (DNNs) and widespread deployment of video cameras have fueled the need for video analytics systems. Despite rapid advances in system design, existing systems treat DNNs largely as "black boxes'' and either deploy models entirely on a camera or compress videos for analysis in the cloud. Both these approaches affect the accuracy and total cost of deployment. In this position paper, we propose a research agenda that involves opening up the black box of neural networks and describe new application scenarios that include joint inference between the cameras and the cloud, and continuous online learning for large deployments of cameras. We present promising results from preliminary work in efficiently encoding the intermediate activations sent between layers of a neural network and describe opportunities for further research.