Reviewerly/exHarmony huggingface dataset:
High-performance Edwards curve aggregate signature (HECAS) for nonrepudiation in IoT-based applications built on the blockchain ecosystem

“Blockchain” is a buzzword that has captured attention from researchers in almost every possible domain. In a blockchain ecosystem, multiple parties across the network have to establish trust in an untrusted environment to ensure seamless nonrepudiation. Nonrepudiation is a crucial factor for secure information auditing in the blockchain. To achieve this, we require an optimal digital signature. In this work, we design a digital signature to address the nonrepudiation challenge in blockchain ecosystems. Our approach developed in the current work completed signing and verification tasks with 10% and 13% shorter processing times, respectively than the conventional digital signature scheme. The adoption of the high-performance Edwards curve aggregate signature (HECAS) in a blockchain ecosystem can yield a 10% improvement in the transaction flow, a 10% improvement in block validation and a 40% decrease in storage costs relative to the same system implemented without HECAS. Finally, we simulated various blockchain-based Internet of Things (IoT) ecosystems and experimented with our scheme. The results show that our work can produce convincing, consistent results across various sensor data types from smart IoT-based blockchain solutions.
Bisimulations for Neural Network Reduction

We present a notion of bisimulation that induces a reduced network which is semantically equivalent to the given neural network. We provide a minimization algorithm to construct the smallest bisimulation equivalent network. Reductions that construct bisimulation equivalent neural networks are limited in the scale of reduction. We present an approximate notion of bisimulation that provides semantic closeness, rather than, semantic equivalence, and quantify semantic deviation between the neural networks that are approximately bisimilar. The latter provides a trade-off between the amount of reduction and deviations in the semantics.
Radiology Report Generation for Rare Diseases via Few-shot Transformer

Reliable automatic radiology report generation is highly desired to reduce the labor-intensive and error-prone workload for healthcare workers. While some multi-modal learning models have been proposed to study on this task, few of them paid attention to the radiology report generation for rare diseases, except for RareGen which solved this problem by enhancing the semantic representations of rare diseases. However, there still exist several open problems to be addressed. The first lies in the low proportion of disease regions in an image, making the visual information redundant or irrelevant to rare diseases to be encoded. The second lies in that correlations modeled in the encoding stage may not be effectively decoded in the decoding stage due to the multi-modal representation. To address these two issues, we propose a few-shot Transformer radiology report generation model, namely TransGen, for rare diseases. It integrates the advantages of Transformer with two key modules assembled. Specifically, in the encoding stage, a Semantic-aware Visual Learning (SVL) module is introduced to capture the regions of rare diseases. Following that, in the decoding stage, a Memory Augmented Semantic Enhancement (MASE) module is proposed to enhance intermediate representations. It could make full use of the semantic information contained in the historical-generated sentences to benefit report generation involving rare diseases. Extensive experiments have been conducted on two public datasets of IU X-Ray and MIMIC-CXR to demonstrate the effectiveness of our proposed model.
Refactorings and Technical Debt in Docker Projects: An Empirical Study

Software containers, such as Docker, are recently considered as the mainstream technology of providing reusable software artifacts. Developers can easily build and deploy their applications based on the large number of reusable Docker images that are publicly available. Thus, a current popular trend in industry is to move towards the containerization of their applications. However, container-based projects compromise different components including the Docker and Docker-compose files, and several other dependencies to the source code combining different containers and facilitating the interactions with them. Similar to any other complex systems, container-based projects are prone to various quality and technical debt issues related to different artifacts: Docker and Docker-compose files, and regular source code ones. Unfortunately, there is a gap of knowledge in how container-based projects actually evolve and are maintained.In this paper, we address the above gap by studying refactorings, i.e., structural changes while preserving the behavior, applied in open-source Docker projects, and the technical debt issues they alleviate. We analyzed 68 projects, consisting of 19,5 MLOC, along with 193 manually examined commits. The results indicate that developers refactor these Docker projects for a variety of reasons that are specific to the configuration, combination and execution of containers, leading to several new technical debt categories and refactoring types compared to existing refactoring domains. For instance, refactorings for reducing the image size of Dockerfiles, improving the extensibility of Docker-compose files, and regular source code refactorings are mainly associated with the evolution of Docker and Docker-compose files. We also introduced 24 new Docker-specific refactorings and technical debt categories, respectively, and defined different best practices. The implications of this study will assist practitioners, tool builders, and educators in improving the quality of Docker projects.
The calculated responses against COVID-19 in Namibia

COVID-19 has spread to many countries and infected a vast number of people around the world. Namibia is not spared from this disease. On the early days of the pandemic in Namibia, the government instituted a four-stage strategy--from a full lockdown in Stage One with gradual relaxation of restrictions in Stage Two and ending with Stage Four. This was proven to be effective since the number of daily new cases were minimal by the time the restrictions were lifted in Stage Four, which allowed many non-essential businesses to resume, and borders to reopen. However, following this, the cases jumped in an alarming pace. The situation was also exacerbated partly by obliviousness of the people to restrictions due to their economic issues. At present, the government struggles to bring the situation back under control. Thus, the government reintroduced new restrictions to mitigate the situation. The country is facing paucity of facilities like personal protective equipment (PPE), health workers, intensive care unit (ICU) equipment and testing kits. To avoid further explosion of cases, Namibia needs to determine relaxation of restrictions based on the indicators of the situation of COVID-19. Assistance to the population and addressing insufficiency of facilities by the government through innovative solutions are of utmost importance in tussling the virus.
Systematic Review of Virtual Reality Solutions Employing Artificial Intelligence Methods

This paper first presents a systematic literature review of artificial intelligence (AI) methods used in virtual reality (VR) solutions. Based on the systematic literature review, a methodology for locating existing studies, selecting and evaluating contributions, performing analyses, and synthesizing data was proposed. We used search engines, such as Google Scholar and databases such as Elsevier's Scopus, ACM Digital Library, and IEEE Xplore Digital Library. A set of inclusion and exclusion criteria was used to select documents. The results showed that the AI scientific technique most applied in VR applications is machine learning. The findings revealed several fields adopting real-world applications that employ AI in VR: human–robot interaction, emotion interaction and behavior recognition, education, agriculture, transport, manufacturing, and health.
Rethinking Lightweight: Multiple Angle Strategy for Efficient Video Action Recognition

Video action recognition task involves modeling spatiotemporal information, and efficiency is critical to capture spatiotemporal dependencies in the video. Most existing models rely on optical flow information to capture the dynamic visual tempos between consecutive video frames. Although impressive performance can be achieved by combining optical flow with RGB, the time-consuming nature of optical flow computation cannot be ignored. Moreover, 3D CNN has successfully modeled spatiotemporal information, yet the enormous computational volume is unsuitable for real-time action recognition. In this letter, we propose a novel lightweight video feature extraction strategy that achieves better recognition performance with lower FLOPs. In particular, we perform convolution on the video cube from three orthogonal angles to learn its appearance and motion features. Compared with the computational volume of 3D CNN, our proposed method is more economical and thus meets the lightweight requirements. Extensive experimental results on public Something Something-V1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\&amp;$</tex-math></inline-formula> V2 and Diving48 datasets show our approach achieves the state-of-the-art performance.
An Algebraic Approach to Clustering and Classification with Support Vector Machines

In this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors.
Automated Identification of Social Media Bots Using Deepfake Text Detection

Social networks are playing an increasingly important role in modern society. Social media bots are also on the rise. Bots can propagate misinformation and spam, thereby influencing economy, politics, and healthcare. The progress in Natural Language Processing (NLP) techniques makes bots more deceptive and harder to detect. Easy availability of readily deployable bots empowers the attacker to perform malicious activities; this makes bot detection an important problem in social networks. Researchers have worked on the problem of bot detection. Most research focus on identifying bot accounts in social media; however, the meta-data needed for bot account detection is unavailable in many cases. Moreover, if the account is controlled by a cyborg (a bot-assisted human or human-assisted bot) such detection mechanisms will fail. Consequently, we focus on identifying bots on the basis of textual contents of posts they make in the social media, which we refer to as fake posts. NLP techniques based on Deep Learning appear to be the most promising approach for fake text detection. We employ an end-to-end neural network architecture for deep fake text detection on a real-world Twitter dataset containing deceptive Tweets. Our experiments achieve the state of the art performance and improve the classification accuracy by 2% compared to previously tested models. Moreover, our content-level approach can be used for fake posts detection in social media in real-time. Detecting fake texts before it gets propagated will help curb the spread of misinformation.
Sequence Prediction using Partially-Ordered Episode Rules

