Reviewerly/exHarmony huggingface dataset:
Can consumers’ visual attention be predictable? A saliency modelling-based approach on fashion advertisements

As collaborative research between engineering and fashion, the purpose of this study was to investigate if saliency models can be applied for predicting consumers’ visual attention to fashion images such as fashion advertisements. A human subject study was conducted to record human visual fixations on 10 colour fashion advertisement images, which were randomly selected from fashion magazines. The participants include 67 college students (26 males and 41 females). All mouse-tracking locations on images were recorded and saved using Psychtoolbox-3 with MATLAB. The locations represent the human fixation points on the images and are used to generate fixation maps. This collaborative research is an innovative and pioneering approach to predict consumers’ visual attention toward fashion images using saliency models. From the results of this study, the engineering area’s saliency models were proven as effective measurements in predicting fashion consumers’ visual attention when looking at fashion images such as advertisements.
Synchronous Multi-Bit Audio Watermarking Based on Phase Shifting

Audio watermarking has been developed to protect the copyright of audio signals. We considered the use of the distribution of the phase spectrum and propose an effective multi-bit audio watermarking method based on phase shifting. The proposed method is implemented on the basis of a frame-wise framework. The phase spectrum of each frame is first calculated, then, in accordance with the embedded bit number of each frame, the phase bins are segmented into several subsets. An artificial phase pattern is constructed in one of these subsets to carry a specific multi-bit watermark. In the water-mark extraction process, each frame is synchronized by identifying the phase pattern, then watermarks are extracted. We investigate the performance of the proposed method, and the results indicated that the proposed method balances the trade-off of inaudibility and robustness. From comparative evaluations, the proposed method also performed better than several other audio watermarking methods.
Recurrent Deep Attention Network for Person Re-Identification

Person re-identification (re-id) is an important task in video surveillance. It is challenging due to the appearance of person varying a wide range across non-overlapping camera views. Recent years, attention-based models are introduced to learn discriminative representation. In this paper, we consider the attention selection in a natural way as like human moving attention on different parts of the visual field for person re-id. In concrete, we propose a Recurrent Deep Attention Network (RDAN) with an attention selection mechanism based on reinforcement learning. The proposed RDAN aims to progressively observe the identity-sensitive regions to build up the representation of individuals. Extensive experiments on three person reid benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed method can achieve competitive performance.
Investigation of Blockchain Cryptocurrencies’ Price Movements Through Deep Learning: A Comparative Analysis

This work shows the results obtained from a comparison between a restricted and a unrestricted Bitcoin price classification, verifying whether the addition of technical indicators to the classic macroeconomic variables leads to an effective improvement in the prediction of Bitcoin price changes. The goal was achieved implementing different machine learning algorithms, such as Support Vector Machine (SVM), XGBoost (XGB), a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) neural network. Macroeconomic variables data were gained from Yahoo Finance website spanning a 4-year interval with a hourly resolution, while technical indicators data are provided by the python talib library. The variance problem on test samples has been taken into account through the cross validation technique which also allowed to evaluate a more reliable estimate of the model's performance. Furthermore, the Grid Search technique was used to find the best hyperparameters values for each implemented algorithm. The results were evaluated in terms of the well known classification metrics, i.e. accuracy, precision, recall and f1 score. Based on the results, it was possible to demonstrate that the unrestricted case outperforms the restricted one, verifying that the addition of the technical indicators to the macroeconomic variables actually improves the accuracy on Bitcoin price classification.
Inductive Contextual Relation Learning for Personalization

Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity’s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity’s heterogeneous contents. Next, we introduce a node embedding term to capture entity’s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation.
Mono-pulse radar angle estimation algorithm under low signal-to-noise ratio

Abstract In the case of low signal-to-noise ratio (SNR), in order to improve the angle measurement accuracy of the mono-pulse radar seeker, a target angle estimation algorithm is proposed. The algorithm takes the target angle as a state variable under the Bayesian framework, expands it to the state vector, performs state modeling and observation modeling on it, and builds a particle-based eye angle estimator, which improves the target angle estimation performance. The effectiveness of the algorithm is verified by the simulation data of the medium pulse repetition frequency radar seeker.
Manipulating Virtual Objects in Augmented Reality Using a New Ball-Shaped Input Device

