Reviewerly/exHarmony huggingface dataset:
Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections

Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.
Augmented Reality in Higher Education: An Evaluation Program in Initial Teacher Training

One of the emerging technologies that have sparked greater interest in pedagogical contexts is augmented reality. This paper aims to assess the impact, practices and attitudes that are generated from augmented reality in the initial training of future teachers, and the presence of these practices in a university training context. The study was carried out with 87 trainee primary teachers. Information was obtained by applying the Wilcoxon test. The qualitative data obtained in open questions were also triangulated. It is emphasized that students do not habitually use this resource at the university, and that with these practices there is sometimes a certain amount of distraction, and even of time being wasted. From the data analyzed, we also highlight that once the availability of resources, class planning and initial teacher training are overcome, augmented reality provides benefits and advantages centered on pedagogies that allow for greater enthusiasm on the part of the students, with significant advantages in creativity, innovation, participation, and especially in the motivation of participants. Coinciding with recent research, our results underline the need for initial training so as to be able to design and apply practices with augmented reality in teaching, and to take advantage of the aforementioned benefits.
Rethinking Triplet Loss for Domain Adaptation

The gap in data distribution motivates domain adaptation research. In this area, image classification intrinsically requires the source and target features to be co-located if they are of the same class. However, many works only take a global view of the domain gap. That is, to make the data distributions globally overlap; and this does not necessarily lead to feature co-location at the class level. To resolve this problem, we study metric learning in the context of domain adaptation. Specifically, we introduce a similarity guided constraint (SGC). In the implementation, SGC takes the form of a triplet loss. The triplet loss is integrated into the network as an additional objective term. Here, an image triplet consists of two images of the same class and another image of a different class. Albeit simple, the working mechanism of our method is interesting and insightful. Importantly, images in the triplets are sampled from the source and target domains. From a micro perspective, by enforcing this constraint on every possible triplet, images from different domains but of the same class are mapped nearby, and those of different classes are far apart. From a macro perspective, our method ensures that cross-domain similarities are preserved, leading to intra-class compactness and inter-class separability. Extensive experiment on four datasets shows our method yields significant improvement over the baselines and has a competitive accuracy with the state-of-the-art results.
A recalling-enhanced recurrent neural network: Conjugate gradient learning algorithm and its convergence analysis

Elman network is a classical recurrent neural network with an internal delay feedback. In this paper, we propose a recalling-enhanced recurrent neural network (RERNN) which has a selective memory property. In addition, an improved conjugate algorithm with generalized Armijo search technique that speeds up the convergence rate is used to train the RERNN model. Further enhancement performance is achieved with adaptive learning coefficients. Finally, we prove weak and strong convergence of the presented algorithm. In other words, as the number of training steps increases, the following has been established for RERNN: (1) the gradient norm of the error function with respect to the weight vectors converges to zero, (2) the weight sequence approaches a fixed optimal point. We have carried out a number of simulations to illustrate and verify the theoretical results that demonstrate the efficiency of the proposed algorithm.
Malicious URL Detection based on Machine Learning

Currently, the risk of network information insecurity is increasing rapidly in number and level of danger.The methods mostly used by hackers today is to attack end-toend technology and exploit human vulnerabilities.These techniques include social engineering, phishing, pharming, etc.One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs).As a results, malicious URL detection is of great interest nowadays.There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques.In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes.Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors.In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning algorithm, and a bigdata technology.The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly.This is suggested that the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.
A Stochastic Line Search Method with Expected Complexity Analysis

For deterministic optimization, line search methods augment algorithms by providing stability and improved efficiency. Here we adapt a classical backtracking Armijo line search to the stochastic optimization setting. While traditional line search relies on exact computations of the gradient and values of the objective function, our method assumes that these values are available up to some dynamically adjusted accuracy which holds with some sufficiently large, but fixed, probability. We bound the expected number of iterations to reach a desired first-order accuracy in the nonconvex, convex, and strongly convex cases and show that this bound matches the complexity bound of deterministic gradient descent up to constants.
A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degrades when the target area of vehicle detection (the target domain) is different from the training data (the source domain). To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low cost. We applied Correlation alignment (CORAL) DA and adversarial DA to our region-based vehicle detector and improved the detection accuracy by over 10% in the target domain. We further improved adversarial DA by utilizing the reconstruction loss to facilitate learning semantic features. Our proposed method achieved slightly better performance than the accuracy achieved with the labeled training data of the target domain. We demonstrated that our improved DA method could achieve almost the same level of accuracy at a lower cost than non-DA methods with a sufficient amount of labeled training data of the target domain.
Noninterpretive Uses of Artificial Intelligence in Radiology

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
Legal challenges and opportunities of blockchain technology in the real estate sector

Purpose Blockchain, which was originally created to enable peer-to-peer digital payment systems (bitcoin), is considered to have several benefits for different sectors, such as the real estate one. In a standard European-wide real estate transaction, several intermediaries are involved. As a consequence, these agreements are usually time-consuming and involve extra difficulties to cross-border operations. As blockchain, combined with smart contracts, may have an important role in these transactions, this paper aims to explore its prospective challenges, limitations and opportunities in the real estate sector and discover how the traditional intermediaries have to face a possible implementation of this technology. Design/methodology/approach This paper analyses the current intermediaries in the real estate sector in European Union (EU), their functions and how can blockchain strengthen the security of these transactions while reducing their time. The author uses a legal methodology to approach it. Findings Blockchain, combined with smart contracts, has both challenges and opportunities for the real estate sector. On the one hand, it may improve procedures, allow EU transactions and the interconnection between public administration. However, to not reduce parties rights, this blockchain should have some special features, such as the possibility of being amended. Originality/value This paper provides a valuable overview of all the intermediaries that could be affected by blockchain protocols. It is of interest of blockchain developers, public administrations and researchers who are working on blockchain and property conveyancing.
Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

