Reviewerly/exHarmony huggingface dataset:
Cluster Centers Provide Good First Labels for Object Detection

Learning object detection models with a few labels, is possible due to ingenious few-shot techniques, and due to clever selection of images to be labeled. Few-shot techniques work with as few as 1 to 10 randomized labels per object class. We are curious if performance of randomized label selection can be improved by selecting 1 to 10 labels per object class in a non-random manner. Several active learning techniques have been proposed to select object labels, but all started with a minimum of several tens of labels. We explore an effective and simple label selection strategy, for the case of 1 to 10 labels per object class. First, the full unlabeled dataset is clustered into N clusters, where N is the desired number of labels. Clustering is based on k-means on embedding vectors from a state-of-the-art pretrained image classification model (SimCLR v2). The image closest to the center is selected to be labeled. It is effective: on Pascal VOC we validate that it improves over randomized selection over 25%, with large improvements especially when having 1 label per object class. We have several benefits to report on this simple strategy: it is easy to implement, it is effective, and it is relevant in practice where one often starts with a dataset without any labels.
A Novel Semi-Supervised Classification Approach for Evolving Data Streams

Classification plays a crucial role in mining the evolving data streams. The concept drift as well as concept evolution are the major issues of stream data classification, which greatly affect the classification performance. Most existing works of concept drift and evolution are supervised in nature, where labeling the data is time and resource consuming. In this paper, for the evolving data streams, a semi-supervised classification approach using partial labeled data is proposed. Firstly, an ensemble model dynamically maintains a series of micro-clusters to capture the concept drift. The ensemble model processes the instances in an online fashion rather than chunk-based. Secondly, the concept evolution detection module is constructed to detect the outliers by the local density. The module examines the current buffer to capture the class emergence in stream data with complex class distribution. For improving the execution efficiency of emerging class detection without compromising performance, several constructive strategies are adopted, including removing part of the buffer and selectively executing samples generating. The abundant experiments are constructed about the popular stream data sets and the process industry stream data, whose results indicate the practicality and effectiveness of the proposed approach for the classification of evolving data streams.
An Improved Face Mask-aware Recognition System Based on Deep Learning

Face mask detection and recognition have been incorporated into many applications in daily life, especially during the current COVID-19 pandemic. To mitigate the spread of coronavirus, wearing face masks has become commonplace. However, traditional face detection and recognition systems utilize main facial features such as the mouth, nose, and eyes to determine a person’s identity. Masks make facial detection and recognition tasks more challenging since certain parts of the face are concealed. Yet, how to improve the performance of existing systems with a face mask overlaid on the original face input images remains an open area of inquiry. In this study, we propose an improved face mask-aware recognition system named ‘MAR’ based on deep learning, which can tackle challenges in face mask detection and recognition. MAR consists of five main modules to handle various kinds of input images. We re-train the CenterNet model with our augmented face mask inputs to perform face mask detection and propose four variations on face mask recognition models based on the pre-trained ArcFace to handle facial recognition. Finally, we demonstrate the effectiveness of our proposed models on the VGGFACE2 dataset and achieve a high accuracy score on both detection and recognition tasks.
An End-to-End Multi-dialect Tibetan Speech Synthesis Method

The end-to-end speech synthesis model, Tacotron2, which is based on neural network theory, drives advancements in speech synthesis technology. This paper proposed an end-to-end multi-dialect Tibetan speech synthesis technique based on the Tacotron2 model. First the Wiley transliteration scheme is used to convert Tibetan characters into corresponding Latin letters, which reduces the size of the training corpus, reduces the workload of front-end text processing and improves the efficiency of speech synthesis. Then an end-to-end multi-dialect shared speech synthesis model is established for Amdo pastoral dialect, Dege-Kham dialect and Yushu-Kham dialect, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms. In the end, the dialect ID is appended to the text to help the model learn the characteristics of three dialects during training. The results of experiments show that the effect of synthesized speech is improved with dialect ID.
A Mapping Solution Search Garden Balsam Optimization for Solving Invariant Point Problems

