Reviewerly/exHarmony huggingface dataset:
Aspect-Based Opinion Mining of Students' Reviews on Online Courses

It is critical for higher education institutions to work on improvement of their teaching and learning strategy by examining feedback of students. Analyzing these feedbacks typically requires manual interventions which are not only labor intensive but prone to errors as well. Therefore, automatic models and techniques are needed to handle textual feedback efficiently. To this end, we propose a model for aspect-based opinion mining of comments of students that are posted in online learning platforms. The model aims to predict some of the key aspects related to an online course from students' reviews and then assess the attitude of students toward these commented aspects. The proposed model is tested on a large-scale real-world dataset which is collected for this purpose. The dataset consists of more than 21 thousand manually annotated students' reviews that are collected from Coursera. Conventional machine learning algorithms and deep learning techniques are used for prediction of the aspect categories and the aspect sentiment classification as well. The obtained results with respect to precision, recall, and F1 score are very promising.
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

We present an end-to-end method for object detection and trajectory prediction utilizing multi-view representations of LiDAR returns and camera images. In this work, we recognize the strengths and weaknesses of different view representations, and we propose an efficient and generic fusing method that aggregates benefits from all views. Our model builds on a state-of-the-art Bird’s-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks. We extend this model with additional LiDAR Range-View (RV) features that use the raw LiDAR information in its native, non-quantized representation. The RV feature map is projected into BEV and fused with the BEV features computed from LiDAR and high-definition map. The fused features are then further processed to output the final detections and trajectories, within a single end-to-end trainable network. In addition, the RV fusion of LiDAR and camera is performed in a straightforward and computationally efficient manner using this framework. The proposed multi-view fusion approach improves the state-of-the-art on proprietary large-scale real-world data collected by a fleet of self-driving vehicles, as well as on the public nuScenes data set with minimal increases on the computational cost.
Non-interactive Zero-Knowledge Arguments for QMA, with Preprocessing

A non-interactive zero-knowledge (NIZK) proof system for a language $$L\in \text {NP} $$ allows a prover (who is provided with an instance $$x \in L$$ , and a witness w for x) to compute a classical certificate $$\pi $$ for the claim that $$x\in L$$ such that $$\pi $$ has the following properties: 1) $$\pi $$ can be verified efficiently, and 2) $$\pi $$ does not reveal any information about w, besides the fact that it exists (i.e. that $$x \in L$$ ). NIZK proof systems have recently been shown to exist for all languages in $$\text {NP} $$ in the common reference string (CRS) model and under the learning with errors (LWE) assumption. We initiate the study of NIZK arguments for languages in $$\text {QMA} $$ . An argument system differs from a proof system in that the honest prover must be efficient, and that it is only sound against (quantum) polynomial-time provers. Our first main result is the following: if LWE is hard for quantum computers, then any language in $$\text {QMA} $$ has an NIZK argument with preprocessing. The preprocessing in our argument system consists of (i) the generation of a CRS and (ii) a single (instance-independent) quantum message from verifier to prover. The instance-dependent phase of our argument system, meanwhile, involves only a single classical message from prover to verifier. Importantly, verification in our protocol is entirely classical, and the verifier needs not have quantum memory; its only quantum actions are in the preprocessing phase. NIZK proofs of (classical) knowledge are widely used in the construction of more advanced cryptographic protocols, and we expect the quantum analogue to likewise find a broad range of applications. In this respect, the fact that our protocol has an entirely classical verification phase is particularly appealing. Our second contribution is to extend the notion of a classical proof of knowledge to the quantum setting. We introduce the notions of arguments and proofs of quantum knowledge (AoQK/PoQK), and we show that our non-interactive argument system satisfies the definition of an AoQK, which extends its domain of usefulness with respect to cryptographic applications. In particular, we explicitly construct an extractor which can recover a quantum witness from any prover who is successful in our protocol. We also show that any language in QMA has an (interactive) proof of quantum knowledge, again by exhibiting a particular proof system for all languages in QMA and constructing an extractor for it.
Polygames: Improved zero learning

Since DeepMind’s AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training against them. Using these features, we release Polygames, our framework for Zero learning, with its library of games and its checkpoints. We won against strong humans at the game of Hex in 19 × 19, including the human player with the best ELO rank on LittleGolem; we incidentally also won against another Zero implementation, which was weaker than humans: in a discussion on LittleGolem, Hex19 was said to be intractable for zero learning. We also won in Havannah with size 8: win against the strongest player, namely Eobllor, with excellent opening moves. We also won several first places at the TAAI 2019 competitions and had positive results against strong bots in various games.
Visual Causality Analysis of Event Sequence Data

