Reviewerly/exHarmony huggingface dataset:
Selfholding: A combined attack model using selfish mining with block withholding attack

Bitcoin is one of the most influential digital cryptocurrencies in the world, of which the security is under serious threat. One of the most serious security problems is facing several attacking strategies on the bitcoin protocol, represented by selfish mining and block withholding attacks. In this paper, we propose SelfHolding as a combined attack model based on the selfish mining and block withholding strategies. We mainly focus on the SelfHolding attacker when (i) attacking one honest mining pool; (ii) attacking two honest mining pools. Based on the proposed model, we compute the expected revenue of the attacker and analyze the factors affecting it. Finally, we compare the revenues of an attacker under some other attacking strategies to those of our proposed strategy. Our experiments show that when the attacker’s computing power is relatively small, SelfHolding attack strategy can obtain higher revenues than the classic selfish mining attack.
Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches

Blind image quality assessment (BIQA) aims to develop quantitative measures to automatically and accurately estimate the visual quality of an image without any prior information about its reference image. This issue has been attracting a great deal of attention for a long time; however, little work has been done on night-time images, which are crucially important for consumer photography and practical applications such as automated driving systems. In this paper, to the best of our knowledge, we conduct the first exploration on subjective and objective quality assessment of night-time images. First, we build a large-scale natural night-time image database (NNID) containing 2240 images with 448 different image contents captured by different photographic equipment in real-world scenarios. Subsequently, we carry out a subjective experiment to evaluate the perceptual quality of all the images in the NNID database. Thereafter, we perform objective assessment of night-time images by proposing a blind night-time image quality assessment metric using brightness and texture features (BNBT). Finally, extensive experiments are conducted to evaluate the performance and efficiency of the proposed BNBT metric on the NNID database. The experimental results demonstrate that this metric outperforms existing state-of-the-art BIQA methods in terms of all evaluation criteria and has an acceptable computational cost at the same time. We have made the NNID database publicly available for downloading at https://sites.google.com/site/xiangtaooo/.
Large-scale instance-level image retrieval

The great success of visual features learned from deep neural networks has led to a significant effort to develop efficient and scalable technologies for image retrieval. Nevertheless, its usage in large-scale Web applications of content-based retrieval is still challenged by their high dimensionality. To overcome this issue, some image retrieval systems employ the product quantization method to learn a large-scale visual dictionary from a training set of global neural network features. These approaches are implemented in main memory, preventing their usage in big-data applications. The contribution of the work is mainly devoted to investigating some approaches to transform neural network features into text forms suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea of our approaches relies on a transformation of neural network features with the twofold aim of promoting the sparsity without the need of unsupervised pre-training. We validate our approach on a recent convolutional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. Its effectiveness has been proved through several instance-level retrieval benchmarks. An extensive experimental evaluation conducted on the standard benchmarks shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.
NeuCheck: A more practical Ethereum smart contract security analysis tool

Summary Ethereum is one of the currently popular trading platform, where any one can exchange, buy, or sell cryptocurrencies. Smart contract, a computer program, can help Ethereum to encode rules or scripts for processing transactions. Because the smart contract usually handles large number of cryptocurrencies worth billions of dollars apiece, its security has gained considerable attention. In this paper, we first investigate the security of smart contracts running on the Ethereum and introduce several new security vulnerabilities that allow adversaries to exploit and gain financial benefits. Then, we propose a more practical smart contract analysis tool termed NeuCheck, in which we introduce the syntax tree in the syntactical analyzer to complete the transformation from source code to intermediate representation, and then adopt the open source library working with XML to analyze such tree. We have built a prototype of NeuCheck for Ethereum and evaluate it with over 52 000 existing Ethereum smart contracts. The results show that (1) our new documented vulnerabilities are prevalent; (2) NeuCheck improves the analysis speed by at least 17.2 times compared to other popular analysis tools (eg, Securify and Mythril; and (3) allows for cross‐platform deployment.
DeepSwarm: Optimising Convolutional Neural Networks Using Swarm Intelligence

In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone information to collectively search for the best neural architecture. Furthermore, by using local and global pheromone update rules our method ensures the balance between exploitation and exploration. On top of this, to make our method more efficient we combine progressive neural architecture search with weight reusability. Furthermore, due to the nature of ACO our method can incorporate heuristic information which can further speed up the search process. After systematic and extensive evaluation, we discover that on three different datasets (MNIST, Fashion-MNIST, and CIFAR-10) when compared to existing systems our proposed method demonstrates competitive performance. Finally, we open source DeepSwarm ( https://github.com/Pattio/DeepSwarm ) as a NAS library and hope it can be used by more deep learning researchers and practitioners.
The significance of securing as a critical component of information security: An Australian narrative