Mapping solution search (MSS), which maps current solution to a mapping solution, increases population diversity and promotes algorithm without the difficulty of premature convergence. This paper presents an MSS based garden balsam optimization (MGBO). This avoids the premature convergence of the algorithm, improves the convergence speed of the algorithm, and increases the possibility that the solution is closer to the global optimum. To evaluate the performance of MGBO, four complex invariant point problems are chosen from the literature. Experimental studies show that the MGBO can solve these problems with great precision compared with some state-of-the-art algorithms.
CVGAN: Image Generation with Capsule Vector-VAE

In unsupervised learning, the extraction of a representational learning space is an open challenge in machine learning. Important contributions in this field are: the Variational Auto-Encoder (VAE), on a continuous latent representation, and the Vector Quantized - VAE (VQ-VAE), on a discrete latent representation. VQ-VAE is a discrete latent variable model that has been demonstrated to learn nontrivial features representations of images in unsupervised learning. It is a viable alternative to the continuous latent variable models, VAE. However, training deep discrete variable models is challenging, due to the inherent non-differentiability of the discretization operation. In this paper, we propose Capsule Vector - VAE(CV-VAE), a new model based on VQ-VAE architecture where the discrete bottleneck represented by the quantization code-book is replaced with a capsules layer. We demonstrate that the capsules can be successfully applied for the clusterization procedure reintroducing the differentiability of the bottleneck in the model. The capsule layer clusters the encoder outputs considering the agreement among capsules. The CV-VAE is trained within Generative Adversarial Paradigm (GAN), CVGAN in short. Our model is shown to perform on par with the original VQGAN, VAE in GAN. CVGAN obtains images with higher quality after few epochs of training. We present results on ImageNet, COCOStuff, and FFHQ datasets, and we compared the obtained images with results with VQGAN. The interpretability of the training process for the latent representation is significantly increased maintaining the structured bottleneck idea. This has practical benefits, for instance, in unsupervised representation learning, where a large number of capsules may lead to the disentanglement of latent representations.
Compressed Phase Coding Based-Optical Image Encryption

With the development of artificial intelligence technology, the traditional inflexible optical image encryption system has been difficult to resist security attacks such as chosen-plaintext attack and known-plaintext attack, and its security has been greatly challenged. Therefore, an optical image encryption method based on compressed phase coding is proposed, in which a spatial light modulator (SLM) is used to encode the phase of the optical system randomly, and then combined with the measurement matrix in the single pixel sampling process to build a flexible key system. In the encryption process, a phase-only SLM is firstly implemented to realize phase encoding of the original plaintext image and loading the phase key. Then the modulated reference light is implemented to further hide the complex amplitude distribution of the encrypted image. Finally, the encoded object light and reference light are coherently superimposed in the digital micromirror device (DMD), and the inner product operation is carried out with the measurement matrix to realize further encryption and collected by the single pixel detector. Both simulation and experiment proved that the proposed compressed phase coding based-image encryption system is similar to "one time one pad" cryptographic system, which greatly improves the security of the optical encryption system.
Towards Generating Surprising Content in 2d Platform Games

Surprise is broadly considered as one of the main driving factors for engagement in video games. In this paper, we present a framework for automatically generating levels for 2D platform games with embedding surprising content.Â The VCL (Violation of Expectation, Caught Off Guard, and Learning) model introduced priorly is used as a conceptual foundation for curating surprise in games by altering certain game elements (called metrics) while generating the game levels. We developed a tile based parametric level generator of a popular 2D platform game (Super Mario Bros.) and generated customized levels based on certain metrics (linearity, density of enemies, density of game elements, pattern variation, gap frequency, player speed and gravity). The study participants were asked to play a couple generated levels of the game. The first level is meant to set the expectations of the game and the second level is meant to violate the expectations by changing the chosen metrics. Player responses and the gameplay data are correlated with the VCL model to explain the phenomenon of surprise in games. Studies demonstrate statistically significant correlation between certain chosen metrics and reported players responses opening up the possibility of automatically generating surprising levels in 2D platform games.
Human-Computer Interaction Tools and Methodologies

HCI's main objective is to improve user-computer interactions by making computers more responsive to the game player's demands. This is done through the following three interfaces: the command line, the graphical user interface, and a standardized user interface.
DeepSR: A Deep Learning Tool for Image Super Resolution

