Reviewerly/exHarmony huggingface dataset:
Smart Contract Vulnerability Analysis and Security Audit

Ethereum started the blockchain-based smart contract technology that due to its scalability more and more decentralized applications are now based on. On the downside this has led to the exposure of more and more security issues and challenges, which has gained widespread attention in terms of research in the field of Ethereum smart contract vulnerabilities in both academia and industry. This article presents a survey of the Ethereum smart contract's various vulnerabilities and the corresponding defense mechanisms that have been applied to combat them. In particular, we focus on the random number vulnerability in the Fomo3d-like game contracts, as well as that attack and defense methods applied. Finally, we summarize the existing Ethereum smart contract security audit methods and compare several mainstream audit tools from various perspectives.
Optimized Edge Detection Technique for Brain Tumor Detection in MR Images

Genetic algorithms (GAs) are intended to look for the optimum solution by eliminating the gene strings with the worst fitness. Hence, this paper proposes an optimized edge detection technique based on a genetic algorithm. A training dataset that consists of simple images and their corresponding optimal edge features is employed to obtain the optimum filter coefficients along with the optimum thresholding algorithm. Qualitative and quantitative performance analyses are investigated based on several well-known metrics. The performance of the proposed genetic algorithm-based cost minimization technique is compared to the classical edge detection techniques, fractional-order edge detection filters, and threshold-optimized fractional-order filters. As an application for the proposed algorithm, a strategy to detect the edges of the brain tumour from a patient's MR scan image of the brain is proposed. First, Balance Contrast Enhancement Technique (BCET) is applied to improve the image features to provide better characteristics of medical images. Then the proposed GA edge detection method is employed, with the appropriate training dataset, to detect the fine edges. A comparative analysis is performed on the number of MR scan images as well. The study indicates that the proposed GA edge detection method performs well compared to both classical and fractional-order edge detection methods.
DRQ: Dynamic Region-based Quantization for Deep Neural Network Acceleration

Quantization is an effective technique for Deep Neural Network (DNN) inference acceleration. However, conventional quantization techniques are either applied at network or layer level that may fail to exploit fine-grained quantization for further speedup, or only applied on kernel weights without paying attention to the feature map dynamics that may lead to lower NN accuracy. In this paper, we propose a dynamic region-based quantization, namely DRQ, which can change the precision of a DNN model dynamically based on the sensitive regions in the feature map to achieve greater acceleration while reserving better NN accuracy. We propose an algorithm to identify the sensitive regions and an architecture that utilizes a variable-speed mixed-precision convolution array to enable the algorithm with better performance and energy efficiency. Our experiments on a wide variety of networks show that compared to a coarse-grained quantization accelerator like “Eyeriss”, DRQ can achieve 92% performance gain and 72% energy reduction with less then 1% accuracy loss. Compared to the state-of-the-art mixed-precision quantization accelerator “OLAccel”, DRQ can also achieve 21% performance gain and 33% energy reduction with 3% prediction accuracy improvement which is quite impressive for inference.
Deep learning in citation recommendation models survey

The huge amount of research papers on the web makes finding a relevant manuscript a difficult task. In recent years many models were introduced to support researchers by providing personalized citation recommendations. Moreover, deep learning methods have been employed in this domain to improve the quality of the final recommendations. However, a thorough study that classifies citation recommendation models and examines their (a) strengths and weaknesses, (b) evaluation metrics used, (c) popular datasets, and challenges faced is missing. Therefore, with this survey, we present a new classification approach for deep learning models that provide citation recommendation. Our approach uses the following six criteria: data factors, data representation methods, methodologies, types of recommendations used, problems addressed, and personalization. Additionally, we present a comparative analysis of those models that use the same set of evaluation metrics and datasets. Moreover, we examine hot upcoming issues and solutions in light of explored literature. Also, the survey discusses and analyzes the evaluation metrics and datasets adopted by the explored models. Finally, we conclude our survey with trends and future directions to further assist research on that domain.
A systematic review of augmented reality tourism research: What is now and what is next?

