Reviewerly/exHarmony huggingface dataset:
Social Media Sentiment Analysis: The Hajj Tweets Case Study

About forty five percent of the world's population use social networks, thinking of using these platforms seemed to find people's opinions and feelings on various topics.Companies that offer their services and products to customers focus on the subject for future improvement.Thus, serious thinking began to analyze the views of people across different social platforms and also to develop the best ways to analyze these views.In this study, we focused on finding the best way for sentiment analysis by using a series of Hajj-related tweets, which is one of the most important rituals performed by Muslims, where the companies responsible for the pilgrimage season seek to complete the season in best way every year.We used the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) as supervised algorithms for machine-learning approach and Text Blob analyzer for lexicon-based approach.Finding shows that, machine learning techniques worked better than the lexicon approach in the classification and analysis of Hajj related tweets.Even the limited availability of Hajj tweets corpus dataset, SVM reaches the best accuracy which was 84%.
TECHNOLOGY FOR DESIGNING A DIGITAL EDUCATIONAL COURSE FOR THE UNIVERSITY STUDENTS

E. Egorova1, N. Bulankina2, O. Mishutina3, V. Tsybaneva4 1Don State Technical University (RUSSIAN FEDERATION) 2Novosibirsk Teacher Upgrading and Retraining Institute (RUSSIAN FEDERATION) 3Novosibirsk State Pedagogical University (RUSSIAN FEDERATION) 4Moscow City University (RUSSIAN FEDERATION)
Towards Bounding-Box Free Panoptic Segmentation

In this work we introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation. Panoptic segmentation is an ideal problem for proposal-free methods as it already requires per-pixel semantic class labels. We use this observation to exploit class boundaries from off-the-shelf semantic segmentation networks and refine them to predict instance labels. Towards this goal BBFNet predicts coarse watershed levels and uses them to detect large instance candidates where boundaries are well defined. For smaller instances, whose boundaries are less reliable, BBFNet also predicts instance centers by means of Hough voting followed by mean-shift to reliably detect small objects. A novel triplet loss network helps merging fragmented instances while refining boundary pixels. Our approach is distinct from previous works in panoptic segmentation that rely on a combination of a semantic segmentation network with a computationally costly instance segmentation network based on bounding box proposals, such as Mask R-CNN, to guide the prediction of instance labels using a Mixture-of-Expert (MoE) approach. We benchmark our proposal-free method on Cityscapes and Microsoft COCO datasets and show competitive performance with other MoE based approaches while outperforming existing non-proposal based methods on the COCO dataset. We show the flexibility of our method using different semantic segmentation backbones and provide video results on challenging scenes in the wild in the supplementary material.
Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative frauds... | Find, read and cite all the research you need on Tech Science Press
Entanglement synthesis based on the interference of a single-mode squeezed vacuum and a delocalized photon

We propose a new approach to generate entangled states, both hybrid and consisting exclusively of continuous variable (CV) states. A single-mode squeezed vacuum is mixed with a delocalized single photon on an arbitrary beam splitter with subsequent registration of measurement outcomes in auxiliary mode. The entangled hybrid states consisting of CV and discrete variable (DV) states are generated whenever any event is measured in auxiliary mode. Negativity is used as a measure of entanglement. Under certain initial conditions, the conditional state becomes as entangled as possible. New types of CV states, either even or odd depending on the parity of the Fock states forming the superpositions, are introduced. If the conditional entangled hybrid state is mixed with a single-mode squeezed vacuum with the subsequent registration of measurement results in DV mode, then the new state becomes CV entangled, i.e., its orthogonal components are already CV states. The entanglement synthesis can be expanded to implement a high-complexity quantum network.
Reinforcement Learning Upside Down: Don't Predict Rewards - Just Map Them to Actions.

