Reviewerly/exHarmony huggingface dataset:
Collapsed Gibbs Sampling of Beta-Liouville Multinomial for Short Text Clustering

With the rise of social media, we have access to more and more text data collected through platforms like Facebook and Twitter. The abundance of these data comes along with short texts challenges. We propose in this paper a collapsed Gibbs Sampling Beta-Liouville Multinomial (CGSBLM) to cope with those challenges. We evaluate the proposed CGSBLM on two datasets extracted from the Google News corpus. Apart from giving a better performance, our approach allows to address the limitations related to short text clustering.
Byzantine-resilient distributed learning under constraints

We consider a class of convex distributed statistical learning problems with inequality constraints in an adversarial scenario. At each iteration, an α-fraction of m machines, which are supposed to compute stochastic gradients of the loss function and send them to a master machine, may act adversarially and send faulty gradients. To guard against defective information sharing, we develop a Byzantine primal-dual algorithm. For α ∈ [0,0.5), we prove that after T iterations the algorithm achieves ~O(1/T+1/√{mT}-+α/√T) statistical error bounds on both the optimality gap and the constraint violation. Our result holds for a class of normed vector spaces and, when specialized to the Euclidean space, it attains the optimal error bound for Byzantine stochastic gradient descent.
Meta-Learning based Siamese Network with Channel-wise Self-attention for Visual Tracking

A great deal of attention has been paid to Siamese networks in visual object tracking. However, the Siamese network often faces the problem of over-fitting when adapting to new scenes, where the training samples for scene adaptation are limited. In this paper, we propose a meta-learning based Siamese network with channel-wise self-attention (CSA) on the basis of SiamFC3s architecture to overcome this limitation, in which meta-learning mechanism is adopted for efficient scene adaptation and CSA is used for better representation of the target object. Specifically, in the meta-learning phase, only part of the neurons' parameters, called hyper parameters are updated, leading to lightweight adaptation, which not only reduces the computational burden of the network but also overcomes the over-fitting problem. Experimental results based on benchmark OTB-100 have shown that our meta-learning based SiamFC3s incorporating the proposed CSA outperforms the baseline method SiamFC3s by 3.4% in success rate and 5.6% in precision rate.
Answer Selection Using Reinforcement Learning for Complex Question Answering on the Open Domain

Multiple-choice question answering for the open domain is a task that consists of answering challenging questions from multiple domains, without direct pieces of evidence in the text corpora. The main application of multiple-choice question answering is self-tutoring. We propose the Multiple-Choice Reinforcement Learner (MCRL) model, which uses a policy gradient algorithm in a partially observable Markov decision process to reformulate question-answer pairs in order to find new pieces of evidence to support each answer choice. Its inputs are the question and the answer choices. MCRL learns to generate queries that improve the evidence found for each answer choice, using iteration cycles. After a predefined number of iteration cycles, MCRL provides the best answer choice and the text passages that support it. We use accuracy and mean reward per episode to conduct an in-depth hyperparameter analysis of the number of iteration cycles, reward function design, and weight of the pieces of evidence found in each iteration cycle on the final answer choice. The MCRL model with the best performance reached an accuracy of 0.346, a value higher than naive, random, and the traditional end-to-end deep learning QA models. We conclude with recommendations for future developments of the model, which can be adapted for different languages using text corpora and word embedding models for each language.
Deep Neural Network Hyper-Parameters Optimization for Face Classification

Recognizing faces is a very challenging problem in the field of image processing. Deep neural network and especially Convolutional Neural Networks are the most widely used techniques for image classification and recognition. Despite these deep neural networks efficiency, choosing their optimal architectures for a given task remains an open problem. In fact, Convolutional Neural Networks performance depends on many hyper-parameters namely the network depth, convolutional layer numbers, the number of the local receptive fields and their respective sizes, convolutional stride and dropout ratio. These parameters thoroughly affect the performance of the classifier. This paper aims to optimize these parameters and develop the optimized architecture face classification and recognition. Intensive simulated experiments and qualitative comparisons have been conducted. The achieved results show that the developed Convolutional Neural Networks configuration provided a remarkable performance improvement in in terms of the network accuracy that exceeds 94%.
Eyephone—Eye Tracking Application for Digital Devices

