Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition

Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity of annotated images, the annotations of caricature attributes are rare. To facility the research in attribute learning of caricatures, we propose a caricature attribute dataset, namely WebCariA. Moreover, to utilize models that trained by face attributes, we propose a novel unsupervised domain adaptation framework for cross-modality (i.e., photos to caricatures) attribute recognition, with an integrated inter- and intra-domain consistency learning scheme. Specifically, the inter-domain consistency learning scheme consisting an image-to-image translator to first fill the domain gap between photos and caricatures by generating intermediate image samples, and a label consistency learning module to align their semantic information. The intra-domain consistency learning scheme integrates the common feature consistency learning module with a novel attribute-aware attention-consistency learning module for a more efficient alignment. We did an extensive ablation study to show the effectiveness of the proposed method. And the proposed method also outperforms the state-of-the-art methods by a margin. The implementation of the proposed method is available at https://github.com/KeleiHe/DAAN .
DCFNet++: More Advanced Correlation Filters Network for Real-Time Object Tracking

Visual object tracking has been widely addressed in Siamese networks, where accurate and fast object tracking can be achieved. However, it is challenging to discriminate foregrounds from semantic backgrounds, because the semantic backgrounds are always considered as distractors, which would hinder the robustness of Siamese trackers. In this paper, we address the visual object tracking problem in complex scenarios, including occlusions, out-of-view, deformation, background cluttering, and other variations. We introduce multiple combinations in the training set to improve the discriminative ability of the learned features. As a result, the robustness of the model can be improved. We also propose an advanced method to fuse multi-layer features, so that the feature representation can be enhanced. Finally, we develop a flow-based tracking method, which makes the tracker very robust to occlusion scenarios. Extensive evaluations on OTB-2015, VOT2018, UAV123, and LaSOT benchmarks demonstrate that the proposed DCFNet++ has strong robustness when facing challenging scenarios. Without bells and whistles, our proposed tracker can run at more than 56 FPS during test time.
Unsupervised Pre-Training for Voice Activation

The problem of voice activation is to find a pre-defined word in the audio stream. Solutions such as keyword spotter “Ok, Google” for Android devices or keyword spotter “Alexa” for Amazon devices use tens of thousands to millions of keyword examples in training. In this paper, we explore the possibility of using pre-trained audio features to build voice activation with a small number of keyword examples. The contribution of this article consists of two parts. First, we investigate the dependence of the quality of the voice activation system on the number of examples in training for English and Russian and show that the use of pre-trained audio features, such as wav2vec, increases the accuracy of the system by up to 10% if only seven examples are available for each keyword during training. At the same time, the benefits of such features become less and disappear as the dataset size increases. Secondly, we prepare and provide for general use a dataset for training and testing voice activation for the Lithuanian language. We also provide training results on this dataset.
RETRACTED: CNN based framework for identifying the Indian currency denomination for physically challenged people

Abstract One of the premier issues confronting the visual hindered individual is money, acknowledgment especially for cash. Be that as it may, the outwardly weakened individual may not think about the estimation of cash, and they endure part in cash trade related issues in their normal life. To address this issue, we have built up a framework for acknowledgment of money, notes, which might be the helpful device for an outwardly debilitated individual. Investigation and trials were done on the money informational collection, which encouraged CNN dependent on the key highlights, for example, watermarks, pictures printed on cash, esteemed composed as words and numbers and the total cash. This paper deals with the utilization of Convolutional Neural Networks (CNNs) for solving this society issues and investigations about the exhibition and evaluation of different CNN models. Here, Alexnet, Googlenet, and Vgg16 models have been considered for assessment. All the models were adjusted as far as preparing and testing the individuals of data sets. Among these three models, Alexnet accomplished better in preparing fulfillment, Vgg16 model indicated the better execution and accomplished 100%, Google net arrives at 88% as far as productivity.
The Induced Finger Movements Effect

The Sense of Embodiment in Virtual Reality is one of the key components to provide users with a convincing experience. Our contribution to a better understanding of the phenomenon focuses on the analysis of the motor reaction of the users to an alien finger movement. We assess quantitatively that the view of an alien movement (i.e. a movement of the self-avatar caused by an alien will) induces a finger posture variation, that we refer to as the Induced Finger Movements Effect. This only happens in case of embodiment, while in a disembodied setup the effect disappears. The principle of the investigation is being tested as a base for neuro-rehabilitation, based on the concept of inducing movements in post-stroke hemiplegic patients.
The interplay of complexity and subjectivity in opinionated discourse