We transform reinforcement learning (RL) into a form of supervised learning (SL) by turning traditional RL on its head, calling this Upside Down RL (UDRL). Standard RL predicts rewards, while UDRL instead uses rewards as task-defining inputs, together with representations of time horizons and other computable functions of historic and desired future data. UDRL learns to interpret these input observations as commands, mapping them to actions (or action probabilities) through SL on past (possibly accidental) experience. UDRL generalizes to achieve high rewards or other goals, through input commands such as: get lots of reward within at most so much time! A separate paper [63] on first experiments with UDRL shows that even a pilot version of UDRL can outperform traditional baseline algorithms on certain challenging RL problems. We also also conceptually simplify an approach [60] for teaching a robot to imitate humans. First videotape humans imitating the robot's current behaviors, then let the robot learn through SL to map the videos (as input commands) to these behaviors, then let it generalize and imitate videos of humans executing previously unknown behavior. This Imitate-Imitator concept may actually explain why biological evolution has resulted in parents who imitate the babbling of their babies.
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians.

We provide sample complexity upper bounds for agnostically learning multivariate Gaussians under the constraint of approximate differential privacy. These are the first finite sample upper bounds for general Gaussians which do not impose restrictions on the parameters of the distribution. Our bounds are near-optimal in the case when the covariance is known to be the identity, and conjectured to be near-optimal in the general case. From a technical standpoint, we provide analytic tools for arguing the existence of global locally small covers from local covers of the space. These are exploited using modifications of recent techniques for differentially private hypothesis selection. Our techniques may prove useful for privately learning other distribution classes which do not possess a finite cover.
Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning (ML) systems are difficult to design because of the “Two Black Boxes” problem that exists at the interface between human and machine. Many algorithms that are used in interactive ML systems are black boxes that are presented to users, while the human cognition represents a second black box that can be difficult for the algorithm to interpret. These black boxes create cognitive gaps between the user and the interactive ML model. In this paper, we identify several cognitive gaps that exist in a previously-developed interactive visual analytics (VA) system, Andromeda, but are also representative of common problems in other VA systems. Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system. These include designing new visual features to help people better understand how Andromeda processes and interacts with data, as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process. We evaluate our designs through both qualitative and quantitative analysis, and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.
MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking

Segmentation of Glioma from three dimensional magnetic resonance imaging (MRI) is useful for diagnosis and surgical treatment of patients with brain tumor. Manual segmentation is expensive, requiring medical specialists. In the recent years, the Brain Tumor Segmentation Challenge (BraTS) has been calling researchers to submit automated glioma segmentation and survival prediction methods for evaluation and discussion over their public, multimodality MRI dataset, with manual annotations. This work presents an exploration of different solutions to the problem, using 3D UNets and self attention for multitasking both predictions and also training (2D) EfficientDet derived segmentations, with the best results submitted for the official challenge leaderboard. We show that end-to-end multitasking survival and segmentation, in this case, led to better results.
Comparative Analysis of Machine Learning Algorithms for Phishing Website Detection

Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Users’ immense dependence on digital platform increases the chance of fraudulence. Phishing attacks are the most common ways of attack in the digital world. Any communication method can be used to target an individual and trick them into leaking confidential data in a fake environment, which can be later used to harm the sole victim or even an entire business depending on the attacker’s intend and the type of leaked data. Researchers have developed enormous anti-phishing tools and techniques like whitelist, blacklist, and antivirus software to detect web phishing. Classification is one of the techniques used to detect website phishing. This paper has proposed a model for detecting phishing attacks using various machine learning (ML) classifiers. K-nearest neighbors, random forest, support vector machines, and logistic regression are used as the machine learning classifiers to train the proposed model. The dataset in this research was obtained from the public online repository Mendeley with 48 features are extracted from 5000 phishing websites and 5000 real websites. The model was analyzed using F1 scores, where both precision and recall evaluations are taken into consideration. The proposed work has concluded that the random forest classifier has achieved the most efficient and highest performance scoring with 98% accuracy.
Research on key technologies for fault knowledge acquisition of power communication equipment

