Reviewerly/exHarmony huggingface dataset:
ELEMENTARY SCHOOL STUDENTS AND THEIR SATISFACTION WITH SMARTPHONE USE IN BIOLOGY CLASSES

According to many, scientific literacy is one of the most important personal competences (Hurd, 1998). To be scientifically literate in biology and related life sciences, a person should acquire not only content knowledge but also skills and attitudes. The development of smartphones has brought many changes and challenges to our lives and also has a great impact on education. The impact of smartphones on youth, for better or worse, has been well researched (Rather & Rather, 2019), but much less is known about the impact of devices on teaching and learning (Martín-Gutiérrez, Mora, Añorbe-Díaz & González-Marrero, 2017). Even less is known about the use of smartphones and their impact in the subject of biology, where teachers can incorporate a variety of digital teaching materials, learning environments, and the use of different functions and sensors of a smartphone, and even less is known about the use of smartphones in lower secondary education.In the study presented in this article, we examine how students learned practically in a distance learning course in biology or science during the closure of elementary schools due to the COVID -19 pandemic. In the following, we focus on smartphone usage, their average daily use for specific activities such as using office tools, playing games, searching for information, social networking, taking photos, recording and watching videos and reading e-books and articles. Survey participants were also asked their opinions about using a smartphone in the classroom and using a smartphone independently for learning.A questionnaire was developed with five different sets of questions. The four sets of questions are related to the use of a smartphone for learning and teaching. In one set of questions, we ask students how they did practical exercises in biology or science during distance learning. Here we were particularly interested in how often the practical exercises were carried out using a smartphone and involving measuring devices. In the pilot study, 85 students from 28 different Slovenian elementary schools participated.The results show that 43.5% had never used a smartphone for teaching and learning purposes. In distance education, practical exercises in biology were most frequently performed by receiving written work instructions and performing the exercise themselves (68.3%). However, they never or rarely performed virtual laboratory exercises and never performed exercises using a smartphone and involving measuring devices (67.1%). 88.2% of the respondents reported owning a smartphone, of which 65.9% used the smartphone several times a day. Only 17.6% of respondents use a smartphone independently for learning and finding explanations or educational content. Less than half (45.9) of all respondents expressed average satisfaction with the use of smartphones in teaching or learning a biology subject.
Efficient color image retrieval method using deep stacked sparse autoencoder

The recent advancement in deep learning-based approaches vastly outperforms the traditional image descriptors. Deep learning models, such as residual networks (ResNet), are well known for finding salient features. Although effective, high-level description often has a high dimensionality that increases computational overhead. The autoencoders find the useful approximation of the input data without losing critical information. Considering this, we propose a content-based image retrieval system for natural color images using a deep stacked sparse autoencoder (DSSA). The DSSA model learns latent features in an unsupervised way from the high-level description obtained using ResNet. The DSSA model achieves a nearly 50% reduction in size compared with the full-length features for the simple distance-based retrieval approach while increasing accuracy. The image retrieval efficacy of the learned latent features is also evaluated for two classifier-based methods using a Softmax classifier. Further, this study investigates the impact of unsupervised feature learning on retrieval using three benchmark natural color image databases of varying complexities, viz., Corel-1K, Corel-10K, and Canadian Institute for Advanced Research (CIFAR)-10. The latent features learned by the DSSA model with the fuzzy class membership-based retrieval method achieve promising improvements and yield a highly competitive retrieval performance with the large-size CIFAR-10 database.
Empirical Analysis of Machine Learning Algorithms for Multiclass Prediction

With the emergence of big data and the interest in deriving valuable insights from ever-growing and ever-changing streams of data, machine learning has appeared as an effective data analytic technique as compared to traditional methodologies. Big data has become a source of incredible business value for almost every industry. In this context, machine learning plays an indispensable role of providing smart data analysis capabilities for uncovering hidden patterns. These patterns are later translated into automating certain aspects of the decision-making processes using machine learning classifiers. This paper presents a state-of-the-art comparative analysis of machine learning and deep learning-based classifiers for multiclass prediction. The experimental setup consisted of 11 datasets derived from different domains, publicly available at the repositories of UCI and Kaggle. The classifiers include Naïve Bayes (NB), decision trees (DTs), random forest (RF), gradient boosted decision trees (GBDTs), and deep learning-based convolutional neural networks (CNN). The results prove that the ensemble-based GBDTs outperform other algorithms in terms of accuracy, precision, and recall. RF and CNN show nearly similar performance on most datasets and outperform the traditional NB and DTs. On the other hand, NB shows the lowest performance as compared to other algorithms. It is worth mentioning that DTs show the lowest precision score on the Titanic dataset. One of the main reasons is that DTs suffer from overfitting and use a greedy approach for attribute relationship analysis.
Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress

&amp;lt;p&amp;gt;Earthquakes forecasting and prediction have long, and in some cases sordid, histories but recent work has rekindled interest in this area based on advances in short-term early warning, hazard assessment for human induced seismicity and successful prediction of laboratory earthquakes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the lab, frictional stick-slip events provide an analog for the full seismic cycle and such experiments have played a central role in understanding the onset of failure and the dynamics of earthquake rupture. Lab earthquakes are also ideal targets for machine learning (ML) techniques because they can be produced in long sequences under a wide range of controlled conditions. Indeed, recent work shows that labquakes can be predicted from fault zone acoustic emissions (AE). Here, we generalize these results and explore additional ML and deep learning (DL) methods for labquake prediction. Key questions include whether improved ML/DL methods can outperform existing models, including prediction based on limited training, or if such methods can successfully forecast beyond a single seismic cycle for aperiodic failure. We describe significant improvements to existing methods of labquake prediction using simple AE statistics (variance) and DL models such as Long-Short Term Memory (LSTM) and Convolution Neural Network (CNN). We demonstrate: 1) that LSTMs and CNNs predict labquakes under a variety of conditions, including pre-seismic creep, aperiodic events and alternating slow and fast events and 2) that fault zone stress can be predicted with fidelity (accuracy in terms of R2 &amp;gt; 0.92), confirming that acoustic energy is a fingerprint of the fault zone stress. We predict also time to start of failure (TTsF) and time to the end of Failure (TTeF). Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of fault creep before an event. We also report on a novel autoregressive forecasting method to predict future fault zone states, focusing on shear stress. This forecasting model is distinct from existing predictive models, which predict only the current state. We compare three modern approaches in sequence modeling framework: LSTM, Temporal Convolution Network (TCN) and Transformer Network (TF). Results are encouraging in forecasting the shear stress at long-term future horizons, autoregressively. Our ML/DL prediction models outperform the state of the art and our autoregressive model represents a novel forecasting framework that could enhance current methods of earthquake forecasting.&amp;lt;/p&amp;gt;
TLSPG: Transfer learning-based semi-supervised pseudo-corpus generation approach for zero-shot translation

Machine Translation (MT) has come a long way in recent years, but it still suffers from data scarcity issue due to lack of parallel corpora for low (or sometimes zero) resource languages. However, Transfer Learning (TL) is one of the directions widely used for low-resource machine translation systems to overcome this issue. Creating parallel corpus for such languages is another way of dealing with data scarcity, yet costly, time-consuming and laborious task. In order to avoid the above listed limitations of parallel corpus formation, we present a TL-based Semi-supervised Pseudo-corpus Generation (TLSPG) approach for zero-shot MT systems. It generates the pseudo corpus by exploiting the relatedness between low resource language pairs and zero-resource language pairs via TL approach. It is further empirically ascertained in our experiments that such relatedness helps improve the performance of zero-shot MT systems. Experiments on zero-resource language pairs show that our approach effectively outperforms the existing state-of-the-art models, yielding improvement of +15.56,+8.13,+3.98 and +2 BLEU points for Bhojpuri→Hindi, Magahi→Hindi, Hindi→Bhojpuri and Hindi→Magahi, respectively.
Deep Learning and Data Mining for Book Recommendation: Retrospect and Expectation

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines, and recommendation system have become the most convenient ways to find relevant information within a short time. The technique of Book Recommendation System (BRS) combines machine learning, data mining, and artificial intelligence, which is regarded as an expert to offer advice according to personal related choices and decisions before. The recommendation system is one of the strongest tools to increase profit and retain buyers. Book Recommendation System finds useful patterns in rating and consumption data between readers and books, then exploits these patterns to guide users to good books. Many of these patterns reflect subsistent phenomena between the various users and books. But there remain some unsolved questions, such as offering advice to persons who are firstly using the system, recommending books when avoiding some taboos like religious taboos. In this paper, I proposed a retrospective study of BRS and also made an expectation of the future development of the domain.
A Fast and Effective Panorama Stitching Algorithm on UAV Aerial Images

