Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence for Demystifying Blockchain Technology Challenges: A Survey of Recent Advances

Blockchain technology has gained lots of traction in the past five years due to the innovations introduced in digital currency, the Bitcoin. This technology is powered by distributed ledger technology, which is a distributed database system. It is often renowned for decentralization, anti-attack, and unfalsified attributes making it a top choice in several non-monetary applications. In fact, the problem of privacy and security of the Internet of Things has been undertaken aggressively with Blockchain. Several problems have been identified with blockchain technology such as large delays and lack of support for real-time transaction processing, authorization, node verification, and consensus mechanisms. This article intends to provide a comprehensive survey on the recent advances and solutions to the problems of blockchain technology by leveraging the artificial intelligence approaches. The outcomes of this study will provide valuable information and guidance on the design of Blockchain-based systems to support time-sensitive and real-time specific applications and processes.
Combinatorial methods for dynamic gray‐box SQL injection testing

Summary This work presents an extended and enhanced gray‐box combinatorial security testing methodology for SQL injection vulnerabilities in web applications. We propose multiple new attack grammars modelling SQLi attacks against MySQL‐compatible databases, each one targeting a different injection context. Additionally, these grammars are also dynamically refined at the beginning of each attack against an endpoint of a web application, as a further optimization of the used attack model by taking into account the specifics of the generated query of that endpoint. Our goal is to enhance existing combinatorial approaches for detecting SQL injection vulnerabilities. The newly developed methodology is implemented in a prototype security testing tool called SQLInjector+ , which is an extension of an earlier prototype developed by us in prior work. This improved tool can attack (i.e. test) any web application that uses a MySQL‐compatible database management system. We evaluate our revised approach and improved prototype tool in a case study comprising of different kinds of web applications to which SQLi is a potential security threat. The case study contains the well‐known verification framework WAVSEP among other five real‐world web applications and one web application firewall. Our generated attack vectors, constructed via combinatorial methods applied to our improved and dynamically optimized attack grammars, are capable of injecting every known vulnerable endpoint in WAVSEP and also of finding new vulnerable parameters in some of the real‐world applications investigated in this paper. Our approach performs equally well or better when compared with existing state‐of‐art of SQL injection security testing tools (sqlmap, w3af, wapiti and fuzzdb) across all tested web applications in the case study.
LEQNet: Light Earthquake Deep Neural Network for Earthquake Detection and Phase Picking

Developing seismic signal detection and phase picking is an essential step for an on-site early earthquake warning system. A few deep learning approaches have been developed to improve the accuracy of seismic signal detection and phase picking. To run the existing deep learning models, high-throughput computing resources are required. In addition, the deep learning architecture must be optimized for mounting the model in small devices using low-cost sensors for earthquake detection. In this study, we designed a lightweight deep neural network model that operates on a very small device. We reduced the size of the deep learning model using the deeper bottleneck, recursive structure, and depthwise separable convolution. We evaluated our lightweight deep learning model using the Stanford Earthquake Dataset and compared it with EQTransformer. While our model size is reduced by 87.68% compared to EQTransformer, the performance of our model is comparable to that of EQTransformer.
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations

In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important. While these attributions are often claimed to hold the potential to improve human "understanding" of the models, surprisingly little work explicitly evaluates progress towards this aspiration. In this paper, we conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. They are challenged both to simulate the model on fresh reviews, and to edit reviews with the goal of lowering the probability of the originally predicted class. Successful manipulations would lead to an adversarial example. During the training (but not the test) phase, input spans are highlighted to communicate salience. Through our evaluation, we observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control. For the BERT-based classifier, popular local explanations do not improve their ability to reduce the model confidence over the no-explanation case. Remarkably, when the explanation for the BERT model is given by the (global) attributions of a linear model trained to imitate the BERT model, people can effectively manipulate the model.
Interactive Search on the Web: The Story So Far

Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the search services from the early days of the web to the present into keyword search engines, semantic search engines, question answering systems, dialogue systems and chatbots. As the first generation of search engines, keyword search engines have adopted keyword-based techniques to find the web pages containing the query keywords and ranking search results. In contrast, semantic search engines try to find meaningful and accurate results on the meaning and relations of things. Question-answering systems aim to find precise answers to natural language questions rather than returning a ranked list of relevant sources. As a subset of question answering systems, dialogue systems target to interact with human users through a dialog expressed in natural language. As a subset of dialogue systems, chatbots try to simulate human-like conversations. The paper provides an overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories.
Joint image demosaicking and denoising with mutual guidance of color channels