With the development of Energy Internet and Smart Grid, power communication equipment has gradually become an important pillar supporting the information transmission. However, when equipment failure occurs, more and more attention has been paid to the rapid realization of equipment fault research and judgment from a large amount of equipment fault information. Based on the fault data of power communication equipment, a fault model of power communication equipment is designed in this paper. It combines BERT and BiLSTM-CRF model for knowledge extraction, and constructs the equipment fault knowledge graph to realize the visual display of power communication equipment. Through this model, fault knowledge extraction can be realized accurately, and a visual model of fault analysis can be formed, which can effectively improve the fault research speed and work efficiency of maintenance personnel.
Clustering-based Inference for Biomedical Entity Linking

Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points.
Demonstrating the Evolution of GANs Through t-SNE

Generative Adversarial Networks (GANs) are powerful generative models that achieved strong results, mainly in the image domain. However, the training of GANs is not trivial, presenting some challenges tackled by different strategies. Evolutionary algorithms, such as COEGAN, were recently proposed as a solution to improve the GAN training, overcoming common problems that affect the model, such as vanishing gradient and mode collapse. In this work, we propose an evaluation method based on t-distributed Stochastic Neighbour Embedding (t-SNE) to assess the progress of GANs and visualize the distribution learned by generators in training. We propose the use of the feature space extracted from trained discriminators to evaluate samples produced by generators and from the input dataset. A metric based on the resulting t-SNE maps and the Jaccard index is proposed to represent the model quality. Experiments were conducted to assess the progress of GANs when trained using COEGAN. The results show both by visual inspection and metrics that the Evolutionary Algorithm gradually improves discriminators and generators through generations, avoiding problems such as mode collapse.
Two-factor Password-authenticated Key Exchange with End-to-end Security

We present a secure two-factor authentication (TFA) scheme based on the user’s possession of a password and a crypto-capable device. Security is “end-to-end” in the sense that the attacker can attack all parts of the system, including all communication links and any subset of parties (servers, devices, client terminals), can learn users’ passwords, and perform active and passive attacks, online and offline. In all cases the scheme provides the highest attainable security bounds given the set of compromised components. Our solution builds a TFA scheme using any Device-enhanced Password-authenticated Key Exchange (PAKE), defined by Jarecki et al., and any Short Authenticated String (SAS) Message Authentication, defined by Vaudenay. We show an efficient instantiation of this modular construction, which utilizes any password-based client-server authentication method, with or without reliance on public-key infrastructure. The security of the proposed scheme is proven in a formal model that we formulate as an extension of the traditional PAKE model. We also report on a prototype implementation of our schemes, including TLS-based and PKI-free variants, as well as several instantiations of the SAS mechanism, all demonstrating the practicality of our approach. Finally, we present a usability study evaluating the viability of our protocol contrasted with the traditional PIN-based TFA approach in terms of efficiency, potential for errors, user experience, and security perception of the underlying manual process. 1
Geometric characteristics and optical performance of ACPCs for integration with roofing structure of buildings

Stationary compound parabolic concentrators (CPC) are usually oriented in the east–west direction and tilted towards the equator for efficient radiation concentration. However, the slope (β) of roofing structure integrated with CPCs may be not equal to site latitude (λ) in buildings, thus asymmetric CPCs (ACPC) should be used. In this work, geometric characteristics and optical performance of ACPCs integrated onto roofs of buildings are investigated. To perform this work, a mathematical procedure, in which three and four reflections within ACPCs with flat-plate (ACPC-1) and tubular (ACPC-2) absorbers are respectively considered, is suggested and validated by ray-tracing analysis. Analysis shows that the geometric concentration of ACPCs on a south-facing roof tilted at an angle (α=λ-β) relative to the site latitude is not only dependent on angular extent (2θa) of solar rays required for acceptance on the cross-section of linear ACPCs, but also dependent on the geometry of absorbers and α. Calculation results indicate that given θa, the geometric concentration ratio (Ct) of horizontally truncated ACPC-1 decreases with an increase of α but that of ACPC-2 increases. Compared to similar symmetric CPC, an appropriately designed ACPC not only reduce the use of reflector materials but also increase the annual collectible radiation (Sa). It is found that as compared to similar symmetric CPC, for ACPC-1 with θa=35.5° and ρ=0.9, 7° and 10° of |a| result in reduction of Sa less than 1%, and 2%, respectively; whereas for ACPC-2, 10° and 15° of |a| result in reduction of Sa less than 1%, and 2%, respectively.
The Role of Mobile Technologies in Impacting Learner Autonomy in an EFL Context