I introduce an open source tool that eases and automates the entire process of the design and implementation of deep learning (DL) algorithms for single image super resolution reconstruction (SISR). It ensures rapid prototyping of DL models with a simple and easy framework, and performing experimental and computational studies in a fully automated way. Its end-to-end processing mechanism makes it also possible to accomplish multiple experiments with detailed results. Besides, it saves researchers from time consuming, challenging and repetitive workloads as it is an all-in-one tool that accompanying many components and features out-of-the-box than typically needed for developing DL models for SISR.
A Secure and Lightweight Chaos Based Image Encryption Scheme

In this paper, we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm. The method works on the principle of confusion and diffusion. The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes. Initially, an image (red, green, and blue components) is partitioned into blocks with an equal number of pixels. Each block is then processed with Tinkerbell Chaotic Map (TBCM) to get shuffled pixels and shuffled blocks. Composite Fractal Function (CFF) change the value of pixels of each color component (layer) to obtain a random sequence. Through the obtained random sequence, three layers of plain image are encrypted. Finally, with each encrypted layer, Brownian Particles (BP) are XORed that added an extra layer of security. The experimental tests including a number of statistical tests validated the security of the presented scheme. The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.
Literature Review on performance Evaluation of recommendation system with different dimensions of metrics

In today's world, recommendation systems assist people in suggesting the greatest item among the most regularly used goods or the best item with a high level of popularity. The majority of the commendations are based on machine learning algorithms that perform learning using training datasets and a standard collection of programs that enhance prediction using the underlined datasets. In recent years, customer and product growth has surged, providing substantial challenges for recommender systems. They entail giving high-quality recommendations and completing numerous recommendations per second for millions of consumers and items, and many of them use the Collaborative Filtering technique to develop recommender systems. It predicts an item's utility for a specific user based on the user's previous interests and takes other users' feedback into account. Much of the early research focused on the "accuracy" of recommendation systems. However, a good recommendation in terms of accuracy must be combined with other factors. This paper proposes metrics for evaluating elements of recommendation systems other than accuracy. Other factors to consider are:(1) Coverage, which is the percentage of a data collection that is covered.(2) confidence metrics that can assist users in making decisions that a recommender system is capable of recommending, judgments that are more effective (3) computing time, which measures how quickly an algorithm can produce worthy recommendations, (4) novelty/serendipity, which measure whether a recommendation is novel, and (5) robustness which measure the ability of the algorithm to make virtuous estimates in the presence of noisy or sparse data.
Pedestrian Attribute Recognition with Feature Combination in Transformer with Attention Model

In computer vision, pedestrian attribute recognition is one of the hot topics. It often learns convolutional networks to perform feature extraction as well as multi-label classification for pedestrian attributes. Nowadays, researchers often employ attention mechanisms to improve the network performance. In this work, we focus on the less studied aspect to learn the relationships between different pedestrian attributes. For that, we propose a method based on Feature Combination in Transformer with the attention model (referred to as FCT) to learn the inconsistency relationship between attributes, where the attention mechanism is built based on visual transformers. Extensive experiments conducted on three benchmark datasets (i.e., DukeMTMC-reID, PA-100K and PETA) clearly demonstrate the effectiveness of our proposed FCT method.
The Resilience of Cryptocurrency Market Efficiency to Covid-19 Shock

We examine the price disorder and market efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during COVID-19 pandemic period. Using permutation entropy and Fisher information measure (FIM), we construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply sliding time window approach to study the temporal evolution of efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano is the most efficient. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryptocurrencies usage for liquidity risk diversification strategy.
Virtual Reality Assembly of Physical Parts: The Impact of Interaction Interface Techniques on Usability and Performance

In the last few years there has been increased interest in Virtual Reality technology application in the professional context. The technology is used in industry for training. There are several reported successful pilot applications in the industrial training of assembly of physical parts. However, our understanding of how interaction techniques affect performance on these tasks is still limited. In this research, we study the usability of three common hand-based interaction methods: controllers, camera-based hand tracking, and dataglove interaction in the virtual assembly of physical parts. We investigate how performance on the task is affected by the interaction method. We measure the usability, task time, task accuracy, and interaction errors in a study with 12 participants (n = 12) - with and without prior Virtual Reality experience. The results show that users rate the usability of controllers higher than camera based tracking, while datagloves are rated lower than camera tracked solutions. Users also complete the task faster, and with fewer interaction errors when using a controller, than when using a camera-tracking solution, or dataglove. However users execute actions more precisely when using a dataglove. Participants gender does not play a significant rule on the reported usability or task performance. However, as participants experience with Virtual Reality increases they can become less precise at executing actions with the controller, and more precise with the dataglove. The results of this study can help to design systems for the Virtual Reality assembly of physical parts, and to make decisions on which interaction method is suitable based on which aspects of the system are most important.
Teacher Preparation Programs in Iraq

