Reviewerly/exHarmony huggingface dataset:
Passing the Message: Representation Transfer in Modular Balanced Networks

Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.
Anomaly Detection Using Dynamic Time Warping

Analyzing network traffic behavior is essential for detecting network anomalies. However, it remains a challenge to effectively analyze this behavior for anomaly diagnosis. One promising approach is to decompose network traffic into control and data planes, and statistically analyze each plane's packet features. Both control and data planes behave similarly during benign traffic. However, any difference in the behavior of these planes may indicate an anomaly. In this work, We show that under normal conditions, the packet count distance between the two planes falls within a range of values. Consecutive outliers to these values may reveal the presence of anomalies. We exploit Dynamic Time Warping (DTW) to get the best alignment of the two planes and measure the Euclidean distance between their corresponding instances. We investigate our approach using recent Internet traffic captured at King Saud University. Results support our argument and show that the distance between the TCP control plane and corresponding data plane falls within a certain range of values during benign applications and exceeds these values during anomalous activities.
Learning higher-order logic programs

Abstract A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for higher-order definitions to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs, which in turn reduces the size of the hypothesis space and sample complexity. We implement our idea in two new MIL systems: the Prolog system $$\text {Metagol}_{ho}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>Metagol</mml:mtext> <mml:mrow> <mml:mi>ho</mml:mi> </mml:mrow> </mml:msub> </mml:math> and the ASP system $$\text {HEXMIL}_{ho}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>HEXMIL</mml:mtext> <mml:mrow> <mml:mi>ho</mml:mi> </mml:mrow> </mml:msub> </mml:math> . Both systems support learning higher-order programs and higher-order predicate invention, such as inventing functions for and conditions for . We conduct experiments on four domains (robot strategies, chess playing, list transformations, and string decryption) that compare learning first-order and higher-order programs. Our experimental results support our theoretical claims and show that, compared to learning first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times.
Designing a Blockchain Enabled Supply Chain

The paper reports a case study of a consortium in the UK’s construction sector trying to pilot a smart contract initiative. At the pre-pilot stage, we use the theory of sensemaking to understand how supply chain actors contextualise the potential impact of blockchain on their businesses, diagnose a new technology’s symptoms and then develop assumptions, expectations and knowledge of the new technology, which then shape their subsequent actions during the pilot. During the pilot, we use the design science approach in combination with theoretical lens of business model to actively shape the deployment of blockchain. Design science enables us a deep engagement with the field problem and practitioners while the business model allows us a generic design of blockchain deployment while test this generic approach in a given context, i.e. the smart contract pilot. This research offers valuable insights on how the consortium members design a blockchain enabled supply chain and various issues they have encountered.
Automatic shadow detection and removal using image matting

Extraction of shadows from a single image (also known as shadow matting) is a difficult problem and often requires user interaction. This paper presents an automatic method to extract and remove shadows from real images using the tricolor attenuation model (TAM) and intensity information. Trimap using a guided filter is used for rough segmentation of shadow region. Robust matting is then used to refine the shadowed region in the image and the shadow is removed by relighting the segmented region in RGB space. Finally, the shadow edges are recovered by employing inpainting technique near shadow boundary. Compared with existing shadow removal techniques, the proposed method accurately preserves the color and texture of the images under variety of scenarios without user intervention.
Related-Key Differential Cryptanalysis of the Reduced-Round Block Cipher GIFT

GIFT is a lightweight block cipher that was proposed by Banik et al. at CHES 2017, which is said to be a direct improvement over PRESENT since “that provides a much increased efficiency in all domains (smaller and faster)” and improves the security weaknesses of the latter. At Asiacrypt in 2014, Sun et al. introduced a bit-oriented mixed integer linear programming (MILP) method to search for the differential characteristics of block ciphers. In this paper, we use the differential cryptanalysis method based on this automated tool to analyse GIFT. We propose 12-round and 13-round related-key differential characteristics of GIFT- 64 and 7-round and 10-round related-key differential characteristics of GIFT- 128. By using them as distinguishers, we apply key recovery attacks on the 19-round and 20-round reduced GIFT-64 with data complexities of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">47</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">56</sup> plaintexts, respectively, which mean that the data complexities are lower. Furthermore, we improve the GIFT-64 key recovery attack using differential cryptanalysis by one round over the previous differential cryptanalysis.
Variance-preserving deep metric learning for content-based image retrieval

