Reviewerly/exHarmony huggingface dataset:
A* CCG Parsing with a Supertag and Dependency Factored Model

Combinatory Categorial Grammar (CCG) is a strongly lexicalized grammatical formalism, in which the vast majority of parsing decisions involve assigning a supertag to indicate the correct syntactic role. We propose an A* parsing model for CCG that exploits this characteristics, by modeling the probability of a tree through the supertags and resolving the remaining ambiguities by its syntactic dependencies. The key of our method is that it predicts the probabilities of supertags and dependency heads independently using a strong unigram model defined over bi-directional LSTMs. The factorization allows precomputation of probabilities for all possible trees for a sentence, which, combined with an A* algorithm, enables very efficient decoding. The proposed model achieves the state-of-the-art results on English and Japanese CCG parsing. In addition, we conduct Recognizing Textual Entailment (RTE) experiments by integrating the proposed parser within logic-based RTE systems. We demonstrate that such integration leads to improved performance in English RTE experiments.
From State Estimation for Dogs to the Internet of Dogs

This paper proposes the concept of the Internet of dogs, and uses its perceptual tracking to track the behavioral trajectory of various dogs around the world, constructs a framework for analyzing the spatial and temporal trajectory of dogs, and answers our more unsolved mysteries. For noise data, this paper adopts the sliding filter algorithm. Which makes the behavior data significantly enhance the smoothness, and constructs a new police dog motion attitude fusion method. Compared with the Kalman filter method, it has higher real-time performance under the premise of ensuring accuracy. A new multi-sensor police dog data vest is designed and implemented according to the biological movement characteristics of the police dog. Based on multiple small and low power consumption sensors, a new police dog motion attitude fusion method is constructed, which can capture different posture data of the police dog, including standing, sitting, lying, etc.
Did You See What I Saw?: Comparing User Synchrony When Watching 360° Video In HMD Vs Flat Screen

This study examined whether the high level of immersion provided by HMDs encourages participants to synchronise their attention during viewing. 39 participants watched the 360° documentary “Clouds Over Sidra” using either a HMD or via a flat screen tablet display. We found that the HMD group showed significantly greater overall ISC did the tablet group and that this effect was strongest during transition between scenes.
Copy-Move Forgery Detection Algorithm using Frequency Transforms, SURF and MSER

The number of digital images shared in the internet is growing faster, increasing the risk that these images be modified causing moral or economic damage. Thus, it is necessary to develop efficient image authentication algorithms that be able to detect malicious modification on the digital images. To solve this problem a tampering detection algorithm is proposed to detect if a digital image was tampered, using the Copy-Move technique. In the proposed scheme the input image is segmented into N×M blocks whose similarity is analyzed using frequency domain techniques, together with SURF or MSER detectors. The evaluation results show the desirable features of proposed approach.
80% of Block Propagation Rate is Enough - Towards Secure and Efficient PoW-based Blockchain Consensus (poster)

Recently, Samsung released Galaxy S10 supporting the cryptowallet feature [5]. However, it is still questionable whether cryptocurrency can be popularly used for mobile payments because processing transactions in existing blockchain systems are too slow. For example, public blockchain systems such as Bitcoin [6] (7 transactions per second (TPS)) and Ethereum (15 TPS) are significantly slower than mainstream payment systems such as Visa (2,000 TPS) using a centralized database.
A Smart Contract to Facilitate Goods Purchasing Based on Online Haggle

Blockchain technology is growing very rapidly, not only in the field of cryptocurrency transactions but also in other fields such as health care services, service companies, manufacturing. Smart contracts are a series of instruction codes, which automatically verify, execute, and tamper with. With the integration of blockchain technology, it can perform tasks in real time with a high level of security. In this study, we built a smart contract model based on online haggling in determining the purchase of a product. Buyers and sellers haggle with each other for the products they want via online the seller web app. Finally, we propose a smart contract design model using online haggling, which can help provide decision-making in determining the appropriate price from the seller and buyer agreement.
Analysis of cluster center initialization of 2FA‐kprototypes analogy‐based software effort estimation

Abstract Analogy‐based estimation is one of the most widely used techniques for effort prediction in software engineering. However, existing analogy‐based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA‐kprototypes was proposed and evaluated. 2FA‐kprototypes is based on the use of the fuzzy k‐prototypes clustering technique. Although fuzzy k‐prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers. In this paper, the impact of cluster center initialization on improving the prediction accuracy of 2FA‐kprototypes was analyzed and discussed using two cluster initialization techniques: centrality‐based initialization and density‐based initialization. The performance of 2FA‐kprototypes using these two initialization techniques was evaluated and compared with that of 2FA‐kprototypes using random initialization over four datasets: ISBSG, COCOMO81, USP05‐FT, and USP05‐RQ. The results showed an improvement in the performance of 2FA‐kprototypes in terms of estimation accuracy when the all‐in method is used.
Localizing the Gaze Target of a Crowd of People

