Reviewerly/exHarmony huggingface dataset:
Heuristic Approach for Countermeasure Selection Using Attack Graphs

Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.
Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine

Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in which the endpoint carbon content and temperature are two important indicators. In BOF steelmaking, the quality of raw materials varies greatly between different batches, which would lead to the inaccurate predictions for these two indicators. Additionally, there are imbalance problems in production process data of BOF steelmaking. For the time-varying problem, a novel similarity criterion based on von-Mises Fisher mixture model (VMM) is proposed in this paper and applied for sample selection of just-in-time-learning (JITL)-based endpoint carbon content and temperature prediction model. The V-shaped transfer function is utilized to develop weighted extreme learning machine (WELM) as local regression model to address the imbalance problems. The performance of the proposed methods is compared with other methods under JITL framework. The experimental results show that the proposed online model can provide a more accurate prediction.
Exploring the Personal Informatics Analysis Gap: “There's a Lot of Bacon”

Personal informatics research helps people track personal data for the purposes of self-reflection and gaining self-knowledge. This field, however, has predominantly focused on the data collection and insight-generation elements of self-tracking, with less attention paid to flexible data analysis. As a result, this inattention has led to inflexible analytic pipelines that do not reflect or support the diverse ways people want to engage with their data. This paper contributes a review of personal informatics and visualization research literature to expose a gap in our knowledge for designing flexible tools that assist people engaging with and analyzing personal data in personal contexts, what we call the personal informatics analysis gap. We explore this gap through a multistage longitudinal study on how asthmatics engage with personal air quality data, and we report how participants: were motivated by broad and diverse goals; exhibited patterns in the way they explored their data; engaged with their data in playful ways; discovered new insights through serendipitous exploration; and were reluctant to use analysis tools on their own. These results present new opportunities for visual analysis research and suggest the need for fundamental shifts in how and what we design when supporting personal data analysis.
An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.
IMAGINE: Image Synthesis by Image-Guided Model Inversion

We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations via matching multi-level feature representations in the classifier, associated with adversarial training with an external discriminator. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process. With extensive experimental results, we demonstrate qualitatively and quantitatively that IMAGINE performs favorably against state-of-the-art GAN-based and inversion-based methods, across three different image domains (i.e., objects, scenes, and textures).
Deterministic one-way logic gates on a cloud quantum computer

One-way quantum computing is a promising candidate for fault-tolerant quantum computing. Here, we propose new protocols to realize a deterministic one-way CNOT gate and one-way $X$-rotations on quantum-computing platforms. By applying a delayed-choice scheme, we overcome a limit of most currently available quantum computers, which are unable to implement further operations on measured qubits or operations conditioned on measurement results from other qubits. Moreover, we decrease the error rate of the one-way logic gates, compared to the original protocol using local operations and classical communication (LOCC). In addition, we apply our deterministic one-way CNOT gate in the Deutsch-Jozsa algorithm to show the feasibility of our proposal. We demonstrate all these one-way gates and algorithms by running experiments on the cloud quantum-computing platform IBM Quantum Experience.
Astra: Autonomous Serverless Analytics with Cost-Efficiency and QoS-Awareness

With the ability to simplify the code deployment with one-click upload and lightweight execution, serverless computing has emerged as a promising paradigm with increasing popularity. However, there remain open challenges when adapting data-intensive analytics applications to the serverless context, in which users of serverless analytics encounter with the difficulty in coordinating computation across different stages and provisioning resources in a large configuration space. This paper presents our design and implementation of Astra, which configures and orchestrates serverless analytics jobs in an autonomous manner, while taking into account flexibly-specified user requirements. Astra relies on the modeling of performance and cost which characterizes the intricate interplay among multi-dimensional factors (e.g., function memory size, degree of parallelism at each stage). We formulate an optimization problem based on user-specific requirements towards performance enhancement or cost reduction, and develop a set of algorithms based on graph theory to obtain optimal job execution. We deploy Astra in the AWS Lambda platform and conduct real-world experiments over three representative benchmarks with different scales. Results demonstrate that Astra can achieve the optimal execution decision for serverless analytics, by improving the performance of 21% to 60% under a given budget constraint, and resulting in a cost reduction of 20% to 80% without violating performance requirement, when compared with three baseline configuration algorithms.
SurveilNet: A Lightweight Anomaly Detection System for Cooperative IoT Surveillance Networks

The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process. However, the isolated setup of existing surveillance systems makes ADM inefficient and susceptible to staleness due to the lack of resource sharing and continuous learning (CL). CL is the incremental development of models that adapts continuously to the external world. Thus, for efficient CL in surveillance systems, devices must share resources and cooperate with neighbor sites. Yet, solutions from the literature focus on the isolated environment thereby neglecting the need for resource sharing and CL. To address this gap, this paper proposes a cooperative surveillance system called SurveilNet that allows for resource sharing between surveillance sites under the control of a cooperator node. We further propose a lightweight subscription scheme that allows for a joint specialized model development process that continually adapts to the dynamics of the secured environment. Our proposed scheme offers the ability to learn from the neighboring site’s data without compromising data privacy. The performance of our scheme is evaluated using a reclassified UCF-Crime dataset with the result showing the efficiency of our proposed scheme when compared to the state-of-the-art.
Hybrid Bird Mating Optimizer With Single-Based Algorithms for Combinatorial Optimization Problems

