Reviewerly/exHarmony huggingface dataset:
Teaching Distributed Software Architecture by Building an Industrial Level E-Commerce Application

Software architecture design is a key component in software engineering. Teaching it effectively requires instructors come up with challenging real world projects, in which students need to recognize the architectural problems, figure out solutions, compare alternatives and make decisions. On the other hand, keeping students motivated and interested in this topic is also critical. However, truly understanding and appreciating a certain software architecture are not easy for most undergraduate students. Most architecture courses either focus too much on the theoretical concepts or fail to immerse students enough in complex industrial level projects unless the instructors are practicing software engineers. In this paper, we propose a Project-Based Learning experience, which brings an open-source full-fledged system to classroom in order to effectively teach distributed software architecture. Students are introduced the best practices that are widely used in industry to solve some of today’s common architectural problems. Our ultimate goal is to establish a public code repository of realistic projects from popular industrial sectors, so that instructors can reference to improve software architecture’s learning experience even though they are not practicing software engineers.
Towards a Smart Contract Verification Framework in Coq.

We propose a novel way of embedding functional smart contract languages into the Coq proof assistant using meta-programming techniques. Our framework allows for developing the meta-theory of smart contract languages using the deep embedding and provides a convenient way for reasoning about concrete contracts using the shallow embedding. The proposed approach allows to make a connection between the two embeddings in a form of a soundness theorem. As an instance of our approach we develop an embedding of the Oak smart contract language in Coq and verify several important properties of a crowdfunding contract. The developed techniques are applicable to all functional smart contract languages.
ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in Bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
Dlion: Decentralized distributed deep learning in micro-clouds

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds---small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2X speedup in an Amazon GPU cluster, and up to 2X speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.
Priming vs. Inhibition of Optional Infinitival “to”

The word “to” that precedes verbs in English infinitives is optional in at least two environments: in what Wasow et al. (2015) previously called the “do-be” construction, and in the complement of “help”, which we explore in the present work. In the “do-be” construction, Wasow et al. found that a preceding infinitival “to” increases the use of following optional “to”, but the use of “to” in the complement of help is reduced following “to help”. We examine two hypotheses regarding why the same function word is primed by prior use in one construction and inhibited in another. We then test predictions made by the two hypotheses, finding support for one of them.
An Improved Feature Selection Method for Short Text Classification

Abstract Text has become one of the widest means of communication on mobile devices due to cheap rate and convenience for instance short text, web document, emails, instant messages. The exponential growth of text documents shared among users globally has increased the threat of misclassification associated with mobile devices such as Spam, Phishing, License to kill, Malware and privacy issues. Existing studies have shown that the major problem associated with text message classification is the poor representation of feature thus reducing accuracy and increasing f-measure rate. Thus, a modified Genetic Algorithm (GA) for improve feature selection and Artificial Immune System (AIS) algorithm was proposed for effective text classification in mobile short messages. The system will be deployed on an Android OS.
Egocentric Analysis of Dash-Cam Videos for Vehicle Forensics

Video acquisition using dashboard-mounted cameras has recently achieved massive popularity around the world. One of the major developments following the dash-cam's popularity is that videos captured by them can be used as testimony during scenarios, like traffic violations and accidents. The widespread deployment of dash-cams brings new problems ranging from the compromise of privacy by uploading these videos on public websites using videos captured from other cars for making fraudulent claims. Therefore, there is a compelling need to address the problems associated with the usage of dash-cam videos. In this paper, we discuss and highlight the importance of the emerging area of multimedia vehicle forensics. We propose an algorithm for linking a dash-cam video to a specific car. The proposed algorithm is useful for various applications, for example, insurance companies can authenticate the origin of video before processing the claim. In a different scenario of illegitimate video upload on the Web, the video can be traced back to the car it originated from. To this end, we make use of motion blur extracted from dash-cam videos for generating a discriminative feature. We observe that the subtle motion pattern of every vehicle can serve as its unique signature. We extract motion blur from dash-cam videos and use random forest trees for classifying the vehicle correctly. The experimental results on thousands of frames obtained from dash-cam videos of several cars show the effectiveness of our approach. We further investigate the process of forging the signature of a car and propose a counter forensics method to detect such forgery. Also, we discuss the application of our technique to other potential platforms where the camera can be mounted, for example, on the chest of a person. We believe that ours is the first work that describes this new area of research.
Neural network gradient-based learning of black-box function interfaces

Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions. We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions. We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process. At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function. Using this Estimate and Replace paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels. We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.
An Unsupervised Learning Approach for NER Based on Online Encyclopedia

Named Entity Recognition (NER) is a core task of NLP. State-of-art supervised NER models rely heavily on a large amount of high-quality annotated data, which is quite expensive to obtain. Various existing ways have been proposed to reduce the heavy reliance on large training data, but only with limited effect. In this paper, we propose a novel way to make full use of the weakly-annotated texts in encyclopedia pages for exactly unsupervised NER learning, which is expected to provide an opportunity to train the NER model with no manually-labeled data at all. Briefly, we roughly divide the sentences of encyclopedia pages into two parts simply according to the density of inner url links contained in each sentence. While a relatively small number of sentences with dense links are used directly for training the NER model initially, the left sentences with sparse links are then smartly selected for gradually promoting the model in several self-training iterations. Given the limited number of sentences with dense links for training, a data augmentation method is proposed, which could generate a lot more training data with the help of the structured data of encyclopedia to greatly augment the training effect. Besides, in the iterative self-training step, we propose to utilize a graph model to help estimate the labeled quality of these sentences with sparse links, among which those with the highest labeled quality would be put into our training set for updating the model in the next iteration. Our empirical study shows that the NER model trained with our unsupervised learning approach could perform even better than several state-of-art models fully trained on newswires data.
Kernel Mean Matching for Content Addressability of GANs

We propose a novel procedure which adds content-addressability to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.
A novel approach recommendation for hazard analysis

Hazard evaluation generally defines the ranking of hazards in the work environment and ignores the interaction of hazards. This article aims to overcome this drawback using a fuzzy cognitive map (FCM) approach to analyze the interaction of hazards related to sheathing tasks in a construction firm. The FCM approach can be insufficient for hazard evaluation over the long term due to certain budget and time restrictions allocated by the firm. When the firm allocates more time and budget for a process, the firm must limit the allocated time and budget for the other processes. This article aims to overcome this problem so linear programming is used to optimize the goal and resources of the firm. The article contributes to hazard evaluation of sheathing tasks in a construction firm considering the interaction of the hazards and the capacity of the firm.
On Theoretical Analysis of Single Hidden Layer Feedforward Neural Networks with Relu Activations

During past decades, extreme learning machine has acquired a lot of popularity due to its fast training speed and easy-implementation. Though extreme learning machine has been proved valid when using an infinitely differentiable function like sigmoid as activation, existed extreme learning machine theory pays a little attention to consider non-differentiable function as activation. However, other non-differentiable activation function, rectifier linear unit (Relu) in particular, has been demonstrated to enable better training of deep neural networks, compared to previously wide-used sigmoid activation. And today Relu is the most popular choice for deep neural networks. Therefore in this note, we consider extreme learning machine that adopts non-smooth function as activation, proposing that a Relu activated single hidden layer feedforward neural network (SLFN) is capable of fitting given training data points with zero error under the condition that sufficient hidden neurons are provided at the hidden layer. The proof relies on a slightly different assumption from the original one but remains easy to satisfy. Besides, we also found that the squared fitting error function is monotonically non-increasing with respect to the number of hidden nodes, which in turn means a much wider SLFN owns much expressive capacity.
ERAMIS: A Reference Architecture-Based Methodology for IoT Systems

Opportunities arising from IoT-enabled applications are significant, but market growth is inhibited by concerns over security and complexity. To address these issues, we propose the ERAMIS methodology, which is based on instantiation of a reference architecture that captures common design features, embodies best practice, incorporates good security properties by design, and makes explicit provision for operational security services and processes.
Annular Feature Pyramid Network for Salient Object Detection

With the rapid development of deep learning, some attempts based on the fully convolutional networks have shown outstanding performance in salient object detection. Visual multi-context information is beneficial to detect salient regions so that how to better integrate multi-level convolutional features becomes essential. In this paper, we develop an annular feature pyramid network to augment information flow and enhance feature hierarchy. Our network contains a top-down path with lateral connections to help shallow layers locate salient regions and a bottom-up path with lateral connections to help deep layers retain fine object boundary. These feature maps at each resolution are combined to generate a final saliency prediction, which can take full advantage of high-level semantic features with low-level fine details. Comprehensive experiments demonstrate that our network performs favorably against state-of-the-art algorithms in term of different evaluation metrics.
Cryptocurrencies and Anti-money Laundering Laws: The Need for an Integrated Approach