Predicting the next event (or symbol) of a sequence has many applications such as network event prediction, webpage prefetching, activity recommendation and stock price prediction. An effective approach for event prediction that has the benefit of being interpretable, is to extract episode rules to then apply them for prediction. This paper improves upon this approach by using a novel type of episode rules called partially-ordered episode rules. These rules are more general than standard episode rules as they loosen the ordering constraint on events in each rule antecedent and consequent. Hence, a partially-ordered episode rule can replace multiple standard episode rules. Experiments on several benchmark datasets show that partially-ordered episode rules can provide more accurate predictions than standard episode rules.
A Method for Reducing Time-to-Solution in Quantum Annealing Through Pausing

Recent research has shown that alternative annealing schedules provide the means for improving performance in modern quantum annealing devices. One such type of schedule is forward annealing with a pause, in which there is a period of time when system evolution is paused. While the results from using this type of schedule have been promising, effectively using a pause is not a trivial task. One challenge associated with introducing a pause into the schedule is determining the point in the anneal at which the pause will start. Additionally, tuning the schedule in real-time requires a significant amount of time. A second challenge is that while a pause may increase the number of correct solutions returned from the annealer, the time-to-solution, a standard metric for measuring performance in quantum annealing, will not necessarily be improved. We propose a method for constructing annealing schedules containing a pause that avoids the costly process of determining the optimal pause location in an online manner. We also evaluate our method on the subset sum problem, a problem of practical significance, and show that our method is able to achieve a 70% reduction in time-to-solution from a standard schedule containing no pause.
Pedestrian Positioning Scheme Based on the Fusion of Smartphone IMU Sensors and Commercially Surveillance Video

Existing pedestrian positioning technologies have difficulty balancing multiple aspects such as positioning accuracy, system maintenance, equipment and deployment costs. We propose a pedestrian positioning scheme employing inertial measurement unit (IMU) sensors of smartphones and surveillance video. We apply pedestrian dead reckoning (PDR) information to mark the pedestrians to be located and use surveillance video to track all pedestrians in the field of view. For video pedestrian tracking, we use a modified Markov model to reduce the probability of trajectory merging and interruption. In the fusion of video trajectories, we use single-step feature weighting and multi-step dynamic time warping (DTW) to improve the matching accuracy of trajectories, respectively. The experimental results show that the average positioning error of our proposed fusion scheme is less than 1.5 m.
Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development

With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available.
A comprehensive review of the video-to-text problem

Research in the Vision and Language area encompasses challenging topics that seek to connect visual and textual information. When the visual information is related to videos, this takes us into Video-Text Research, which includes several challenging tasks such as video question answering, video summarization with natural language, and video-to-text and text-to-video conversion. This paper reviews the video-to-text problem, in which the goal is to associate an input video with its textual description. This association can be mainly made by retrieving the most relevant descriptions from a corpus or generating a new one given a context video. These two ways represent essential tasks for Computer Vision and Natural Language Processing communities, called text retrieval from video task and video captioning/description task. These two tasks are substantially more complex than predicting or retrieving a single sentence from an image. The spatiotemporal information present in videos introduces diversity and complexity regarding the visual content and the structure of associated language descriptions. This review categorizes and describes the state-of-the-art techniques for the video-to-text problem. It covers the main video-to-text methods and the ways to evaluate their performance. We analyze twenty-six benchmark datasets, showing their drawbacks and strengths for the problem requirements. We also show the progress that researchers have made on each dataset, we cover the challenges in the field, and we discuss future research directions.
A Chinese Text Classification Method Based on BERT and Convolutional Neural Network

Text classification has always been an important task in natural language processing. In recent years, text classification has been widely used in emotion analysis, intention recognition, intelligent question answering and other fields. In this paper, the word vector is generated based on the Bert model, and the text features extracted by Convolutional Neural Network (CNN) are fused to get more effective features, so as to complete the Chinese text classification. Experiments are conducted on the public data set. Compared with the text classification model in recent years, it is proved that the Bert+CNN model can accurately classify Chinese text, effectively prevent over fitting, and has good generalization.
Logics with Multiteam Semantics

