Reviewerly/exHarmony huggingface dataset:
An Efficient and Effective Model Based on Mean Positive Examples for Social Image Annotation

Nowadays, with the rapid growth of imaging and social network, huge volumes of image data are produced and shared on social media. Social image annotation has been an important and challenging task in the fields of computer vision and machine learning, which can facilitate large-scale image retrieval, indexing, and management. The four most challenges of social image annotation are semantic gap, tag refinement, label-imbalance, and annotation efficiency. To address these issues, we propose an efficient and effective annotation method based on the Mean of Positive Examples (MPE) corresponding to each label. First, we refine user-provided noisy tags by our proposed local smoothing process, and consider the refined tags as key features in contrast to the previous methods that consider them as side information, which significantly improves annotation performance. Second, we propose a weighted trans-media similarity measure method that fuses all modality information in identifying proper neighbors, which promotes the semantic level and eases image annotation. Third, our MPE model gives equal importance to all labels, thus, improving the annotation performance of infrequent labels without sacrificing that of frequent labels. Fourth, our MPE model can dramatically decrease space-time overheads, since the time cost of annotating an image is unaffected by the size of the training image dataset, but relying on the size of label vocabulary. Therefore, our proposed method can be applied to real-world large-scale online social image repositories. Extensive experiments on both benchmark datasets demonstrate the effectiveness and efficiency of our MPE model.
EMOTIONAL BURNING OF PERSONALITY: PSYCHOLOGICAL ANALYSIS OF THE PROBLEM

The article provides a theoretical and methodological analysis of the phenomenon of emotional burnout. It has been determined that emotional burnout is a complex, multifaceted phenomenon, arising as a stress response to production and emotional demands arising from excessive devotion to one's work with the accompanying contempt for family life or leisure. This condition occurs due to the internal accumulation of negative emotions without the appropriate "discharge" or "release" from them. It is noted that this phenomenon is a symptom complex, the action of which covers all major structural levels of personality. The main approaches, principles, features, factors that contribute to the emergence of this syndrome are analyzed. Personal factors of emotional burnout include: introversion, reactivity, authoritarianism, low self-esteem, low empathy, dissatisfaction with the profession and professional growth. The role risk factors of burnout include: role conflict, role uncertainty, role overload. The organizational factor is related to the conditions and features of the specialist. It is emphasized that emotional burnout is a combination of three factors: physical, emotional and cognitive exhaustion or fatigue. Models of emotional burnout are described, the peculiarities of the influence of this syndrome on the personality in professional activity are revealed. It is established that burnout is a specific human condition that occurs due to long-term stress and manifests itself in various life situations. There are types of reactions to stress, similar to the effects of "burnout": the reaction of emotional imbalance, characterized by a clear predominance of negative emotions; pessimistic situational reaction, expressed, first of all, by change of world outlook, negative thoughts and estimations of the world and itself, restructuring of system of values; situational reaction of demobilization, which is determined by the refusal of habitual contacts, their significant limitation, which causes persistent, prolonged and painful experiences of loneliness; situational reaction of the opposition, characterized by the degree of aggression, high sharpness of negative assessments of others and their activities; situational reaction of disorganization, which is based on an alarming component.
Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the â€œACDSee Ultimate 2020â€ software to evaluate the performance of the proposed system.
Projected Minimal Gated Recurrent Unit for Speech Recognition

Recurrent neural network (RNN) has the ability to learn long-term dependencies, which makes it suitable for acoustic modeling in speech recognition. In this paper, we revise RNN model used in acoustic modeling, namely, mGRUIP with Context module (mGRUIP-Ctx), and propose an advanced model which named Projected minimal Gated Recurrent Unit (PmGRU). The paper demonstrates two major contributions: firstly, in the case that adding context information to context module in mGRUIP-Ctx will bring about large amount of parameter, we propose to insert a smaller output projection layer after the mGRUIP-Ctx cell's output to form the PmGRU, which is inspired by the idea of low-rank decomposition of matrix. The output projection layer has been proved to be able to save most of the effective information with the reduction of model parameters. Secondly, in the case that too much context information of previous layer introduced by context module will cause declining of model performance, we adjust the ratio of context information of the previous layer to the current layer by moving the position of batch normalization layer, and the final RNN model Normalization Projected minimal Gated Recurrent Unit (Norm-PmGRU) is generated. In the five automatic speech recognition (ASR) tasks, the Norm-PmGRU has been proved more effectively in the experiments compared with mGRUIP-Ctx, TDNN-OPGRU, TDNN-LSTMP and other RNN baseline acoustics models.
Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations

Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on the estimated hierarchical CNN features and previous prediction. Our design is intended to automatically learn hierarchical representations of video and their temporal dynamics. Convolutional Long Short-Term Memory (ConvLSTM) is used in combination with skip connections so as to separately capture the sequential structures of multiple levels of hierarchy of features. We adopt Scheduled Sampling for training our recurrent network in order to facilitate convergence and to produce high-quality sequence predictions. We evaluate our method on the Bouncing Balls, Moving MNIST, and KTH human action dataset, and report favorable results as compared to existing methods.
Long Short-Term Memory Networks Based on Particle Filter for Object Tracking

