Reviewerly/exHarmony huggingface dataset:
Towards an efficient Malayalam Named Entity Recognizer Analysis on the Challenges

Named Entity Recognition (NER) also known as entity extraction plays an important role in identifying and classifying the named entities into different categories like name of person, place, organization, things, quantity, monetary value etc. that appear in a document. NER has applications in various Natural Language Processing (NLP) tasks such as information retrieval, question answering system, Machine Translation, Sentiment Analysis. NER systems can be developed using rule based, machine learning or hybrid approach. Now Deep learning is being used to develop efficient NER as these models are capable of learning patterns easily and efficiently. Quite a large number of work has been done in English compared to the work done for Indian Languages. The focus of this paper is to highlight the challenges in building an efficient NER for one of the south Indian language namely Malayalam. The different issues that we need to address in order to develop an efficient NER for Malayalam is presented.
Deductive Synthesis of Bubble–Sort Using Multisets

We demonstrate the possibility of automated synthesis of the Bubble–Sort algorithm as a rewrite program, a functional program, and an iterative program, starting from the specification. First a rewrite set of clauses for the algorithm Max–Sort is generated from the automatic proof of the synthesis conjecture, representing the main algorithm as well as the necessary auxiliary functions. This is then transformed into a tail recursive Bubble–Sort and by logical analysis the possibility of adding a flag for avoiding unnecessary recursions is identified. This new, more efficient algorithm is then transformed into a functional program, and finally into an imperative program. The practical experiments are performed using the Theorema system.
Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

This paper addresses the nontrivial task of Twitter financial disambiguation (TFD), which is relevant to filter financial domain tweets (e.g., alloy steel or coffee prices) when no unique identifiers (e.g., cashtags) are adopted. To automate TFD, we propose a transfer learning approach that uses freely labeled news titles to train diverse one-class and two-class classification methods. These include different text handling transforms, adaptations of statistical measures and modern machine learning methods, including support vector machines (SVM), deep autoencoders and multilayer perceptrons. As a case study, we analyzed the domain of alloy steel prices, collecting a recent Twitter dataset. Overall, the best results were achieved by a two-class SVM fed with TFD statistical measures and topic model features, obtaining an 80% and 71% discrimination level when tested with 11,081 and 3000 manually labeled tweets. The best one-class performance (78% and 69% for the same test tweets) was obtained by a term frequency-inverse document frequency classifier (TF-IDFC). These models were further used to generate a Financial User Relevance rank (FUR) score, aiming to filter relevant users. The SVM and TF-IDFC FUR models obtained a predictive user discrimination level of 80% and 75% when tested with a manually labeled test sample of 418 users. These results confirm the proposed joint TFD-FUR approach as a valuable tool for the selection of Twitter texts and users for financial social media analytics (e.g., sentiment analysis, detection of influential users).
Automatic Color Scheme Extraction from Movies

A color scheme is an association of colors, i.e., a subset of all possible colors, that represents a visual identity. We propose an automated method to extract a color scheme from a movie. Since a movie is a carefully edited video with different objects and heterogeneous content embodying the director's messages and values, it is a challenging task to extract a color scheme from a movie as opposed to a general video filmed at once without distinction of shots or scenes. Despite such challenges, color scheme extraction plays a very important role in film production and application. The color scheme is an interpretation of the scenario by the cinematographer and it can convey a mood or feeling that stays with the viewer after the movie has ended. It also acts as a contributing factor to describe a film, like the metadata fields of a film such as a genre, director, and casting. Moreover, it can be automatically tagged unlike metadata, so it can be directly applied to the existing movie database without much effort. Our method produces a color scheme from a movie in a bottom-up manner from segmented shots. We formulate the color extraction as a selection problem where perceptually important colors are selected using saliency. We introduce a semi-master-shot, an alternative unit defined as a combination of contiguous shots taken in the same place with similar colors. Using real movie videos, we demonstrate and validate the plausibility of the proposed technique.
Code Reviews in C++