What target is focused on by many people? Analysis of the target is a crucial task, especially in a cinema, a stadium, and so on. However, it is very difficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera. Therefore, we introduce a new approach for localizing the gaze target focused on by a crowd of people. The proposed framework aggregates the individually estimated results of each person's gaze. It enables us to localize the target being focused on by them even though each person's gaze localization from a low-resolution image is inaccurate. We analyze the effects of an aggregation method on the localization accuracy using images capturing a crowd of people in a tennis stadium under the assumption that all of the people are focusing on the same target, and also investigate the effect of the number of people involved in the aggregation on the localization accuracy. As a result, the proposed method showed the ability to improve the localization accuracy as it is applied to a larger crowd of people.
Towards Real-Time Contextual Touristic Emotion and Satisfaction Estimation with Wearable Devices

Following the technical progress and growing touristic market, demand on guidance systems is constantly increasing. Current systems are not personalized, they usually provide only a general information on sightseeing spot and do not concern about the tourist's perception of it. To design more adjustable and context-aware system, we focus on collecting and estimating emotions and satisfaction level, those tourists experience during the sightseeing tour. We reducing changes in their behaviour by collecting two types of information: conscious (short videos with impressions) and unconscious (behavioural pattern recorded with wearable devices) continuously during the whole tour. We have conducted experiments and collected initial data to build the prototype system. For each sight of the tour, participants provided an emotion and satisfaction labels. We use them to train unimodal neural network based models, fuse them together and get the final prediction for each recording. As tourist himself is the only source of labels for such system, we introduce an approach of post-experimental label correction, based on paired comparison. Such system built together allows us to use different modalities or their combination to perform real-time tourist emotion recognition and satisfaction estimation in-the-wild, bringing touristic guidance systems to the new level.
Rigid First-Order Hybrid Logic

Hybrid logic is usually viewed as a variant of modal logic in which it is possible to refer to worlds. But when one moves beyond propositional hybrid logic to first or higher-order hybrid logic, it becomes useful to view it as a systematic modal language of rigidification. The key point is this: @ can be used to rigidify not merely formulas, but other types of symbol as well. This idea was first explored in first-order hybrid logic (without function symbols) where @ was used to rigidify the first-order constants. It has since been used in hybrid type-theory: here one only has function symbols, but they are of every finite type, and @ can rigidify any of them. This paper fills the remaining gap: it introduces a first-order hybrid language which handles function symbols, and allows predicate symbols to be rigidified. The basic idea is straightforward, but there is a slight complication: transferring information about rigidity between the level of terms and formulas. We develop a syntax to deal with this, provide an axiomatization, and prove a strong completeness result for a varying domain (actualist) semantics.
Sentiment Analysis for Automated Email Response System

Email is the most widely used form of written communication, especially for teacher-student communication. Most routine email responses are repetitive in nature but consume a lot of academic time. An automated email response system can save much of this time. This research is focused on detection and classification of sentiment in student-teacher email conversations. This study aims to find out markers of politeness and impoliteness within email. This will ultimately help in generating more human-like response. Taking into account Ekman's basic emotions, we have proposed a method to find out these emotions using deep learning. Limiting our research to student-teacher conversations, gives us a close domain for finding sentiment and its intensity.
Conditional nearest regularized subspace classifiers: a fast classification approach for HSI