The application of augmented reality in tourism is flourishing and promising, bringing an emerging body of studies. While virtual reality might be a virtual threat to the travel and tourism as being a potential substitute, augmented reality allows users to interact with the real environment that could potentially enhance visitors’ experience. Distinguishing from reviews that combine studies of augmented reality and virtual reality, this study systematically investigates the current state of augmented reality research exclusively in the tourism literature. The results identify five established and emerging research clusters, with one predominant cluster that focuses on user acceptance of augmented reality, commonly applying the technology acceptance model. A meta-analysis of a subset of four empirical studies reveals that perceived ease of use has an overall influence of 52.79% on perceived usefulness. Lastly, a concept map visually presents the constructs that have been explored across the clusters. Based on our review, future research directions are proposed to advance knowledge in the emerging area of gamification, to explore the potential negative consequences of augmented reality, and to apply more innovative methods and study designs.
Hyperledger Fabric Blockchain: Chaincode Performance Analysis

Hyperledger Fabric, created and supported by the Linux Foundation and IBM, is one of the most popular open-source blockchain permissioned platforms that has been already used in many industrial scenarios. One of the main characteristics of this platform is that it provides a smart contract system that relies on general-purpose languages instead of an ad hoc one. In fact, a chaincode in the Fabric platform (the equivalent of the Ethereum smart contract) is a software program which encapsulates the business logic for the creation and modification of logical assets in the ledger that can be written in different general-purpose programming languages (currently Java, Go, and Node.js). This paper analyses the transaction performance of the Fabric platform by identifying at a fine-grained degree level the factors that most contribute to the overall overhead. In particular, we focus on how the transaction latency is affected by the programming language adopted for implementing the chaincode and by varying the number of participating endorser peers. Finally, the paper shows a thorough test assessment aimed at evaluating the impact of the different chaincode implementation on performance overhead. As it emerges from our experimental results, Go is the most performing programming language.
Convergence Time Minimization of Federated Learning over Wireless Networks

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, with the considered model, wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected and transmit their local FL model parameters to the BS at each learning step. Meanwhile, since each user has unique training data samples and the BS must wait to receive all users' local FL models to generate the global FL model, the FL performance and convergence time will be significantly affected by the user selection scheme. In consequence, it is necessary to design an appropriate user selection scheme that enables all users to execute an FL scheme and efficiently train it. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time while optimizing the FL performance. To address this problem, a probabilistic user selection scheme is proposed using which the BS will connect to the users, whose local FL models have large effects on its global FL model, with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission, which enables the BS to include more users' local FL models to generate the global FL model so as to improve the FL convergence speed and performance. Simulation results show that the proposed ANN-based FL scheme can reduce the FL convergence time by up to 53.8%, compared to a standard FL algorithm.
Wolverine: Fast, Scalable, and Communication-Efficient Zero-Knowledge Proofs for Boolean and Arithmetic Circuits

Efficient zero-knowledge (ZK) proofs for arbitrary boolean or arithmetic circuits have recently attracted much attention. Existing solutions suffer from either significant prover overhead (i.e., high memory usage) or relatively high communication complexity (at least κ bits per gate, for computational security parameter κ). In this paper, we propose a new protocol for constant-round interactive ZK proofs that simultaneously allows for an efficient prover with asymptotically optimal memory usage and significantly lower communication compared to protocols with similar memory efficiency. Specifically:•The prover in our ZK protocol has linear running time and, perhaps more importantly, memory usage linear in the memory needed to evaluate the circuit non-cryptographically. This allows our proof system to scale easily to very large circuits.•for statistical security parameter ρ = 40, our ZK protocol communicates roughly 9 bits/gate for boolean circuits and 2–4 field elements/gate for arithmetic circuits over large fields.Using 5 threads, 400 MB of memory, and a 200 Mbps network to evaluate a circuit with hundreds of billions of gates, our implementation (ρ = 40, κ = 128) runs at a rate of 0.45 μs/gate in the boolean case, and 1.6 μs/gate for an arithmetic circuit over a 61-bit field.We also present an improved subfield Vector Oblivious Linear Evaluation (sVOLE) protocol with malicious security that is of independent interest.
Natural language to SQL