Code reviews are an essential part of quality assurance in modern software projects. But despite their great importance, they are still carried out in a way that relies on human skills and decisions. During the last decade, there have been several publications on code reviews using eye tracking as a method, but only a few studies have focused on the performance differences between experts and novices. To get a deeper understanding of these differences, the following experiment was developed: This study surveys expertise-related differences in experts’, advanced programmers’, and novices’ eye movements during the review of eight short C++ code examples, including correct and erroneous codes. A sample of 35 participants (21 novices, 14 advanced and expert programmers) were recruited. A Tobii Spectrum 600 was used for the data collection. Measures included participants’ eye movements during the code review, demographic background data, and cued retrospective verbal comments on replays of their own eye movement recordings. Preliminary results give proof for experience-related differences between participants. Advanced and expert programmers performed significantly better in case of error detection and the eye tracking data implies a more efficient reviewing strategy.
Proxy re-encryption for fine-grained access control: Its applicability, security under stronger notions and performance

Proxy Re-encryption (PRE) offers an efficient solution for enforcing access control on outsourced data through delegation of decryption rights of a delegator to a delegatee. However, to meet practical security requirements of an access control model, the delegator must control these delegations such that a re-encryption key enables the delegation of decryption rights of only a subset of the delegator's ciphertexts. In this paper, we focus on a category of PRE-based primitives, which we refer to as "PRE with controlled delegation". In these primitives, instead of the re-encryption key alone, the re-encryption key and authorization of the delegatee for a data item collectively determine whether the ciphertext transformation results in a valid re-encrypted ciphertext under the delegatee's public key. This paper provides an exhaustive functional, security and performance analysis of all the existing schemes for PRE with controlled delegation in a concrete fine-grained access control model. We show that the traditional PRE security notions are insufficient to address all the security aspects of the access control model. Motivated by our analysis, we formulate stronger security notions and state the desirable efficiency requirements for PRE schemes applicable in the concrete fine-grained access control model. We show the validity of the proposed security notions by formally proving the insecurity of a conventional PRE scheme and security of one of the PRE schemes with controlled delegation under the proposed stronger PRE security notions. We critically analyze all schemes for PRE with controlled delegation under the proposed stronger security notions and with respect to the efficiency requirements. We show that no scheme for PRE with controlled delegation simultaneously satisfies the efficiency and security requirements formulated in this paper. Finally, we present possible future research directions to obtain a PRE-based solution that is secure under the proposed stronger security notions and satisfies all desirable performance requirements in a fine-grained access control model.
Safe and optimal navigation for autonomous multi-rotor aerial vehicle in a dynamic known environment by a decomposition-coordination method

In this paper, we present a new solution for the Autonomous navigation problem, using a Decomposition-Coordination Method (DCM) 1. The main purpose of this work is to compute an optimal and safe path for the multi-rotor Unmanned Aerial Vehicle (UAV) in a dynamic environment, moving from an initial location to the desired state. We assume that the flight environment is totally known to a supervisory unit, and the positions and trajectories of dynamic obstacles could be known in real-time, thus to perform in such environment a high reactivity is required as well as good connectivity with the supervisory unit that provides the safe path, each time one obstacle or more are detected on the road, so that the UAV could autonomously diverts from the unsafe path to the new safe one, and avoid the potential collisions. First and foremost, we choose a generalized nonlinear model for the multi-rotors in view of the rotational and translational dynamics of the UAV. We then associate that model with the objective functions. After that, we proceed to the resolution of the multi-objective optimization problem using our approach of decomposition-coordination. The principle of this method consists in decomposing the system into several smaller subsystems to simplify the treatment. Then we achieve the coordination afterward using Lagrange multipliers. To prove the convergence and stability of our method we make use of a Lyapunov function chosen particularly for this system. In the last section we present the simulation results, to confirm the reliability of our method.
The relationship between human resource management practices and organisational citizenship behaviour

Purpose: This study aims at providing empirical evidence on the extent to which organisational commitment mediates and strengthens the relationship between human resource management practices and organisational citizenship behaviour amongst administrative staff working in Jordanian universities. Design/methodology/approach: Data were gathered using a questionnaire. The population of the study consisted of administrative staff working at 29 universities. A total of 350 employees participated in the study. Data analysis involved the computation of descriptive statistics, as well as structural equation modelling, factor analysis, regression and correlation analysis. Finding/results: The results of the study indicated that the human resource management practices, organisational commitment, and organisational citizenship behaviour were statistically significant. Moreover, it was found that human resource management practices through organisational commitment impact organisational citizenship behaviour. Practical implication: The literature supports that organisational citizenship behaviour promotes the effective functioning of any organisation as well as contributing to the overall success of the organisations. Therefore, there is a need to conduct studies that concentrate on how to improve organisational citizenship behaviour so that organisations can sustain performance. Originality/value: The conceptual model developed in this study provides relationships of human resource management practices through organisational commitment that can be used by the management of organisations as a guide to positively influence the organisational citizenship behaviour of the employees in their organisations.
Knowledge-based automated planning system for StereoElectroEncephaloGraphy: A center-based scenario