This article reviews previous research conducted on the role of mobile devices that support learner autonomy (LA) in English as a foreign/second language (EFL) context from 2010 to 2021 based on a number of criteria that included research design, publication type, period, field of study, and use of technology. Mainly, the review examined design features of the empirical studies on mobile technologies and learner autonomy (LA) in EFL contexts and the ways mobile technologies impact LA. A coding sheet was used to analyze the papers (N = 23) having met the selection criteria in terms of education form and level, instrumentation, location, context, gender, use of technology, English as a foreign/second language (EFL) skills, and learning impact. It was revealed that all of the examined studies were conducted in Asia and Europe. Also, most of the studies had male and female participants. In addition, most studies on utilizing mobile devices to impact LA were investigated in higher education informally. Moreover, literature has paid more attention to reading and writing skills, vocabulary/grammar skills, and EFL in general. Finally, the analysis showed that mobile devices played a vital role in impacting LA in EFL contexts in a positive sense. Future research gaps and recommendations are suggested based on the findings of this review.
The South West Hub In-Situ Laboratory – A Facility for CO2 Injection Testing and Monitoring in a Fault Zone

The In-Situ Laboratory Project entails a configuration of wells at approximately 400 m depth for monitoring a small volume of CO2 for testing purposes in a fault zone at the South West Hub CCS Flagship project in Western Australia. This is one of four wells drilled to obtain data from a greenfield site for potential commercial scale geological storage of carbon dioxide. The Harvey-2 well intersects a major fault zone with a maximum throw up to 1,000 m. This provides an opportunity to establish a complementary monitoring well configuration around the original well to conduct a deeper, more complex shallow release test than others conducted in Australia to date. The purpose of the In-Situ Lab project is to evaluate the ability to monitor and detect unwanted leakage of carbon dioxide from a storage complex at the South West Hub CCS site, a site which will contribute to broadening the global portfolio of shallow CO2 release tests. The project will develop the first part of an enduring research facility at the South West Hub to enable further research of the characterisation of CO2 migration in fault zones and the shallow groundwater environment.
Evaluation of Federated Learning in Phishing Email Detection

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric.
Group Encryption: Full Dynamicity, Message Filtering and Code-Based Instantiation

Group encryption (GE), introduced by Kiayias, Tsiounis and Yung (Asiacrypt’07), is the encryption analogue of group signatures. It allows to send verifiably encrypted messages satisfying certain requirements to certified members of a group, while keeping the anonymity of the receivers. Similar to the tracing mechanism in group signatures, the receiver of any ciphertext can be identified by an opening authority - should the needs arise. The primitive of GE is motivated by a number of interesting privacy-preserving applications, including the filtering of encrypted emails sent to certified members of an organization.
The Potential Use of Blockchain Technology in Co-creation Ecosystems

Research literature on blockchain technology is starting to emerge. Blockchain technology is primarily associated with cryptocurrencies, raising interest in an application in other business sectors, thanks to its characteristics. The decentralized nature of blockchain creates the new concept of a token economy, which can help to trace and valorize intellectual property - one of the essential challenges in co-creation. This paper considers how blockchain technology and tokenization can solve challenges in co-creation projects consisting of large companies, startups and entrepreneurial accelerators. The paper proposes an exploratory analysis based on interviews conducted with French companies and startups. The findings include a framework of (1) challenges, lack of operating rules, trust, and traceability, and (2) the potential benefits of blockchain and tokenization to solve issues that arise. The findings can be useful for both startups and large companies as a starting point for understanding and adopting blockchain technology.JEL Codes: O3, L2