Reviewerly/exHarmony huggingface dataset:
selp: <i>A Single-Shot Epistemic Logic Program Solver</i>

Abstract Epistemic logic programs (ELPs) are an extension of answer set programming (ASP) with epistemic operators that allow for a form of meta-reasoning, that is, reasoning over multiple possible worlds. Existing ELP solving approaches generally rely on making multiple calls to an ASP solver in order to evaluate the ELP. However, in this paper, we show that there also exists a direct translation from ELPs into non-ground ASP with bounded arity. The resulting ASP program can thus be solved in a single shot. We then implement this encoding method, using recently proposed techniques to handle large, non-ground ASP rules, into the prototype ELP solving system “selp,” which we present in this paper. This solver exhibits competitive performance on a set of ELP benchmark instances.
Automatic Demirci-Selçuk Meet-in-the-Middle Attack on SKINNY with Key-Bridging

Demirci-Selçuk meet-in-the-middle ( $$\mathcal {DS}$$ - $$\mathsf {MITM}$$ ) attack is an effective and generic method for analyzing iterative block ciphers. It reaches the best results on attacking AES in the single-key model. In ASIACRYPT 2018, a tool for finding $$\mathcal {DS}$$ - $$\mathsf {MITM}$$ attack automatically based on general constraint programming was put forward, which can not only enumerate $$\mathcal {DS}$$ - $$\mathsf {MITM}$$ distinguishers, but also partly automate the key-recovery process. However, the constraint programming models generated by this tool do not consider the key-bridging technique, which has been shown to be effective in reducing the complexities of many cryptanalytic attacks. In this work, we build a general constraint model for SKINNY-128–384 (the same target as the ASIACRYPT 2018 paper) integrated with the key-bridging technique. As a result, the time complexity of the key-recovery attack on SKINNY-128–384 is significantly reduced from $$2^{382.46}$$ to $$2^{366.28}$$ .
Density Based Clustering Data Association Procedure for Real–Time HFSWRs Tracking at OTH Distances

In order to efficiently cover maritime areas at over the horizon (OTH) distances and thus increase marine safety in a nation's exclusive economic zone (EEZ), a network of maritime sensors built around High Frequency Surface Wave Radars (HFSWR) can be an excellent choice.The critical parameter for success of the deployed sensor network is a real time tracking of all detected vessels.During the tracking process, data association (DA) is the first step and it defines the complexity and thus the speed of the whole tracking process.This paper presents a density based clustering DA procedure where the cluster complexity determines the applied DA procedure within the cluster itself.It is demonstrated that the great majority of clusters (over 98 % of all clusters in the worst case) may be processed in a timely manner with an optimal DA procedure, or more precisely, a Joint Probability Data Association (JPDA).However, a small number of unusually large clusters (less than 2 % of all clusters) requires the application of a sub-optimal DA procedure, more accurately, the Roecker's suboptimal JPDA algorithm, in order to maintain real time performance of the whole tracking process.Moreover, unlike standard JPDA procedure which tends to be inapplicable for real time tracking in heavily cluttered environment, the density based clustering DA procedure presented here provides real time performances in the very same environment.The whole analysis is done on real HFSWR data obtained from two HFSWR, located in the Gulf of Guinea.The data set used for the experiments includes data obtained during a month and a half of constant HFSWR operation.
Visualization for Quality Healthcare: Patient Flow Exploration

The health and medical institutes and hospitals are facing several challenges in the short and long term, i.e. demographic changes, demands on improving quality, limited resources and cost requirements. To cope with these challenges, the health care sector needs to become more efficient, while maintaining and improving the quality of care. Efficient and transparent information flow across the whole system plays a key role even in this context. Being able to analyze the patient flows as the first step will provide the possibility to more efficiently manage medical resources and better serve incoming patients. In this work, we have developed a web-based portal for interactive analysis of patient flow data to assist hospital authorities to improve and optimize the time and quality of the provided services. The developed tool facilitates short and long term optimization of resource allocation by analyzing the past, as well as current, patient flows, identifying bottlenecks and exploring the reasons for the occurred waiting times.
Subspace Clustering with Active Learning

Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering. It assumes that data originate from a union of subspaces, and clusters the data depending on the corresponding subspace. In practice, it is reasonable to assume that a limited amount of labels can be obtained, potentially at a cost. Therefore, algorithms that can effectively and efficiently incorporate this information to improve the clustering model are desirable. In this paper, we propose an active learning framework for subspace clustering that sequentially queries informative points and updates the subspace model. The query stage of the proposed framework relies on results from the perturbation theory of principal component analysis, to identify influential and potentially misclassified points. A constrained subspace clustering algorithm is proposed that monotonically decreases the objective function subject to the constraints imposed by the labelled data. We show that our proposed framework is suitable for subspace clustering algorithms including iterative methods and spectral methods. Experiments on synthetic data sets, motion segmentation data sets, and Yale Faces data sets demonstrate the advantage of our proposed active strategy over state-of-the-art.
An Analysis of Automated Technical Debt Measurement

Background: Measuring and understanding Technical Debt (TD) is quite complex since there are a number of different definitions and techniques that have been proposed in the last few years and it is not clear which ones should be used in which conditions. The approaches proposed are almost never based on the existing ones and their validation is often performed in a very limited number of projects. For this reasons, practitioners are confused and find difficult to apply such approaches in their projects. Goals: This paper investigates the available techniques for evaluating TD using automated tools aiming at helping practitioners and researcher in understanding the available options and apply them correctly. Method: The study has been performed as a Systematic Literature Review (SLR) applied to 835 studies obtained from the three largest digital libraries and databases. Results: After applying all filtering stages, 38 papers out of 835 have been selected and analyzed in depth. Almost all of them propose novel approaches to measure TD using different criteria and they do not extend or validate existing approaches. Conclusions: The area is not mature and it lacks independent evaluations of the models proposed. Authors focus on proposing new approaches and no consolidation can be identified. Moreover, almost all the approaches proposed are automated only partially and through prototype tools designed just to support the studies analyzed in the paper in which the approach is proposed and rarely maintained. These facts makes difficult the application of such methods by practitioners.
A Brief Analysis of Data Mining Techniques

Today, we live in the age of information. And we all know, where there is information, there is data. And currently, the amount of information present is humongous. Worse, every day the amount of information present is increasing. As a direct consequence of that, there is an enormous amount of data that is being generated on a day to day basis and the majority of that data is generally noise data (meaningless data). The data that is useful to us is quite less and is hidden in between all the useless data. Finding the useful data is like finding a pin in the haystack. This is where Data Mining and its techniques come to play. The data mining is the process of searching and then segmenting the data by discovering useful and interesting patterns as well as descriptive and predictive models from large scale data [1]. The objective of this paper is to review, analyze and then have the application of the different techniques which are used in Data mining and the discovery of knowledge in databases.
Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection

Internet of Things (IoT) enabled geriatric healthcare systems have gained importance in the recent years due to an increase in the number of elderly people living alone. The application of machine learning (ML) in areas of geriatric healthcare such as fall detection, has, consequently been an area of active research. Wearable systems for fall detection has the advantage of being light-weight and low power-consuming, yet reasonably accurate without being overly intrusive. However, the accuracy of fall detection using wearable systems depends, among other factors, on the types of sensors embedded in them. The use of inertial motion unit (IMU) sensors for fall detection, combined with machine learning classifiers applied on datasets collected from IMU sensors is an area of active research. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We compare the performance of various ML algorithms on the dataset we collected for this purpose. We also perform statistical analysis to examine the relative importance of the various features on the behavior of ML classifiers.
MODEA Based on Multi-Population Strategy With Adaptive Weight and Its Application to Electromagnetic Device Optimization

Various intelligent algorithms are applied in optimization design, and the differential evolution (DE) algorithm is widely applied with its excellent convergence speed and convergence precision. This study analyzed the advantages and disadvantages of the existing multi-objective differential evolution (MODE) algorithm, and developed a MODE algorithm based on the adaptive weight and the multi-population strategy (MODE/AWMS). The proposed algorithm was verified using test functions. MODE/AWMS exhibited certain advantages compared with several other multi-objective optimization algorithms. Taking a polarized magnetic relay as an example, MODE/AWMS was used to optimize its key parameters by establishing a rapid calculation model of its electromagnetic mechanism. The electromagnetic force (EMF) of the release position was improved, which verified the validity of MODE/AWMS.
Video Person Re-Identification using Learned Clip Similarity Aggregation