Translating natural language to SQL (NL2SQL) has received extensive attention lately, especially with the recent success of deep learning technologies. However, despite the large number of studies, we do not have a thorough understanding of how good existing techniques really are and how much is applicable to real-world situations. A key difficulty is that different studies are based on different datasets, which often have their own limitations and assumptions that are implicitly hidden in the context or datasets. Moreover, a couple of evaluation metrics are commonly employed but they are rather simplistic and do not properly depict the accuracy of results, as will be shown in our experiments. To provide a holistic view of NL2SQL technologies and access current advancements, we perform extensive experiments under our unified framework using eleven of recent techniques over 10+ benchmarks including a new benchmark (WTQ) and TPC-H. We provide a comprehensive survey of recent NL2SQL methods, introducing a taxonomy of them. We reveal major assumptions of the methods and classify translation errors through extensive experiments. We also provide a practical tool for validation by using existing, mature database technologies such as query rewrite and database testing. We then suggest future research directions so that the translation can be used in practice.
PyTorchFI: A Runtime Perturbation Tool for DNNs

PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for the popular PyTorch deep learning platform. PyTorchFI enables users to perform perturbations on weights or neurons of DNNs at runtime. It is designed with the programmer in mind, providing a simple and easy-to-use API, requiring as little as three lines of code for use. It also provides an extensible interface, enabling researchers to choose from various perturbation models (or design their own custom models), which allows for the study of hardware error (or general perturbation) propagation to the software layer of the DNN output. Additionally, PyTorchFI is extremely versatile: we demonstrate how it can be applied to five different use cases for dependability and reliability research, including resiliency analysis of classification networks, resiliency analysis of object detection networks, analysis of models robust to adversarial attacks, training resilient models, and for DNN interpertability. This paper discusses the technical underpinnings and design decisions of PyTorchFI which make it an easy-to-use, extensible, fast, and versatile research tool. PyTorchFI is open-sourced and available for download via pip or github at: https://github.com/pytorchfi.
Decentralized Federated Learning via SGD over Wireless D2D Networks

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets and computing resources with limited disclosure of local data. Communication is a critical enabler of large-scale FL due to significant amount of model information exchanged among edge devices. In this paper, we consider a network of wireless devices sharing a common fading wireless channel for the deployment of FL. Each device holds a generally distinct training set, and communication typically takes place in a Device-to-Device (D2D) manner. In the ideal case in which all devices within communication range can communicate simultaneously and noiselessly, a standard protocol that is guaranteed to converge to an optimal solution of the global empirical risk minimization problem under convexity and connectivity assumptions is Decentralized Stochastic Gradient Descent (DSGD). DSGD integrates local SGD steps with periodic consensus averages that require communication between neighboring devices. In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference. The proposed protocols are based on graph coloring for scheduling and on both digital and analog transmission strategies at the physical layer, with the latter leveraging over-the-air computing via sparsity-based recovery.
Financial inclusion and human development: Evidence from Sub-Saharan Africa

Despite the rapid economic growth recorded since the 1990s, inequality, poverty and unemployment levels remain high in most African countries. As such, achieving socio-economic goals has been the major focus of policymakers. The objective of this study is to examine the effect of financial inclusion on human development in Sub-Saharan Africa (SSA). Access to and usage of financial services may encourage business start-ups, allow individuals to invest in health and education, manage risk and lessen the burden of financial shocks, and therefore, impact positively on human development. The study employs the panel data approach and utilises the Generalised Method of Moments (GMM) technique. The results show that financial inclusion has a positive effect on human development. Therefore, it is recommended that policymakers implement measures that reduce the costs of access to and usage of financial services, such as investments in infrastructure, and raise awareness of the available financial services.
COSE: Configuring Serverless Functions using Statistical Learning