Deep network based methods have shown the advantages in image demosaicking and denoising for raw images. However, how to fully utilize the correlations of color channels in raw data still needs further investigations. In this paper, we propose a novel end-to-end network for joint demosaicking and denoising (JDD) with mutual guidance of color channels (MGCC), which utilizes the strengths of all color channels comprehensively. Specifically, RGB channels are reconstructed by three branches respectively and each color channel can guide the reconstruction of others by a designed channel guiding block (CGB), which fuses features from different color channel reconstruction branches spatial-adaptively. Moreover, considering the amount of data in the additional channel, i.e., the extra green channel in RGGB Bayer pattern, features from its reconstruction branch are applied as guidance first. In this way, each color channel is capable of learning the advantages of other color channels adaptively in different regions to recover itself better. Through this mechanism, our method can achieve results with more details and less artifacts. Experimental results on both synthetic and realistic datasets demonstrate that our method outperforms state-of-the-art JDD methods both quantitatively and qualitatively. The code is publicly available on: https://github.com/yongzhangwhu/MGCC-JDD.
An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

Mammography continues to play a central part in the early breast masses diagnosis and has raised several challenges in breast cancer detection. Nevertheless, it remains still difficult to detect abnormalities in a dense breast and some benign lesions may have similar appearance with masses. This study proposes a novel Computer-Aided Diagnosis (CADx) allowing benign and malignant mass classification. Accordingly, relevant textural-shape descriptors are proposed, which is the aggregation of a new Local Binary based feature, namely the Monogenic Gray Level and Local Difference (MGLLD), where both texture characteristics and breast tissue density information, and the Zernike moments are incorporated. A heuristic algorithm is then devised for optimizing the proposed features with respect to inter-class and intra-class distribution, by a convenient ponderation. Then, a set of classifiers are applied for opting to the best decision making solution. The Deep CNN yields the best accuracy, with an Area Under Curve (AUC) of 0.98 on Curated Breast Imaging Subset of DDSM (CBIS-DDSM). In addition, the authors are based on a subjective approval of the extracted Region Of Interest (ROI) by experts in radiology. The proposed scheme proves its effectiveness especially on some challenging breast cancer cases, corresponding to higher breast tissue density. In medical image processing, these results open new perspectives with respect to the use of the deep learning solutions, where the small sample-number is known to be a limitation (because pathologies cannot be intentionally provoked).
Lightweight ID-based broadcast signcryption for cloud–fog-assisted IoT

Balancing the security and efficiency of the internet of things (IoT) is difficult due to its numerous heterogeneous devices, different trust levels of stakeholders, and asymmetric resource distribution among layers. Identity-based broadcast encryption (IBBE) is a promising technique to ensure data confidentiality, simplify the certification management issue, and achieve one-to-many data sharing while reducing overheads simultaneously. However, broadcast encryption (BE) schemes without source authentication are not applicable in the real world as they cannot prevent invalid messages from malicious users. Meanwhile, the computation cost of IBBE is heavy and not suitable for constrained devices. This paper proposes a lightweight ID-based broadcast signcryption scheme (LIBBSC), which takes advantage of fog nodes to temporarily store ciphertext and to partially designcrypt for end users. A fog node transforms the ciphertext based on the specific user’s public identity key and then sends the outdesigncrypted ciphertext to the end-user (this step transforms the broadcast to the one-to-one communication). Finally, only the designated end-user with the associated secret key can rapidly designcrypt with minimal computational cost. Our scheme is selective-ID secure against chosen-ciphertext attacks in the random oracle model. And the implementation and evaluation of the proposed scheme show that it is efficient and suitable for IoT devices.
Switch-GPT: An Effective Method for Constrained Text Generation under Few-Shot Settings (Student Abstract)

In real-world applications of natural language generation, target sentences are often required to satisfy some lexical constraints. However, the success of most neural-based models relies heavily on data, which is infeasible for data-scarce new domains. In this work, we present FewShotAmazon, the first benchmark for the task of Constrained Text Generation under few-shot settings on multiple domains. Further, we propose the Switch-GPT model, in which we utilize the strong language modeling capacity of GPT-2 to generate fluent and well-formulated sentences, while using a light attention module to decide which constraint to attend to at each step. Experiments show that the proposed Switch-GPT model is effective and remarkably outperforms the baselines. Codes will be available at https://github.com/chang-github-00/Switch-GPT.
Tourism Development and Income Inequality in Sub-Saharan Africa: Does Governance Matter?