Nowadays, UAV(Unmanned Aerial Vehicle) has been widely used in many areas, such as agriculture, rescue and disaster relief, video shooting. In some applications, the whole environment needs to be perceived, so image stitching comes in handy. It is time-consuming for present methods to stitch aerial images, whose bottleneck is to generate dense point clouds. Besides, traditional image stitching algorithm may encounter accumulative error which makes the result distortion. In this paper, a fast UAV image stitching algorithm is proposed by re-placing generation of dense point clouds with image composition algorithm and taking full advantages of GPS information to initialize cameras&#x0027; rotation to weaken the impact of accumulative error. The proposed model is much simpler than classical models in UAV image stitching, it can handle large scene very quickly. Furthermore, the effect of stitching is better than the current commercial software(Pix4D). When overlap ratio between two images is low, the proposed algorithm can generate a good stitching result, while commercial software may generate some black areas.
Dark Web in Modern World Theoretical Perspective: A survey

The Internet as a whole is a large network of interconnected computer networks and their supporting infrastructure which is divided into 3 parts. The web is a list of websites that can be accessed using search engines like Google, Firefox, and others, this is called as Surface Web. The Internet&#x0027;s layers stretch well beyond the surface material that many people can quickly reach in their everyday searches. The Deep Web material, which cannot be indexed by regular search engines like Google, is a subset of the internet. The Dark Web, which extends to the deepest reaches of the Deep Web, contains data that has been purposefully hidden. Tor may be used to access the dark web. Tor employs a network of volunteer devices to route users&#x0027; web traffic via a succession of other users&#x0027; computers, making it impossible to track it back to the source. We will analyze and include results about the Dark Web&#x0027;s presence in various spheres of society in this paper. Further we take dive into about the Tor metrics how the relay list is revised after users are determined based on client requests for directories (using TOR metrics). Other way we can estimate the number of users in anonymous networks. This analysis discusses the purposes for which it is frequently used, with a focus on cybercrime, as well as how law enforcement plays the adversary position. The analysis discusses these secret Dark Web markets, what services they provide, and the events that take place there such as cybercrime, illegal money transfers, sensitive communication etc. Before knowing anything about Dark Web, how a rookie can make mistake of letting any threat or malware into his system. This problem can be tackled by knowing whether to use Windows, or any other OS, or any other service like VPN to enter Dark world. The paper also goes into the agenda of how much of illegal community is involved from India in these markets and what impact does COVID-19 had on Dark Web markets. Our analysis is carried out by searching scholarly journal databases for current literature. By acting as a reference guide and presenting a research agenda, it contributes to the field of the dark web in an efficient way. This paper is totally built for study purposes and precautionary measures for accessing Dark Web.
Structure-aware unsupervised tagged-to-cine MRI synthesis with self disentanglement

Cycle reconstruction regularized adversarial training—e.g., CycleGAN, DiscoGAN, and DualGAN—has been widely used for image style transfer with unpaired training data. Several recent works, however, have shown that local distortions are frequent, and structural consistency cannot be guaranteed. Targeting this issue, prior works usually relied on additional segmentation or consistent feature extraction steps that are task-specific. To counter this, this work aims to learn a general add-on structural feature extractor, by explicitly enforcing the structural alignment between an input and its synthesized image. Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities. The translator and structure encoder are updated, following an alternating training protocol. In addition, the information w.r.t. imaging modality can be eliminated with an asymmetric adversarial game. We train, validate, and test our network on 1,768, 416, and 1,560 unpaired subject-independent slices of tagged and cine magnetic resonance imaging from a total of twenty healthy subjects, respectively, demonstrating superior performance over competing methods.
ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to adversarial perturbation and may cause classification task failure. In this work, we propose an adversarial attack model using the Artificial Bee Colony (ABC) algorithm to generate adversarial samples without the need for a further gradient evaluation and training of the substitute model, which can further improve the chance of task failure caused by adversarial perturbation. In untargeted attacks, the proposed method obtained 100%, 98.6%, and 90.00% success rates on the MNIST, CIFAR-10 and ImageNet datasets, respectively. The experimental results show that the proposed ABCAttack can not only obtain a high attack success rate with fewer queries in the black-box setting, but also break some existing defenses to a large extent, and is not limited by model structure or size, which provides further research directions for deep learning evasion attacks and defenses.
Evaluating Electric Guitar Strumming Form as Musically Correct Rhythm And Sharpness Using Wrist-Wom Inertial Motion-Tracking Device