Due to the uncertainty of object motion, object tracking is a more difficult state estimation problem. The traditional tracking method based on particle filter has come into wide use, but it has high complexity and poor real-time performance in the process of tracking. As long as there are enough training data, the method based on deep neural network can fit any mapping well. In this paper, a structured Long Short-Term Memory Network based on Particle Filter(LSTM-PF) is proposed to learn and model video sequences with high uncertainty. This network draws on the idea of particle filter, which uses a set of weighted particles to approximate the latent variable and updates the latent state distribution through the LSTM gating structure according to Bayesian rules. We conduct a comprehensive experiment on two benchmark datasets: OTB100 and VOT2016. The experimental results show that our tracker has better performance than other trackers, which can effectively reduce the calculation redundancy and improve the tracking accuracy.
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods is unsatisfactory on real datasets where the new target domain comprises of heterogeneous sub-domains (e.g. diverse weather characteristics). We point out that carefully reasoning about the multiple modalities in the target domain can improve the robustness of adaptation models. To this end, we propose a condition-guided adaptation framework that is empowered by a special attentive progressive adversarial training (APAT) mechanism and a novel self-training policy. The APAT strategy progressively performs condition-specific alignment and attentive global feature matching. The new self-training scheme exploits the adversarial ambivalences of easy and hard adaptation regions and the correlations among target sub-domains effectively. We evaluate our method (DCAA) on various adaptation scenarios where the target images vary in weather conditions. The comparisons against baselines and the state-of-the-art approaches demonstrate the superiority of DCAA over the competitors.
The GJU Website Development Process and Best Practices

This paper introduces a website development process comprised of six phases: requirements, content, design, development, launch, and maintenance. In this context, a website is considered the primary marketing channel for a university and has different characteristics than web-portals. In the content phase, a flat content structure with fielded content types has been defined to make the website content-rich and intuitive to navigate. In the design phase, reusable page templates and engaging UI components were proposed to improve website appearance, usability, and responsiveness. In the development phase, the development framework, technology stack, and development steps to build non-interactive websites have been suggested. Best practices to test, secure, and monitor the website before launching it were also covered in the launch phase. Hosting a website in-house requires addressing issues like quality assurance and system administration in the maintenance phase. Yet, the analytics reports and survey results showed that the suggested process enabled producing a high-quality website.
Architecture aware compilation of quantum circuits via lazy synthesis

Qubit routing is a key problem for quantum circuit compilation. It consists in rewriting a quantum circuit by adding the least possible number of instructions to make the circuit compliant with some architecture&amp;apos;s connectivity constraints. Usually, this problem is tackled via either SWAP insertion techniques or re-synthesis of portions of the circuit using architecture aware synthesis algorithms. In this work, we propose a meta-heuristic that couples the iterative approach of SWAP insertion techniques with greedy architecture-aware synthesis routines. We propose two new compilation algorithms based on this meta-heuristic and compare their performances to state-of-the-art quantum circuit compilation techniques for several standard classes of quantum circuits and show significant reduction in the entangling gate overhead due to compilation.
An Ensemble Learning-Based Prediction Model for Image Forensics From IoT Camera in Smart Cities

Recent years witnessed a surge in the number of IoT cameras in smart cities. In this article, an ensemble learning-based prediction model for image forensics from IoT camera is proposed. In particular, our goal is to obtain human body measurements from 2D images taken from two views. Firstly, 24 body part features are extracted by the DensePose algorithm from the two views. Secondly, the features of the upper body part are integrated with height and body weight features. Ensemble learning is then performed with the LightGBM algorithm and a regression prediction model is constructed. The proposed noncontact image prediction method is simple and workable. Its feasibility and validity are verified on an experimental dataset. Experimental results demonstrate that the proposed method is highly reliable in the size prediction of different body parts. Specifically, the mean absolute errors of chest circumference, waistline and hip circumference are about 2.5 cm, while the mean absolute errors of other predictions are about 1 cm.
Adaption of Big Data Technology for ITG Trust Framework: A Coherent Framework

Utilization of Big Data technology in the industrial era 4.0 is basically used to get huge profits in data processing and utilization.In Big Data technology, processing and utilizing large volumes of data, fast changing data, varied data, and data complexity are indispensable for the effectiveness and efficiency of service strategies, preparation of IT infrastructure, and needs of corporate governance or ITG.On the other hand, ITG trust aims to provide timely, reliable, meaningful and adequate data services.Apparently, this goal is the same as the goal of Big Data technology services offered to support ITG.We propose an ITG trust framework influenced by Big Data technology.The ITG trust framework presents criteria different from existing criteria at the data quality level.It focuses on timely, reliable, meaningful, and sufficient data services, focusing on what data trust attributes should be achieved based on the data trust attributes of Big Data services.In addition to the quality level of Big Data, the personal information protection strategy and the data disclosure/accountability strategy are also needed to achieve goals and to prevent problems.This paper performed case analysis based on the ITG trust Framework on the National Education Service in High School Institution.Big Data services in the public sectorare especially on education field hope to improve the quality of student education's in that country.ITG trust and its trust framework are the essential components for the realization of Big Data service.
DAMPAK SISTEM PEMBELAJARAN DARING TERHADAP KEGIATAN BELAJAR MENGAJAR PADA MASA PANDEMI COVID 19 DI SDN 3 SRIMINOSARI