This paper brings together cutting-edge, quantitative corpus methodologies and discourse analysis to explore the relationship between text complexity and subjectivity as descriptive features of opinionated language. We are specifically interested in how text complexity and markers of subjectivity and argumentation interact in opinionated discourse. Our contributions include the marriage of quantitative approaches to text complexity with corpus linguistic methods for the study of subjectivity, in addition to large-scale analyses of evaluative discourse. As our corpus, we use the Simon Fraser Opinion and Comments Corpus (SOCC), which comprises approximately 10,000 opinion articles and the corresponding reader comments from the Canadian online newspaper The Globe and Mail, as well as a parallel corpus of hard news articles also sampled from The Globe and Mail. Methodologically, we combine conditional inference trees with the analysis of random forests, an ensemble learning technique, to investigate the interplay between text complexity and subjectivity. Text complexity is defined in terms of Kolmogorov complexity, that is, the complexity of a text is measured based on its description length. In this approach, texts which can be described more efficiently are considered to be linguistically less complex. Thus, Kolmogorov complexity is a measure of structural surface redundancy. Our take on subjectivity is inspired by research in evaluative language, stance and Appraisal and defined as the expression of evaluation and opinion in language. Drawing on a sentiment analysis lexicon and the literature on stance markers, a custom set of subjectivity and argumentation markers is created. The results show that complexity can be a powerful tool in the classification of text into different text types, and that stance adverbials serve as distinctive features of subjectivity in online news comments.
Differential confocal measurement for surface topography with microstructures based on spiral scanning and wavelet filter

This paper proposes a novel spiral-scanning laser differential confocal measurement method (SSLDCM) for fast and precise measurement of surface topography with microstructures. Spiral plane scanning is used to eliminate frequent acceleration and deceleration problems in traditional raster-scanning differential confocal measurement systems and helps to keep the measuring process efficient and stable. To solve the problem of uneven sampling distribution during spiral scanning, a variable sampling rate method is adopted to distribute the sampling points at equal intervals, which would help to reduce the time of the 3D imaging process. A denoising method based on an adaptive wavelet threshold is proposed to filter the existing noise during the measuring process. An experimental measurement platform based on SSLDCM is constructed, and the axial response curve is tested and analyzed. The linear region range of the experimental platform reaches 13 µm, and the slope is about 164.15 mV/µm. In addition, the measurement results of a silicon wafer specimen by SSLDCM show good consistency with a commercial high-precision microscope, and the largest deviation is less than 2.71%. The SSLDCM has great potential to be used in various noncontact surface measurement applications with high efficiency and accuracy.
WITHDRAWN: Contactless attendance system using Siamese neural network based face recognition

One of the most important and fast growing biometrical technological advancement is face recognition, used for identification of the person without any human intervention. In the proposed work, one shot learning has been used on Siamese convolution neural network (SCNN) for face recognition and attendance of the employee is entered in the MySQL database. Application Programmable interface used for uploading database is APACHE Web Server. The advantage of one shot learning algorithm is that it requires single training set for similarity measure. Get well soon message is send to absent employees through Gmail using send mail transfer protocol. Twelve images have been successfully tested and the system observed state-of –art performance with all face recognized correctly.
Expert System for Diagnosing Osteoarthritis with Fuzzy Tsukamoto Method

Abstract Osteoarthritis is a degenerative joint disease involving the cartilage, the lining of the joints, ligaments, and bones, causing pain and stiffness in the joints that suffer from these symptoms. People often ignore pain that occurs when moving the shoulder, plate, knee and hand pain when it has great potential are the symptoms of the disease Osteoarthritis. Expert systems can be used as a system to identify disease Osteoarthritis. With Osteoarthritis disease diagnosis expert system then expected to help people to detect diseases early Osteoarthritis and can consult with a doctor before continuing to a more dangerous stage. In this research using Fuzzy Tsukamoto Algorithm method, which method is an extension of the reasoning Tsukamoto monotonous, every consequent formed IF-Then rules should be presented with a fuzzy set. As a result, output inference of each rule is given explicitly by predicate. Result is obtained by using a weighted average (center average defuzzifier). Symptoms of Osteoarthritis serve as fuzzy values which will then be sorted according to a predetermined membership function. After fuzzy value of the calculation has been obtained, then fuzzy value will be converted into firm value using a weighted average. From these results it can be concluded that the accuracy of the expert system that has been built to have an accuracy of 90% that is 18 of the 20 patients who had been tested using an expert system that has been built
A Fuzzy-PD Controller Optimized by Artificial Bee Colony Algorithm Applied to a Small-Scale Pasteurization Plant

