Reviewerly/exHarmony huggingface dataset:
Residual Proportion Multilayer Perceptron For Few-Shot Classification

Traditional deep learning methods require large amounts of data to ensure the excellent performance of the training model. The emergence of few-shot learning reduces the model’s dependence on data. Few-shot classification aims to identify novel categories with a small number of labeled samples. In this work, we tackle the few-shot classification task in the metric-based meta-learning method, which contains two parts: feature encoder and metric network. We used Graph Neural Network (GNN) as metric network and proposed RP-MLP (Residual Proportion MLP) to learn better the relationship among the samples. Our model is verified in two public datasets mini-ImageNet and tiered-ImageNet. We also conduct a cross-domain classification experiment on CUB200-2011. The results demonstrate that the method we proposed compares favorably against other state-of-the-art algorithms in few-shot classification tasks.
The effect of corruption on Foreign Direct Investment

Foreign Direct Investment (FDI) is an element that can promote the growth of the country that will receive the resource.One factor that can influence the decision of investors and foreigners to choose the country in which they will invest is the governance environment of the host country.Based on this logic, this study analyzed the influence of corruption in attracting FDI.Data were collected from 184 countries between 2002 and 2016.The Panel Data technique with fixed effect was used, using two variables to represent corruption (control of corruption and corruption) and nine control variables (market size, domestic investment, infrastructure, interest rate, openness, labor force, GDP growth, donations of resources and quality of institutions).The proposed model showed that high corruption negatively affects the flow of FDI.In addition, the control variables that remained in the model were domestic investment, interest rate, labor force and GDP growth.Policy makers should be aware that the political environment is a crucial factor for FDI flow and policies should be properly structured to meet the basic requirements of attracting investment and thus economic growth in general.
Assisting Example-Based API Misuse Detection via Complementary Artificial Examples

Application Programming Interfaces (APIs) allow their users to reuse existing software functionality without implementing it by themselves. However, using external functionality can come at a cost. Because developers are decoupled from the API’s inner workings, they face the possibility of misunderstanding, and therefore misusing APIs. Prior research has proposed state-of-the-art example-based API misuse detectors that rely on existing API usage examples mined from existing code bases. Intuitively, without a varied dataset of API usage examples, it is challenging for the example-based API misuse detectors to differentiate between infrequent but correct API usages and API misuses. Such mistakes lead to false positives in the API misuse detection results, which was reported in a recent study as a major limitation of the state-of-the-art. To tackle this challenge, in this paper, we first undertake a qualitative study of 384 falsely detected API misuses. We find that around one third of the false-positives are due to missing alternative correct API usage examples. Based on the knowledge gained from the qualitative study, we uncover five patterns which can be followed to generate artificial examples for complementing existing API usage examples in the API misuse detection. To evaluate the usefulness of the generated artificial examples, we apply a state-of-the-art example-based API misuse detector on 50 open source Java projects. We find that our artificial examples can complement the existing API usage examples by preventing the detection of 55 false API misuses. Furthermore, we conduct a pre-designed experiment in an automated API misuse detection benchmark (MUBench), in order to evaluate the impact of generated artificial examples on recall. We find that the API misuse detector covers the same true positive results with and without the artificial example, i.e., obtains the same recall of 94.7 percent. Our findings highlight the potential of improving API misuse detection by pattern-guided source code transformation techniques.
Team MohammadHabash at Mowjaz Multi-Topic Labelling Task

Multi-label text classification is an important problem with the growing size of data and the difficulties in assigning a single label to each text sample because of the tendency of internet users to assign multiple labels to describe documents, emails, posts, etc. Our goal is to predict the category (topic) of an article given its text. The dataset which is used in this work contains articles from Mowjaz. Mowjaz is an Arabic topical content aggregation mobile application for news, sport, entertainment and other topics from top publishers that users can follow. This paper describes the approach to classify articles using Bi-directional Gated Recurrent Unit (Bi-GRU) with AraVec embeddings. The F1-score of this system is 0.8344 which shows a significant improvement over the baseline models.
Incremental Without Replacement Sampling in Nonconvex Optimization

Minibatch decomposition methods for empirical risk minimization are commonly analyzed in a stochastic approximation setting, also known as sampling with replacement. On the other hand, modern implementations of such techniques are incremental: they rely on sampling without replacement, for which available analysis is much scarcer. We provide convergence guaranties for the latter variant by analyzing a versatile incremental gradient scheme. For this scheme, we consider constant, decreasing or adaptive step sizes. In the smooth setting, we obtain explicit complexity estimates in terms of epoch counter. In the nonsmooth setting, we prove that the sequence is attracted by solutions of optimality conditions of the problem.
MKSTD: Multiple Knowledge Sourcing Approach for Topic Directories Construction and Linking Web Pages Using Fusion Semantics