Classification is an important problem in a large variety of applications, which makes it an open-ended forum for researchers in various disciplines. In this paper, the proposed two approaches are mostly focused on Nearest Regularized Subspace (NRS) classifier. The proposed pair of variants are: (1) reducing the computational complexity of NRS classifier termed as Conditional Fast Nearest Regularized Subspace (CFNRS) classifier and (2) incorporation of dissimilarity feature termed as Conditional Dissimilarity-based Nearest Regularized Subspace (CDNRS) classifier. Regarding the first approach, the simple k-NN classifier result is used as a condition to evaluate NRS classifier. Regarding the second approach, an intra-feature dissimilarity measure is considered to create a pair of dictionary which contains a conditional binary matrix and a dissimilarity feature matrix. Each conditional binary matrix is a collection of distinct sub-spaces representing their respective classes. The incoming data is classified with the help of a dictionary and classification performance is validated over other state-of-art approaches. As Hyper-Spectral (HS) data have a strong spectral correlation in terms of spectral signatures, various such HS data are included for validation of our approach. The experimental study reveals the pros and cons of different classifiers along with the effectiveness of our proposed approach. The proposed conditional classifier is found to be able to compete with other, standard classifiers in terms of accuracy and computational complexity, mostly where features possess some sort of sequence information. The suitability of the proposed classifier is also verified for both binary and multi-class classification problems. Along with the experimental validation, the statistical significance of the proposed conditional measure is assessed over other standard state-of-art methods, and the proposed one outperformed the others. The experimental results reveal the importance of dissimilarity features for classification and also suggest to incorporate a simple classifier such as k-NN, prior to any other classifier. This is mainly meant to reduce the computational complexity of the selected classifier without compromising the accuracy.
Security Cryptanalysis of NUX for the Internet of Things

In order to adopt the restricted environment, such as radio frequency identification technology or sensor networking, which are the important components of the Internet of Things, lightweight block ciphers are designed. NUX is a 31-round iterative ultralightweight cipher proposed by Bansod et al . In this paper, we examine the resistance of NUX to differential and linear analysis and search for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mn mathvariant="normal">1</mml:mn><mml:mo>~</mml:mo><mml:mn mathvariant="normal">31</mml:mn></mml:math>-round differential characteristics and linear approximations. In design specification, authors claimed that 25-round NUX is resistant to differential and linear attack. However, we can successfully perform 29-round differential attack on NUX with the 22-round differential characteristic found in this paper, which is 4 rounds more than the limitation given by authors. Furthermore, we present the key recovery attack on 22-round NUX using a 19-round linear approximation determined in this paper. Besides, distinguishing attack, whose distinguisher is built utilizing the property of differential propagation through NUX, is implemented on full NUX with data complexity 8.
Crack recognition and reconstruction with coarse–fine discontinuous digital image correlation

The recognition of structural cracks and reconstruction of displacement fields around cracks are challenging tasks in engineering. By improving the standard digital image correlation (DIC) method and applying it in measuring the discontinuity of cracks, this study presents a coarse–fine search strategy based on a discontinuous DIC for rapid crack recognition and accurate reconstruction of the displacement fields. The technique introduces a split-subset model with a vector that represents the direction of crack opening to overcome the limitation of the standard DIC in analyzing discontinuous areas. A specially designed coarse–fine search strategy is applied to support the calculation model for achieving higher accuracy. The proposed approach is validated using numerically synthesized crack images as well as images obtained from notch tensile experiments in the laboratory. The results show that the proposed method can recognize the crack faces; in addition, it works well for reconstructing the displacement fields of both continuous and discontinuous (cracked) areas with average absolute errors of approximately 0.02. The proposed technique is also valid for slightly noised images.
Named Entity Recognition Using Gazetteer of Hierarchical Entities

This paper presents a named entity recognition method which finds predetermined entities in an unstructured text. The method uses word similarities based on typical word transformations (lemmatization and stemming), word embeddings and character level based similarity to map those entities onto words in the text. The approach is language independent, though language-dependent components are used for lemmatization, stemming and word embedding, and works on any given set of entities. Special attention is given to the entities which are represented in a hierarchical form with the hypernymy-hyponymy relation. The proposed method has the following advantages: it finds the normalized form of the recognized entity name; it is easy to adjust to a new domain; it respects the hierarchical organization of entities; and due to the modular approach can be constantly improved just by updating components for lemmatization, stemming or word embedding. The proposed entity recognition method was tested on a test set of tourist queries and hierarchical entities collected from Slovenia.info tourist portal.
A Skeleton Object Detection-Based Dynamic Gesture Recognition Method

A skeleton-based object detection method is proposed to recognize the dynamic gesture. The Kinect depth camera is utilized to capture the skeleton of human beings in the dynamic gesture motions, all gestures of the same dynamic gestures are marked and trained. Then a modified Single Shot MultiBox Detector (SSD) network is adopted to locate the arm in the skeleton images. The dynamic gesture is recognized based on the arm skeleton movements in the motion. To balance the precision and recognition time in the identification period, a corresponding comprehensive index is constructed to find a suitable proportion of arm skeleton images for detection by the trained neural network. It can reduce the redundancy and improve detection efficiency. Moreover, it will decrease the impact of noise and enhance the recognition accuracy. At last, an experimental analysis validated the effectiveness of the proposed method.
On Network Performance Indicators for Network Promoter Score Estimation

