Reviewerly/exHarmony huggingface dataset:
Target tracking via recursive Bayesian state estimation in cognitive radar networks

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.
Quantum walks for the determination of commutativity of finite dimensional algebras

Quantum walks provide a framework for the construction of quantum algorithms.Based on this approach, we consider different walks for testing the commutativity of a finite dimensional algebra.In particular, we consider Szegedy's and Santo's quantum walks constructed from complete and torus graphs.Results of numerical experiments are presented, showing that for some choices of the quantum walks we can obtain better detections probabilities than with previous quantum algorithms addressing this problem that were based on Grover's quantum search.Additionally, we introduce a general notion of quantum detection system that encompasses all the methods consider in this work, among others, and develop the idea of quantum hitting time for this kind of systems as an extension of Szegedy's seminal ideas.
Exploring the frequency and change proneness of dynamic feature pattern instances in PHP applications

Although numerous technologies are available for developing web applications, PHP holds the lions' share of web content today. PHP offers several features that enable developers to easily produce dynamically extendible code, forming an entire ecosystem of standard as well as more 'exotic' opportunities that can be exploited. One reason that drives developers to rely on the dynamic features of a scripting language is to enable effortless functionality extensions. The aim of this work is twofold: initially, we (a) provide an overview of all possible dynamically extendible code patterns (i.e., either through method invocation, or object instantiation) and (b) investigate their frequency by mining the code base of ten milestone PHP projects to identify the subset of patterns that developers actually use. Next, in order to investigate whether the expected flexibility of these patterns stands in practice, we examine if code chunks that instantiate them are more stable than other parts of the code. In particular, we study whether methods that employ dynamic invocation and instantiation patterns are less change prone than the other methods. The findings imply that although a small subset of all the theoretically feasible patterns is actually put to use, the code that is developed upon such patterns is less change prone.
Discovering Latent Discriminative Patterns for Multi-Mode Event Representation

Representation of videos is essential since it conveys an understanding of video content and enables many higher level tasks to be tackled efficiently. However, it is challenging to propose a rational representation for complex event videos, as most video information is either noisy or redundant. In this paper, we propose a compact event representation method that can concisely describe the inner modes of events. We deem that an optimal event representation scheme should reflect the long-term and high-level visual semantics (visual topics) of events, so different from previous frame-level video semantics representation methods and concept-based video representation methods, we investigate the problem from the perspective of segment-level video representations. We then present three appealing properties of segment-level visual semantics. Based on the observation, we propose different algorithms that rely on a novel deep-visual-word-based video encoding method to discover latent discriminative patterns of events. Finally, our multi-mode event representation is obtained by concatenating the discovered patterns as inner modes. We adopt our event representation for representative event parts mining, which can highlight the visual topics of events and remarkably prune the raw videos. We validate our event representation method based on complex event detection task. Experimental results on two standard benchmarking datasets, MED11 and CCV Dataset, show that the proposed method can significantly outperform the state-of-the-art approaches.
Latent Dirichlet allocation-based temporal summarization

Purpose During crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online. However, relevant content can be disseminated in document streams. The available information can also contain redundant content published by different sources. Therefore, the need of automatic construction of summaries that aggregate important, non-redundant and non-outdated pieces of information is becoming critical. Design/methodology/approach The aim of this paper is to present a new temporal summarization approach based on a popular topic model in the information retrieval field, the Latent Dirichlet Allocation. The approach consists of filtering documents over streams, extracting relevant parts of information and then using topic modeling to reveal their underlying aspects to extract the most relevant and novel pieces of information to be added to the summary. Findings The performance evaluation of the proposed temporal summarization approach based on Latent Dirichlet Allocation, performed on the TREC Temporal Summarization 2014 framework, clearly demonstrates its effectiveness to provide short and precise summaries of events. Originality/value Unlike most of the state of the art approaches, the proposed method determines the importance of the pieces of information to be added to the summaries solely relying on their representation in the topic space provided by Latent Dirichlet Allocation, without the use of any external source of evidence.
Gradient-aware blind face inpainting for deep face verification

ID face photos are widely used for identity verification in many business authentication situations. To avoid any infringement and misuse, the ID photos provided by the relevant government agencies and business organizations are always corrupted with designed watermarks, such as random wave lines or meshes. These corrupted images are further compressed with JPEG algorithm to reduce their storage size. The artifacts caused by the random meshes and JPEG compression seriously destroy the original image information and quality, which makes the face verification between the corrupted ID faces and daily life images extremely difficult. To tackle these issues, a preprocessing step called blind inpainting is needed to recover the corrupted ID faces. In this paper, we present a new framework to address this blind face inpainting problem. We use an improved Deep Recursive Residual Network (IDRRN) to learn an effective non-linear mapping between the corrupted and clean ID image pairs. To train the IDRRN model, a unified Euclidean loss function considering both 0- and 1st-order pixel residuals is proposed to enhance the image pixel as well as gradient reconstruction. In addition, we collect a dataset of clean ID images and develop a simulation procedure to generate corresponding corrupted ID face images. Final experiments demonstrate that the recovered ID face images inferred from our IDRRN model achieve the best performance in terms of perceptual error and verification accuracy.
Theorizing About Socio-Technical Approaches to HCI