As information security is called upon to operate in increasingly unstable spaces, the role of the information security practitioner becomes ever more complex. Successful information security practice depends on tactics and strategies that situate the need for protection within organisational goals. These tactics and strategies have become progressively important as organisations are disrupted by digital technology. The locus of control is unpredictably distributed across groups within an organisation. Finding consensus about the need for security thus becomes challenging. Information security controls are an important part of the control structure but increasingly they are negotiated controls, making the process of securing as important as the security mechanisms themselves. We employ broader political and social theories of security, most notably Smith (2005), to analyse data from nine semi-structured interviews of Australian information security practitioners. Our findings delineate the interlinking concepts of securing and security. Security is straightforward. It is a state of being secure. Securing, on the other hand, is complex. It is a consensus-seeking, value-engagement process that enables the attainment of security. Through this analysis, we identify processes and techniques of securing tacitly employed by the participants. Central to effective securing practice is a participant's ability to, ‘get the security message right.’ The message is used to create an agreed value consensus across conflict-ridden environments. We contend that securing is often undervalued and not recognised as a distinct theoretical part of the discipline of information security. However, given the complexity and uncertainty of information security practice, we argue that securing needs to be considered as a critical component of being secure.
Realtime and Accurate 3D Eye Gaze Capture with DCNN-Based Iris and Pupil Segmentation

This paper presents a realtime and accurate method for 3D eye gaze tracking with a monocular RGB camera. Our key idea is to train a deep convolutional neural network(DCNN) that automatically extracts the iris and pupil pixels of each eye from input images. To achieve this goal, we combine the power of Unet\cite{ronneberger2015u-net:} and Squeezenet\cite{iandola2017squeezenet:} to train an efficient convolutional neural network for pixel classification. In addition, we track the 3D eye gaze state in the Maximum A Posteriori (MAP) framework, which sequentially searches for the most likely state of the 3D eye gaze at each frame. When eye blinking occurs, the eye gaze tracker can obtain an inaccurate result. We further extend the convolutional neural network for eye close detection in order to improve the robustness and accuracy of the eye gaze tracker. Our system runs in realtime on desktop PCs and smart phones. We have evaluated our system on live videos and Internet videos, and our results demonstrate that the system is robust and accurate for various genders, races, lighting conditions, poses, shapes and facial expressions. A comparison against Wang et al.[3] shows that our method advances the state of the art in 3D eye tracking using a single RGB camera.
SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction

In this paper, we propose the use of semantic segmentation to incorporate scene information for better understanding of human motion in crowded environments. Our proposed SSeg-LSTM method leverages SegNet, which is a semantic segmentation encoder-decoder architecture, to extract semantically meaningful scene features. We then train the Social Scene LSTM (SS-LSTM) model with the contextual information regarding dynamics, social neighborhood, and scene semantics to predict future trajectory points of pedestrians. Experimental evaluation on public datasets show better performance for SSeg-LSTM than SS-LSTM which highlights the utility of semantic encoding for trajectory prediction.
Raphtory: Streaming analysis of distributed temporal graphs

Temporal graphs capture the development of relationships within data throughout time. This model fits naturally within a streaming architecture, where new events can be inserted directly into the graph upon arrival from a data source and be compared to related entities or historical state. However, the vast majority of graph processing systems only consider traditional graph analysis on static data, with some outliers supporting batched updating and temporal analysis across graph snapshots. In this work we define a temporal graph model which can be updated via event streams and discuss the challenges of distribution and graph maintenance. To solve these challenges, we introduce Raphtory, a distributed temporal graph management system which maintains the full graph history in memory, leveraging this to insert streamed events directly into the model without batching or centralised ordering. Raphtory additionally provides an API to perform both approximative analysis on the most up-to-date version of the graph, as well as temporal analysis throughout its full history; executed in parallel with ingestion.
Knowledge socialism: the rise of peer production - collegiality, collaboration, and collective intelligence

The terms ‘knowledge economy’ and ‘knowledge capitalism’ have been used with increasing frequency since the 1990s as a way of describing the latest phase of capitalism in in the process of global r...
An adaptive latent factor model via particle swarm optimization

Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial applications. Stochastic gradient descent (SGD) is an effective algorithm to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a meaningful issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on four HiDS matrices from real industrial applications indicate that an ALF model obvious outperforms an LF model according to convergence rate, and maintain competitive prediction accuracy for missing data.
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network

Person-job fit has been an important task which aims to automatically match job positions with suitable candidates.Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited.We study the domain adaptation problem for person-job fit.We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively.Furthermore, we extend the match network and implement domain adaptation in three levels, i.e., sentence-level representation, sentence-level match, and global match.Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data.
Optical flow estimation using channel attention mechanism and dilated convolutional neural networks

Learning optical flow based on convolutional neural networks has made great progress in recent years. These approaches usually design an encoder-decoder network that can be trained end-to-end. In encoder part, high-level feature information is extracted through a series of strided convolution, which is similar to most image classification networks. In contrast to classification task, spatial feature maps are then enlarged to full scale of input by conducting successive deconvolution layer in decoder part. However, optical flow estimation is a pixel-level task, and blurry flow fields are usually generated, which is caused by unrefined features and low-resolution. To address this problem, we propose a novel network, which combines attention mechanism and dilated convolutional neural network. In this network, the channel-wise features are adaptively weighted by building interdependencies among channels, which can weaken the weights of useless features and can enhance the directivity of feature extraction. Meanwhile, spatial precision is achieved by employing dilated convolution which improves the receptive field without large computational source and keeps the spatial resolution of feature map unchanged. Our network is trained on FlyingChairs and FlyingThings3D datasets in a supervised manner. Extensive experiments are conducted on MPI-Sintel and KITTI datasets to verify the effectiveness of the proposed method. The experimental results show that attention mechanism and dilated convolution are beneficial for optical flow estimation. Moreover, our method achieves better accuracy and visual improvements comparing to most of recent approaches.
A Machine Learning Approach for Detecting Malicious Websites using URL Features

