Reviewerly/exHarmony huggingface dataset:
Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

The Convolutional Neural Network (CNN) based on deep learning is introduced to propose two deep face detection algorithms and design an embedded face recognition system, in an effort to apply the deep learning algorithm to face detection and explore the embedded face recognition system. First, an optimized Multi-task Cascaded Convolutional Network (MTCNN) algorithm is proposed for the simulation transformation and crop preprocessing of the face image, denoted as OMTCNN. Second, a lightweight face recognition algorithm based on CNN is proposed to reduce the computational complexity of face recognition in the embedded system, denoted as LCNN. Finally, OMTCNN and LCNN are combined to construct the multi-core embedded face recognition system. Results demonstrate that OMTCNN shows good performance in determining face identity, and its training accuracy can reach 95.78%, significantly better than the unimproved MTCNN algorithm. On the Labeled Faces in the Wild (LFW) dataset, LCNN provides a correct rate of 98.13%, and the reduction in the complexity of the model itself increases the computational speed by as much as 6.4 times. The test results suggest that the designed face recognition system has good applicability on the embedded platform. The increase in the accuracy of the face recognition module and the introduction of the calculation acceleration module have significantly improved the face detection and recognition performance of the embedded system. The embedded face recognition system based on deep learning has practical application value.
Social Media for Scholarly Communication in Central Asia and Its Neighbouring Countries

The current digital era has led to a surge in the use of Social Media in academia. Worldwide connectivity has brought to the fore a scarce participation of Central Asia and adjoining regions in scientific discussions. Global perspectives in science may not be recorded due to such communication disparities. An equal representation of all ethnic groups is essential to have a rounded picture of the topic at hand. The extent of use of social media platforms in various regions is determined by social, economic, religious, political, cultural and ethnic factors, which may limit participation. The paper aims to examine the use of social media by academicians in the Central Asian countries, China and Mongolia. It also focusses on the linguistic skills of the Central Asian, Chinese and Mongolian population and their eagerness to be involved in global discussions. Understanding the factors limiting participation from specific regions is the first step in this direction.
Low-Light Demosaicking and Denoising for Small Pixels Using Learned Frequency Selection

Low-light imaging is a challenging task because of the excessive photon shot noise. Color imaging in low-light is even more difficult because one needs to demosaick and denoise simultaneously. Existing demosaicking algorithms are mostly designed for well-illuminated scenarios, which fail to work with low-light. Recognizing the recent development of small pixels and low read noise image sensors, we propose a learning-based joint demosaicking and denoising algorithm for low-light color imaging. Our method combines the classical theory of color filter arrays and modern deep learning. We use an explicit carrier to demodulate the color from the input Bayer pattern image. We integrate trainable filters into the demodulation scheme to improve flexibility. We introduce a guided filtering module to transfer knowledge from the luma channel to the chroma channels, thus offering substantially more reliable denoising. Extensive experiments are performed to evaluate the performance of the proposed method, using both synthetic datasets and real data. Results indicate that the proposed method offers consistently better performance over the current state-of-the-art, across several standard evaluation metrics.
Income Levels, Governance and Inclusive Human Development in Sub-Saharan Africa

This study examines how income-driven governance affects inclusive human development in Sub-Saharan Africa with data for the period 2000–2012. The empirical evidence is based on the Generalised Method of Moments (GMM) and Tobit regressions. Nine bundled and unbundled concepts of governance are used: political (voice & accountability and political stability/no violence), economic (government effectiveness and regulation quality) and institutional (corruption-control and the rule of law) governances. The main finding is that ‘middle income’-driven governance has a higher effect on inclusive human development than ‘low income’-driven governance. Policy implications are discussed in the light of: (i) the contemporary relevance of findings; (ii) the pivotal role of a higher income level in the post-2015 sustainable development agenda; and (iii) inconsistent strands in the literature and in foreign aid policies.
Using Artificial Intelligence to Visualize the Impacts of Climate Change

Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment.One reason for this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract.To overcome these challenges, we propose to use cuttingedge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer.It will allow a user to enter an address-be it their house, their school, or their workplace--and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires.This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.
Bitcoin spot and futures market microstructure

Abstract We study Bitcoin (BTC) trading at the Chicago Mercantile Exchange (CME) and four settlement spot exchanges that transact $146 million per day in the BTC/USD pair. Spot market median trade sizes are under $1,300 but exceed $18,000 on the CME. Bid‐ask spreads average 0.0298%. Trade sizes of over $1 million move markets by less than 1%. 2.5% of trades and 15.5% of cancellations on Coinbase take place within 50 ms. Bid‐ask spreads exceed 0.8% for only 226 s. Most executions trade‐through better quotes, with estimated losses of $36 million. The CME leads price discovery. BTC leads Ethereum price adjustment.
To BAN or Not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection

Abstract Hate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the results of the proposed approach on hate speech detection problems in several languages. Additionally, we test whether affective dimensions can enhance the information extracted by the BERT model in hate speech classification. Our experiments show that Monte Carlo dropout provides a viable mechanism for reliability estimation in transformer networks. Used within the BERT model, it offers state-of-the-art classification performance and can detect less trusted predictions.
Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume

This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.
NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2)

Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, and websites that provide recommendations. This research proposes a novel method to develop a knowledge-based recommender system from unstructured (text) data. The method is based on applying an opinion mining algorithm, extracting aspect-based sentiment score per text item, and transforming text into a structured form. An action rule mining algorithm is applied to the data table constructed from sentiment mining. The proposed application of the method is the problem of improving customer satisfaction ratings. The results obtained from the dataset of customer comments related to the repair services were evaluated with accuracy and coverage. Further, the results were incorporated into the framework of a web-based user-friendly recommender system to advise the business on how to maximally increase their profits by introducing minimal sets of changes in their service. Experiments and evaluation results from comparing the structured data-based version of the system CLIRS (Customer Loyalty Improvement Recommender System) with the unstructured data-based version of the system (CLIRS2) are provided.
CGP: Cluster-based gossip protocol for dynamic resource environment in cloud

Cloud computing offers computational, storage, and applications capabilities as subscription oriented services. It offers a platform explore computational assets and use them based on "pay per use arrangement". Thus it opens ways to access boundless assets with negligible equipment and programming at the customers' end. This paper focuses on the advancement of a cloud administration's provisioning structure by building up a dynamic load-balancer for the cloud. It proposes a framework and protocol for the resource environment in the cloud. Distributed Hash Table (DHT) protocol has been utilized for a service query to perform a job agreed by the user. For load balancing, gossip protocol has been used for inter/intra-cluster gossip. For inter-cluster gossip, the load is balanced among the leaders of every cluster. The proposed protocol uses the inter-cloud resource management, where a leader is selected from the cloud that interacts to other cloud and decides on virtual machine (VM) migration. The decision about job allocation is not acknowledged by a single machine, which generates the scalable architecture of the proposed protocol. The protocol considers the current load situation and decides at the time of request submission. This protocol is adaptable, reliable and scalable and supports green computing by utilizing server solidification.
DNS covert channel detection method using the LSTM model

DNS is a kind of basic network protocol that is rarely blocked by firewalls; therefore, it is used to build covert channels. Malicious DNS covert channels play an important role in data exfiltration and botnets and do great harm to the network environment. To detect DNS covert channels, researchers extract multiple features from different perspectives of DNS traffic. At present, many detection methods using machine learning are based on manual features, which usually include complex data preprocessing and feature extraction. Additionally, these methods seriously rely on expert knowledge, and some potential features are hard to discover. To address these problems, we propose a DNS covert channel detection method using the LSTM model, which does not rely on feature engineering. First, we use the FQDNs of DNS packets as the input and implement an end-to-end detection approach using LSTM. Then, we filter the detection results of the LSTM model with the grouped filtering method to further reduce the false positive rate. Using the packets from the Internet and the packets generated by running different DNS covert channel tools, we construct our datasets, in which generalization test datasets are included in addition to the FQDN and the DNS packet datasets for model training. Our method achieves an accuracy rate of 99.38% on the test dataset and a recall rate of 98.52% on the generalization test dataset, which are better than the state-of-the-art methods. This method is also tested in a real network environment and has detected multiple malicious DNS covert channel events.
Linking textual and contextual features for intelligent cyberbullying detection in social media

The rapid and vast adoption of social media and the 21st century technologies is changing the bullying process., i.e., deliberate misuse of power in relationships through social behavior that intends to cause social and psychological harm. In this context, Cyberbullying can be defined as the process of using technology to hurt someone else by sending hurtful messages, pictures, or comments. A successful Cyberbullying detection technique requires an effective combination of big data (generated from social, open, and private data islands), features, and algorithms. While many Cyberbullying detection techniques exist for working with machine learning data and algorithms, support for thinking of new features (to be extracted from images, images' metadata, and textual information generated around images) remains poor. In this paper, we present an intelligent Cyberbullying detection pipeline to: (i) extract features (from an image, image meta-data and textual content generate); (ii) contextualize the extracted features by developing a crowdsourced feedback loop and drawing on human knowledge; and (iii) combining features using a Neural Network to identify and build potentially useful features. We adopt a typical scenario for analyzing social media content generated on Instagram and Twitter to identify Cyberbullying activities. We discuss how the proposed approach significantly improves the quality of extracted knowledge compared to classical natural language analysis techniques.
OCoR