We address the challenging task of video-based person re-identification. Recent works have shown that splitting the video sequences into clips and then aggregating clip-based similarity is appropriate for the task. We show that using a learned clip similarity aggregation function allows filtering out hard clip pairs, e.g. where the person is not clearly visible, is in a challenging pose, or where the poses in the two clips are too different to be informative. This allows the method to focus on clip-pairs which are more informative for the task. We also introduce the use of 3D CNNs for video-based re-identification and show their effectiveness by performing equivalent to previous works, which use optical flow in addition to RGB, while using RGB inputs only. We give quantitative results on three challenging public benchmarks and show better or competitive performance. We also validate our method qualitatively.
Reinforcement Learning based Autonomous Vehicle for Exploration and Exploitation of Undiscovered Track

This research focuses on autonomous traversal of land vehicles through exploring undiscovered tracks and overcoming environmental barriers. Most of the existing systems can only operate and traverse in a distinctive mapped model especially in a known area. However, the proposed system which is trained by Deep Reinforcement learning can learn by itself to operate autonomously in extreme conditions. The dynamic double deep Q-learning (DDQN) model enables the proposed system not be confined to only in known environments. The ambient environmental obstacles are identified through Faster R-CNN for smooth movement of the autonomous vehicle. The exploration and exploitation strategies of DDQN enables the autonomous agent to learn proper decisions for various dynamic environments and tracks. The proposed model is tested in a gaming environment. It shows the overall effectiveness in traversing of autonomous land vehicles. The goal is to integrate Deep Reinforcement learning and Faster R-CNN to make the system effective to traverse through undiscovered paths by detecting obstacles.
Wi-Fi Enabled Speech Automated Guided Vehicle using Android and NodeMCU

Given the high mortalities on our roads its imperative that a solution be implemented to mitigate this issue. Although, different conventional methods of providing safety for vehicle operators are in place, there are needs for further improvements. This paper implements an automated guided vehicle that uses NodeMCU as the central intelligence of the system, with the use of ultrasonic sensor for the detection and avoidance of obstacles. It uses voice commands from an Android smartphone using Wi-Fi communication. Experimental studies were conducted to investigates the response of vehicle to the given voice instruction for different directional movement, and obstacle detection with the avoidance of collision. The study achieved very promising results with accuracy, sensitivity and precision rates of 100% for directional movement, with sensitivity and accuracy rates of 70%, and 85% for obstacle detection in the right direction and sensitivity and accuracy rates of 80%, and 90% for obstacle detection in the left direction with the precision rates of 100% at both left and right directions.
A Modular Diversity Based Reviewer Recommendation System

A new approach for solving the problem of reviewer recommendation for conference or journal submissions is proposed. Instead of assigning one best reviewer and then looking for a second-best match, we want to start from a single reviewer and look for a diverse group of other possible candidates, that would complement the first one in order to cover multiple areas of the review. We present the idea of an overall modular system for determining a grouping of reviewers, as well as three modules for such a system: a keyword-based module, a social graph module, and a linguistic module. The added value of modular diversity is seen primarily for larger groups of reviewers. The paper also contains a proof of concept of the method.
Blind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3D‐DCT coefficients

There is an urgent need for a robust video quality assessment (VQA) model that can efficiently evaluate the quality of a video content varying in terms of the distortion and content type in the absence of the reference video. Considering this need, a novel no reference (NR) model relying on the spatiotemporal statistics of the distorted video in a three-dimensional (3D)-discrete cosine transform (DCT) domain is proposed in this study. While developing the model, as the first contribution, the video contents are adaptively segmented into the cubes of different sizes and spatiotemporal contents in line with the human visual system (HVS) properties. Then, the 3D-DCT is applied to these cubes. Following that, as the second contribution, different efficient features (i.e. spectral behaviour, energy variation, distances between spatiotemporal frequency bands, and DC variation) associated with the contents of these cubes are extracted. After that, these features are associated with the subjective experimental results obtained from the EPFL-PoliMi video database using the linear regression analysis for building the model. The evaluation results present that the proposed model, unlike many top-performing NR-VQA models (e.g. V-BLIINDS, VIIDEO, and SSEQ), achieves high and stable performance across the videos with different contents and distortions.
CompressNet: Generative Compression at Extremely Low Bitrates