Bird mating optimizer (BMO) is a population-based metaheuristic that has been recently extended to solve combinatorial optimization problems. Even though the algorithm shows promising performance in solving combinatorial optimization problems, it suffers from slow convergence and poor efficiency which leads to poor solution quality for some problem instances. This is due to the limited capability of BMO in exploiting the search space and identifying more promising regions. Therefore, in this work we propose a hybrid BMO with five single-based metaheuristics: hill-climbing, late acceptance hill-climbing, simulated annealing, iterated greedy heuristic and variable iterated greedy heuristic. Each of these algorithms is used inside the BMO to exploit the search space, and improve the quality of solution generated from the BMO population. This work also compares which one of these five is better for hybridizing with BMO. The performance of these algorithms is tested on two combinatorial problems: travelling salesman problem and berth allocation problem. Experimental results demonstrate that the hybrid algorithm is superior to BMO when applied to both problems and it improved the BMO by 1.13% for BAP and by 4.13% for TSP. Furthermore, the hybrid algorithm is able to match the best-known results for most of the instances. In addition, the proposed hybrid approaches perform well over both tested domains and obtain competitive results when compared to the best-known results that have previously been presented in the scientific literature.
Does government education expenditure affect educational outcomes? New evidence from sub‐Saharan African countries

Abstract The human capital crisis, reflected in the weak global competitiveness of African education, has questioned the effectiveness of public spending in increasing educational outcomes on the continent. Thus, this article examines the impact of government education expenditure on educational outcomes in 31 sub‐Saharan African (SSA) countries from 2000 to 2019 based on a generalized method of moments (GMM). The study sheds light on the priorities of government education spending on the continent. Findings showed that the effect of government education spending on educational outcomes in SSA was driven by the measure of educational outcome used. Government spending in Africa had focused mainly on primary and secondary education to the detriment of tertiary education because it is convenient and generates political gains. Due to institutional rigidities that emanate from the governance structure, the inequitable allocation of government funding had made higher education in Africa less responsive to the changes in global knowledge and labour market demands. Therefore, the following policy agenda becomes imperative in SSA: (i) government education spending should equitably target all education levels to improve the aggregate human capital development indicators in the region; (ii) there is a need to enhance government institutions' capacity to increase their level of effectiveness and performance.
A Novel Automating Irrigation Techniques based on Artificial Neural Network and Fuzzy Logic

Abstract India is primarily an agrarian society and it is important to develop various irrigation techniques as the nature of soil varies drastically spatially. Irrigation scheduling are mainly depends upon monitoring of soil, crop, water available and weather conditions. Major limitation of this system is the under-irrigation or over-irrigation, which affects the SAR ratio of soil or excess runoff generation respectively. Effective implementation of irrigation technique is required to enhance the productivity of less fertile soil. Irrigation with variable rate and intelligent control-based system is required for the enhancement of irrigation system and higher crop yield. Study was conducted for the performance evaluation of irrigation system using ANN and Fuzzy Logic Toolbox of MATLAB. Moisture available, rainfall, evapotranspiration and water supply for wheat crop were taken as input and output variable were performance of irrigation system. Results of different irrigation techniques were evaluated and best technique were identified under their specification.
An effective decomposition-enhancement method to restore light field images captured in the dark

Light field (LF) cameras can capture the scenes from different directions, which enables many applications, such as depth estimation and saliency detection. However, they can be seriously affected when there is poor luminance. This work focuses on LF image restoration under low-light conditions, which is a challenging task to deal with noise, color distortion and multiple views. We develop a learning-based decomposition-enhancement method to decouple this complex task into several sub-tasks. First, there is a lightweight DecomNet with spatial-angular residual modules to decompose each input LF image into its reflectance and illumination. Then, there is a reflectance EnhanceNet for noise suppression and color correction, and an illumination EnhanceNet for luminance recovery. They integrate a multi-scale pooling attention mechanism to extract more informative spatial and angular features, and a deep spatial-angular feature fusion group with selective fusion units to fully encode and fuse the spatial-angular information with high efficiency. Extensive experimental results have demonstrated that our method can restore the spatial details, geometric structures, luminance and color of LF images with various light levels effectively.
Machine Translation of English Language to Mizo Language