Code retrieval helps developers reuse code snippets in the open-source projects. Given a natural language description, code retrieval aims to search for the most relevant code relevant among a set of code snippets. Existing state-of-the-art approaches apply neural networks to code retrieval. However, these approaches still fail to capture an important feature: overlaps. The overlaps between different names used by different people indicate that two different names may be potentially related (e.g., "message" and "msg"), and the overlaps between identifiers in code and words in natural language descriptions indicate that the code snippet and the description may potentially be related.
Deep learning-Based 3D inpainting of brain MR images

Abstract The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. Various methods were used to assess the overall similarity between the inpainted and original 3D data. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. Brain anatomy details were efficiently recovered by the proposed neural network. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data.
A Bernoulli Track-Before-Detect Filter for Interacting Targets in Maritime Radar

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range-azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.
The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework

The aim of this study is to identify the practical benefits and associated risks generated by the implementation of artificial intelligence (AI) in retail and capitalize on the results by developing a conceptual framework for integrating AI technologies in the information systems of retail companies.To this end, a systematic study of recent literature was conducted by carefully examining the topic of AI implementations.The main results of the documentation were used to substantiate the conceptual framework introduced by the paper.The research revealed a variety of advanced solutions, benefits, but also risks that AI generates in retail, in different segments of the value chain, abbreviated CECoR, from improving the customer experience (Customer Experience, CE) with the help of virtual agents (chatbots, virtual assistants, etc.), to cost reductions (Cost, Co) by using smart shelves, and to increasing revenues (Revenue, R) due to product recommendations and personalized offers or discounts.The proposed conceptual framework is focused on customer profiles and includes recommendations on AI implementations in a retail company, from the perspective of CECoR drivers.The results of the research can be capitalized by practitioners and researchers in the field, who are presented with concrete examples of benefits, challenges, and risks generated by AI technologies.The CECoR framework could be a useful tool for both retail and AI specialists, providing common and clear guidelines for initiating and overseeing projects for integrating AI in a company's information systems.Literature-based CECoR analysis dimensions have allowed the restriction of the research area, which is particularly wide, at the confluence of retail and AI.The originality of the article lies in the CECoR orientation of the research and the conceptual framework focused on customer profiling.
Efficient algorithms for mining frequent high utility sequences with constraints

An important data mining task is to discover all high utility sequences in a quantitative sequence database. Although useful, the number of discovered sequences is often very large. To find patterns that are more tailored to a user’s needs, this paper studies the problem of mining frequent high utility sequences satisfying item constraints. This article proposes a novel algorithm named C-FHUSM to quickly obtain these sequences from two concise representations discovered from a quantitative sequence database, namely frequent generator high utility sequences and frequent closed high utility sequences. The first set is extracted using a novel algorithm named FGenHUSM, while an existing algorithm is applied to extract the second set. C-FHUSM integrates novel pruning techniques to ignore sequences that do not satisfy item constraints early by checking only a small number of representative sequences at the beginning of the mining process. Experimental results show that C-FHUSM can be more than ten times faster and has better scalability than a modified version of the state-of-the-art EHUSM algorithm for mining sequences with item constraints. Moreover, it is found that using C-FHUSM is beneficial when a user frequently changes constraints as results can be updated without rescanning the database.
AutoFS: Automated Feature Selection via Diversity-Aware Interactive Reinforcement Learning

In this paper, we study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is to find the optimal feature subset from large-scale feature space, and is a fundamental intelligence for machine learning and predictive analysis. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection methods (e.g., K-Best, decision tree based ranking, mRMR) are mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection methods automatically navigate feature space to explore the best subset, but are usually inefficient. Are automation and efficiency always apart from each other? Can we bridge the gap between effectiveness and efficiency under automation? Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method. To that end, we propose an Interactive Reinforced Feature Selection (IRFS) framework that guides agents by not just self-exploration experience, but also diverse external skilled trainers to accelerate learning for feature exploration. Specifically, we formulate the feature selection problem into an interactive reinforcement learning framework. In this framework, we first model two trainers skilled at different searching strategies: (1) KBest based trainer; (2) Decision Tree based trainer. We then develop two strategies: (1) to identify assertive and hesitant agents to diversify agent training, and (2) to enable the two trainers to take the teaching role in different stages to fuse the experience of the trainers and diversify teaching process. Such a hybrid teaching strategy can help agents to learn broader knowledge, and thereafter be more effective. Finally, we present extensive experiments on real-world datasets to demonstrate the improved performances of our method: more efficient than reinforced selection and more effective than classic feature selection.