Reviewerly/exHarmony huggingface dataset:
Subpath Queries on Compressed Graphs: A Survey

Text indexing is a classical algorithmic problem that has been studied for over four decades: given a text T, pre-process it off-line so that, later, we can quickly count and locate the occurrences of any string (the query pattern) in T in time proportional to the query’s length. The earliest optimal-time solution to the problem, the suffix tree, dates back to 1973 and requires up to two orders of magnitude more space than the plain text just to be stored. In the year 2000, two breakthrough works showed that efficient queries can be achieved without this space overhead: a fast index be stored in a space proportional to the text’s entropy. These contributions had an enormous impact in bioinformatics: today, virtually any DNA aligner employs compressed indexes. Recent trends considered more powerful compression schemes (dictionary compressors) and generalizations of the problem to labeled graphs: after all, texts can be viewed as labeled directed paths. In turn, since finite state automata can be considered as a particular case of labeled graphs, these findings created a bridge between the fields of compressed indexing and regular language theory, ultimately allowing to index regular languages and promising to shed new light on problems, such as regular expression matching. This survey is a gentle introduction to the main landmarks of the fascinating journey that took us from suffix trees to today’s compressed indexes for labeled graphs and regular languages.
Multi-Scale Boosting Feature Encoding Network for Texture Recognition

Texture recognition remains a challenging visual task due to the complex appearance variations caused by scale changes in the real world. In most existing texture recognition methods, textures are represented at a single scale; thus, multi-scale texture information is not fully utilized, resulting in insufficient representation and inaccurate recognition. In this study, with the goal of addressing the challenge of scale changes, we propose a novel multi-scale boosting feature encoding network (MSBFEN) for accurate texture recognition. MSBFEN first extracts multi-scale features with multi-scale texture structure information under the guidance of texture priors using a novel prior-guided feature extraction (PFE) method. Then, a multi-scale texture encoding (MSTE) method is devised to capture discriminative multi-scale texture representations by encoding the extracted features. Finally, to fully utilize the multi-scale texture representations for accurate texture recognition, a novel multi-scale boosting learning (MSBL) method is proposed. In MSBL, the learning procedure for multi-scale texture recognition is boosted in a hierarchical, progressively reinforced manner, significantly addressing the challenge of scale changes and greatly enhancing the recognition accuracy. In addition, a novel outlier-aware texture encoding (OTE) method is proposed for robust texture encoding at each scale of MSTE. OTE can resist the influence of background interference and can further enhance the robustness of MSBFEN. In extensive experiments conducted on six challenging texture recognition datasets, namely, KTH-TIPS2b, FMD, DTD, MINC, GTOS and GTOS-mobile, MSBFEN achieves accuracies of 86.2%, 86.4%, 77.8%, 85.3%, 86.4% and 87.57%, respectively, representing state-of-the-art texture recognition performance.
Capacity Limits of Fully Binary CNN

Convolutional Neural Networks (CNNs) have achieved a state-of-the-art performance on different real world information processing tasks. However, CNNs are computationally and power intensive, which makes them difficult to run on wearable and embedded systems. Using binary models instead of more complex models, was shown to significantly reduce the memory and computational resources; however, at the cost of a lower accuracy. The aim of the paper is to provide the further exploration of the binarization effect on the model capacity. We study a multiple-valued thresholding (binarization) of the input images and we combine features of several binary networks to perform a classification task. Each image is fitted to a separate binary CNN model. In this manner, we decompose the real world problem to several binary sub-problems. The separate binary models are then assembled into a final classifier and are used to predict the class label. The results show that while for MNIST the accuracy is very close to the full precision counterpart, for the more complex dataset, CIFAR-10, the binarization and the representational power of CNNs is strongly affected.
Behavioral flexibility in Belief-Desire- Intention (BDI) architectures