Optimization problems are widely used in numerous fields of science, engineering, and industry. On many occasions, such optimization problems involve a large number of decision variables and complex structured objectives. Applying fuzzy logic theory to the classical or traditional control strategies often requires the use of optimization techniques to shape the membership functions better. This paper presents a fuzzy-PD controller's design using an Artificial Bee Colony optimization algorithm, which is based on the intelligent foraging behavior of honeybee swarm. The proposed optimized controller is then applied to control the temperature of a heat exchanger in a scale pasteurization plant. The simulation results obtained are finally compared with those obtained by a classical PID controller and a non-optimized fuzzy PD+I controller.
Parametric Model for Video Streaming Services With Different Spatial and Temporal Resolutions

Parametric models of video quality are designed for service and network planning as well as video quality monitoring. They are widely applied in a broad range of applications especially when video streams are encrypted or even unavailable at all. Designing metrics in these models remains challenge due to limited available information. In this paper, a spatio-temporal resolution-adaptive parametric (STRAP) model is proposed to evaluate the quality of video streaming services considering the spatial and temporal resolutions. This work serves as a follow-up study for ITU Rec. P.1203.1 that we were previously involved. The relationship between the content complexity and the spatial and temporal resolutions are analyzed and incorporated into the proposed model. Moreover, the effect of video up/down-scaling in display devices on the perceived video quality is further taken into consideration. Experimental results showed that the proposed model can be used as a reliable indicator for video streaming providers to improve their services performance.
Interpretable Unsupervised Anomaly Detection For RAN Cell Trace Analysis

The high complexity of modern communication networks requires an increasing degree of automation for performance and fault management tasks. A key task is the classification and identification of anomalous operation modes (and faults). This is important to separate them from normal operation conditions. In addition, these diagnoses should be interpretable by domain experts to (a) gain acceptance by these experts and (b) support effective root cause analysis and localisation. In this paper, we investigate the analysis of multivariant network data in order to identify anomalous data instances. Root cause analysis benefits from this by filtering features whose values lead (to some extent) to such anomalies. We are using Deep Neural Networks (DNNs), a powerful tool for anomaly detection in the telecommunication domain. We demonstrate the effectiveness of autoencoders (an unsupervised technique) to detect multivariant anomalies and anomalous features. To overcome the black box nature of neural networks (and thus increase their acceptance by domain experts), we apply SHapley Additive exPlanations (SHAP), which are used to explain the output model of a neural network.
Zero-Shot Transfer Learning of a Throwing Task via Domain Randomization

Deep reinforcement learning (DRL) on continuous robot control has received a wide range of interests over the last decade. Collecting data directly from real robots results in high sample complexities and can cause safety accidents, so simulators are widely used as efficient alternatives for real robots. Unfortunately, policies trained in the simulation cannot be directly transferred to real-world robots due to a mismatch between the simulation and the reality, which is referred to as ‘reality gap’. To close this gap, domain randomization (DR) is commonly used. DR guarantees better transferability in the zero-shot setting, i.e. training agents in the source domain and testing them on the previously unseen target domain without fine-tuning. In this work, we identify the positive influence of DR on zero-shot transfer between the sim-to-sim setting with an object throwing task.
Dynamic Performance Evaluation of Blockchain Technologies

In recent years, the rapid development of blockchain technologies have attracted worldwide attention. Its application has been extended to many fields, such as digital finance, supply chain management, and digital asset transactions. For some enterprises and users, how to choose the most effective platform from many blockchains to control costs and share data is an important issue. To comprehensively evaluate the blockchain technologies, we first construct three-level evaluation indicators in terms of technical, market, and popularity indicators. Then, we propose an improved global DEA-Malmquist index without explicit inputs to assess the dynamic performance of blockchain technologies. Finally, we carry out an empirical analysis to evaluate 31 public blockchains' performance from May 2018 to April 2020. The results indicate that the overall performance of blockchain technologies is basically on the rise. Some blockchain technologies that have not yet received widespread attention have shown good dynamic performance.
A Novel Approach Using Steganography and Cryptography in Business Intelligence