Mapping of Knowledge sources is one of the preliminary strategies for making Semantic Search foundational. The Web is flooded with information but making it meaningful requires the integration of useful resources and attenuated semantics. In this paper, an approach for integrating knowledge from several heterogeneous sources such as the knowledge bases, crowdsourced knowledge, and traditional web directories using an integration of semantic similarity and Simpson’s Diversity Index has been proposed for the construction of hierarchical topic directories, and yield topic-specific Linked Open Data. The proposed MKSTD framework is a knowledge sourcing and integration scheme for Linked Open Data generation and further leveraging the web pages to the generated topics based on the index words and the content words of the web pages for facilizing the Semantic Search. The MKSTD is evaluated on the Bookshelf dataset from the Gutenberg Project categorizations and an average F-Measure of 95.98% has been achieved with an nDCG score of 0.91 and a very low FDR of 0.06 for leveraging the indexed web pages with the topic hierarchies.
Surfacing Paradigms underneath Research on Human and Social Aspects of Software Engineering

Software engineering is a wide field with topics ranging from coding to organizational aspects. In the last two decades, researchers have developed a growing interest in the human and social aspects of software development. To investigate these phenomena, researchers have often employed methods common in social sciences such as case study and ethnography. These methods usually have specific underpinnings about the relationship between knowledge and the world, the so-called research paradigms. Although these paradigms are essential to define what is legitimate knowledge and how research should be performed, so far, this topic has been barely discussed in the software engineering community. In this paper, our goal is to explore how different research paradigms reflected in the current studies of human and social factors in software engineering. To achieve this goal, we present an overview of the main research paradigms: positivism, postpositivism, constructivism-interpretivism, critical theory, and pragmatism. Then, we analyze the papers published in the Technical Track of the International Conference on Software Engineering (ICSE) that focus on human and social aspects to explore if and how these paradigms influence Software Engineering research. Our results show that these studies generally do not explicate the paradigms they are following but, from several aspects, it is possible to relate the studies to a pragmatic perspective with a strong postpositivistic influence. We also discuss topics on which research could be enriched by employing diverse paradigms.
Fully automatic CNN design with inception blocks

Convolutional Neural Networks (CNNs) are widely used in image classification tasks and have demonstrated promising classification accuracy results. Designing a CNN architecture requires a manual adjustment of parameters through a series of experiments as well as sufficient knowledge both in the problem domain and CNN architecture design. Therefore, it is extremely difficult for users without prior experience to design a promising CNN for their purposes. To solve this issue, various solutions on the automatic construction of CNN architectures are proposed, including but not limited to a genetic algorithm AE-CNN 1 for automatically evolving CNN design using ResNet and DenseNet blocks. In this paper, a significant improvement of the afore-mentioned solution by the addition of Inception blocks is introduced. Performance of the algorithm is assessed on the CINIC-10 benchmark dataset without and with the usage of Inception blocks. As it can be observed from the outcome of the experiment, the addition of Inception blocks positively affects the final classification accuracy. The proposed algorithm does not only improve the current solution but also keeps the advantages of automatic CNN without requesting any manual interventions.
The Deep Learning Method for Image Segmentation to Improve the Efficiency of Data Processing Without Compromising the Accuracy of an Autonomous Driving Country-Road Pilot System After Image Classification

Autonomous driving requires object recognition for vehicles to automatically generate a path according to their recognised environment, the conditions of which have different dims of light, from daylight to night. High-resolution images require high amounts of expensive storage as automated driving moves from urban to rural areas, where driving at night and recognising traffic signs and lights are necessary for all light conditions. Therefore, a reliable source of input, allowing for the intended performance of an autonomous driving system such as the country or rural road pilot, is necessary for adequate deployment of its functionality in its target environment. For quality criteria such as intended performance, functional reliability, safety, and correct driving behaviour are to be ensured; accuracy metrics can be a substantial contribution to the product quality criteria. Furthermore, since autonomous technology faces the challenge of being costly, thus any new innovative methods for saving costs, without comprising quality, would help to develop and enhance the chance of this developing technology being installed into more advanced automated or autonomous driving vehicles once the product safety as quality criteria can be validated on target roads. Part of this work’s limitation was that only a simulation environment was used for testing the image processing and autonomous driving accuracy models. Through research, certain algorithms were found that may be used in storage size minimisation for taking a high-resolution image; its size had to be reduced for use without compromising accuracy in the classification process. Further research in their validation may be necessary.
Deep Neural Exposure: You Can Run, But Not Hide Your Neural Network Architecture!