A wrist-worn inertial motion-tracking device using a commercially available sensor chip has been investigated for a motion evaluation on a chord strumming technique of electric guitar. Several motion parameters such as elbow-swinging angle, wrist-twisting angle, and angular jerk on the wrist-twisting axis were examined for the strumming diagnosis. Three professional guitarists and amateur players having a wide range of playing experience participated in the chord strumming evaluation experiments. As a result, it was found that musically important parameters including playing rhythm deviation and strumming sharpness were able to be estimated without using sound recorders or cameras. A newly developed evaluation algorithm for the strumming sharpness may clearly distinguish the professional and amateur guitarists. In addition, application software for PC enabled the guitar players to compare their performances with those of professional guitarists as well as of themselves in the past. A guitar strumming task finder based on the elbow-swinging angle signal enabled the user to locate a specific timing for evaluation. An experimental guitar lesson class with the easy-to-use motion analysis system showed that both the task annotation and the performance evaluation can be performed without introducing significant cost in terms of time and environmental care.
Augmented Reality Communication on Active Construction Sites: A Pilot Study Exploring Non-Technological Factors

The construction industry requires effective communication between project stakeholders in various locations, which has traditionally been facilitated through in-person site visits or phone calls. These methods can lead to wasted time driving to remote sites for in-person visits or misunderstandings from omitting critical site details on phone calls. Augmented Reality (AR) has the potential to enable a consistent understanding of site conditions provided through in-person meetings with the efficiency of phone calls. However, the majority of studies exploring AR in construction present results from controlled implementations or laboratory environments. Conversely, this investigates AR calls between practitioners on active construction sites to identify factors and behaviors relevant to successful use of AR in real workspace. Furthermore, interviews with participants enabled the authors to understand the perceptions of the practitioner participants that may have guided their behavior. The results helped explore about six drawbacks of AR application in real workspaces and their associated solutions. Manipulation, color of annotation feature, and the time needed to upload large files are examples of the experimented drawbacks. Practising, training sessions, and the use of other available colors for annotation are explored solutions to overcome some of the revealed drawbacks. By altering their behavior and with more practice and repetition, users overcame some of the explored challenges. These findings will inform researchers and practitioners interested in using AR to effectively support communication on construction projects.
Cryptocurrencies:

Fraud follows the money, and as finance evolves so too will fraud. We provide an overview of cryptocurrencies and introduce the concept of crypto-enabled and crypto-dependent frauds to examine over twenty-five different types of cryptocurrency scams. The differences between cryptocurrency fraud and traditional fraud are highlighted, showing how cryptocurrencies provide both boons and curses for fraud prevention. Four key areas important for detection and prevention emerged, outlining a path for future research: data integrity and tooling, protocol design, education, and regulation. While cryptocurrencies offer new opportunities for fraud, the unique transparency they provide could also be used to mitigate it.
Multi-Level Contrastive Learning for Cross-Lingual Alignment

Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks. This paper proposes a multi-level contrastive learning (ML-CTL) framework to further improve the cross-lingual ability of pre-trained models. The proposed method uses translated parallel data to encourage the model to generate similar semantic embeddings for different languages. However, unlike the sentence-level alignment used in most previous studies, in this paper, we explicitly integrate the word-level information of each pair of parallel sentences into contrastive learning. Moreover, cross-zero noise contrastive estimation (CZ-NCE) loss is proposed to alleviate the impact of the floating-point error in the training process with a small batch size. The proposed method significantly improves the cross-lingual transfer ability of our basic model (mBERT) and outperforms on multiple zero-shot cross-lingual downstream tasks compared to the same-size models in the Xtreme benchmark.
Efficient Wavelet Entropy-Based Face Recognition System

In the presented paper, the face images modeling in different circumstances such as occlusion, poses, and illumination was investigated. A novel research based on a new combination of the discrete wavelet transform (DWT) and wavelet packet (WP) methods in conjunction with log energy and sure entropies is proposed. Probabilistic neural network (PNN) was used for classification. The performance of the proposed method was investigated using a collection of experiments with different challenges ORL face database and extended Yale face database was conducted. For testing the proposed face recognition system on the ORL database, many classification scores were calculated for different training/testing sets. The comparative results obtained on the ORL database for different published methods based on the efficiency were performed and showed that our method was superior with rate reached 98.5% and relatively small, elapsed time about 4.4 sec was found. For the extended Yale face database B, the recognition rate was averaged at each time for 42 iterations. Furthermore, the recognition rate was calculated for different training/testing sets and excellent results were achieved. The contribution of the study was to use wavelet entropy for the face recognition task by finding a best combination of the method that leads to achieving a competent form guarantying better results. The major contribution of this study is that it is possible to use just a few real face images to model the illumination conditions in the extended Yale face database B.