The Belief-Desire-Intention (BDI) model is a popular approach to design flexible agents. The key ingredient of BDI model, that contributed to concretize behavioral flexibility, is the inclusion of the practical reasoning. On the other hand, researchers signaled some missing flexibility’s ingredient, in BDI model, essentially the lack of learning. Therefore, an extensive research was conducted in order to extend BDI agents with learning. Although this latter body of research is important, the key contribution of BDI model, i.e., practical reasoning, did not receive a sufficient attention. For instance, for performance reasons, some of the concepts included in the BDI model are neglected by BDI architectures. Neglecting these concepts was criticized by some researchers, as the ability of the agent to reason will be limited, which eventually leads to a more or less flexible reasoning, depending on the concepts explicitly included. The current paper aims to stimulate the researchers to re-explore the concretization of practical reasoning in BDI architectures. Concretely, this paper aims to stimulate a critical review of BDI architectures regarding the flexibility, inherent from the practical reasoning, in the context of single agents, situated in an environment which is not associated with uncertainty. Based on this review, we sketch a new orientation and some suggested improvements for the design of BDI agents. Finally, a simple experiment on a specific case study is carried out to evaluate some suggested improvements, namely the contribution of the agent’s “well-informedness” in the enhancement of the behavioral flexibility.
Evaluating the Accuracy of Password Strength Meters using Off-The-Shelf Guessing Attacks

In this paper we measure the accuracy of password strength meters (PSMs) using password guessing resistance against off-the-shelf guessing attacks. We consider 13 PSMs, 5 different attack tools, and a random selection of 60,000 passwords extracted from three different datasets of real-world password leaks. Our results show that a significant percentage of passwords classified as strong were cracked, thus suggesting that current password strength estimation methods can be improved.
Self Supervision for Attention Networks

In recent years, the attention mechanism has become a fairly popular concept and has proven to be successful in many machine learning applications. However, deep learning models do not employ supervision for these attention mechanisms which can improve the model's performance significantly. Therefore, in this paper, we tackle this limitation and propose a novel method to improve the attention mechanism by inducing "self-supervision". We devise a technique to generate desirable attention maps for any model that utilizes an attention module. This is achieved by examining the model's output for different regions sampled from the input and obtaining the attention probability distributions that enhance the proficiency of the model. The attention distributions thus obtained are used for supervision. We rely on the fact, that attenuation of the unimportant parts, allows a model to attend to more salient regions, thus strengthening the prediction accuracy. The quantitative and qualitative results published in this paper show that this method successfully improves the attention mechanism as well as the model's accuracy. In addition to the task of Visual Question Answering(VQA), we also show results on the task of Image classification and Text classification to prove that our method can be generalized to any vision and language model that uses an attention module.
Machine Learning-Based Similarity Attacks for Chaos-based Cryptosystems

When the chaotic block cryptographic algorithms are performed on hardware devices, the leakages of power consumption etc. are crucial information which can be used to analyse the security of the cryptosystems. Template Attack (TA) can recover the secret key. However, there are still some challenges for TA such as irreversible covariance matrix and exponentiation calculation overflow. Machine Learning-based Similarity Attacks (MLSAs) are proposed to effectively analyse the sensitive information of the chaotic block cryptosystem. The proposed method consists of three steps: parameter tuning, learning and attacking. For the parameter tuning, the profiling traces are categorised according to the Hamming weights of sensitive intermediate data. Then a 10-fold cross-validation is executed to determine the corresponding parameter settings for learning algorithms. In the learning step, the profiling traces and Hamming weight labels are used to train machine learning models, and in the attacking step different similarity measure methods are used to calculate similarities between actual and hypothetical Hamming weight labels to attack the secret keys. Performance analyses demonstrate that the proposed MLSAs have higher success rates than TA and lower computational time consumptions under most of scenarios. Therefore, the MLSAs can efficiently attack and analyse hardware security of chaotic block cryptosystems.
Coverage Path Planning with Proximal Policy Optimization in a Grid-based Environment

We present an on-line approach for coverage path planning in 2D grid environments based on reinforcement learning. We used actor-critic architecture with convolutional layers to learn an agent's policy from simulated limited-range sensor observations. We experimented with different reward functions and network architectures to get a minimal repetition rate. Our results show that model generalizes well to unseen environments with complex geometry and dynamic obstacles and demonstrates the ability to learn some optimal trajectory patterns like circular and boustrophedon motion. An approach may as well be suitable for a multi-agent setting with minor adjustments, as shown by our simulations.
Open Science and Data Science

Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections. “Open Science by Design” (OSD), i.e., making artefacts such as data, metadata, models, and algorithms available and re-usable to peers and beyond as early as possible, is a pre-requisite for a flourishing DS landscape. However, a few major aspects can be identified hampering a fast transition: (1) The classical “Open Science by Publication” (OSP) is not sufficient any longer since it serves different functions, leads to non-acceptable delays and is associated with high curation costs. Changing data lab practices towards OSD requires more fundamental changes than OSP. 2) The classical publication-oriented models for metrics, mainly informed by citations, will not work anymore since the roles of contributors are more difficult to assess and will often change, i.e., other ways for assigning incentives and recognition need to be found. (3) The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science (OS). (4) Finally, OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain—“one data-domain” as George Strawn calls it—which is still not visible due to differences about the technological key pillars. OS therefore is a need for DS, but it will take much more time to implement it than we may have expected.
HybridHadoop: CPU-GPU Hybrid Scheduling in Hadoop

As a GPU has become an essential component in high performance computing, it has been attempted by many works to leverage GPU computing in Hadoop. However, few works considered to fully utilize the GPU in Hadoop and only a few works studied utilizing both CPU and GPU at the same time. In this paper, we propose a CPU-GPU hybrid scheduling in Hadoop, where both CPUs and GPUs in a node are exploited as much as possible in an adaptive manner. The technical barrier stands in that the optimal number of GPU tasks is not known in advance, and the total number of Containers in a node cannot be changed once a Hadoop job starts. In the proposed approach, we first determine the initial number of Containers as well as the hybrid execution mode, then the proposed dynamic scheduler adjusts the number of Containers for a GPU and a CPU with the help of a GPU monitor during the job execution. It also employs a load-balancing algorithm for the tail. The experiments with various benchmarks show that the proposed CPU-GPU hybrid scheduling achieves 3.79 × of speedup on average against the 12-core CPU-only Hadoop.
Local thinning of 3D stereo images based on symmetric decryption algorithm

In traditional 3D image thinning methods, because the thinning data is not symmetrical, the skeleton connectivity of 3D image is reduced, and the thinned image is seriously distorted. Based on the symmetric decryption algorithm, this method acquires the symmetric information of 3-D stereo image, filters out the pseudo-feature points, extracts the local detail features of 3-D stereo image by detecting template, and realizes the local thinning of image by stereo matching. According to the test results, the skeleton of 3D stereo image obtained by the method of local thinning has the characteristics of connectedness and symmetry, while the skeleton of 3D stereo image obtained by the traditional method has the phenomena of fragmentation and asymmetry.
Construction of Differentially Private Empirical Distributions from a Low-Order Marginals Set Through Solving Linear Equations with $$l_2$$ Regularization

We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginals set tHrough solving linear Equations with $$l_2$$ Regularization (CIPHER), that produces differentially private empirical joint distributions from a set of low-order marginals. CIPHER is conceptually simple and requires no more than decomposing joint probabilities via basic probability rules to construct a linear equation set and subsequently solving the equations. Compared to the full-dimensional histogram (FDH) sanitization, CIPHER has drastically lower requirements on computational storage and memory, which is practically attractive especially considering that the high-order signals preserved by the FDH sanitization are likely just sample randomness and rarely of interest. Our experiments demonstrate that CIPHER outperforms the multiplicative weighting exponential mechanism in preserving original information and has similar or superior cost-normalized utility to FDH sanitization at the same privacy budget.
Minimization of fraudulent activities in land authentication through Blockchain-based system

In Sri Lanka, the land administration process has not been digitized, which has led to plenty of conflicts in determining the real ownership of lands and drastically increasing the land transaction processing time, which has resulted in many fraudulent activities concerning land management. The existing system is majorly paper-based and centralized. Administrators who have the powers to alter the system according to their wishes hold a huge threat for information security and is a centralized system that bears the risk of a single point of failure. Hence, transitioning into another system that could mitigate the drawbacks in the current land authentication system has become a vital need and currently, the knowledge regard this area is very much limited. The decentralized nature of the blockchain-based system has the potential of diluting major limitations in the current system. The overall objective of this research is to mitigate fraudulent activities in land authentication systems through the blockchain-based technological approach. A prototype of a smart contract based model has been created and verified with the involvement of the actual users. Since the smart contract-based land authentication model verifies the land ownership within a short period, the transaction processing time narrows down from a few months to a couple of minutes. The results show signs of considerable improvements in the efficiency and the security of the land authentication process, the users who interacted with the prototype and presented positive comments.
An Improved Adaptive Window Stereo Matching Algorithm