Compressing images at extremely low bitrates (<; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. With the increasing need to transfer large amounts of images with limited bandwidth, compressing images to very low sizes is a crucial task. However, the existing methods are not effective at extremely low bitrates. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bi-trates (<; 0.1 bpp). We benchmark the performance of our proposed method on the Cityscapes dataset, evaluating over different metrics at very low bitrates showing that our method outperforms the other state-of-the-art. In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods.
Masking Salient Object Detection, a Mask Region-Based Convolutional Neural Network Analysis for Segmentation of Salient Objects

In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Object Detection (SOD) context. Studies in the SOD literature usually explore architectures based in FCNs to detect salient regions and objects in visual scenes. However, besides the promising results achieved, FCNs showed issues in some challenging scenarios. Fairly recently studies in the SOD literature proposed the use of a Mask-RCNN approach to overcome such issues. However, there is no extensive comparison between the two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs over FCN when segmenting salient objects. Aiming to effectively show the superiority of Mask-RCNNs over FCNs in the SOD context, we compare two variations of Mask-RCNNs with two variations of FCNs in eight datasets widely used in the literature and in four metrics. Our findings show that in this context Mask-RCNNs achieved an improvement on the F-measure up to 47% over FCNs.
Optimizing Latency Sensitive Applications for Amazon's Public Cloud Platform

Recent cloud technologies enable a diverse set of novel applications with capabilities never seen before. Cloud native programming, microservices, serverless architectures are novel paradigms reducing the burden on both software developers and operators while enabling cloud-grade service deployments. Several types of applications fit in well with the new concepts, however, latency sensitive applications with strict delay constraints pose additional challenges on the platforms. Can we run these applications on today's public cloud platforms making use of the brand new tools and techniques? In this paper, we try to answer this question by addressing one of the most widely used and versatile public cloud platforms, namely Amazon's AWS, and we propose a novel mechanism to optimize the software "layout" based on dynamic performance measurements. Our contribution is threefold. First, we define a combined performance and cost model on CaaS/FaaS (Container/Function as a Service) platforms, specifically for AWS, based on a comprehensive performance analysis, and we also provide an application model capturing the performance requirements. Second, we formulate an optimization problem which minimizes the deployment costs on AWS while meeting the latency constraints. A polynomial algorithm finding the optimal solution is also given. Third, we evaluate the model and the algorithm for different scenarios and investigate the performance on today's system.
Blockchain - Bringing Accountability in the Public Distribution System

The Central and State governments of India uses the Public Distribution System(PDS) to distribute food grains to the citizen of India at subsidized rates to ensure food security. The system is prone to losses of food grains because of prevalent corruption among the officials serving the system and also due to physical losses during storage and transport. Blockchains have become a commonly used audit trail verification utility because of its inherent nature, that it is a distributed ledger for recording transactions. This makes it a key candidate to replace the current centralized system that exists for the tracking of commodities being distributed by the PDS. Like any centralized system the current centralized system can be sabotaged by the people in administration, as it relies heavily on trust on the central authority. In this paper we propose an intelligent scheme for tracking the commodities in PDS using IoT based sensors that monitor the arrival and dispatch of commodities and generate corresponding transactions. These transactions should be stored permanently to keep track of the commodities for which a decentralized Blockchain based system is used that continuously logs every valid transaction. As the amount of transactions increase so does the data to be stored on the blockchain, hence we propose a hierarchical blockchain approach where in transaction summaries of a local blockchain are recorded as transactions in a higher level blockchain after which the local blockchains can start afresh recording newer transactions only. This approach results in reduction in the amount of data to be stored on the blockchain. At the same time one can find aggregate information at a higher level which can be used to determine the supply demand ratio. Anyone can verify the current availability of the food grains throughout the distribution network of the PDS and track the commodities right from the network of warehouses to the end point sales-the fair price shops where the food grains are delivered to the consumers. These transactions can be audited at any time as all transactions are recorded in the Blockchain which can prevent any misuse or corruption.