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.
Relational data synthesis using generative adversarial networks

The proliferation of big data has brought an urgent demand for privacy-preserving data publishing. Traditional solutions to this demand have limitations on effectively balancing the tradeoff between privacy and utility of the released data. Thus, the database community and machine learning community have recently studied a new problem of relational data synthesis using generative adversarial networks (GAN) and proposed various algorithms. However, these algorithms are not compared under the same framework and thus it is hard for practitioners to understand GAN's benefits and limitations. To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates applying GAN to relational data synthesis. We introduce a unified GAN-based framework and define a space of design solutions for each component in the framework, including neural network architectures and training strategies. We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches. Through extensive experiments, we find that GAN is very promising for relational data synthesis, and provide guidance for selecting appropriate design solutions. We also point out limitations of GAN and identify future research directions.
Interactive POS-aware network for aspect-level sentiment classification

Existing aspect-level sentiment-classification models completely rely on the learning from given datasets. However, these are easily misled by biased samples, resulting in learning some ill-suited rules that limit their potential. The information of some specific part-of-speech (POS) categories often indicates the word sentiment polarity, which can be introduced as prior knowledge to facilitate prediction of the model. Accordingly, we propose an interactive POS-aware network (IPAN) that explicitly introduces the POS information as reliable guidance to assist the model in accurately predicting sentiment polarity. We distinguish the information of different POS categories using a POS-filter gate and reinforce the features extracted from adjectives, adverbs, and verbs via a POS-highlighting attention mechanism. This enables the model to concentrate on the words that contain significant sentiment orientations and to obtain the most practical learning experience. To emphasize the target information, we construct a target-context gate that enables the interaction of the target information with contexts; consequently, the model considerably focuses on target-related sentiment features. The experiments on SemEval2014 and Twitter datasets verify that our IPAN consistently outperforms the current state-of-the-art methods.
Review on Implementation Techniques of Chatbot

Chatbots are an intelligent system being developed using artificial intelligence (AI) and natural language processing (NLP) algorithms. It successfully interfaces and answers the inquiries of the users. Dialogue/conversation operators are predominately utilized by organizations, government associations, and non-benefit associations. They are habitually conveyed by money related associations like a bank, charge card organizations, organizations like online retail locations and new businesses. These conversational specialists are utilized in organizations running from exceptionally small start-ups to enormous partnerships. There are numerous chatbot advancement systems accessible in the market both code-based and interface based. However, they don't have the handiness and adaptability in growing real dialogues. Among popular intelligent personal assistants includes “Amazon's Alexa, Microsoft's Cortana and Google's Google Assistant”. The elements of these specialists are restricted. The current chatbots are created utilizing rule-based techniques, simple machine learning algorithms or retrieval based techniques but they do not generate good results. This paper provides a critical review of chatbots and the current strategies are exhaustively explored and talked discussed.
Compact, Efficient and UC-Secure Isogeny-Based Oblivious Transfer

Oblivious transfer (OT) is an essential cryptographic tool that can serve as a building block for almost all secure multiparty functionalities. The strongest security notion against malicious adversaries is universal composability (UC-secure). An important goal is to have post-quantum OT protocols. One area of interest for post-quantum cryptography is isogeny-based crypto. Isogeny-based cryptography has some similarities to Diffie-Hellman, but lacks some algebraic properties that are needed for discrete-log-based OT protocols. Hence it is not always possible to directly adapt existing protocols to the isogeny setting. We propose the first practical isogeny-based UC-secure oblivious transfer protocol in the presence of malicious adversaries. Our scheme uses the CSIDH framework and does not have an analogue in the Diffie-Hellman setting. The scheme consists of a constant number of isogeny computations. The underlying computational assumption is a problem that we call the computational reciprocal CSIDH problem, and that we prove polynomial-time equivalent to the computational CSIDH problem.
Discovering Fuzzy Periodic-Frequent Patterns in Quantitative Temporal Databases

Periodic-frequent pattern mining is a challenging problem of great importance in many applications. Most previous works focused on finding these patterns in binary temporal databases and did not take into account the quantities of items within the data. This paper proposes a novel model of fuzzy periodic-frequent pattern (FPFP) that may exist in a quantitative temporal database (QTD). Finding FPFPs in QTD is a non-trivial and challenging task due to its huge search space. A novel pruning technique, called improved maximum scalar cardinality, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FPFPs in real-world very large databases practicable. An efficient algorithm has also been presented to find all FPFPs in a QTD. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in air pollution database.
Human Fall Detection Algorithm Based on YOLOv3

With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. In view of the above problems, this paper proposes a fall detection method based on video in complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, YOLOv3 network model is proposed for detection algorithm. Secondly, the human fall detection data set is constructed by referring to Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.
Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier

Abstract Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.
GSCA-UNet: Towards Automatic Shadow Detection in Urban Aerial Imagery with Global-Spatial-Context Attention Module

As an inevitable phenomenon in most optical remote-sensing images, the effect of shadows is prominent in urban scenes. Shadow detection is critical for exploiting shadows and recovering the distorted information. Unfortunately, in general, automatic shadow detection methods for urban aerial images cannot achieve satisfactory performance due to the limitation of feature patterns and the lack of consideration of non-local contextual information. To address this challenging problem, the global-spatial-context-attention (GSCA) module was developed to self-adaptively aggregate all global contextual information over the spatial dimension for each pixel in this paper. The GSCA module was embedded into a modified U-shaped encoder–decoder network that was derived from the UNet network to output the final shadow predictions. The network was trained on a newly created shadow detection dataset, and the binary cross-entropy (BCE) loss function was modified to enhance the training procedure. The performance of the proposed method was evaluated on several typical urban aerial images. Experiment results suggested that the proposed method achieved a better trade-off between automaticity and accuracy. The F1-score, overall accuracy, balanced-error-rate, and intersection-over-union metrics of the proposed method were higher than those of other state-of-the-art shadow detection methods.
Securing CNN Model and Biometric Template using Blockchain

Blockchain has emerged as a leading technology that ensures security in a distributed framework. Recently, it has been shown that blockchain can be used to convert traditional blocks of any deep learning models into secure systems. In this research, we model a trained biometric recognition system in an architecture which leverages the blockchain technology to provide fault tolerant access in a distributed environment. The advantage of the proposed approach is that tampering in one particular component alerts the whole system and helps in easy identification of `any' possible alteration. Experimentally, with different biometric modalities, we have shown that the proposed approach provides security to both deep learning model and the biometric template.
A Leak in PRNU Based Source Identification—Questioning Fingerprint Uniqueness

Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task.Its uniqueness ensures that the sensor pattern noises extracted from different cameras are strongly uncorrelated, even when they belong to the same camera model.However, with the advent of computational photography, most recent devices heavily process the acquired pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's distinctiveness, especially when several exemplars of the same device model are involved in the analysis.Considering that PRNU is an image forensic technology that finds actual and wide use by law enforcement agencies worldwide, it is essential to keep validating such technology on recent devices as they appear.In this paper, we perform an extensive testing campaign on over 33.000 Flickr images belonging to 45 smartphone and 25 DSLR camera models released recently to determine how widespread the issue is and which is the plausible cause.Experiments highlight that most brands, like Samsung, Huawei, Canon, Nikon, Fujifilm, Sigma, and Leica, are strongly affected by this issue.We show that the primary cause of high false alarm rates cannot be directly related to specific camera models, firmware, nor image contents.It is evident that the effectiveness of PRNU based source identification on the most recent devices must be reconsidered in light of these results.Therefore, this paper is intended as a call to action for the scientific community rather than a complete treatment of the subject.Moreover, we believe publishing these data is important to raise awareness about a possible issue with PRNU reliability in the law enforcement world.
Unified Cross-domain Classification via Geometric and Statistical Adaptations

Domain adaptation aims to learn an adaptive classifier for target data using the labelled source data from a different distribution. Most proposed works construct cross-domain classifier by exploring one-sided property of the input data, i.e., either geometric or statistical property. Therefore they may ignore the complementarity between the two properties. Moreover, many previous methods implement knowledge transfer with two separated steps: divergence minimization and classifier construction, which degrades the adaptation robustness. In order to address such problems, we propose a unified cross-domain classification method via geometric and statistical adaptations (UCGS). UCGS models the divergence minimization and classifier construction in a unified way based on structural risk minimization principle and coupled adaptations theory. Specifically, UCGS constructs an adaptive model by simultaneously minimizing the structural risk on labelled source data, using Maximum Mean Discrepancy (MMD) criterion to implement statistical adaptation, and flexibly employing the Nyström method to explore the geometric connections between domains. A domain-invariant graph is successfully constructed to link the two domains geometrically. The standard supervised methods can be used to instantiate UCGS to handle inter-domain classification problems. Comprehensive experiments show the superiority of UCGS on several real-world datasets.