• SEEG trajectory planning is a time-consuming patient specific task. • The analysis of retrospective planned trajectories offers valuable information. • Trajectory optimization can be adapted to the specific medical workflow. • Trajectory profiles allows tailored optimization strategies. Surgical planning for StereoElectroEncephaloGraphy (SEEG) is a complex and patient specific task, where the experience and medical workflow of each institution may influence the final planning choices. To account for this variability, we developed a data-based Computer Assisted Planning (CAP) solution able to exploit the knowledge extracted by past cases. By the analysis of retrospective patients’ data sets, our system proposes a pool of trajectories commonly used by the institution, which can be selected to initialize a new patient plan. An optimization framework adapts those to the patient’s anatomy by optimizing clinical requirements (e.g. distance from vessel, gray matter recording and insertion angle), and adapting its strategy based on the trajectory type selected.The system has been customized based on the data of a single institution. Two neurosurgeons, working in a high-volume hospital, have validated it by using 15 retrospective patient data sets, with more than 200 trajectories reviewed. Both surgeons considered ~ 81 % of the optimized trajectories as clinically feasible ( 75 % inter-rater reliability). Quantitative comparison of distance from vessels, insertion angle and gray matter recording index showed that the optimized trajectories reached superior or comparable values with respect to the original manual plans. The results suggest that a tailored center-based solution could increase the acceptance rate of the automated trajectories proposed.
PHY-IDS

In modern connected healthcare applications, wearable devices supporting real-time monitoring and diagnosis have become mainstream. However, wearable systems are exposed to massive cyberattacks that threaten not only data security but also human safety and life. One of the fundamental security threats is device impersonation. We therefore propose PHY-IDS; a lightweight real-time detection system that captures spoofing attacks leveraging on body motions. Our system utilizes time series of physical layer features and builds on the fact that it is non-trivial to inject malicious frames that are indistinguishable with legitimate ones. With the help of statistical learning, our system characterizes the signal behavior and flags deviations as anomalies. We experimentally evaluate PHY-IDS's performance using bodyworn devices in real attack scenarios. For four types of attackers with increasing knowledge of the deployed detection system, the results show that PHY-IDS detects naive attackers with high accuracy above 99.8\% and maintains good accuracy for stronger attackers at a range from 81.0% to 98.9%.
Accurate Estimation of Body Height From a Single Depth Image via a Four-Stage Developing Network

Non-contact measurement of human body height can be very difficult under some circumstances.In this paper we address the problem of accurately estimating the height of a person with arbitrary postures from a single depth image. By introducing a novel part-based intermediate representation plus a four-stage increasingly complex deep neural network, we manage to achieve significantly higher accuracy than previous methods. We first describe the human body in the form of a segmentation of human torso as four nearly rigid parts and then predict their lengths respectively by 3 CNNs. Instead of directly adding the lengths of these parts together, we further construct another independent developing CNN that combines the intermediate representation, part lengths and depth information together to finally predict the body height results.Here we develop an increasingly complex network architecture and adopt a hybrid pooling to optimize training process. To the best of our knowledge, this is the first method that estimates height only from a single depth image. In experiments our average accuracy reaches at 99.1% for people in various positions and postures.
Efficient Online Interest-Driven Exploration for Developmental Robots

A major challenge for online and data-driven model learning in robotics is the high sample complexity. This hinders its efficiency and practical feasibility for lifelong learning, in particular, for developmental robots that autonomously bootstrap their sensorimotor skills in an open-ended environment. In this work, we propose new methods to mediate this problem in order to permit the learning of robot models online, from scratch, and in learning while behaving fashion. Exploration is utilized and autonomously driven by a novel intrinsic motivation signal which combines knowledge-based and competence-based elements and surpasses other state-of-the-art methods. In addition, we propose an episodic online mental replay to accelerate online learning, to ensure sample efficiency, and to update the model online rapidly. The efficiency as well as the applicability of our methods are demonstrated with a physical 7-DoF Baxter manipulator. We show that our learning schemes are able to drastically reduce the sample complexity and learn the data-driven model online, even within a limited time frame.
Gaussian Mixture Variational Autoencoder for Semi-Supervised Topic Modeling