Deep Neural Networks (DNNs) are at the heart of many of today's most innovative technologies. With companies investing lots of resources to design, build and optimize these networks for their custom products, DNNs are now integral to many companies' tightly guarded Intellectual Property. As is the case for every high-value product, one can expect bad actors to increasingly design techniques aimed to uncover the architectural designs of proprietary DNNs. This paper investigates if the power draw patterns of a GPU on which a DNN runs could be leveraged to glean key details of its design architecture. Based on ten of the most well-known Convolutional Neural Network (CNN) architectures, we study this line of attack under varying assumptions about the kind of data available to the attacker. We show the attack to be highly effective, attaining an accuracy in the 80 percentage range for the best performing attack scenario.
An implementation of Rabin-p cryptosystem and affine cipher in a hybrid scheme to secure text

Abstract One way to keep digital information secure is by using cryptographic techniques. With respect to its keys, cryptography generally is divided into two types: symmetric and asymmetric. Symmetric cryptography algorithm uses the same key in both encryption and decryption while asymmetric uses different keys for each of these processes. Asymmetric cryptography algorithm is known to be ineffective to encrypt big messages since it takes more CPU time to do the computation and the resulting ciphertext is usually much bigger than the original message. To eliminate these problems, in this research, we combine Rabin-p cryptosystem, an asymmetric cryptography algorithm, and affine cipher, a symmetric cryptography algorithm, in a hybrid scheme in order to secure messages from unwanted parties. The affine cipher is used for message encryption while Rabin-p is used for affine cipher’s key encryption. The result of this research is a hybrid cryptography scheme that can encrypt and decrypt message in the form of *.pdf files and be able to restore the message without losing integrity.
Combining E-Commerce and Blockchain Technologies to Solve Problems and Improve Business Results

The internet and digital transformation have changed our relations with the market. These technologies have been developing continuously, creating opportunities for new business models, and e-commerce has grown overwhelmingly worldwide, changing the consumption process of a large part of the world's population. Companies are increasingly using blockchain technology to improve and create new global trading business models. Blockchain had its first application in cryptocurrencies, but it has quickly become a major solution in all sorts of activity sectors, providing increased security in commercial transactions. An important question is how the blockchain can leverage e-commerce in solving problems and improving business results. It was concluded that blockchain could leverage e-commerce in the four fundamental areas of (1) e-commerce financial transactions, (2) e-commerce supply chain management, (3) e-commerce forecasting and contractual relations, and (4) e-commerce transactions systems' trust and credibility.
A TensorFlow and System Simulator Integration Approach to Estimate Hardware Metrics of Convolution Accelerators

GPUs became the reference platform for both training and inference phases of Convolutional Neural Networks (CNN), due to their tailored architecture to the CNN operators. However, GPUs are power-hungry architectures. A path to enable the deployment of CNNs in energy-constrained devices is adopting hardware accelerators for the inference phase. The design space exploration of CNNs using standard approaches, such as RTL, is limited due to their complexity. Thus, designers need frameworks enabling design space exploration that delivers accurate hardware estimation metrics do deploy CNNs. This work aims to propose a framework to explore hardware accelerators' design space, providing power, performance, and area (PPA) estimations. The heart of the framework is a system simulator with TensorFlow as front-end and as back-end performance estimations obtained from the physical synthesis. Results evaluate the energy trade-off varying the number of convolutional layers, showing that it can be necessary to spend approximately 40% more energy to increase 0.02% in the accuracy.
An Integrated Approach of Conventional and Deep Learning Method for Underwater Image Enhancement