Abstract In order to solve the problem that the existing adaptive window stereo matching algorithms have insufficient feature extraction in low-texture regions, resulting in low matching accuracy. An adaptive window stereo matching algorithm based on the gradient is proposed. Firstly, the Sobel operator is used to extract the gradient value of each pixel in the image. Then, each pixel is divided into high, medium and low texture regions according to the gradient value. Next, different arm length thresholds are assigned to different region pixels, and matching windows are generated dynamically according to arm length and color threshold. Finally, the pixels closer to the center of the window are given higher weights by generating windows several times. It solves the problem that the stereo matching algorithm can not select a matching window dynamically. Experimental results on Middlebury dataset show that the proposed method improves the matching accuracy by 5.5% compared with the latest adaptive window stereo matching algorithm.
Towards Contextual Learning in Few-shot Object Classification

Few-shot Learning (FSL) aims to classify new concepts from a small number of examples. While there have been an increasing amount of work on few-shot object classification in the last few years, most current approaches are limited to images with only one centered object. On the opposite, humans are able to leverage prior knowledge to quickly learn new concepts, such as semantic relations with contextual elements.Inspired by the concept of contextual learning in educational sciences, we propose to make a step towards adopting this principle in FSL by studying the contribution that context can have in object classification in a low-data regime. To this end, we first propose an approach to perform FSL on images of complex scenes. We develop two plug-and-play modules that can be incorporated into existing FSL methods to enable them to leverage contextual learning. More specifically, these modules are trained to weight the most important context elements while learning a particular concept, and then use this knowledge to ground visual class representations in context semantics. Extensive experiments on Visual Genome and Open Images show the superiority of contextual learning over learning individual objects in isolation.
OWI: Open-World Intent Identification Framework for Dialog Based System

Automated task-oriented dialog based system, generally stated as Chatbot, is widely used nowadays by service-oriented platforms such as banking, mobile service providers and travel management firms. The most imperative part of the task-oriented dialog system is to distinguish the intent of the queries asked. If the system erroneously identifies the intent of the query, then the given answer is either incorrect or not related to the query asked. This raises the risk of deteriorating the reliability of the entire system and the organization. Such kind of systems struggle when a user asks queries that contain words for which training classes are not available. These classes may be termed as unseen classes. Our aim is to find the unseen classes in an automated task-oriented dialog system. This paper focuses on open-world learning. Specifically, we propose a deep learning-based Intent Identification framework, OWI, to identify unseen classes for an automated dialog-based system. The OWI framework is based on convolutional neural network with a 1-vs-rest output layer to identify the unseen classes. The proposed model is evaluated on various performance matrices. In addition, we compare OWI with an existing state-of-the-art model. The experimental results show that the OWI outperforms the existing model with respect to identifying unseen classes.
High-Performance Deterministic Stochastic Computing Using Residue Number System

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> This article discusses how to reduce the latency of stochastic computations. The authors represent an integer number as a set of remainders with respect to a set of relatively prime moduli. Operations such as multiplication, implemented using a deterministic version of stochastic computing, work directly on the remainders, thus yielding a partitioning of the original computation and a significant decrease in the number of clock cycles required. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vincent T. Lee, Facebook Reality Labs Research</i>
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition

Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels. Each label represents images of a specific domain. In particular, a set of two labels per subject, one for the NIR images and one for the VIS images, are used for training a NIR-VIS face recognition model. The classification of images into different domains reduces the intra-class variation and lessens the negative impact of data imbalance in training. To train a network with sets of labels, we introduce a domain-based angular margin loss and a maximum angular loss to maintain the inter-class discrepancy and to enforce the close relationship of labels in a set. Quantitative experiments confirm that DBLFace significantly improves the rank-1 identification rate by 6.7% on the EDGE20 dataset and achieves state-of-the-art performance on the CASIA NIR-VIS 2.0 dataset.