This chapter attempts to clarify and describe the legal and regulatory framework for cryptocurrency with special focus on Malaysia and the threats that it poses from the anti-money laundering perspective. Currently, very few countries have legislations that regulate cryptocurrency. Nonetheless, the crazy surge in prices (to more than 20-folds at some point) has sent both legitimate investors and criminals flocking to cryptocurrencies. This chapter analyses and compares the official reports from various governments, writings of government officials, experts and scholars in journals and newspapers, interviews and draws conclusions on the legal framework of cryptocurrency, and money laundering challenges. The study notes that the decision of the US regulators in allowing Bitcoin futures to trade on major exchanges to be one of the reasons behind the sudden surge. The study also finds that the South Korean regulators’ approach in banning its financial institutions from dealing with virtual currency is a positive one. The chapter stresses that it is not adequate for regulators to warn the public to act with extreme caution and increase their understanding on the risks they take on if they choose to invest in cryptocurrencies. Instead, it is necessary to have comprehensive international and national laws and regulations for the control and management of cryptocurrencies. In addition, the anti-money laundering legal framework must be improved to cater to the new threats posed by cryptocurrency.
Outlier Detection Method based on Improved Two-step Clustering Algorithm and Synthetic Hypothesis Testing

For the detection of outliers in unsupervised mixed attribute datasets, this paper proposes an outlier detection method based on improved two-step clustering algorithm and synthetic hypothesis testing. Firstly, a two-step clustering algorithm based on clustering feature tree is improved to be suitable for mixed attribute datasets. In the clustering stage, RA-Clust algorithm is used to pre-select the clustering center to improve the algorithm performance, and the original complex data distribution is simplified into the superposition of data distribution under sub-clusterings. Then using the method of synthetic hypothesis testing, the outliers are judged according to the change rule of data in the sub-clusterings of sample data. Experimental results show that the algorithm has better clustering performance, and the synthetic hypothesis testing can accurately detect most of the outliers and maintain over 90% of the outlier detect efficiency under different data sizes.
Towards Language-Parametric Semantic Editor Services Based on Declarative Type System Specifications (Brave New Idea Paper)

Editor services assist programmers to more effectively write and comprehend code. Implementing editor services correctly is not trivial. This paper focuses on the specification of semantic editor services, those that use the semantic model of a program. The specification of refactorings is a common subject of study, but many other semantic editor services have received little attention. We propose a language-parametric approach to the definition of semantic editor services, using a declarative specification of the static semantics of the programming language, and constraint solving. Editor services are specified as constraint problems, and language specifications are used to ensure correctness. We describe our approach for the following semantic editor services: reference resolution, find usages, goto subclasses, code completion, and the extract definition refactoring. We do this in the context of Statix, a constraint language for the specification of type systems. We investigate the specification of editor services in terms of Statix constraints, and the requirements these impose on a suitable solver.
A comprehensive framework for evaluating web services composition methods

Purpose Web services composition engineering (WSCE) is a big challenge because of the increasing complexity, openness and extensibility of services based on the information system (IS). In the absence of an adequate framework for IS engineering, the authors assume that the overall problem is consequently no longer easy to resolve. This paper aims to explore some of the issues underlying WSCE through a framework, which is built based on the state of the art. The proposed framework is structured around five views that mainly highlight aspects of personalization and security concerns. The main objectives of this framework are: to help understand and clarify the basics of the WSCE domain; to evaluate web services composition (WSC) methods; to analyze and compare existing personalization, secure methods and identify new research axes; and to identify the main criteria of the ongoing approach for the design of a secure IS based on personalized WSC. Design/methodology/approach This work develops a framework that is used as an analytical study to compare the existing WSCE methods and come up with research issues. Then, the proposed framework is considered as an abstract model for the new WSCE approach. Findings A set of criteria that the proposed framework should consider when developing a new approach to a secure IS based on personalized WSs composition. Research limitations/implications The paper has theoretical implications as the personalization and security issues provide a research roadmap toward the realization of an approach for the design of a secure IS based on personalized WSs composition. Practical implications As proof, the authors are interested in a web services repository of a real mall. To do this, the authors deployed the application in a cloud environment and observed the results of personalization and security concepts in WSCE. Originality/value None of the existing comparison frameworks has raised both personalization and security issues in WSs composition, while personalization and security must be present in the whole composition process.