The communication between different regional languages is always inevitable fact which requires a lot of effort to make meaningful in terms of technology. The machine translation methods have been endeavoured to fill this impediment. The Neural Machine Translation is one of the efforts which recently gained a remarkable enhancement in the matter of human judgement over the conventional approaches like phrase-based machine translation and statistical machine translation. Thus, the advances of neural machine translation approach influence the challenge in the mind of people. The different types of online translation and mobile application tools such as Google Translate, DeepL, SYSTRAN, etc. applications were came up to tackle this issue. But such kinds of convenience translation tools are not available for English-Mizo parallel languages. In this paper, we have tried to enhance the ability of language translation by owing the supremacy capability of Neural Machine Translation to improve the English to Mizo conversion in BiLingual Evaluation Understudy metric.
Garbage In, Garbage Out: Poisoning Attacks Disguised With Plausible Mobility in Data Aggregation

Location data are often aggregated to support a wide range of applications, such as mobility management, point-of-interest recommendation, map inferences, and so on. However, these aggregated results might suffer from poisoning attacks when adversaries deliberately send poisoned locations to the aggregator, namely, garbage in, garbage out. Most existing work focuses on poisoning attacks in anomaly detection, crowd sensing, recommendation, and machine learning models. One observation motivating our work is that poisoning attacks in the context of data aggregation introduce new challenges along with distinguished features compared to anomaly detection, recommendation systems and machine learning models. As such, in this paper, we concentrate on the inputs to data aggregation and introduce the first optimal attack framework that launches <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> oisoning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ttacks on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> ocation <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ggregation (PALDA) and disguises the attacking behaviors. Specifically, we first formalize the poisoning attack as a min-max optimization problem and then design an iterative algorithm to address the optimization problem. Moreover, we theoretically prove the convergence of the proposed optimization algorithm and extend the attack strategy optimization to more practical settings where PALDA is applicable to any linear decomposable aggregation model executed by an aggregator. Finally, simulations on six real-world mobility datasets have demonstrated the risks entailed by poisoning attacks with data aggregation.
A 4.29nJ/pixel Stereo Depth Coprocessor With Pixel Level Pipeline and Region Optimized Semi-Global Matching for IoT Application

The semi-global matching (SGM) algorithm in stereo vision is a well-known depth-estimation method since it can generate dense and robust disparity maps. However, the real-time processing and low power dissipation, the specifications of the Internet-of-Thing (IoT) applications, are challenging for their computational complexity. In this paper, we propose a hardware-oriented SGM algorithm with pixel-level pipeline and region-optimized cost aggregation for high-speed processing and low hardware-resource usage. Firstly, the matching costs in a region are integrated with an optimization strategy to significantly reduce memory usage and improve the processing speed of the cost aggregation. Then, a two-layer parallel two-stage pipeline (TPTP) architecture, which enables pixel-level processing, is designed to calculate two directions (0° and 135°) aggregation to further solve the crucial computational bottleneck of the SGM algorithm. Finally, the architecture is demonstrated on a low-cost XILINX Spartan-7 device and an advanced Stratix-V FPGA device for VGA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> ) depth estimation. The experimental results show that the proposed architecture with compact hardware architecture also ensures accuracy. The pixel-level pipeline architecture enables a processing speed of 355 frames per second (fps) at 109MHz on the Spartan-7 FPGA device and 508 fps at 156MHz on the Stratix-V FPGA. Besides, the coprocessor respectively achieves an energy efficiency of 4.74 nJ/pixel with a power dissipation of 517mW and 4.29nJ/pixel with a power dissipation of 669mW on these two FPGAs.
Thermal-Mask – A Dataset for Facial Mask Detection and Breathing Rate Measurement

This paper demonstrates the usability of thermal video for facial mask detection and the breathing rate measurement. Due to the lack of available thermal masked face images, we developed a dataset based on the SpeakingFaces set, by generating masks for the unmasked thermal images of faces. We utilize the Cascade R-CNN as the thermal facial mask detector, identifying masked and unmasked faces, and whether the mask colour indicates a inhale or exhale state. The latter is used to calculate the breathing rate. The proposed Cascade R-CNN is a multi-stage object detection architecture composed of detectors trained with increasing Intersection-of-Unions thresholds. In our experiments on the Thermal-Mask dataset, the Cascade R-CNN achieves 99.7% in precision, on average, for the masked face detection, and 91.1% for recall. To validate our approach, we also recorded a small set of videos with masked faces to measure the breathing rate. The accuracy result of 91.95% showed a promising advance in identifying possible breath abnormalities using thermal videos, which may be useful in screening subject for COVID-19 symptoms.
Entanglement Dynamics Governed by Time-Dependent Quantum Generators

In the article, we investigate entanglement dynamics defined by time-dependent linear generators. We consider multilevel quantum systems coupled to an environment that induces decoherence and dissipation, such that the relaxation rates depend on time. By applying the condition of partial commutativity, one can precisely describe the dynamics of selected subsystems. More specifically, we investigate the dynamics of entangled states. The concurrence is used to quantify the amount of two-qubit entanglement in the time domain. The framework appears an efficient tool for investigating quantum evolution of entangled states driven by time-local generators. In particular, non-Markovian effects can be included to observe the restoration of entanglement in time.
Modeling and evaluating beat gestures for social robots

Abstract Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation .