Reviewerly/exHarmony huggingface dataset:
Learned Multi-View Texture Super-Resolution

We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. Our architecture unifies the concepts of (i) multi-view super-resolution based on the redundancy of overlapping views and (ii) single-view super-resolution based on a learned prior of high-resolution (HR) image structure. The principle of multi-view super-resolution is to invert the image formation process and recover the latent HR texture from multiple lower-resolution projections. We map that inverse problem into a block of suitably designed neural network layers, and combine it with a standard encoder-decoder network for learned single-image super-resolution. Wiring the image formation model into the network avoids having to learn perspective mapping from textures to images, and elegantly handles a varying number of input views. Experiments demonstrate that the combination of multi-view observations and learned prior yields improved texture maps.
Design of Hybrid Sorting Unit

Sorting is the fundamental function of any process for most of the application, The widely used hardware sorting algorithm in VLSI are Bitonic merge sort and Bitonic odd even sort with 24 and 19 comparator unit. In this paper a new sorting algorithm (Hybrid sort) is proposed which has 23 comparator units and provides the better performance than existing one when the bit width of input increases. The pipelining concept is applied along with parallel processing to the entire hardware sorting algorithm which improved processing time of the sorting. The proposed system used three stages of the pipeline; therefore, the speed is increased three times more than the existing method. Experiment results shows that the proposed sorting unit gives better performance when the bit width of input increase.
Predicting Rogue Content and Arabic Spammers on Twitter

Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.
Identification and prevention of social engineering attacks on an enterprise

In recent times, the integration of technology in everyday tasks helps in making most of the cumbersome work more convenient. This integration has brought about a positive wave in aiding and assisting humans in various sectors such as the military, health, education, finance, etc. Conversely, convenience does come with a cost, i.e. it increases the concern for security in those systems. Attackers with various motives try to exploit these systems for personal gain. Some of the popular attacks like Man In The Middle, Cross-Site Request Forgery(CSRF), Phishing and Code Injection can be used to compromise the systems. However, the easiest way to gain control over a system is through Social engineering because it can be performed within a short time and without much technical expertise. Social Engineering targets humans by using various psychological weaknesses of human cognizance. Such attacks are often used to attack enterprises, as their weakest links are the human employees who are prone to be deceived and manipulated. Hence, the enterprise must be prepared for any kind of attack that may be deployed to exploit the weaknesses. This paper discusses the broad spectrum of Social Engineering threats that an enterprise faces throughout its life and various effective measures to prevent such attacks. This paper helps the reader to gain insight into how social engineering can be used against enterprises.
Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.
Digital Forensics for Drone Data – Intelligent Clustering Using Self Organising Maps

Drones or unmanned aerial vehicles (UAVs) have been rapidly adopted for a range of applications over the past decade. Considering their capabilities of information acquisition and surveillance for intelligence, and increasing access to the common public, have led to a rise in the threat associated with cyber crime associated with drones, in recent times. In order to mitigate the threats and to prevent cyber-crime, digital forensics on drone data is both critical as well as lacking in terms of efficacy studies. In this paper, we define a digital forensic methodology for analyzing drone data, and we propose a self organizing map (SOM)-based method for aiding such analysis. Experiments were conducted on two images obtained from the CFReDS project, namely, ArduPilot DIY Drone and DJI Phantom 4, with the purpose of producing admissible digital forensic evidence for the court of law, as part of a cyber-crime investigation. We also highlight the individual capabilities of several digital forensic tools based on experiments conducted on drone data.
Artificial Intelligence for Ecological and Evolutionary Synthesis

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the world's biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logic's ability to represent complex mathematical ideas with probability theory's ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.
Metadedup: Deduplicating Metadata in Encrypted Deduplication via Indirection

Encrypted deduplication combines encryption and deduplication in a seamless way to provide confidentiality guarantees for the physical data in deduplication storage, yet it incurs substantial metadata storage overhead due to the additional storage of keys. We present a new encrypted deduplication storage system called Metadedup, which suppresses metadata storage by also applying deduplication to metadata. Its idea builds on indirection, which adds another level of metadata chunks that record metadata information. We find that metadata chunks are highly redundant in real-world workloads and hence can be effectively deduplicated. In addition, metadata chunks can be protected under the same encrypted deduplication framework, thereby providing confidentiality guarantees for metadata as well. We evaluate Metadedup through microbenchmarks, prototype experiments, and trace-driven simulation. Metadedup has limited computational overhead in metadata processing, and only adds 6.19% of performance overhead on average when storing files in a networked setting. Also, for real-world backup workloads, Metadedup saves the metadata storage by up to 97.46% at the expense of only up to 1.07% of indexing overhead for metadata chunks.
The Adversarial Machine Learning Conundrum: Can the Insecurity of ML Become the Achilles' Heel of Cognitive Networks?