Team semantics is the mathematical basis of modern logics of dependence and independence. In contrast to classical Tarski semantics, a formula is evaluated not for a single assignment of values to the free variables, but on a set of such assignments, called a team. Team semantics is appropriate for a purely logical understanding of dependency notions, where only the presence or absence of data matters, but being based on sets, it does not take into account multiple occurrences of data values. It is therefore insufficient in scenarios where such multiplicities matter, in particular for reasoning about probabilities and statistical independencies. Therefore, an extension from teams to multiteams (i.e. multisets of assignments) has been proposed by several authors. In this paper we aim at a systematic development of logics of dependence and independence based on multiteam semantics. We study atomic dependency properties of finite multiteams and discuss the appropriate meaning of logical operators to extend the atomic dependencies to full-fledged logics for reasoning about dependence properties in a multiteam setting. We explore properties and expressive power of a wide spectrum of different multiteam logics and compare them to second-order logic and to logics with team semantics. In many cases the results resemble what is known in team semantics, but there are also interesting differences. While in team semantics, the combination of inclusion and exclusion dependencies leads to a logic with the full power of both independence logic and existential second-order logic, independence properties of multiteams are not definable by any combination of properties that are downwards closed or union closed and thus are strictly more powerful than inclusion-exclusion logic. We also study the relationship of logics with multiteam semantics with existential second-order logic for a specific class of metafinite structures. It turns out that inclusion-exclusion logic can be characterised in a precise sense by the Presburger fragment of this logic, but for capturing independence, we need to go beyond it and add some form of multiplication. Finally, we also consider multiteams with weights in the reals and study the expressive power of formulae by means of topological properties.
MAPPING THE MINERAL ZONATION AT THE ERNEST HENRY IRON OXIDE COPPER-GOLD DEPOSIT: VECTORING TO Cu-Au MINERALIZATION USING MODAL MINERALOGY

Abstract Iron oxide copper-gold (IOCG) deposits form in spatial and genetic relation to hydrothermal iron oxide-alkali-calcic-hydrolytic alteration and thus show a mappable zonation of mineral assemblages toward the orebody. The mineral zonation of a breccia matrix-hosted orebody is efficiently mapped by regularly spaced samples analyzed by the scanning electron microscopy-integrated mineral analyzer technique. The method results in quantitative estimates of the mineralogy and allows the reliable recognition of characteristic alteration as well as mineralization-related mineral assemblages from detailed mineral maps. The Ernest Henry deposit is located in the Cloncurry district of Queensland and is one of Australia’s significant IOCG deposits. It is known for its association of K-feldspar altered clasts with iron oxides and chalcopyrite in the breccia matrix. Our mineral mapping approach shows that the hydrothermal alteration resulted in a characteristic zonation of minerals radiating outward from the pipe-shaped orebody. The mineral zonation is the result of a sequence of sodic alteration followed by potassic alteration, brecciation, and, finally, by hydrolytic (acid) alteration. The hydrolytic alteration primarily affected the breccia matrix and was related to economic mineralization. Alteration halos of individual minerals such as pyrite and apatite extend dozens to hundreds of meters beyond the limits of the orebody into the host rocks. Likewise, the Fe-Mg ratio in hydrothermal chlorites changes systematically with respect to their distance from the orebody. Geochemical data obtained from portable X-ray fluorescence (p-XRF) and petrophysical data acquired from a magnetic susceptibility meter and a gamma-ray spectrometer support the mineralogical data and help to accurately identify mineral halos in rocks surrounding the ore zone. Specifically, the combination of mineralogical data with multielement data such as P, Mn, As, P, and U obtained from p-XRF and positive U anomalies from radiometric measurements has potential to direct an exploration program toward higher Cu-Au grades.
Artificial Intelligence-Based Methods For Filtering Spam Messages In Email Services

In this paper describes methods for classify spam messages in electronic mail services. Considered algorithms of machine learning based spam filtering. The characteristics and effectiveness of spam filters based on artificial intelligence methods are analyzed.