Today's Augmented Reality (AR) technology allows users to explore the real world enriched with digital artifacts, learn from it, or shape it (i.e., creating your own virtual objects). To properly use virtual objects in AR space, users must be able to manipulate them (i.e., rotate or move them). The prerequisite for manipulation is an intuitive interaction technique controlled by an input device. To explore novel AR interaction techniques, a new ball-shaped input device called BIRDY is combined with the HoloLens for the first time.
A Multiple Decoder Cnn For Inverse Consistent 3d Image Registration

The application of deep learning approaches in medical image registration has decreased the registration time and increased registration accuracy.Most of the learning-based registration approaches considers this task as a one directional problem.As a result, only correspondence from the moving image to the target image is considered.However, in some medical procedures bidirectional registration is required.Here, we propose a registration framework with inverse consistency.The proposed method learns in an unsupervised manner a bidirectional transformation that approximates a diffeomorphism.We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate its strong performance.
Informational Measures of the Shape of the Amoroso Distributions for the Research of Dispersed Materials

For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. The article proposes a uniform model for setting the interval of information uncertainty of non-symmetric particle size distributions. Based on the analysis of statistical and information uncertainty intervals, new shape coefficients of distribution models are constructed, these are the entropy coefficients for shifted and non shifted distributions of the Amoroso family. Graphics of dependence of entropy coefficients of non-symmetrical distributions show that distributions well-known are distinguish at small of the shapes parameters. Also it is illustrated for parameters of the form more than 2 that it is preferable to use the entropy coefficients for the unshifted distributions.The material contains also information measures for the well-known logarithmic normal distribution which is a limiting case of distribution Amorozo.
Indonesian Spontaneous Speech Recognition System using Deep Neural Networks

The existing Indonesian speech recognition system has an accuracy that is still not good for spontaneous speech recognition. The system was trained using the HMM-GMM acoustic model. In this study, spontaneous speech data collected in Indonesian with a duration of 14 hours and speech recognition system performance was improved by replacing the acoustic model with a neural network-based model. The neural network topology used are Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Time Delay Neural Network (TDNN). In this paper, the baseline is the HMM-GMM acoustic model trained with dictated speech, the WER obtained by 73.87%. Then the model was trained on data augmented with noise, the WER value dropped to 71.15%. Then the adaptation technique is applied to the model so that the WER drops to 62.75%. Then adaptation model added noise augmentation and WER dropped to 62.16%. In subsequent experiments, the model was trained with mixed training data between dictated and spontaneous speech, the WER value dropped to 57.59%. Furthermore, the acoustic model was replaced with a neural network-based model. In the DNN model, the WER value drops to 50.02%. While on the CNN model, the WER value dropped to 47.58%. The smallest WER value was obtained in acoustic modeling using TDNN topology. The WER value of the model is 40.63%.
SGXoMeter

Intel's Software Guard Extensions (SGX) are currently the most wide-spread commodity trusted execution environment, which provides integrity and confidentiality of sensitive code and data. Thereby, it offers protection even against privileged attackers and various forms of physical attacks. As a technology that only became available in late 2015, it has received massive interest and undergone a rapid evolution. Despite first ad-hoc attempts, there is so far no standardised approach to benchmark the SGX hardware, its associated environment, and techniques that were designed to harden SGX-based applications.
Human-Aware Reinforcement Learning for Fault Recovery Using Contextual Gaussian Processes