The holy grail of networking is to create cognitive networks that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fields of research. But are the ML models foolproof and robust to security attacks to be in charge of managing the network? Unfortunately, many modern ML models are easily misled by simple and easily-crafted adversarial perturbations, which does not bode well for the future of ML-based cognitive networks unless ML vulnerabilities for the cognitive networking environment are identified, addressed, and fixed. The purpose of this article is to highlight the problem of unsecure ML and to sensitize the readers to the danger of adversarial ML by showing how an easily crafted adversarial ML example can compromise the operations of the cognitive self-driving network. In this article, we demonstrate adversarial attacks on two simple yet representative cognitive networking applications (namely, intrusion detection and network traffic classification). We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.
LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have become indispensable for solving machine learning tasks in speech recognition, computer vision, and other areas that involve high-dimensional data. A CNN filters the input feature using a network containing spatial convolution operators with compactly supported stencils. In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators. This coupling leads to immense computational cost in the training and prediction phase. In this paper, we introduce LeanConvNets that are derived by sparsifying fully-coupled operators in existing CNNs. Our goal is to improve the efficiency of CNNs by reducing the number of weights, floating point operations and latency times, with minimal loss of accuracy. Our lean convolution operators involve tuning parameters that controls the trade-off between the network's accuracy and computational costs. These convolutions can be used in a wide range of existing networks, and we exemplify their use in residual networks (ResNets). Using a range of benchmark problems from image classification and semantic segmentation, we demonstrate that the resulting LeanConvNet's accuracy is close to state-of-the-art networks while being computationally less expensive. In our tests, the lean versions of ResNet in most cases outperform comparable reduced architectures such as MobileNets and ShuffleNets.
MRec-CRM: Movie Recommendation based on Collaborative Filtering and Rule Mining Approach

Recent years have observed an excellent success of recommender system in the field of movies, social network, tourism and e-commerce (such as eBay and Amazon). Recommender system transforms geophysical restrictions and allows business and individuals to form transaction anytime and anywhere. This approach is widely used by e-commerce organizations to exploit product recommender systems in order to improve user experience and enhance sales. In this paper, we are interested in proposing a collaborative filtering based movie recommendation using rule mining to satisfy individual's requirement. This paper also provides a comparative analysis of the several types of similarity measures namely Cosine, Correlation, Euclidean, Jaccard and Manhattan. Rule mining is done on the rating matrix using TopSeq rule for providing perfect recommendation. Recommendation accuracy is measured using precision, recall and F1 score measure. Finally, it was found that correlation measure based rule mining yields better F1 Score.
CloudSafe: A Tool for an Automated Security Analysis for Cloud Computing

Cloud computing has been adopted widely, providing on-demand computing resources to improve performance and reduce operational costs. However, these new functionalities also bring new ways to exploit the cloud computing environment. To assess the security of the cloud, graphical security models can be used, such as Attack Graphs and Attack Trees. However, existing models do not consider all types of threats, and also automating the security assessment functions are difficult. In this paper, we propose a new security assessment tool for the cloud named CloudSafe, an automated security assessment for the cloud. The CloudSafe tool collates various tools and frameworks to automate the security assessment process. To demonstrate the applicability of the CloudSafe, we conducted security assessment in Amazon AWS, where our experimental results showed that we can effectively gather security information of the cloud and carry out security assessment to produce security reports. Users and cloud service providers can use the security report generated by the CloudSafe to understand the security posture of the cloud being used/provided.
Threat modelling and agile software development: Identified practice in four Norwegian organisations

Threat modelling is considered a key activity in secure software engineering. However, despite its documented benefits it has not (yet) been widely adopted by agile software development projects. In this paper we present results from a qualitative study of how it is performed in practice by four different organisations. The findings show that, even though they all consider threat modelling to lead to a more secure product, they all struggle with practical aspects of the established theory.
A Lightweight Moving Vehicle Classification System Through Attention-Based Method and Deep Learning