This study examines the relationship between tourism development, governance, and income inequality in 31 sub-Saharan African (SSA) countries. In the main, the study aims at examining whether tourism inflows ameliorate or exacerbate income inequality in the studied countries. The study also aims to examine whether institutional governance modulates the impact of tourism development on income inequality. For robustness, four proxies of governance indicators have been used, namely, the rule of law, voice and accountability, political stability, and government effectiveness, thereby leading to four separate specifications with four interaction variables. The Generalized Method of Moments (GMM), which accounts for endogeneity, has been used to examine this linkage. Contrary to some of the previous studies, the results of our study show that an increase in tourist arrivals leads to a decrease in income inequality in the studied SSA countries. The results also show that the favorable negative impact of tourism on income inequality could switch to an unfavorable positive impact if the following levels of governance are exceeded: 1.6842, 3.1429, and 0.7436 for voice and accountability, political stability, and government effectiveness, respectively. Policy implications are discussed.
Adaptive Micro-service based Cloud Monitoring and Resource Orchestration

The ultimate goal of cloud management is to get a reliable, mature and automatic elastic model, which can handle workload changes and system resources capacity scaling instantly without SLA violation and with cheapest cost. We propose a new monitoring and orchestration model that is based on micro-services architecture style and presents elasticity scaling measured by four aspects: scaling accuracy, resources orchestration time, monitoring scalability and communication complexity. We achieved an excellent elastic scaling with minimum delay using dynamic 2-3-4 tree structure using integrated self-monitoring and finite state machine configuration. We implemented this model using Java programming language by creating a time events simulator for cloud workload and resources. We validated the model by running real cloud traces and compare our model in monitoring and orchestration of the resources.
Evaluating digital mathematical games in improving the basic mathematical skills of university students

Technical skills related to basic mathematics in higher education have been a key foundation for further allied courses across primary programmes. While the traditional learning strategies appear to be inadequate to enhance the basic mathematics skills of university students, digital mathematical games pose the potential to improve such a set of skills by means of mobile learning. This work focuses on investigating the influence of digital mathematical games on students' basic mathematics skills under four various academic programmes. To illustrate the proposed scheme, a case study is conducted in a state university in the Philippines across undergraduate programmes in information technology, hospitality management, industrial engineering, and education. It was found that the use of digital games can improve the skills of students in basic mathematics. Thus, the educational system may be redesigned along with the positive effects of digital games on traditional skill enhancement initiatives.
Solving String Problems on Graphs Using the Labeled Direct Product

Suffix trees are an important data structure at the core of optimal solutions to many fundamental string problems, such as exact pattern matching, longest common substring, matching statistics, and longest repeated substring. Recent lines of research focused on extending some of these problems to vertex-labeled graphs, although using ad-hoc approaches which in some cases do not generalize to all input graphs. In the absence of a ubiquitous tool like the suffix tree for labeled graphs, we introduce the labeled direct product of two graphs as a general tool for obtaining optimal algorithms: we obtain conceptually simpler algorithms for the quadratic problems of string matching (SMLG) and longest common substring (LCSP) in labeled graphs. Our algorithms are also more efficient, since they run in time linear in the size of the labeled product graph, which may be smaller than quadratic for some inputs, and their run-time is predictable, because the size of the labeled direct product graph can be precomputed efficiently. We also solve LCSP on graphs containing cycles, which was left as an open problem by Shimohira et al. in 2011. To show the power of the labeled product graph, we also apply it to solve the matching statistics (MSP) and the longest repeated string (LRSP) problems in labeled graphs. Moreover, we show that our (worst-case quadratic) algorithms are also optimal, conditioned on the Orthogonal Vectors Hypothesis. Finally, we complete the complexity picture around LRSP by studying it on undirected graphs.
Reliable Label-Supervised Pixel Attention Mechanism for Weakly Supervised Building Segmentation in UAV Imagery