This work addresses the iterated nonstationary assistant selection problem, in which over the course of repeated interactions on a mission, an autonomous robot experiencing a fault must select a single human from among a group of assistants to restore it to operation. The assistants in our problem have a level of performance that changes as a function of their experience solving the problem. Our approach uses reinforcement learning via a multi-arm bandit formulation to learn about the capabilities of each potential human assistant and decide which human to task. This study, which is built on our past work, evaluates the potential for a Gaussian-process-based machine learning method to effectively model the complex dynamics associated with human learning and forgetting. Application of our method in simulation shows that our method is capable of tracking performance of human-like dynamics for learning and forgetting. Using a novel selection policy called the proficiency window, it is shown that our technique can outperform baseline selection strategies while providing guarantees on human use. Our work offers an effective potential alternative to dedicated human supervisors, with application to any human–robot system where a set of humans is responsible for overseeing autonomous robot operations.
An Efficient Resource Utilization in VM Live Migration Techniques using CloudSim a Simulation tool

Abstract In the era of cloud resources and edge computing the transferring of data from one node to another node server is a complicated task in which there should be no disruption. While any user is accessing the cloud services then it is required that shifting a virtual machine load from anywhere must be without disturbing the services on front end. There is a key problem which is high resource usage such as network usage, management of various cloud resources, tolerating the fault occurrence, consumption of energy etc. It is also required that the resource consumption should be more efficient when shifting the virtual machine load from one physical host to another particular physical host. This paper identifies a solution to manage the workload on different migration techniques without disruption of services in cloud computing environment.
Beds: Bagging Ensemble Deep Segmentation For Nucleus Segmentation With Testing Stage Stain Augmentation

Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for aggregating weak learners to become a strong learner. Random forest is one of the most powerful machine learning algorithms before deep learning era, whose superior performance is driven by fitting bagged decision trees (weak learners). Inspired by the random forest technique, we propose a simple bagging ensemble deep segmentation (BEDs) method to train multiple U-Nets with partial training data to segment dense nuclei on pathological images. The contributions of this study are three-fold: (1) developing a self-ensemble learning framework for nucleus segmentation; (2) aggregating testing stage augmentation with self-ensemble learning; and (3) elucidating the idea that self-ensemble and testing stage stain augmentation are complementary strategies for a superior segmentation performance. Implementation Detail: https://github.com/xingli1102/BEDs.
Adaptive Transfer Learning To Enhance Domain Transfer In Brain Tumor Segmentation

Supervised deep learning has greatly catalyzed the development of medical image processing. However, reliable predictions require a large amount of labeled data, which is hard to attain due to the required expensive manual efforts. Transfer learning serves as a potential solution for mitigating the issue of data insufficiency. But up till now, most transfer learning strategies for medical image segmentation either fine-tune only the last few layers of a network or focus on the decoder or encoder parts as a whole. Thus, improving transfer learning strategies is of critical importance for developing supervised deep learning, further benefits medical image processing. In this work, we propose a novel strategy that adaptively fine-tunes the network based on policy value. Specifically, the encoder layers are fine-tuned to extract latent feature followed by a fully connected layer that generates policy value. The decoder is then adaptively fine-tuned according to these policy value. The proposed approach has been applied to segment human brain tumors in MRI. The evaluation has been performed using 769 volumes from public databases. Domain transfer from T2 to T1, T1ce, and Flair shows state-of-the-art segmentation accuracy.
PREPARATION OF FUTURE TEACHERS FOR DIGITALIZATION IN EARLY LEARNING OF FOREIGN LANGUAGES

The education system must respond to changes in society, its demands and needs of students at all levels, so the mission of modern pedagogical institutions of higher education is to train teachers ready and able to accept such challenges. The article presents the experience of the Department of Foreign Languages at the Pedagogical Institute of Borys Grinchenko Kyiv University in training students majoring in 012 "Preschool Education" and 013 "Primary Education" and methods of teaching them to implement digital tools in the process of forming foreign language communicative competence of 3-10 year-old children at preschool institutions and the New Ukrainian School. It should be noted that this work took place within the research on the scientific topic: "Strategy of foreign language teacher training in the context of European integration." The basis of the study is the understanding of the specifics of language as a means of communication, and therefore the emergence of new digital ways and tools of communication, their widespread penetration into various spheres of life naturally requires their use for educational purposes. In addition, the normative documents of the Ministry of Education and Science of Ukraine recognize information and digital competence as a component of professional competence of a teacher of any subject, which also requires modernization of components of teacher training in early foreign language learning. In the process of research we relied on the levels of digital competence of teachers defined in DigCompEdu and aimed to prepare future teachers of early foreign language teaching for the practical application of digital tools and instruments taking into account Generation Theory, Alpha children, current foreign language programs in modern conditions of educational institutions in Ukraine. In the process of research in the curriculum of disciplines "Modern technologies of teaching preschool children a foreign language" (specialty "Preschool education") and "Modern technologies of foreign language teaching in primary school" (specialty "Primary education") content modules "Organizational and pedagogical conditions of application of modern technologies of teaching foreign languages (FL) to preschool (PS) children / FL in PS”, “Digital technologies of teaching FL to preschool children / FL in PS”, “Media-based learning in early foreign language learning in PS / in primary school”,“ Technologies of blended learning of foreign languages in primary school” are introduced, within which theoretical, practical and methodical work takes place, the purpose of which is the formation of digital competence of future teachers.
Locating Various License Numbers in The Wild: An Effective Approach