Specially abled people who cannot make use of their hands for typing and operating smartphones, computers, etc., or people suffering from diseases like cerebral palsy, amyotrophic lateral sclerosis who cannot move or even speak; eyes can prove to be an effective channel of communication for them. Products available in the market for such people are expensive. Our aim is to design an application, eyephone, to help them access their own phones without assistance. The application consists of different phases like face recognition, eye tracking, and finally operation activation. Eye blinking and face gestures will be used for operation activation. Basically, the application will provide a keyboard to the users divided into appropriate sections. User will look at the section of his/her interest and operations such as typing, opening an application will be activated. As mentioned earlier, specially abled people will be able to make use of our application in order to use their smartphones. They will not need assistance for using their smartphones anymore and can be more independent and self-sufficient.
Evaluation of Cooking Recipes Using Their Texts and Images

Many people are using smartphone applications and websites with respect to cooking recipes in almost everyday life. By using cooking recipe sites, we easily search the cooking recipe for dishes that we want to eat and cook the dish based on the recipe. When we search cooking recipes, we refer to the two information that are pictures of cooked food and texts of cooking recipe. In this study, to investigate good cooking recipes, we built a multimodal coupled network to use these two kinds of information of cooking recipe sites. In this multimodal coupled network, as pre-learning, we learned images and texts separately. To learn texts, we first used word2vec to vectorize for each text, and then we use the deep averaging network to identify the objective function. To learn images, we performed transfer learning of images using VGG16 which is a famously trained model. We combined these two kinds of 300-dimensional feature vectors extracted from the final layer of the two models obtained by pre-learning, and then we learned a total of 600-dimensional feature vectors. As a result, the multimodal coupled network that combines image and text data has the best accuracy.
A simulation-extrapolation approach for the mixture cure model with mismeasured covariates

We consider survival data from a population with cured subjects in the presence of mismeasured covariates. We use the mixture cure model to account for the individuals that will never experience the event and at the same time distinguish between the effect of the covariates on the cure probabilities and on survival times. In particular, for practical applications, it seems of interest to assume a logistic form of the incidence and a Cox proportional hazards model for the latency. To correct the estimators for the bias introduced by the measurement error, we use the simex algorithm, which is a very general simulation based method. It essentially estimates this bias by introducing additional error to the data and then recovers bias corrected estimators through an extrapolation approach. The estimators are shown to be consistent and asymptotically normally distributed when the true extrapolation function is known. We investigate their finite sample performance through a simulation study and apply the proposed method to analyse the effect of the prostate specific antigen (PSA) on patients with prostate cancer.
Power Trading Model for Distributed Power Generation Systems Based on Consortium Blockchains

Distributed power generation systems based on clean energy experience a large development worldwide. However, existing centralized power trading models cannot support direct transactions between distributed power generation systems and power consumers. Existing centralized power trading models suffer from multi-agent untrustworthiness and risk of data tampering. In addition, the direct transactions involve a large number of producers and consumers, thus requiring high efficiency of transaction processing in power trading models. To tackle with the problems, we propose a weakly centralized power trading model based on consortium blockchains in this paper. The trading model facilitate the power trading process by using smart contracts on consortium blockchains. In addition, we propose an approach based on the Hotelling theory to determine the pricing for transaction in our power trading model. The experimental results show that our power trading model provides a stable throughput as the request rate of transactions increases. The power trading model proposed in this paper is more practical than those based on public blockchains.
Tile-Related Factors in Modern User Interface and Their Effects on Gaze Pointing Movement Time

This study investigated the feasibility of using gaze pointing in modern user interface (UI). Experiments were conducted to assess the impact of the modern UI layout (tile size, dynamic or static target tile, and interference tile) on gaze pointing movement time. A total of 13 participates were recruited, and their gaze pointing operation movement time under 12 experimental settings were measured. The scenarios contained different tile sizes as well as a dynamic or static target tile and an interference tile. When the interference tile was static, the gaze pointing movement time decreased as the tile size increased. However, when the tile size was larger than 96 pixels, the gaze pointing movement time no longer changed as tile size increased. The individual differences of the participants had a substantial impact on the operational performance of the gaze pointing interface, which is a challenge to be overcome when gaze pointing and the modern UI are integrated for future applications.
A Survey: Load Balancing Algorithm in Cloud Computing