Topic models are widely explored for summarizing a corpus of documents. Recent advances in Variational AutoEncoder (VAE) have enabled the development of black-box inference methods for topic modeling in order to alleviate the drawbacks of classical statistical inference. Most existing VAE based approaches assume a unimodal Gaussian distribution for the approximate posterior of latent variables, which limits the flexibility in encoding the latent space. In addition, the unsupervised architecture hinders the incorporation of extra label information, which is ubiquitous in many applications. In this paper, we propose a semi-supervised topic model under the VAE framework. We assume that a document is modeled as a mixture of classes, and a class is modeled as a mixture of latent topics. A multimodal Gaussian mixture model is adopted for latent space. The parameters of the components and the mixing weights are encoded separately. These weights, together with partially labeled data, also contribute to the training of a classifier. The objective is derived under the Gaussian mixture assumption and the semi-supervised VAE framework. Modules of the proposed framework are appropriately designated. Experiments performed on three benchmark datasets demonstrate the effectiveness of our method, comparing to several competitive baselines.
Tao: A Trilateral Awareness Operation For Human Parsing

Human parsing, which segments a human image into semantic regions, is a fundamental task in human-centric analysis. Recently, numerous human parsing approaches based on convolutional neural networks (CNNs) have made significant progress. However, these models still suffer the high time-complexity and poor efficiency issues, since they always introduce too many and complex hypothetical priors. Therefore, in this paper, we attempt to solve above issues by mining the deep information hidden in data, such as the scale, spatial information and statistics of the image descriptors, etc. By this way, we propose an effective structure, named the trilateral awareness operation (TAO), which can boost the scale, spatial and fine-grained awareness of a CNN. In practice, it is a simple yet effective architecture without using bells and whistles (such as human pose and edge information), to address human parsing tasks. Comprehensive experiments and corresponding results on three public datasets have demonstrated that the proposed TAO is superior to the state-of-the-art methods.
Text Sentiment Classification Based on Improved BiLSTM-CNN

Deep learning methods are popular in natural language processing methods. Because different models will extract different features in the text, traditional machine learning models still have room for expansion in optimization. Based on this fact, this paper proposes a sentiment classification algorithm based on improved BiLSTM-CNN. Firstly, the user's words are converted into vectors by the frequency of words in the text, and the sequence features in the text are extracted through BiLSTM. Then further feature extraction is performed on the data through CNN, so that more dimensional user information can be used for classification. The experimental results show that the model constructed according to this method takes into account the relationship between different features in the text, and effectively optimizes traditional algorithms. Compared with related traditional network models, the performance is slightly improved in text sentiment classification.
Residual Convolutional Neural Network Based In-Loop Filter with Intra and Inter Frames Processed Respectively for Avs3

Inspired by that deep learning based methods have made great progress in image enhancement tasks, we propose a deep residual convolutional neural network based in-loop filter to suppress compression artifacts for the third generation of Audio Video Standard (AVS3). The proposed network consists of several residual blocks, with quantization parameters (QPs) fed into the model. The QP map can help the network to learn the relationship between QPs and the degree of compression distortion. Therefore, different reconstructed frames with different distortion degrees can be enhanced by a single model. In addition, we analyze the difference between intra prediction and inter prediction in terms of compression distortion, and propose to process intra and inter frames respectively. Comparing to the AVS3 reference software (HPM-5.0), we achieve 8.66% and 8.75% BD-rate reduction on Y component under RA and LD configurations separately.
An Implementation of ResNet on the Classification of RGB-D Images

Facial recognition is to identify human faces from an image. It is becoming more and more important these days as it can be applied in multiple industries, such as bank, airport, e-business, etc. Because of the broad application prospects, face recognition is actively developed and researched by many people, companies and academic organizations. In this paper, we customize one of the facial recognition models developed in the recent years – ResNet on the Intellifusion 3D face dataset [1]. And then we evaluate the performance of the algorithm by adjusting the depth of the network, pre-processing steps of the pictures and also the learning rate.