Mechanized tag acknowledgment is fundamental in a few street imaging applications. For the above frameworks conveyed in the US, variety in the Middle of wards on character Distance, dispersing, the presence of commotion origin (e.g., hefty outline, non- constant enlightenment, different optical calculations, helpless difference, etc) in attendance LP pictures form it trying for the acknowledgment precision and versatility of AUTOMATED LICENSE PLATE RECOGNITION (ALPR) frameworks. Text style along with the  plate-format variety beyond wards additionally exacerbates appropriate character Divide and create the degree of manual clarification required for each state to prepare classifiers, which can result in exorbitant overhead and cost of operation.. In this paper, we suggest another  AUTOMATED LICENSE PLATE RECOGNITION work process that incorporates narrative techniques for division and comment free AUTOMATED LICENSE PLATE RECOGNITION, just as developed plate limitation and mechanization for disappointment recognizable proof. We come up with work process starts to the restricting the Limited Picture locale in the caught  Image using a two-stage technique that first focuses on a bunch of candidate districts using a helpless meager winnow classifier organization and In the corresponding step, they are then channeled using a strong convolutionary neural organization (CNN) classifier.Pictures Which bomb an essential certainty test for plate confinement are additionally arranged to distinguish limitati disappointments, for example, LP not present, LP excessively brilliant, LP excessively dull, Or didn't locate a car. We conduct division and optical character in the restricted plate locale acknowledgment together with an utilizing a contingency deduction technique Based on concealed Markov models (HM Ms) where by applying the Viterbi calculation the most likely code succession is dictated. To minimise the manual comment needed to prepare OCR classifiers, we suggested the utilization of either misleadingly produced manufactured Limited Picture or character tests procured via prepared ALPR frameworks previously working in different locales. The presentation hole because of contrasts among preparing and target space conveyances is limited utilizing an unaided area variation. We assessed the exhibition of our proposed techniques on LP pictures caught in a few US wards under practical conditions.
A Comprehensive Review of Mobile Technology-Assisted English Learning

Mobile English language learning has drawn global attention. This study systematically examined the literature in the past 11 years. It visualized the general trend of the number of related publications in a decade, discussed the attitudes of teachers and learners toward mobile device-assisted English learning, explored the effect of motivation on mobile device assisted English learning, discussed the influence of mobile device-assisted English learning on output and input English language skills, presented the influence of mobile device-assisted English learning on vocabulary knowledge acquisition, demonstrated the factors influencing the effectiveness of mobile English language learning, showed the general learning outcomes in mobile device-assisted English learning, together with the disadvantages of mobile technology-assisted English learning and co-citations of publications. When designing mobile devices, designers could include and optimize learning functions based on the findings.
Machine translation and legal tech in legal translation training

The study aims to analyze the use of legal tech and machine translation software in legal translation training. It presents a comparative research of various IT tools and applications in foreign language learning environments, their capabilities and limitations in higher education institutions, as well as the author's recommendations on integrating them in the ESP-teaching process. This research's primary methods are case study method, analysis of software teaching materials, descriptive statistics, and translation studies methods.
On fuzzy alternate control systems

Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.