Abstract Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.
Novel soft sensor development using echo state network integrated with singular value decomposition: Application to complex chemical processes

It is of great importance to develop advanced soft sensors for ensuring the safety and stability of complex industrial processes. Unluckily, with the increasing scale of chemical processes, it becomes more and more demanding to develop soft sensor with high accuracy. In addition, most of industrial processes are dynamic. As a result, the soft sensors developed using static models cannot achieve acceptable performance. In order to handle this problem, the Echo state network (ESN) as a kind of recurrent neural network is selected. However, the output weights of ESN are calculated linearly. On one hand, the collinear in the reserve layer outputs may decrease the performance; on the other hand, the over-fitting problem may occur. To enhance and improve the ESN performance, singular value decomposition based ESN (SVD-ESN) is presented. In the SVD-ESN method, the singular value decomposition instead of the traditional least square is adopted to calculate the weights between the output layer and the reserve layer. Through singular value analysis in the outputs of the reserve layer, appropriate defining parameters are selected to enhance the accuracy and ensure the computing speed. As a result, the collinearity and over-fitting problem is solved; then the performance of ESN is enhanced. To test and validate the performance of SVD-ESN, the proposed SVD-ESN is developed as soft sensor for the High Density Polyethylene (HDPE) production process and Purified Terephthalic Acid (PTA) production process. Compared with the conventional ESN, Extreme Learning Machine (ELM), Dynamic Window based ELM (DW-ELM) and Long Short-Term Memory (LSTM), the simulation results show that the proposed SVD-ESN model obtains better performance in terms of prediction accuracy, which conforms that the proposed SVD-ESN can be used as an effective dynamic model for developing accurate soft sensors.
Influence of initialization on the performance of metaheuristic optimizers

All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of fitness evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.
Achieving O(log³n) Communication-Efficient Privacy-Preserving Range Query in Fog-Based IoT

The advance of Internet-of-Things (IoT) techniques has promoted an increasing number of organizations to explore more mission-critical solutions. However, the response latency, bandwidth usage, and reliability are still challenging issues in the traditional IoT. To tackle these challenges, the fog-based IoT has become popular and the range query is one of the most frequently used operations in fog-based IoT, where given a range query, a fog node will return the aggregated data from IoT devices to the query user. Because the fog nodes are not fully trusted, there is a desire to design a privacy-preserving range query scheme in the fog-based IoT. However, most of existing privacy-preserving range query schemes are not efficient in terms of communication overhead, especially for a large-size range. Therefore, it is still a challenging issue to design a communication-efficient range query in fog-based IoT. Aiming at this challenge, in this article, we propose a new privacy-preserving range query scheme in the fog-based IoT. Specifically, we first devise an efficient homomorphic encryption scheme for maintaining data privacy and security in a range query. Then, we present a novel range decomposition technique to compile the range query, which can transform a given range query [L, U], where 0 ≤ L ≤ U ≤ n - 1, into a semi-triangular structure, and enable our proposed scheme to achieve O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> n) communication efficiency. The detailed security analysis shows that our proposed scheme is really privacy preserving, and the extensive performance evaluation demonstrates that our proposed scheme is efficient in terms of low communication overhead and the computational cost.
SibNet: Sibling Convolutional Encoder for Video Captioning

Visual captioning, the task of describing an image or a video using one or few sentences, is a challenging task owing to the complexity of understanding the copious visual information and describing it using natural language. Motivated by the success of applying neural networks for machine translation, previous work applies sequence to sequence learning to translate videos into sentences. In this work, different from previous work that encodes visual information using a single flow, we introduce a novel Sibling Convolutional Encoder (SibNet) for visual captioning, which employs a dual-branch architecture to collaboratively encode videos. The first content branch encodes visual content information of the video with an autoencoder, capturing the visual appearance information of the video as other networks often do. While the second semantic branch encodes semantic information of the video via visual-semantic joint embedding, which brings complementary representation by considering the semantics when extracting features from videos. Then both branches are effectively combined with soft-attention mechanism and finally fed into a RNN decoder to generate captions. With our SibNet explicitly capturing both content and semantic information, the proposed model can better represent the rich information in videos. To validate the advantages of the proposed model, we conduct experiments on two benchmarks for video captioning, YouTube2Text and MSR-VTT. Our results demonstrate that the proposed SibNet consistently outperforms existing methods across different evaluation metrics.
Toward Realistic Face Photo–Sketch Synthesis via Composition-Aided GANs

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It covers wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this article, we propose using facial composition information to help the synthesis of face sketch/photo. Especially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs, including a face photo/sketch and the corresponding pixelwise face labels for generating a sketch/photo. Next, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. In addition, we employ a perceptual loss function to encourage the synthesized image and real image to be perceptually similar. Finally, we use stacked CA-GANs (SCA-GANs) to further rectify defects and add compelling details. The experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. In addition, our method significantly decreases the best previous Fréchet inception distance (FID) from 36.2 to 26.2 for sketch synthesis, and from 60.9 to 30.5 for photo synthesis. Besides, we demonstrate that the proposed method is of considerable generalization ability.
ViCE

The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly accurate models still require verification by domain experts. In addition, end-users of a model also want to understand the reasons behind specific decisions. Thus, the need for interpretability is increasingly paramount. In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions. Each sample is assessed to identify the minimal set of changes needed to flip the model's output. These explanations aim to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions. The results are effectively displayed in a visual interface where counterfactual explanations are highlighted and interactive methods are provided for users to explore the data and model. The functionality of the tool is demonstrated by its application to a home equity line of credit dataset.