In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy.
Inferring the Complete Set of Kazakh Endings as a Language Resource

The Kazakh language belongs to low-resource languages. For application of actual modern branches as artificial intelligence, machine translation, summarization, sentiment analysis, etc. to the Kazakh language needs increasing the number of electronic language resources. Although neural machine translation (NMT) has shown impressive results for many world languages, it does not solve the problem of low-resource languages. Therefore, the development of resources and tools perfecting the use of NMT for low-resource languages is relevant. For perfect use of NMT for the Kazakh language needs bilingual parallel corpora, but also needs a perfect method of the segmentation source text. By the opinion of authors, one of the effective ways for source text segmentation is morphological segmentation. The authors propose to use for morphological segmentation of Kazakh text a table of a complete set of Kazakh words' endings. In this paper is described the inferring of the complete set of Kazakh words' endings. Development of the table of the complete set of word' endings of the Kazakh language will allow in one-step (by reference to the table of endings of the language) to perform the segmentation of the word's ending into suffixes. The complete set of endings of the Kazakh language allows guaranteeing the analysis of any word of the Kazakh language, as this is determined by the inferring of the complete system of words' endings of the language.
A novel colour image encryption based on fractional order Lorenz system

(2021). A novel colour image encryption based on fractional order Lorenz system. Systems Science & Control Engineering: Vol. 9, Recent Developments on Complexity Science; Guest editors: Zhijian Ji, Yang Liu and Lei Zou, pp. 141-150.
Exact Asymptotics for Learning Tree-Structured Graphical Models With Side Information: Noiseless and Noisy Samples

Given side information that an Ising tree-structured graphical model is homogeneous and has no external field, we derive the exact asymptotics of learning its structure from independently drawn samples. Our results, which leverage the use of probabilistic tools from the theory of strong large deviations, refine the large deviation (error exponents) results of Tan et al. (2011) and strictly improve those of Bresler and Karzand (2020). In addition, we extend our results to the scenario in which the samples are observed in random noise. In this case, we show that they strictly improve on the recent results of Nikolakakis et al. (2019). Our theoretical results demonstrate keen agreement with experimental results for sample sizes as small as that in the hundreds.
Predicting Career Decisions Among Graduates of Tafseer and Hadith

The overall aim of the research was to identify influential factors that best predict career decisions or job choice among graduates of the Department of Tafseer and Hadith at the Universitas Islam Negeri Sultan Syarif Kasim Riau. Instead of using a longitudinal, cohort study using statistical analysis, machine learning techniques such as Decision Tree and Naïve Bayes was applied to search for unknown patterns or rules. This study compares the performance of the machine learning methods in discovering hidden patterns or factors that influenced the alumni career decisions. One of the primary concern of the university is whether their career choice after graduation are relevant or match to their field of studies. Our studies show that CGPA, cohort, additional expertise, and gender are the main factors that influenced alumni career success. We found that a cohort of graduate students was unable to find relevant professions in their field of studies. The experimental result shows that Naïve Bayes outperforms Decision Tree with the best accuracy score of 97.1 % and 92.6% subsequently. Thus, it can be concluded that the prediction model and analysis using Naïve Bayes have the potential to be used effectively. Despite its low performance, Decision Tree able to extract the main factors that influenced an alumnus career efficiently. These findings are valuable and useful both for the institution to better understand and improve the quality of its program and graduates, and also the community of machine learning in understanding the techniques behaviors with small datasets.
Most used project management tools and techniques in information systems projects

Purpose The purpose of this paper is to increase the understanding of practice in information systems (IS) project management (PM) by analyzing the use of tools and techniques by IS project managers. Design/methodology/approach The authors carried out an international questionnaire-based survey with experienced IS project managers. Findings Results reveal that, notwithstanding the similarities between the tools and techniques used in IS projects and projects from other areas, there are also significant differences concerning those more frequently used. The top five tools and techniques most used are “kick-off meeting,” “progress meetings,” “progress reports,” “requirements analysis” and “activity list.” However, the low use of some tools and techniques, from management areas such as risk and quality management, or related to the project monitoring and control, should raise concern. Research limitations/implications Through the results of this research, researchers, organizations and practitioners can identify ways of developing and enhancing PM by examining the tools and techniques identified as the most used and those that are not being used as frequently as expected. Originality/value It provides a useful benchmarking basis for evaluating the most applicable tools and techniques, designing training and teaching programs and identifying academic research opportunities in IS PM.