The process of preparing the teacher is one of the topics that persists and still preoccupies the specialists in educational affairs in the countries of the world, because the teacher is the main factor in the success of the educational process. The need to reconsider the teacher and teacher preparationTeacher preparation programs in faculties of education and basic education faculties in IraqIraq is increasing in the recent period, as a result of what is happening in the world in terms of changes and developments. Teacher preparationTeacher preparation programs based on standards, competencies, skills, and roles have gained global attention. This chapter presents the teacher preparationTeacher preparation process and programs in IraqIraq that include academic, educational (professional), and cultural aspectsCultural aspects.
A Fast and Efficient K-Nearest Neighbor Classifier Using a Convex Envelope

In this paper, we propose a fast and efficient method to classify all kinds of patterns using the classical k-nearest neighbor (kNN) classifier. The kNN is one of the most popular supervised classification strategies. However, –for large data collections, the process can be very time consuming due to the tedious distance calculations. Our aim is to provide a generic strategy for all kinds of data collections by calculating fewer distances as in the classical approach. For that reason we propose a data selection technique that reduces the original data to a limited one which contains only some class prototypes. The prototypes are representatives of each class and are selected based on the notion of convex envelope. The experiments on multiple benchmark data collections such as MNIST, Fashion-MNIST and Lampung characters show a considerable speed up (up to 12x) in the classification, while reporting similar or slightly less classification figures than the classification results obtained using the complete data.
Research on Anti-tampering Mechanism of Massive Electric Energy Data Based on Blockchain

Aiming at the problems of malicious tampering and difficult to identify, single point of failure attacks faced by massive electric energy data storage, a blockchain-based anti-tampering mechanism for massive electric energy data is proposed by combining the decentralized, tamper-proof and highly scalable features of blockchain. The mechanism mainly includes three parts: electric energy data metadata extraction, electric energy metadata deposition, and data tampering identification, and is developed and validated based on Hyperledger Fabric system. Based on the characteristics of large amount of electric energy data, strong timeliness, and complex data structure, the electric energy data deposition chain is constructed, and the hash value, data size, data storage location, and data The experiments prove that 10G data is stored on the chain, and the data is divided into 1 10G packet, 10 1G packets, 20 0.5G packets, 40 0.25G packets, and uploaded on the chain. The time required to calculate a 10G packet is about 19 s, the time required for concurrent calculation after data splitting is about 7.8 s, the time required for uploading operation is about 30 s, 74 s, 121 s, 236 s, and the time required to verify whether the 10G data is tampered is about 23 s, which provides strong support for trustworthy sharing of electric energy data.
Determining Optimal Epsilon (eps) on DBSCAN Using Empty Circles

In this work, we have proposed a new approach to determine an optimal epsilon (eps) related to DBSCAN using empty circles in computational geometry. DBSCAN is sensitive to two key parameters, viz., epsilon and minimum number of data points. The radii of empty circles are effectively used to evaluate epsilon in order to run the traditional DBSCAN. Our proposed method is implemented with Python with valid experiment on a benchmark artificial data set. We compared our results with traditional DBSCAN and it shows the novelty of our proposed method.
Effect of Pareto Optimality in Multi Objective Optimization Under Fuzzy Environment Using Rank and Diversity

Study of multi objective optimization takes a major contribution in the field of research activities widely by the researchers due to the conflicting nature of objectives.various works have been progressed to find details about pareto optimal solution.In continuation to this, researchers developed so many algorithms for more details about this work.Keeping inspired by Genetic algorithm, authors intend to show the consequences of pareto optimality of multi objective problems. Due to the volatile nature of decision maker, the concept of Fuzzy optimization has been introduced in correlating with the theory of optimization using several objectives. Here, authors discuss the result of pareto optimality under the influence of fuzzy parameters in terms of Rank,Diversity and Pareto efficiency. Different case studies have been analysed for more details about study of pareto optimality and it’s effects in involvement of fuzzy parameters.