Building segmentation for Unmanned Aerial Vehicle (UAV) imagery usually requires pixel-level labels, which are time-consuming and expensive to collect. Weakly supervised semantic segmentation methods for image-level labeling have recently achieved promising performance in natural scenes, but there have been few studies on UAV remote sensing imagery. In this paper, we propose a reliable label-supervised pixel attention mechanism for building segmentation in UAV imagery. Our method is based on the class activation map. However, classification networks tend to capture discriminative parts of the object and are insensitive to over-activation; therefore, class activation maps cannot directly guide segmentation network training. To overcome these challenges, we first design a Pixel Attention Module that captures rich contextual relationships, which can further mine more discriminative regions, in order to obtain a modified class activation map. Then, we use the initial seeds generated by the classification network to synthesize reliable labels. Finally, we design a reliable label loss, which is defined as the sum of the pixel-level differences between the reliable labels and the modified class activation map. Notably, the reliable label loss can handle over-activation. The preceding steps can significantly improve the quality of the pseudo-labels. Experiments on our home-made UAV data set indicate that our method can achieve 88.8% mIoU on the test set, outperforming previous state-of-the-art weakly supervised methods.
Human capital impact on organizational performance of commercial banks operating in Yemen

This study investigates the relationship between human capital and improvements in organizational performance in Yemeni commercial banks operating in Sana’a. The individual human capital dimensions such as knowledge, experience, skills, innovation, and employees’ morale were taken as human capital variables. A quantitative methodology was used, and a Likert-scale questionnaire was distributed to collect the data. A combination of convenience and random sampling techniques was adopted to distribute the questionnaire to eight banks and 162 employees of these banks. A descriptive approach was used to analyze the data statistically. The results indicated that all the human capital factors had a varying level of practical effect on the organizational performance and all of these were statistically significant. Overall, 76.4% change in performance was observed if human capital elements were employed effectively. Each element such as knowledge, experience, skills, innovation, and employees’ morale had a varying level of positive impact on performance as observed. It is recommended to adopt and put in place knowledge and skills improvement systems in organization to achieve a sustained organizational growth. AcknowledgmentThis research publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Data-Aware Resource Allocation of Linear Pipeline Applications in a Distributed Environment

The data stored on distributed platforms, such as cloud and fog environments, are typically processed by linear pipeline applications (LPAs). All of the component tasks of an LPA job should be assigned to the same computational resource, in order to avoid a high volume of data retrievals. On the other hand, the load balancing of the resources should also be taken into account. To this end, in this paper we investigate resource allocation strategies for LPA jobs in a distributed environment, where the input data requiring processing are not available on all of the resources. Two commonly used routing techniques are adapted in order to leverage data locality. The proposed data-aware routing policies are compared to their non-data-aware counterparts via simulation, under different workload conditions and data retrieval overhead factors. The performance of the routing techniques is examined from the mean response time and fairness perspectives. The simulation results provide useful insights into how the workload conditions and the data retrieval overhead affect the examined routing strategies.
Improving Security of Web Servers in Critical IoT Systems through Self-Monitoring of Vulnerabilities

IoT (Internet of Things) systems are complex ones that may comprise large numbers of sensing and actuating devices; and servers that store data and further configure the operation of such devices. Usually, these systems involve real-time operation as they are closely bound to particular physical processes. This real-time operation is often threatened by the security solutions that are put in place to alleviate the ever growing attack surface in IoT. This paper focuses on critical IoT domains where less attention has been paid to the web security aspects. The main reason is that, up to quite recently, web technologies have been considered unreliable and had to be avoided by design in critical systems. In this work, we focus on the server side and on how attacks propagate from server to client as vulnerabilities and from client to unprotected servers; we describe the concerns and vulnerabilities introduced by the intensive usage of web interfaces in IoT from the server templating engines perspective. In this context, we propose an approach to perform self monitoring on the server side, propagating the self monitoring to the IoT system devices; the aim is to provide rapid detection of security vulnerabilities with a low overhead that is transparent to the server normal operation. This approach improves the control over the vulnerability detection. We show a set of experiments that validate the feasibility of our approach.