This article aims to describe the impact of teaching and learning activities using an online learning system during the Covid 19 pandemic at SDN 3 Sriminosari, Labuhan Maringgai District. This research uses descriptive qualitative method, data collection is done by interview, documentation and observation. The results showed that the impact of teaching and learning activities with the online learning system during the Covid 19 period led to increased parental concern for children in accompanying learning activities and breaking new clusters of the spread of Covid 19, but this was not matched by good children's learning outcomes this was due to not all teachers and parents of students are able to operate technology, weak internet networks, unstable public financial conditions, no moral guidance from teachers, and the material presented is very unattractive to students. The expected positive impact of this article as a media of information requires that it is necessary to be prepared to implement an online learning system by paying attention to supporting facilities and infrastructure; and socialization of applications used in learning activities so that all users can use them well.
Photons can hide where they have been

Recently, the nested Mach–Zehnder interferometer [Phys. Rev. Lett. 111, 240402 (2013)] was modified by adding Dove prisms in a paper [Quantum Stud.: Math. Found. 2, 255 (2015)], and an interesting result is that, after the Dove prisms were inserted, a signal at the first mirror of the nested interferometer was obtained. But, according to the former original paper, the photons have never been present near that mirror. In this work, we interpret this result naturally by resorting to the three-path interference method. Moreover, we find that even though the photons have been somewhere, they can hide the trace of being there.
Extracting numerical data from unstructured Arabic texts (ENAT)

&lt;span id="docs-internal-guid-5dcc170c-7fff-e8e4-10d4-4a07701ca923"&gt;&lt;span&gt;Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The inference engine is based on the Arabic linguistic and morphological rules. The applied method encompasses rules of numerical nouns with Arabic morphological rules, in order to achieve high accurate extraction method. Arithmetic operations are applied to convert the numerical phrase into integer value. The proper operation is determined depending on linguistic and morphological rules. It will be shown that applying Arabic linguistic rules together with arithmetic operations succeeded in extracting numerical data from Arabic unstructured text with high accuracy reaches to 100%.&lt;/span&gt;&lt;/span&gt;
Critical Textile Topologies X Planet City: the intersection of design practice and research

Holly McQuillan's work, in the field of zero waste fashion design, articulates sustainable systems thinking and practice.She focuses on issues such as the impact of technology and how these can challenge established design, production and use practices.She co-authored Zero Waste Fashion Design, co-curated Yield: Making fashion without making waste, and developed the award winning Make/Use.Her work always seeks to broaden the impact of zero waste and sustainable fashion design through research, publication, workshops and lectures.
Ways to form a teacher’s professionalism

The role of the teacher at the present stage is transformed into a high mission of creating a professional personality, which is intended to instill in students readiness to acquire knowledge and improve professional skills throughout their lives. According to the authors, the goal of pedagogical education is continuous general and professional development of a university teacher to achieve high results in the practical professional activity. In general, the professional competence of a University teacher is a synthesis of professionalism (scientific, special, methodological, psychological and pedagogical training), creativity (creativity of relationships, the optimal use of tools, techniques, teaching methods) and art (acting and public speaking). And today it becomes obvious that it is impossible to “add up” a competent professional from a simple sum of knowledge, because in addition, a University teacher should have a great sense of moral responsibility when teaching the modern generation of students.
Inferring the Dynamics of the State Evolution During Quantum Annealing

To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this article we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called slicing, we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.
Image Encryption Based on Matrix Factorization

In this paper, we present a matrix decomposition-based approach for image cryptography. The proposed method consists of decomposing the image into different component and scrambling the components to form the image encryption technique. We use two different type of matrix decomposition techniques to check the efficiency of proposed encryption method. The decomposition techniques used are Independent component analysis (ICA) and Non-Negative Matrix factorization (NMF). The proposed technique has unique user defined parameters (key) such as decomposition method, number of decomposition components and order in which the components are arranged. The unique encryption technique is designed on the basis of these key parameters. The original image can be reconstructed at the decryption end only if the selected parameters are known to the user. The design examples for both decomposition approaches are presented for illustration purpose. We analyze the complexity and encryption time of cryptography system. Results prove that the proposed scheme is more secure as it has less correlation between the input image and the encrypted version of the same as compared to state-of-art methods. The computation time of the proposed approach is found to be comparable.