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless functions requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present COSE, a framework that uses Bayesian Optimization to find the optimal configuration for serverless functions. COSE uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time, to select the "best" configuration parameters for running a single or a chain of functions, while satisfying customer objectives. In addition, COSE has the ability to adapt to changes in the execution time of a serverless function. We evaluate COSE not only on a commercial cloud provider, where we successfully found optimal/near-optimal configurations in as few as five samples, but also over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.
Fuzzing: Challenges and Reflections

We summarize the open challenges and opportunities for fuzzing and symbolic execution as they emerged in discussions among researchers and practitioners in a Shonan Meeting and that were validated in a subsequent survey.
SAR Speckle Removal Using Hybrid Frequency Modulations

Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention.
FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications

In the last two decades, a wide variety of wearable fall detection systems have been proposed. Most of them were based on machine learning. While the reported results give the impression that the problem is almost solved, crucial questions raise about the representation capacity of the considered datasets and accordingly the reliability of performance evaluation. In this article, the limitations of a multitude of state-of-the-art fall detection datasets are discussed. Particularly, limitations related to the sensors that are used to capture the motion signals, the positions of these wearable sensors, the sampling frequency and the measurement range are discussed. Moreover, limitations concerning the simulation protocol e.g. the considered types of falls are discussed. A comprehensive data acquisition system and simulation protocol are proposed to overcome these limitations. Consequently, a large dataset, namely FallAllD, is proposed. It consists of 26 420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications. The performance of deep learning and classical learning-based algorithms is evaluated on the proposed dataset as well as some reference datasets. The results show significant performance differences when considering different datasets. The reasons underlying these differences are discussed and the advantages of FallAllD are highlighted. Moreover, challenges of acceleration-based fall detection are deeply analyzed. This analysis reveals the main reasons underlying false positives and false negatives. FallAllD could be used in fall detection, fall prevention and human activity recognition contexts.
Towards using count-level weak supervision for crowd counting

Most existing crowd counting methods require object location-level annotation which is labor-intensive and time-consuming to obtain. In contrast, weaker annotations that only label the total count of objects can be easy to obtain in many practical scenarios. This paper focuses on the problem of weakly-supervised crowd counting which learns a model from a small amount of location-level annotations (fully-supervised) and a large amount of count-level annotations (weakly-supervised). Our study reveals that the most straightforward, that is, directly regressing the integral of density map to the object count, fails to provide satisfactory performance. As an alternative solution, we propose a method by taking advantage of the fact that the total count can be estimated via different-but-equivalent density maps. Our key idea is to enforce the consistency between those density maps and total object count on weakly labeled images as regularization terms. We realize this idea by using multiple density map estimation branches and a carefully devised asymmetry training strategy, called Multiple Auxiliary Tasks Training (MATT). Through extensive experiments on existing datasets and a newly proposed dataset, we validate the effectiveness of the proposed weakly-supervised method and demonstrate its superior performance over existing solutions.
From Facebook to Instagram: Exploring user engagement in an academic library

It is trendy for university libraries to offer services on social media (SM) platforms. As many millennials prefer to use apps of photo-snapping, libraries unavoidably start to use Instagram to connect with users. This study examines the effectiveness of the use of Facebook and Instagram in The University of Hong Kong Libraries (HKUL), by a mixed-method approach to analyze the posts on the two SM sites of HKUL and user feedback collected from interviews on campus. The content analysis surprisingly revealed that both Facebook and Instagram received very low user engagement, and the most frequently posted contents were library operational news and library events. The interviewees opined that they welcomed HKUL's use of Facebook and Instagram, but they urged HKUL to change its uninteresting style on the SM sites and make better use of the SM features to attract users' attention. This study provides insights for librarians for their effective management and adoption of SM, especially for content design and management.