Supervised deep metric learning led to spectacular results for several Content-based Information Retrieval (CBIR) applications. The success of these approaches slowly led to the belief that image retrieval and classification are just slightly different variations of the same problem. However, recent evidence suggests that learning highly discriminative representation for a (limited) set of training classes removes valuable information from the representation, potentially harming both the in-domain, as well as the out-of-domain retrieval precision. In this paper, we propose a regularized discriminative deep metric learning method that aims to not only learn a representation that allows for discriminating between different classes, but it is also capable of encoding the latent generative factors separately for each class, overcoming this limitation. This allows for modeling the in-class variance and, as a result, maintaining the ability to represent both sub-classes of the in-domain data, as well as objects that belong to classes outside the training domain. The effectiveness of the proposed method, over existing supervised and unsupervised representation/metric learning approaches, is demonstrated under different in-domain and out-of-domain setups and three challenging image datasets.
Do End-Users Want Explanations? Analyzing the Role of Explainability as an Emerging Aspect of Non-Functional Requirements

Software systems are getting more and more complex. Their ubiquitous presence makes users more dependent on them and their correctness in many aspects of daily life. Thus, there is a rising need to make software systems and their decisions more comprehensible. This seems to call for more transparency in software-supported decisions. Therefore, transparency is gaining importance as a non-functional requirement. However, the abstract quality aspect of transparency needs to be better understood and related to mechanisms that can foster it. Integrating explanations in software to leverage systems' opacity has been discussed often. Yet, an important first step is to understand user requirements with respect to explainable software behavior: Are users really interested in transparency, and are explanations considered an adequate mechanism to achieve it? We conducted a survey with 107 end-users to assess their opinion on the current status of transparency in software systems, and what they consider main advantages and disadvantages of explanations embedded in software. The overall attitude towards embedded explanations was positive. However, we also identified potential disadvantages. We assess the relation between explanations and transparency and analyze its possible impact on software quality.
How do Practitioners Capture and Utilize User Feedback During Continuous Software Engineering?

Continuous software engineering (CSE) evolved as a process for rapid software evolution. Continuous delivery enables developers to frequently retrieve user feedback on the latest software increment. Developers use these insights for requirements validation and verification. Despite the importance of users, reports about user feedback in CSE practice are sparse. We conducted 20 interviews with practitioners from 17 companies that apply CSE. We asked practitioners how they capture and utilize user feedback. In this paper, we detail the practitioners' answers by posing three research questions. To improve continuous user feedback capture and utilization with respect to requirements engineering, we derived five recommendations: First, internal sources should be approached, as they provide a rich source of user feedback; second, existing tool support should be adapted and extended to automate user feedback processing; third, a concept of reference points should be established to relate user feedback to requirements; fourth, the utilization of user feedback for requirements validation should be increased; and last, the interaction with user feedback should be enabled and supported by increasing developer-user communication. We conclude that a continuous user understanding activity can improve requirements engineering by contributing to both the completeness and correctness of requirements.
BPRF: Blockchain-based privacy-preserving reputation framework for participatory sensing systems

Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.
SA-Net: A deep spectral analysis network for image clustering

Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image clustering. In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering. While spectral analysis is established with solid theoretical foundations and has been widely applied to unsupervised data mining, its essential weakness lies in the fact that it is difficult to construct a proper affinity matrix and determine the involving Laplacian matrix for a given dataset. In this paper, we propose a SA-Net to overcome these weaknesses and achieve improved image clustering by extending the spectral analysis procedure into a deep learning framework with multiple layers. The SA-Net has the capability to learn deep representations and reveal deep correlations among data samples. Compared with the existing spectral analysis, the SA-Net achieves two advantages: (i) Given the fact that one spectral analysis procedure can only deal with one subset of the given dataset, our proposed SA-Net elegantly integrates multiple parallel and consecutive spectral analysis procedures together to enable interactive learning across different units towards a coordinated clustering model; (ii) Our SA-Net can identify the local similarities among different images at patch level and hence achieves a higher level of robustness against occlusions. Extensive experiments on a number of popular datasets support that our proposed SA-Net outperforms 11 benchmarks across a number of image clustering applications.
Virtually Unexpected: No Role for Expectancy Violation in Virtual Reality Exposure for Public Speaking Anxiety

In the current study, we examined the role of expectancy violation and retrospective reasoning about the absence of feared outcomes in virtual reality exposure therapy (VRET). Participants fearful of public speaking were asked to give speeches in virtual reality. We asked each participant individually to report their expectancies about feared outcomes in public speaking situations and which of these could be tested in VRET. Each of the expectancies was categorized as being related to: (1) participants' own reactions, (2) (overt) reactions of the audience, or (3) (covert) negative evaluation. We examined whether the proportion of testable expectancies could predict treatment outcome and which type of expectancies were evaluated as being more testable in VRET. Additionally, we experimentally manipulated retrospective reasoning about whether or not expectancies related to the overt reactions of the audience could be violated by providing verbal information after VRET about whether or not the virtual audience was interactive. A reduction in public speaking anxiety was observed from pre- to post-VRET. Treatment effects were, however, not predicted by the individually reported proportions of testable expectancies. Participants evaluated expectancies about their own reactions as being more testable in VRET compared to expectancies about reactions of the audience or about being negatively evaluated. In addition, we did not find evidence that the experimental manipulation regarding whether or not the audience was interactive influenced treatment effects. In conclusion, the results of the current study suggest that the effects of VRET are not univocally explained by the mechanism of expectancy violation.
Transfer learning for vehicle detection using two cameras with different focal lengths