Cloud computing gives on-demand access to conveyed assets on paid premise. Everyone needs to utilize these administrations to decrease the expense of framework and support, in this way the load on cloud is expanding step by step. Balancing the load is a standout amongst the most critical issue that distributed computing is confronting today. The load should to be appropriated reasonably among every one of the hubs. Load balancing can reduce the energy utilization. Every one of these algorithms works in various ways and has a few favorable circumstances and restrictions. The most imperative for load balancing calculations is to consider the qualities like throughput, adaptation to internal failure, overhead, execution, and reaction time and asset usage. This paper primarily centers on the idea of load Balancing, writing review on load Balancing systems and diverse estimation parameters.
Interactive Multi-Model Tracking Based on Time Difference Information

Target location and tracking technology are widely used in military and civilian fields. In recent years, development of related technology illustrates higher level of requirements for the positioning and tracking. Due to the constraints of the environment and equipment, the target state model presents a highly nonlinear state in practical applications that make the solution more difficult. In this paper, a tracking algorithm based on time difference of arrival (TDOA) is studied in detail. It is proposed to use an interactive multiple model algorithm improved by unscented Kalman filter to track it and to improve the shortcomings of Kalman filter (KF) in the traditional interacting multiple model (IMM) algorithm.
Blind Quality Assessment of Super-Resolution Images using Relative Gradient Statistics of Salient and Non-salient Objects

In recent years, due to the exponential increase in the usage of multimedia content in our everyday lives, blind image quality assessment (BIQA) has gained a lot of importance. Distortions of many types may be introduced in the images due to limitations in modern technology that needs to be assessed. Blind assessment of super-resolution (SR) images has attracted many researchers to address the limitations in BIQA algorithms. In this paper, a new technique for blind quality assessment of SR images is proposed. We have used the visual saliency technique to extract salient and non-salient objects from the input SR images using you only look once (YOLO) deep learning model. The relative gradient orient features are extracted from salient and non-salient objects, which are then used to predict the score by using Ada-boosting back propagation neural network (Ada-Boosting BP NN). The overall score of the SR image is calculated by the weighted average of the scores obtained from salient and non-salient objects. We have evaluated the performance of the proposed method on three independent SR image databases i.e., MY, Quality assessment database for SRI (QADS), and super-resolution image quality assessment Javeriana (SRIJ). Experimental results signify that the proposed method shows better performance on all three SR databases.
Newspapers Do Work: Quick and Effective Entanglement Material for Speculation

This research is motivated by the increasing significance of situated and sketching activities to co-speculate alternative futures. Our purpose is to present newspapers as imaginary material and how they relate to rich speculation in domestic networked-objects with regard to the emerged entanglement experience. As most studies aim at providing representation of everyday life and imaginative triggers as entanglement material for speculation, this research focuses on the relationship between news headlines and evocative objects. We then explore how collaging and sketching with everyday objects, app-icon stickers together with the present and the past newspapers help to speculate the utopian, or dystopian worlds. Findings show that newspapers become an effective catalyst to slightly defamiliarize our daily life and break the inertial thinking. Based on the collages, we design speculative products that explore the diversity of the possible IOT futures. Finally, we present illustrative cases with interpretations and heuristic implications.
Vulnerability Turning into a Key Design Criterion for Smart Mobility Concepts

What is the impact of mobility on vulnerable groups? Vice versa we ask about the impact of vulnerable groups on the innovation design process. The poster will be practitioner oriented reflecting the #MobilityMovesMinds movement that converges digital, physical, and mental mobility. It looks into the building yet learning patterns of resilience. The exploration of resilience build is a two years’ effort of Barbara Flügge, experts around the globe, vulnerable groups, and the homeless community. We chose the poster format to have an engaging element that the audience can carry on. As a carry-on tool the poster will be presented in audio and video format accessible in onsite and offline modes.
E-commerce Perspective in Kids and Toys Sector Purchase Process: Design Opportunity

Online sales for companies have become indispensable to reach the global market's consumers. To facilitate the purchasing process, however, it's essential to design websites taking attention to all those aspects that contribute to determining their quality and ability to meet the needs of both the e-commerce and the end-user. In particular, when we refer to kids’ products sale, personalized e-commerce services are necessary, bearing in mind the role of emotions on customers’ behaviour and the particular attention that parents pay to products quality. The paper aims to understand behaviours and buying habits’ changes of parents and adults in general, with a particular focus on the processes of choice, purchase and post-sale concerning online sales platforms for children's products. The research reports an analysis of 16 major online retail platforms currently existing globally, divided between child-specific and generic, evaluating aspects related to on-line Customer Journey and User Experience (UX).
When Curation Becomes Creation