With the advancement in technology, the internet has become a platform for wide range of illegal activities ranging from spam advertising to financial fraud. Some of these activities are carried out by embedding malware programs in the URLs. Blacklisting services classify URLs but, the constant creation of newer websites poses a challenge. To overcome this challenge Machine Learning approach is used to classify URLs as malicious or benign. The URL dataset, after addressing the issue of class imbalance is fed into several classification models built using a plethora of classification algorithms. Further, feature selection technique is incorporated to reduce the number of features required for classification and used to rank them based on their importance. Also, rule mining algorithms such as Apriori, FP-Growth and Decision Tree Rules is used to generate IF-THEN rules which helps to establish relationship among the features.
Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns

Abstract Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set.
Effects of Information and Communication Technology Usage by Individuals, Businesses, and Government on Human Development: An International Analysis

The effects of information and communication technology (ICT) on development have been studied primarily from the perspective of their contributions to a country's economic growth.However, ICT can offer individuals much more than just monetary income-it can improve many aspects of their quality of life.In fact, according to the human development paradigm, income is only one resource that helps individuals to satisfy their economic needs.Using the capabilities approach as a theoretical framework, this study applied data panel technique to a sample of 145 countries to investigate how the use and adoption of ICT, by individuals, enterprises, and governments, affects human development, as measured by the Social Progress Index and Human Development Index (HDI).The results show that regardless of a country's level of development, the individual use of ICT has a positive impact on human development; especially on the dimensions measured by HDI (having a long and healthy life, being knowledgeable, and having a decent standard of living).Furthermore, the use of ICTs for commercial purposes has a positive impact on human development at the global level, but if we make the analysis considering only developed countries, the relationship of this variable with human development is no longer significant.Regarding the impact of government use of ICT on human development, it has been verified that it is significant on developed countries.This study can be used as a tool by policymakers, especially in developing countries, to reinforce their intentions to support the use and implementation of ICTs.
Participatory Mapping in a Developing Country Context: Lessons from South Africa

Digital participatory mapping improves accessibility to spatial information and the way in which knowledge is co-constructed and landscapes co-managed with impoverished communities. However, many unintended consequences for social and epistemic justice may be exacerbated in developing country contexts. Two South African case studies incorporating Direct-to-Digital participatory mapping in marginalized communities to inform land-use decision-making, and the ethical challenges of adopting this method are discussed. Understanding the past and present context of the site and the power dynamics at play is critical to develop trust and manage expectations among research participants. When employing unfamiliar technology, disparate literacy levels and language barriers create challenges for ensuring participants understand the risks of their involvement and recognize their rights. The logistics of using this approach in remote areas with poor infrastructure and deciding how best to leave the participants with the maps they have co-produced in an accessible format present further challenges. Overcoming these can however offer opportunity for redressing past injustices and empowering marginalized communities with a voice in decisions that affect their livelihoods.
Securing IoT Apps

Users increasingly rely on Internet of Things (IoT) apps to manage their digital lives through the overwhelming diversity of IoT services and devices. Are the IoT app platforms doing enough to protect the privacy and security of their users? By securing IoT apps, how can we help users reclaim control over their data?
Exploring the Bounds of the Utility of Context for Object Detection

The recurring context in which objects appear holds valuable information that can be employed to predict their existence. This intuitive observation indeed led many researchers to endow appearance-based detectors with explicit reasoning about context. The underlying thesis suggests that stronger contextual relations would facilitate greater improvements in detection capacity. In practice, however, the observed improvement in many case is modest at best, and often only marginal. In this work we seek to improve our understanding of this phenomenon, in part by pursuing an opposite approach. Instead of attempting to improve detection scores by employing context, we treat the utility of context as an optimization problem: to what extent can detection scores be improved by considering context or any other kind of additional information? With this approach we explore the bounds on improvement by using contextual relations between objects and provide a tool for identifying the most helpful ones. We show that simple co-occurrence relations can often provide large gains, while in other cases a significant improvement is simply impossible or impractical with either co-occurrence or more precise spatial relations. To better understand these results we then analyze the ability of context to handle different types of false detections, revealing that tested contextual information cannot ameliorate localization errors, severely limiting its gains. These and additional insights further our understanding on where and why utilization of context for object detection succeeds and fails.
A Semi-automated Ontology Construction for Legal Question Answering

The internet and the development of the semantic web have created the opportunity to provide structured legal data on the web. However, most legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language question. One approach is to develop systems of legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable legal rules. The purpose of the ontology is to provide reasoning support to a legal question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other question statement (Q), so whether Bg entails Q based on the application of the legal rules. The key contribution of this paper is the methodology and the semi-automated legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).