Reviewerly/exHarmony huggingface dataset:
Component-based Feature Saliency for Clustering

Simultaneous feature selection and clustering is a major challenge in unsupervised learning. In particular, there has been significant research into saliency measures for features that result in good clustering. However, as datasets become larger and more complex, there is a need to adopt a finer-grained approach to saliency by measuring it in relation to a part of a model. Another issue is learning the feature saliency and advanced model parameters. We address the first by presenting a novel Gaussian mixture model, which explicitly models the dependency of individual mixture components on each feature giving a new component-based feature saliency measure. For the second, we use Markov Chain Monte Carlo sampling to estimate the model and hidden variables. Using a synthetic dataset, we demonstrate the superiority of our approach, in terms of clustering accuracy and model parameter estimation, over an approach using a model-based feature saliency with expectation maximisation. We performed an evaluation of our approach with six synthetic trajectory datasets obtaining an average clustering accuracy of 97 percent. To demonstrate the generality of our approach, we applied it to a network traffic flow dataset obtaining an accuracy of 93 percent for intrusion detection. Finally, we performed a comparison with state-of-the-art clustering techniques using three real-world trajectory datasets of vehicle traffic. Our approach achieved an average clustering accuracy of 96 percent compared to 77-95 percent for the other techniques. In conclusion, for the datasets considered, component based feature saliency measures gave improved clustering over those based on whole models.
Farming within limits

In this forum we highlight innovative thought, design, and research in the area of interaction design and sustainability, illustrating the diversity of approaches across HCI communities. --- Roy Bendor, Editor
Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images

Because of the optical properties of medical fluorescence images (FIs) and hardware limitations, light scattering and diffraction constrain the image quality and resolution. In contrast to device-based approaches, we developed a post-processing method for FI resolution enhancement by employing improved generative adversarial networks. To overcome the drawback of fake texture generation, we proposed total gradient loss for network training. Fine-tuning training procedure was applied to further improve the network architecture. Finally, a more agreeable network for resolution enhancement was applied to actual FIs to produce sharper and clearer boundaries than in the original images.
TagDeepRec: Tag Recommendation for Software Information Sites Using Attention-Based Bi-LSTM

Software information sites are widely used to help developers to share and communicate their knowledge. Tags in these sites play an important role in facilitating information classification and organization. However, the insufficient understanding of software objects and the lack of relevant knowledge among developers may lead to incorrect tags. Thus, the automatic tag recommendation technique has been proposed. However, tag explosion and tag synonym are two major factors that affect the quality of tag recommendation. Prior studies have found that deep learning techniques are effective for mining software information sites. Inspired by recent deep learning researches, we propose TagDeepRec, a new tag recommendation approach for software information sites using attention-based Bi-LSTM. The attention-based Bi-LSTM model has the advantage of deep potential semantics mining, which can accurately infer tags for new software objects by learning the relationships between historical software objects and their corresponding tags. Given a new software object, TagDeepRec is able to compute the confidence probability of each tag and then recommend top-k tags by ranking the probabilities. We use the dataset from six software information sites with different scales to evaluate our proposed TagDeepRec. The experimental results show that TagDeepRec has achieved better performance compared with the state-of-the-art approaches TagMulRec and FastTagRec in terms of Recall@k, Precision@k and $$F1-score@k$$ .
VICINITY: IoT Semantic Interoperability Based on the Web of Things

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile computing; learning (artificial intelligence); autonomous aerial vehicles; protocols; computer network security; data privacy; data analysis; telecommunication traffic.
Randex: Mitigating Range Injection Attacks on Searchable Encryption

Searchable Encryption enables search functions over encrypted data on an untrusted server without the need of accessing data or queries in plaintext. To boost search time, most of the Searchable Encryption schemes leak access pattern. Unfortunately, by harnessing access pattern, a variation of a chosen-query attack, named range injection attack, can efficiently recover sensitive data in any encrypted tuple. The privacy leakage under a range injection attack is severe, and it is imperative to strengthen the privacy of searchable encrypted data. In this paper, we devise an efficient mechanism, referred to as Randex, to mitigate leakage on searchable encrypted data. Specifically, we apply pre-encryption obfuscation by deploying Randomized Response, which obfuscates access pattern. Randex renders minimal tradeoffs to the correctness of range queries, and is compatible with any Searchable Encryption scheme. We formally prove that Randex achieves ∊ -local differential privacy and rigorously analyze an adversary's guessing probability against range injection attacks. We implement Randex and conduct extensive experiments on a synthetic dataset with 1 million tuples and a real-world dataset with 299 thousand tuples. Our results suggest that, with only 4% false negatives and no false positives, Randex can suppress an adversary's guessing probability to 0.17, which is significantly lower than the guessing probability of 1 without the privacy protection offered by Randex.
Smart Sensor Efficient Signal Processing for Earthquake Early Detection