In this paper, we theorize about Socio-Technical approaches to HCI. The Socio-Technical tradition indicates that looking at design only or mainly from a technical design side is insufficient to design systems for work and workers; instead the social and the technical need to be co-designed and contingent on characteristics of the context, the organisation, and its historical development. However, it also argued that this tradition does not provide enough handles for the design of interactive technologies for users. We present Socio-Technical HCI as a distinct field of knowledge outlining the Socio-Technical traditions where it is rooted, and illustrate these with three different conceptual frameworks that have been used to support the design, development, and evaluation of interactive systems. These frameworks are Cognitive Work Analysis, Human-Work Interaction Design, and Technological Frames. These frameworks are compared and analysed in terms of what are a balanced and comprehensive way to in address socio-technical, contextual, and design issues in HCI. It is argued why Human-Work Interaction Design is best placed to address these issues.
Material Image Segmentation with the Machine Learning Method and Complex Network Method

The study of the relationship among the manufacturing process, the structure and the property of materials can help to develop the new materials. The material images contain the microstructures of materials, therefore, the quantitative analysis for the material images is the important means to study the characteristics of material structures. Generally, the quantitative analysis for the material microstructures is based on the exact segmentation of the materials images. However, most material microstructures are shown with various shapes and complex textures in images, and they seriously hinder the exact segmentation of the component elements. In this research, machine learning method and complex networks method are adopted to the challenge of automatic material image segmentation. Two segmentation tasks are completed: on the one hand, the images of the titanium alloy are segmented based on the pixel-level classification through feature extraction and machine learning algorithm; on the other hand, the ceramic images are segmented with the complex networks theory. In the first task, texture and shape features near each pixel in titanium alloy image are calculated, such as Gabor filters, Hu moments and GLCM (Gray-Level Cooccurrence Matrix) etc.. The feature vector for the pixel can be obtained by arraying these features. Then, classification is performed with the random forest model. Once each pixel is classified, the image segmentation is completed. In the second task, a complex network structure is built for the ceramic image. Then, a clustering algorithm of complex network is used to obtain network connection area. Finally, the clustered network structure is mapped back to the image and getting the contours among the component elements. The experimental results demonstrate that these methods can accurately segment material images.
Fake News and the Limits of Freedom of Speech

In this chapter, the author considers the ethical acceptability of censoring fake news. Fake news has a number of harmful effects. It deceives people into believing falsehoods, sometimes systematically distorting people’s worldviews. It leaves many sceptical of news sources in general, lessening people’s ability to acquire accurate information. Fake news in a digital environment is just not the same thing as “made-up stories” in a non-digital environment. The essential feature of fake news is that it misleads about its origin. The fake news purveyors who seek a particular political advantage through getting people to believe their stories may intend people to believe their stories. Fake news is speech, and restrictions on speech are typically thought to require a much stronger justification than many other sorts of restrictions, because, as F. Schauer puts it, “Speech is special”. Speech is both a means for developing the capacity for autonomy and a way to exercise it.
Digital Escapism

In contemporary archaeology, we can identify a clash of two apparently antagonistic "paradigms": the "(re)turn to things" and the "digital turn". The latter has had great impact on archaeological museums, traditionally focused on material objects, and in this article I analyse this in terms of a phenomenon that I call "digital escapism". Digital escapism embraces ontological and epistemological questions that stem from the digital turn in archaeology - it might denote the dismissal of artefacts as subjects of scientific interest, or a shift in focus to digital methods as a means to creating an immersive past reality. It may also relate to big data in archaeology and the attempt to (re)connect this archaeology with science by way of informatics. My analysis of the exhibition presented in Rynek Underground, a museum in Krakow, provides a case study of digital escapism within museum space.
Learning what and where to attend

Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than bottom-up saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.
Differentially private Markov chain Monte Carlo

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy-guarantees are not subject to unrealistic assumptions on Markov chain convergence and that is applicable to posterior inference in arbitrary models. Our algorithm is based on a decomposition of the Barker acceptance test that allows evaluating the Renyi DP privacy cost of the accept-reject choice. We further show how to improve the DP guarantee through data subsampling and approximate acceptance tests.
Introduction to Neural Networks Using PyTorch

Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because it was invented in 1950s.
Bi-directional Value Learning for Risk-aware Planning Under Uncertainty

Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learning process provides a long-range and risk-aware base policy. A forward value learning process ensures local optimality and updates the policy via forward simulations. We consider a class of scalable and continuous-space rover navigation problems (RNP) to assess the safety, scalability, and optimality of the proposed algorithm. The results demonstrate the capabilities of the proposed algorithm in evaluating long-range risk/safety of the planner while addressing continuous problems with long planning horizons.
Defeasible normative reasoning