The convolutional neural network (CNN) has shown excellent benefits in the classification of objects in the latest years.An important job in the context of intelligent transportation is to properly identify and classify vehicles from videos into various kinds (e.g., car, truck, bus, etc.).For monitoring, tracking and counting purposes, the classified vehicles can be further evaluated.At least two major difficulties stay, however; excluding the uninteresting area (e.g., swinging movement, noise, etc.) and designing an effective and precise system.In order to obviously differentiate the interesting region (moving car) from the un-interesting region (the rest of the area), we introduce a novel attention-based approach.Finally, to significantly increase the classification efficiency, we feed the deep CNN with the respective interesting region.We use several challenging outdoor sequences from the CDNET 2014 (baseline, bad weather and camera jitter classes), and our own dataset to assess the proposed approach.Experimental results show that it costs around ∼85 fps in GPU (and ∼50 fps in CPU) to classify moving vehicles and maintaining a highly accurate rate.Compared with other state-of-the-art object detection approaches, our method obtains a competitive detection accuracy.In addition, we also verify the result of the proposed approach by comparing with recent 3D CNN method, called saliency tubes.
EchoWrite: An Acoustic-based Finger Input System Without Training

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30- minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works.
<i>Nerdalicious scientainment</i>: A network analysis of English libfixes

Libfixes are parts of words that share properties with both blends, compounds and affixes. They are deliberate formations, often with a jocular character, e.g. nerdalicious ‘delicious for nerds’, or scientainment ‘scientific entertainment’. These are not one-off formations – some libfixes have become very productive, as evidenced by high type frequency in a single corpus. Libfix constructions are particularly interesting for a network analysis for three reasons: they do not always have discrete morpheme boundaries, they feature a wide variety of bases (including phrases, as in give-me-a-break-o-meter), and they may be the source of back formations such as infotain. In this paper, we present a corpus-based analysis of eight English libfixes (cracy, fection, flation, gasm, licious, (o-)meter, tainment, and tastic), detailing their formal and semantic properties, as well as their differences and similarities. We argue that libfixes are most fruitfully analysed in a Bybeean network model, in which nodes are connected on the basis of phonological similarity, which allows for both fully compositional and non-compositional constructions to be linked without an exhaustive analysis into morphemes.
Macroeconomic factors and stock market development in sub-Saharan Africa: does the measure of stock market development matter?

We examine the impact of macroeconomic factors on stock market development in sub-Saharan Africa. More specifically, we examine the sensitivity of the impact of macroeconomic factors on stock market development to the choice of measure of stock market development. We apply the Feasible Generalised Least Squares (FGLS) estimator on a panel dataset of 12 countries over the period 2000–2015. The empirical results indicate that income, trade openness, financial openness, macroeconomic instability, financial intermediary development, savings, and investment influence stock market development. We provide evidence to suggest that the impact of macroeconomic factors on stock market development is sensitive to the choice of measure of stock market development. The implication of our findings is that the multidimensionality of stock market development should not be jettisoned in making policy prescriptions for stock market development in sub-Saharan Africa.
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System

In real-world question-answering (QA) systems, ill-formed questions, such as wrong words, ill word order and noisy expressions, are common and may prevent the QA systems from understanding and answering them accurately. In order to eliminate the effect of ill-formed questions, we approach the question refinement task and propose a unified model, QREFINE, to refine the ill-formed questions to well-formed question. The basic idea is to learn a Seq2Seq model to generate a new question from the original one. To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards. Experimental results on real-world datasets show that the proposed QREFINE method can generate refined questions with more readability but fewer mistakes than the original questions provided by users. Moreover, the refined questions also significantly improve the accuracy of answer retrieval.
Entangled Ethnography: Towards a collective future understanding

In this work, we develop a vision for entangled ethnography, where constellations of people, artefacts, algorithms and data come together to collectively make sense of the relations between people and objects. This is grounded in New Materialism's picture of a world understood through entanglement, through resonant constellations, through a multiplicity of unique individual viewpoints and their relationships. These perspectives are especially relevant for design ethnography, in particular for research around smart connected products, which collect data about their environment, the networks they are a part of, and the ways they are used. However, we are concerned about the current trend of many connected systems towards surveillance capitalism, as data is colonised, machinations are hidden, and a narrow definition of value is extracted. There is a key tension that while design, particularly of networked objects, attempts to go beyond human centeredness, the infrastructures that support it are moving towards a less than human perspective in their race to accumulate and dispossess. Our work tries to imagine the situations where participants in networked systems are richly engaged, rather than exploited. We hope for a future where human agency is central to a respectful and acceptable collaborative development of understanding.