This paper presents a new method for earthquake early warning alert that uses a smart sampling technique that expose the signal information in a way that it is simpler to infer knowledge. The objective is to estimate, from the first few seconds of the P wave, if the incoming earthquake is destructive or not. The proposed method is described and compared to conventional approaches. Performance results for real seismic data are shown highlighting the results for earthquakes of different magnitudes. Preliminary results are excellent for inferring damage based on the approach of a single seismic station.
Evaluation of Compiler-Induced Vulnerabilities

This paper explores computer security vulnerabilities that are generated inadvertently by a compiler. By using a novel approach of examining the assembly language and other intermediate files generated by the compilation process, it has been successfully demonstrated that the compiler’s processing of the high-level source code can create a vulnerable end product. Proper software assurance is intended to provide confidence that software is free from vulnerabilities, and compiler-induced vulnerabilities reduce this confidence level. The discovered vulnerabilities can be related to standard vulnerability classes, side channel attacks, undefined behavior, and persistent state violations. Additionally, the research revealed that the executable machine code generated by the compiler can differ in structure from the original source code due to simplifications and optimizations performed during the compilation process that cannot be disabled. This research examined both the open-source GNU C compiler and the Microsoft C/C++ compiler that is part of the Microsoft Visual Studio package. Both of these compilers are widely used and represent typical compilers in use today.
Signature of complexity in time–frequency domain

We propose a time–frequency based complexity to measure disorder in the long term dynamics of a signal. The disorder is characterized by defining an wavelet spectrogram space in a multiscale coordinate system. The multiscale coordinate system is formed with Wavelet coefficient of the signal. Further, an Weighted entropy measure is implemented to quantify the aforesaid disorder. Numerical results support the proposed method. The proposed entropy is successfully applied to distinguish the ECG signals of normal healthy person and congestive heart failure patients.
A framework for distributed data mining heterogeneous classifier

Distributed Data Mining (DDM) emerged as a huge area by the tremendous growth of geographically distributed data and powerful computational capability of computing. In this, ENcryption, NORMalization, MApping (ENORMA), a privacy preserving heterogeneous classifier framework for universal DDM is proposed. Three algorithms are proposed for maintaining data privacy, retrieval and integration on DDM. For data privacy, privacy-preserving algorithm is designed for protection of data in both the levels; for data retrieval, an algorithm is developed for value normalization and for integration, Mapping algorithm is developed to map the data with schema in global level. Experimental implementation on Electronic Health Records (EHRs), Job Recruitment Records (JRRs) and Agriculture Weather Forecast Records (AWFRs) datasets shows an improved result compared to conventional frameworks.
Tangible interaction meets material culture

column Share on Tangible interaction meets material culture: reflections on the meSch project Author: Daniela Petrelli Sheffield Hallam University Sheffield Hallam UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 5September-October 2019 pp 34–39https://doi.org/10.1145/3349268Published:22 August 2019Publication History 3citation550DownloadsMetricsTotal Citations3Total Downloads550Last 12 Months165Last 6 weeks27 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Concrete Categorical Semantics of Lambda-S

Lambda-S is an extension to first-order lambda calculus unifying two approaches of non-cloning in quantum lambda-calculi. One is to forbid duplication of variables, while the other is to consider all lambda-terms as algebraic linear functions. The type system of Lambda-S have a constructor S such that a type A is considered as the base of a vector space while S(A) is its span. A first semantics of this calculus have been given when first presented, with such an interpretation: superposed types are interpreted as vectors spaces while non-superposed types as their basis. In this paper we give a concrete categorical semantics of Lambda-S, showing that S is interpreted as the composition of two functors in an adjunction relation between the category of sets and the category of vector spaces over C. The right adjoint is a forgetful functor U, which is hidden in the language, and plays a central role in the computational reasoning.
Beneath the Dark Web: Excavating the Layers of Cybercrime's Underground Economy