Underwater imaging has proved its significance in underwater environment monitoring, inspection and providing security. For underwater imaging, large amount of data need to be exchanged between underwater sensors and terrestrial-monitoring device. Sensors deployed underwater for a long time must face the difficulty of having their battery recharged. Underwater images are affected by several underwater environmental factors such as water turbidity, light scattering, light absorption and light reflection. There are many conventional and deep learning techniques that help to compress underwater images for fast transmission. The conventional methods are affected by ‘blocking’ and ‘contouring’ artifacts and require complex computations. The deep learning methods need large dataset for training purpose. To overcome the drawbacks of the existing techniques, this paper proposed a method that provides an energy efficient, free learning convolutional neural compression—decompression network with a pre-processing step. The advantage of the proposed method is that the compression framework is solely dependent on untrained convolutional neural network structure and the pre-processed image. Experiments on three different datasets having different image characteristics accomplish the motive of the proposed method. The datasets considered for comparison are Fish dataset, USR-248 dataset and UIEB dataset. Findings of the study show that the proposed method provides better PSNR results for Fish dataset and USR-248 dataset but lower PSNR values for UIEB dataset when compared with SRCNN and Bi-cubic methods. But on visual examination, color and contrast correction in UIEB dataset using proposed method are better than SRCNN and Bi-cubic methods. This study enables researchers and practitioners to get refined reconstructed image at the receiver side by adopting the presented method.
A Blockchain-Based Internet of Things Forensics Model

The Internet of Things has been proposed for nearly 20 years. Its rich digital trace repository provides insight into people's daily activities at home and elsewhere. On the other hand, IoT devices can become targets of attacks, or even tools for a crime. The number of civil and criminal cases involving IoT devices or services is increasing year by year. IoT forensics refers to the process of collecting, analyzing, storing and providing digital evidence in IoT devices in a legally binding manner. In particular, the traceability, integrity and tamper resistance of the evidence should be maintained. However, traditional IoT forensics architecture may be difficult to achieve these goals. This article aims to explore the combination of blockchain technology and IoT forensics which can make full use of the natural advantages of traceability and tamper resistance. A forensic architecture of the Internet of Things based on blockchain evidence is proposed, including an applications layer and data layer. The applications layer includes various APP applications, and the data layer includes the evidence analysis platform and the blockchain platform. Furthermore, an IoT forensics model including blockchain storage and supervision is proposed which can ensure the traceability and tamper-proof of forensic data.
JWSAN: Japanese word similarity and association norm

Abstract We present a new Japanese dataset, Japanese Word Similarity and Association Norm (JWSAN), comprising human rating scores of similarity and association for 2145 word pairs, with a clear distinction between word similarity and word association. Computational models of human semantic memory or mental lexicon, such as distributed semantic models, must predict not only association but also similarity. People can distinguish between word similarity and association. However, although the SimLex-999 dataset is publicly available for English, there is no Japanese similarity dataset with a clear distinction between the two types of word relatedness. JWSAN is the first large Japanese dataset with similarity and association ratings, containing noun, verb, and adjective word pairs. It is also characterized by data collection from a sufficient number of age- and-gender-controlled assessors, with similarity and association ratings obtained via a web-based survey conducted of 6450 native speakers of Japanese. In addition, the effects of the gender and age of the raters were also examined; these factors were only given scant consideration in the past. This dataset can act as a benchmark for improving distributed semantic models in Japanese.
Ethical Design of Social Technology

Social technology has become ubiquitous in everyday life. Developers of social technologies seek design elements and new technologies, such as machine learning algorithms, aimed at increasing user engagement. Increased user engagement with products or services is sought after by both companies, which benefit from increased sales and customers who desire technology which they are motivated to use. However, increased user engagement also results in increased demand on user attention. High demand on user attention results in problems for social technology users, including decreased task performance, decrements in working memory, increased anxiety, and more. Developers of social technology should take these negative effects on users into account when implementing new features into their products or services. This chapter proposes a framework for the ethical design of social technology, with a specific emphasis on the balance between user engagement and attentional demands on the user.
Low Light Image Enhancement in USV Imaging System Via U-Net and Attention Mechanism

Images captured by Unmanned Surface Vessel (USV) have a wide range of applications in various fields, such as maritime object detection, remote sensing, and autonomous transportation. However, cameras often suffer from a low light environment, resulting in low contrast, high noise, and poor quality image, causing identification difficulties and machine decision errors. In recent years, convolutional neural networks have developed rapidly, which have strong generalization ability and can extract different levels of information, especially high-level information. Therefore, to preprocess low light images before advanced computer vision tasks of USV, we proposed a deep learning-based end-to-end convolutional network for low light enhancement in USV imaging system. The advantage of our model is using U-Net as the basic architecture to gain multi-scale feature maps with improvements, including attention mechanism and dense connection. Besides, we pay attention to edge information given images' edge loss. With the unique network structure, our model can effectively increase the brightness and contrast of dark aquatic images. Experiments have been carried out on testing images to analyze our proposed method with several latest imaging methods. The experimental results show its outstanding performance in both subjective and objective evaluation.