This paper proposes a vehicle detection method using transfer learning for two cameras with different focal lengths. A detected vehicle region in an image of one camera is transformed into a binary map. After that, the map is used to filter convolutional neural network (CNN) feature maps which are computed for the other camera’s image. We also introduce a robust evolutionary algorithm that is used to compute the relationship between the two cameras in an off-line mode efficiently. We capture video sequences and sample them to make a dataset that includes images with different focal lengths for vehicle detection. We compare the proposed vehicle detection method with baseline detection methods, including faster region proposal networks (Faster-RCNN), single-shot-multi-Box detector (SSD), and detector using recurrent rolling convolution (RRC), in the same experimental context. The experimental results show that the proposed method can detect vehicles at a wide range of distances accurately and robustly, and significantly outperforms the baseline detection methods.
A model-driven approach for the development of native mobile applications focusing on the data layer

The data layer access design is a critical task for mobile applications which need constant access to remote data, making them available offline in case of network connectivity problems. Moreover, the variety of mobile operating systems and platforms (fragmentation phenomenon) that handles data storage differently affects the portability of mobile applications. This issue suggests the use of Model-Driven Development (MDD) approach that may ease the smartphone application development for different platforms. Therefore, we propose an extension of the Model-Oriented Web Approach (MoWebA) for the development of native mobile applications focusing on the data layer. MoWebA Mobile (as we name the proposal) covers data persistence concepts to achieve offline applications in case of network connectivity problems. It defines meta-models and the Architecture-Specific Model (ASM) for data persistence and data provider to design the data sources of mobile applications. As well, we propose transformation rules for the generation of Android and Windows Phone applications. The MoWebA Mobile process was illustrated by means of modeling, design, and development of a typical mobile application. Despite the learning curve of the approach, the first evaluation suggests that MoWebA Mobile has the potential for supporting the mobile design applications considering the persistence of data.
DeepLIMa: Deep Learning Based Lesion Identification in Mammograms

Mammography is a major technique for early detection of breast cancer, typically through detection of masses or calcifications. However, how to help radiologists efficiently recognize these lesions remains a challenging problem. In this paper, we propose comprehensive deep learning based solutions to respectively detect masses and segment calcifications in mammograms. To achieve the optimal mass detection performance, our method combines Faster R-CNN with Feature Pyramid Networks, Focal Loss, and Non-Local Neural Networks. We thoroughly compare the proposed method and competing methods on three public datasets and an in-house dataset. The best detection results on our in-house dataset are an average precision of 0.933 and a recall of 0.976. Regarding calcification segmentation, we design a series of pre-processing methods including window adjustment, breast region extraction and artifact removal to normalize mammograms. A U-Net model with group normalization is then applied to segment calcifications. The proposed method is validated on our in-house dataset using a newly designed evaluation metric. The experimental results have demonstrated the great potential for this task.
ArcFace for Disguised Face Recognition

Even though deep face recognition is extensively explored and remarkable advances have been achieved on large-scale in-the-wild dataset, disguised face recognition receives much less attention. Face feature embedding targeting on intra-class compactness and inter-class discrepancy is very challenging as high intra-class diversity and inter-class similarity are very common on the disguised face recognition dataset. In this report, we give the technical details of our submission to the DFW2019 challenge. By using our RetinaFace for face detection and alignment and ArcFace for face feature embedding, we achieve state-of-the-art performance on the DFW2019 challenge.
A Deep Learning Approach for Arabic Text Classification

Advancement in information technology produced massive textual material that is available online. Text classification algorithms are at the core of many natural language processing (NLP) applications. There are several algorithms which have been implemented to tackle the classification problem for English and other European languages. Few attempts have been carried out to solve the problem of Arabic text classification. In this paper, we demonstrate a feed-forward deep learning (DL) neural network for the Arabic text classification problem. The first layer uses term frequency-inverse document frequency (TF-IDF) vectors constructed from the most frequent words of the document collection. The output of the first layer is used as an input to the second layer. To reduce the classification error rate, we used Adam's optimizer. We conducted a set of experiments on two multi-classes Arabic datasets to evaluate our approach based on standard measures such as precision, recall, F-measure, support, accuracy and time to build the model. We compared our approach with the logistic regression (LR) algorithm. Experiments entailed that the deep learning approach outperformed the logistic regression algorithm for Arabic text classification.