The Dark Web or DarkNet has attracted both considerable media and scholarly attention. This forms part of a broader tradition of analyzing relatively open cybercriminal marketplaces and forums. With the aid of data collected over a 7-year period, the focus of this paper is to help demarcate - beyond the Dark Web alone - all the layers within the world of profit-driven cybercrime. These include: 1) the top layer, which is the most open forums and marketplaces, whether Dark Web or otherwise; 2) the middle layer of more closely vetted forums; 3) the bottom layer of even smaller and more closed groupings; 4) the molten core, which is centered on the offline organization of cybercrime. The purpose of this analysis is to identify key aspects of the underground economy which warrant further scholarly attention, and to suggest possible approaches to engage with these subjects going forward.
Deep Learning based Effective Surveillance System for Low-Illumination Environments

Surveillance cameras are installed in various locations and contribute to security maintenance and safety. Thus, the video quality of surveillance cameras is important for safety. However, in situations such as nighttime, low-illumination often causes poor image quality. To solve this problem, we propose a system to help acquire quality images of general surveillance cameras utilized in various places through a combination of image quality improvement networks and object detect networks. This will improve safety in low-illumination areas at night. It is also possible to establish a more effective monitoring system for situations occurring in low-illumination areas.
Human Action Recognition based on LSTM Model using Smartphone Sensor

We propose a deep learning model for human action recognition in order to quickly detect occurrence of disasters such as fire and terrorism. Using the acceleration and gyroscope sensors built in the smartphone, four kinds of data on human behavior were obtained and human behavior was classified through the LSTM deep learning model. As an experiment, it was confirmed that the LSTM model can be classified 95.47% accurately.
Agile and Quality: A Systematic Mapping Study

Recently, agile methodologies have been adopted in many application areas of developing software. Since the quality is an inherent factor of the right agile product development, so the use of agile leads to increase the quality of software. The objective of this study is to explore the quality that gained by adopting agile methods and how can be achieved. A systematic mapping study (SMS) is conducted to answer five research questions related to the study topic.
Cross-Stream Selective Networks for Action Recognition

Combining multiple information streams has shown obvious improvements in video action recognition. Most existing works handle each stream independently or perform a simple combination on temporally simultaneous samples in multi-streams, which fails to make full use of the streamwise complementary property due to the negligence of the temporal pattern gaps among streams. In this paper, we propose a cross-stream selective network (CSN) to properly integrate and evaluate information in multi-streams. The proposed CSN first introduces a local selective-sampling module (LSM), which can find asynchronous correspondences among streams and construct high-correlated sample groups across multiple information streams. This LSM can effectively deal with the temporal dis-alignment among different streams, leading to a better integration of cross-stream information. We further introduce a global adaptive-weighting module (GAM). It adaptively evaluates the importance weights for each cross-stream sample group and selects temporally more important ones in action recognition. With the integration of cross-stream information, our GAM can obtain more reasonable importance than the existing single-stream weighting schemes. Extensive experiments on benchmark datasets of UCF101 and HMDB51 demonstrate the effectiveness of our approach over previous state-of-the-art methods.
Improved Action-Decision Network for Visual Tracking With Meta-Learning

Visual tracking is a challenging problem since it usually faces adverse factors, such as object deformation, fast motion, occlusion, and background clutter in practical applications. Reinforcement learning based Action-Decision Network (ADNet) has shown great potential for object tracking. However, ADNet has some shortcomings in optimal action selection and action reward, and suffers from inefficient tracking. To this end, an improved ADNet is proposed to enhance the tracking accuracy and efficiency. Firstly, the multi-domain training is incorporated into ADNet to further improve the feature extraction ability of its convolution layers. Then, in the reinforcement learning based training phase, both the selection criteria for optimal action and the reward function are redesigned separately to explore more appropriate action and eliminate useless action. Finally, an effective online adaptive update strategy is proposed to adapt to the appearance changes or deformation of the object during actual tracking. Specifically, meta-learning is utilized to pursue the most appropriate parameters for the network so that the parameters are closer to the optimal ones in the subsequent tracking process. Experimental results demonstrate that the proposed tracker has advantages over ADNet in terms of accuracy and efficiency.
CAPTURE: Cyberattack Forecasting Using Non-Stationary Features with Time Lags

Forecasting cyberattacks before they occur is an important yet challenging task, as exploring early signs of an attack from a large volume of data is not trivial. This paper describes the design and evaluation of a novel automated system, CAPTURE, which uses a broad range of unconventional signals derived from various open sources to forecast cyberattacks towards a target organization anonymized as CorpX. It includes novel approaches to select relevant and significant, but not redundant, lagged signals and treat the non-stationary relationships between the unconventional signals and the cyberattack occurrences. Using cyber incidents recorded by a third party organization and 146 signals from a variety of sources, this paper demonstrates that CAPTURE performs significantly better than a baseline model with various configurations. Furthermore, CAPTURE offers insights to human analysts on which and how specific lagged signals contributed to the forecasts.
Beyond Passwords: Enforcing Username Security as the First Line of Defense