Media platforms today benefit from: (1) discretion to organize content, (2) algorithms for curating user-posted content, and (3) absolution from liability. This favorable regulatory environment results from the current legal framework, which distinguishes between intermediaries and content providers. This distinction is ill-adapted to the modern social media landscape, where platforms deploy powerful data-driven algorithms to play an increasingly active role in shaping what people see, and where users supply disconnected bits of raw content as fodder. Today's platforms have license to monetize whatever content they like, moderate if and when it aligns with their corporate objectives, and curate their content however they wish.
Overview of Chinese Text Classification

[Goal] The goal is to sort out the key development nodes, algorithms and models in the field of Chinese text categorization, so as to facilitate researchers to sort out the development context and provide practical guidance. [Methods] Through CNKI database and VIP database, the related literatures of Chinese text classification were retrieved, and the related algorithms and models of Chinese text classification were classified and summarized. [Result] The advantages and disadvantages of the mainstream text classification algorithms, such as Naive Bayes algorithm, support vector machine algorithm, neural network algorithm and decision tree algorithm, are summarized; [Conclusion] Considerable quantities of machine learning and deep learning theory are used in text classification, which tends to focus on phrase- and sentence-based topic representation.
Steganographic Method to Data Hiding in RGB Images Using Pixels Redistribution Combining Fractal-Based Coding and DWT

This paper presents a method for hiding information in digital images RGB based on different data representation mechanisms through base 64 coding. In combination with a particular coding redistribution based on fractal dimension, using 6-bit Unicode Transformation Format (UTF-6) representation, and lossless compression using Lempel-Ziv-Welch (LZW). It proposes combining the modification of the least significant bit of the pixels in combination with the Discrete Wavelet Transformation (DWT) to provide extensive data protection. An empirical assessment shows a competitive performance when compared to state-of-the-art methods, highlighting the successful embedding images as hidden messages in the stego-images.
The Role of Modelling in Business Software Development: Case Study of Teaching and Industrial Practice in Zrenjanin, Serbia

This paper describes the role of modelling and visualization in artefacts of software development, in the context of diversity in modelling methods, languages and tools, different software development processes and architectures. It particularly addresses issues in reverse engineering, reengineering and modelling with agile software development, which leads to the need for adaptable and adaptive modelling. This paper contributes with the description of the previously presented methodology of integrated modelling approaches in business software development and empirical research with two case studies. First case study shows experiences of modelling business-oriented software in educational process at University of Novi Sad, Technical faculty “Mihajlo Pupin”, Zrenjanin, Serbia, while second case study analyses experiences of IT companies in Zrenjanin region, Serbia. Conclusion of the paper is that educational process in teaching modelling gives mental models to students to use them in industrial practice. Agile software development with constant changes in software products leave companies out of modelling or minimize their use. Adaptive and adaptable modelling and visualization is needed in industrial practice, since it brings lower software development costs.
Diagnosis for School Inclusion in Teacher Training in One Higher Normal School in Mexico

Educational inclusion not only refers to the integration of students with disabilities, it also refers to the fact of minimizing or eliminating barriers that may represent a limitation for student learning and participation, that is, considering social and cultural diversity that influence the acquisition of expected learning, therefore when talking about inclusion, reference is made to the process through which the school seeks and generates the supports that are required to ensure the educational achievement of all students who attend it, and thus be able to guarantee equality of social conditions through education. The objective of this diagnosis was to identify the guidelines that allow an educational intervention assuming the inclusive education approach, rescuing elements such as: diagnosis of the group with which the intervention will be carried out, selection of educational materials and resources, trans-disciplinary work, and definition of the individual's profile for educational intervention. The work presented is an initial stage of a model of educational inclusion. The results obtained, as can be seen in the previous section, are satisfactory and allow us to corroborate the usefulness of the diagnosis presented.
Emotion Analysis Using Deep Learning Methods

With the rapid advancement of technology, the importance of artificial intelligence methods has also increased. Artificial intelligence methods have started to be used frequently in many fields such as health, education, engineering, medicine and agriculture. In this study, the emotion image data set from the open-source access website (Kaggle.com) was classified as normal, angry and happy using VGG16, Resnet50 and DenseNet121 architectures. Among the deep learning architectures used, the best result was obtained with the DenseNet121 algorithm with an accuracy rate of 84.16%.