The paper is motivated by the need of accounting for the practical syllogism as a piece of defeasible reasoning. To meet the need, the paper first refers to ranking theory as an account of defeasible descriptive reasoning. It then argues that two kinds of ought need to be distinguished, purely normative and fact-regarding obligations (in analogy to intrinsic and extrinsic utilities). It continues arguing that both kinds of ought can be iteratively revised and should hence be represented by ranking functions, too, just as iteratively revisable beliefs. Its central proposal will then be that the fact-regarding normative ranking function must be conceived as the sum of a purely normative ranking function and an epistemic ranking function (as suggested in qualitative decision theory). The distinctions defends this proposal with a comparative discussion of some critical examples and some other distinctions made in the literature. It gives a more rigorous justification of this proposal. Finally, it starts developing the logic of purely normative and of fact-regarding normative defeasible reasoning, points to the difficulties of completing the logic of the fact-regarding side, but reaches the initial aim of accounting for the defeasible nature of the practical syllogism.
Incorporating label dependency for ASR error detection via RNN

Recent advances in Recurrent Neural Networks (RNNs) have been successfully used in several natural language processing tasks. This paper addresses the problem of Automatic Speech Recognition (ASR) error detection and proposes a new features-based approach using a variant RNN, where words labels are re-injected as input into the network. Therefore, this study makes a major contribution to research on ASR error detection by demonstrating the utility of considering label dependency in training RNNs. Experiments are made on automatic transcriptions generated by the 2015 Sheffield ASR system applied on the MGB data. The results have shown that the proposed V-RNN greatly outperforms both unidirectional and bidirectional LSTM-RNNs.
Representation Learning of Knowledge Graphs via Fine-Grained Relation Description Combinations

Knowledge representation learning attempts to represent entities and relations of knowledge graph in a continuous low-dimensional semantic space. However, most of the existing methods such as TransE, TransH, and TransR usually only utilize triples of knowledge graph. Other important information such as relation descriptions with relevant knowledge is still used ineffectively. To address these issues, in this paper, we propose a relation text-embodied knowledge representation learning method, in which relation descriptions are adopted as side information for representation learning. More specifically, we explore a convolutional neural model to build representations of fine-grained relation descriptions. Furthermore, knowledge representations of triples and representations of fine-grained relation descriptions are jointly embedding. Our model is evaluated on the tasks of both link prediction and triple classification. The experiment results show that our model exhibits a superior performance than other baselines, which demonstrates the availability of our method with fine-grained relation descriptions and knowledge graph jointly embedding.
Speicher: securing LSM-based key-value stores using shielded execution

We introduce SPEICHER, a secure storage system that not only provides strong confidentiality and integrity properties, but also ensures data freshness to protect against rollback/forking attacks. SPEICHER exports a Key-Value (KV) interface backed by Log-Structured Merge Tree (LSM) for supporting secure data storage and query operations. SPEICHER enforces these security properties on an untrusted host by leveraging shielded execution based on a hardware-assisted trusted execution environment (TEE)-specifically, Intel SGX. However, the design of SPEICHER extends the trust in shielded execution beyond the secure SGX enclave memory region to ensure that the security properties are also preserved in the stateful (or non-volatile) setting of an untrusted storage medium, including system crash, reboot, or migration.More specifically, we have designed an authenticated and confidentiality-preserving LSM data structure. We have further hardened the LSM data structure to ensure data freshness by designing asynchronous trusted counters. Lastly, we designed a direct I/O library for shielded execution based on Intel SPDK to overcome the I/O bottlenecks in the SGX enclave. We have implemented SPEICHER as a fully-functional storage system by extending RocksDB, and evaluated its performance using the RocksDB benchmark. Our experimental evaluation shows that SPEICHER incurs reasonable overheads for providing strong security guarantees, while keeping the trusted computing base (TCB) small.
Feature Selection Algorithms for Data Mining Classification: A Survey

Objectives: This study summarizes the feature selection process, its importance, different types of feature selection algorithms such as Filter, Wrapper and Hybrid.Moreover, it analyses some of the existing popular feature selection algorithms through a literature survey and also addresses the strengths and challenges of those algorithms.Methods/ Statistical Analysis: When there are many methods are in hand to be obtained, then Review of Literature is the best approach to learn about existing methods before going for a new model.Findings: Feature selection is a predominant preprocessing strategy in Data Mining, which helps in advancing the performance of mining, by selecting only the relevant features and avoiding the redundant features.There are plenty Feature Selection algorithms developed and used by most researchers.But still it is an emerging area in machine learning to be focused for data mining and analysis process for pattern recognition.Many feature selection algorithms confront severe challenges in terms of effectiveness and efficiency, because of recent increase in data variety and velocity.Different types of feature selection algorithms are available in literature such as Filter based, Wrapper based and Hybrid algorithms.Moreover, analyses some of the existing popular feature selection algorithms through a literature survey, also addresses the strengths and challenges of those algorithms.Application/Improvements: There is a need for an effective unified framework, which should provide feature selection for any size of dataset without noisy data, low computational complexity and highest accuracy.