Combinations of account identifier (e.g., username) and key phrase (i.e., password) are among the most utilized form of credentials for several types of authentication purposes, such as, user verification, connection to public and private networks, and access to digital resources. Typically, usernames are considered a method of account or user identification, whereas passwords are regarded as the crucial component that protects from attackers and prevents breaches. As a result, the level of security of a set of digital credentials is primarily associated with the strength of the key phase, and most of the attention focused on promoting initiatives for increasing password security. Unfortunately, account identifiers received less consideration. Consequently, users are aware of how to enforce the security of their password, though they might prefer more convenient options. Contrarily, several bad practices are caused by overlooking usernames as the first line of defense. In this paper, we highlight the increasing importance of account names and we overview the main username practices that impact account security. Furthermore, we present the results of a study that evaluated how human factors and individuals' awareness impact username security.
Hardware-efficient interval analysis based collision detection and avoidance for mobile robots

Collision detection and avoidance is challenging when the mobile robot is moving among multiple dynamic obstacles. A hardware-efficient architecture supporting parallel implementation is presented in this work for low-power, faster and reliable collision-free motion planning. An approach based on interval analysis is developed for designing an efficient hardware architecture. The proposed architecture achieves parallelism which can be combined with any robotic task involving multiple obstacles. Interval arithmetic is used for representing the pose of the robot and the obstacle as velocity intervals in a fixed time period. These intervals correspond to sub-intervals such as arcs and line-segments. In particular, the collision detection problem for dynamic objects involves the computation of line segment-arc intersections and segment-segment intersections. The intersection of these boundary curves is carried out in a hardware-efficient manner so that it avoids complex arithmetic computations such as multiplication, division etc and exploits parallelism. We develop several results on intersection of these sub-intervals for collision detection and use them to obtain a hardware-efficient collision detection algorithm that requires only shift and add-type of computations. The algorithm is further used in developing a hardware-efficient technique for finding an exhaustive set of solutions for avoiding collision of the robot with dynamic obstacles. Simulation results in MatLab and experiments with a Field Programmable Gate Array (FPGA)-based robot show that a variety of collision avoidance techniques can be implemented using the proposed solution set that guarantees collision avoidance with multiple obstacles.
Dissatisfied and Critical Citizens: the Political Effect of Complaining

Although complaining is well-recognized by psychologists and economists, its political potential is still far from obvious. Dissatisfaction with social services, non-democratic relationships between authorities and citizens, and the ignoring of significant social identities are communicated in everyday conversations. Even when complaining is perceived as ‘grumbling’, the informal nature of the communicated inconveniences may be more important than the formal participatory instruments through which collective claims are placed. Criticism and communication of grievances can lead to political activism only in particular circumstances. This article provides a conceptual frame of complaining enhancing into political claims. The proposed frame distinguishes between inactive complaining and complaining aimed at making a change, which may be reinforced or reduced. The essential role in this process of complaining is given to the objects of complaints/addresses, and agents such as media, politicians, institutions such as NGOs, social movements, whistleblowers, activists and leaders.
A double weighted fuzzy gamma naive bayes classifier

Classifiers based on Gamma statistical distribution can be found in the scientific literature, but they assume the collected data doesn’t present any errors. However, in some cases, information precision can not be guaranteed, then the fuzzy approach is convenient. Several methods found in the lite rature are not able to ponder the specific contribution of each class and/or feature for the classification tasks. This paper presents a proposal of a new classifier named Doubled Weighted Fuzzy Gamma Naive Bayes network (DW-FGamNB). This new classifier uses two types of weights in order to allow users to ponder the real contribution of each class and feature in the classification task. The theoretical development is presented, as well as results of its application on simulated multidimensional data using Gamma statistical distribution. A comparison among DW-FGamNB, Fuzzy Gamma Naive Bayes classifier, classical Gamma Naive Bayes classifier, Naive Bayes classifier, DecionTree-Naive Bayes, Decision Tree C4.5, Logistic Regression, Multilayer Perceptron Neural Network, Adaboost-M1, Radial Basis Function Network and Random Forest was performed. The results obtained showed that the DW-FGamNB produced the best performance, according to the Overall Accuracy Index, Kappa and Tau Coefficients, and diagnostic tests.