Estimation of user perceived quality of offered services, from massive number of Key Performance Indicator (KPI)'s that are measured in diverse components, has been a necessity for mobile network operators. The goal is first to have a good estimator for poor Quality of Experience (QoE), which can potentially be achieved with machine learning, and then pinpoint the features that are contributing to the poor performance. There is often a tradeoff between accuracy and interpretability of models. In this paper, we address this tradeoff by first developing a robust but complex teacher machine learning model to map the subjective Net Promoter Score (NPS) values computed from the user quality feedback to the underlying subset of KPI metrics. Next, we develop a rather interpretable student model supervised by the pre-trained teacher model. Eventually the compact student decision tree model learns to mimic the behavior of the teacher model with an at least 10 % improved accuracy in testset as compared to conventional way of directly training using the decision tree model. In the last step, we extract the rules and important influential features of the distilled student model.
A Karatsuba Algorithm Based Accelerator for Pairing Computation

Elliptic curve cryptosystem costs smaller calculation complexity compared with RSA. Identity Based Cryptography is a state-of-art scheme in all the elliptic curve cryptosystems. Bilinear pairing calculation over elliptic curves is the most time-consuming part which is also the pivotal to build IBC schemes. We dedicated this paper to the study of accelerating pairing computation. The proposed design uses Karatsuba algorithm-based multiplier and exploits parallelism at different layer of extension field. On the other hand, some algorithm-level techniques are used for square for F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 12. Finally, the design is implemented on Xilinx Virtex-6 platform with the verification the R-ate pairing computation used in Identity-Based Algorithms SM9 issued by China, takes only 5.83ms. The results also outperform the performance using OpenCL.
Spectral Clustering-based Matrix Completion Method for Top-n Recommendation

In the era of big data on the Internet, top-N recommender systems constitute a mission-critical technology in e-commerce applications. However, sparse data is one of the key factors leading to poor performance of the recommendation algorithm. In this paper, we propose a low-rank matrix completion recommendation scheme based on spectral clustering. The underlying idea is that spectral clustering based on rating and item classification information is used to achieve the division of the similar user group. Furthermore, we use low-rank matrix completion to effectively predict the unrated items in the sub-matrix of the spectral clustering. The real dataset experimental results show that our proposed scheme often outperform other state-of-the-art algorithms in top-N recommendation.
Personalized News Media Extraction and Archival Framework with News Ordering and Localization

Indigenous knowledge is a collective fact generated by local communities all over the world about nature, technology, health, natural resources, etc. The preservation and propagation of this valuable resource can be effectively achieved through media. Presently, information and communication technology plays a vital role in daily lives of the people. The news desk journalist works on the principle of accessing new archives and extracting an idea from the past and reworking it with the present. Yet scrolling through this tremendous amount of information can be hectic. The proposed system of personalized news extraction helps the journalists in identifying the semantically matching news by making use of ontology. Localization and news ordering are the two main features included in the proposed sustainable framework. The localization focuses on extracting news about traditional knowledge. One of the potential elements that determine the news value of a news story is its occurring place. News ordering deals with the listing of news for a query that is specific to the location from where it has been broadcasted. Further studies are required to improve news ordering by discovering how each news event affects the lives of the local community.
Cross Site Scripting (XSS) and SQL-Injection Attack Detection in Web Application

Now a days new web application tools generated day by day so the security of web is most important. From the few years back the web attacks was increasing so it is a big problem. But now various scanner vulnerability tools are available so we can find out the attack if it is any in the web application if we aware the attack then able to find the solution of the problem and protect them from the attackers. With the help of tools we can find the type of vulnerability is occur mostly SQL-injection attack and cross site scripting attacks are possible in the web application.
Pedestrian Tracking and Stereo Matching of Tracklets for Autonomous Vehicles

The prediction of the surrounding pedestrians' walking paths is a vital part for autonomous driving systems in the aspect of traffic safety. In this paper, we propose a pipeline which tracks pedestrians captured by a stereo camera system onboard a mobile vehicle, composes the pedestrian tracklets, clusters the tracklets to form trajectories, and matches the trajectories. The output 3D pedestrian trajectories can be used for further applications such as pedestrian trajectory prediction for driverless vehicles. Our algorithm has been compared with various state-of-art pedestrian tracking methods. Our experimental results show that the visual temporal features computed by our algorithm are effective for trajectory representation and that, by incorporating tracklet clustering into the pipeline, the pedestrian tracking performance is improved.