Reviewerly/exHarmony huggingface dataset:
RIMI

With the advent of the Internet of Things, embedded systems have become widely used in various fields. Concurrently, the security of these systems has become a concern for many. However, security features that are already available for high-end systems have not been provided in low-end embedded systems due to its negative impact on cost and power consumption. Thus, to increase security with low overhead, many studies to implement the memory isolation approach to these systems have been conducted. However, existing techniques for this approach have suffered from problems in terms of scalability or performance. To mitigate such problems, we present RIMI, a new instruction extension to provide memory isolation in embedded systems. Thanks to instructions in RIMI, we can implement an instruction-level memory isolation where the access permission is bound to each memory and control transfer instructions. We implemented the RIMI prototype on a RISC-V architecture, which is a prominent open-source instruction set architecture (ISA). Our evaluation results show that existing security solutions, i.e., shadow stacks and in-process isolation, can be efficiently implemented with RIMI.
Viewpoint robust knowledge distillation for accelerating vehicle re-identification

Abstract Vehicle re-identification is a challenging task that matches vehicle images captured by different cameras. Recent vehicle re-identification approaches exploit complex deep networks to learn viewpoint robust features for obtaining accurate re-identification results, which causes large computations in their testing phases to restrict the vehicle re-identification speed. In this paper, we propose a viewpoint robust knowledge distillation (VRKD) method for accelerating vehicle re-identification. The VRKD method consists of a complex teacher network and a simple student network. Specifically, the teacher network uses quadruple directional deep networks to learn viewpoint robust features. The student network only contains a shallow backbone sub-network and a global average pooling layer. The student network distills viewpoint robust knowledge from the teacher network via minimizing the Kullback-Leibler divergence between the posterior probability distributions resulted from the student and teacher networks. As a result, the vehicle re-identification speed is significantly accelerated since only the student network of small testing computations is demanded. Experiments on VeRi776 and VehicleID datasets show that the proposed VRKD method outperforms many state-of-the-art vehicle re-identification approaches with better accurate and speed performance.
Green Cloud Communication System for Big Data Management

This paper makes an effort to survey and study the open challenges in the field of energy-efficient and green Cloud infrastructures. The useful software that offers the possibility to implement and evaluate Cloud environments is CloudSim, which we also use in order to demonstrate and propose our idea. Moreover, we consider the CloudSim's simulator architecture in order to achieve our proposal. Consequently, we investigate and propose a system framework for better use of Big Data management, based on Cloud federated network. Additionally, we propose an algorithm for achieving an energy-efficient resource allocation technique for Big Data management on Green Cloud environment. The experimental results demonstrate that our proposed model has immense potential as it offers significant performance gains regarding the cost saving and the better data management under large workload scenarios.
An annotated association mining approach for extracting and visualizing interesting clinical events

This work aims at deriving interesting clinical events using association rule mining based on a user-annotated order of clinical features. A user specifies a partial temporal order of features by indexing features of interest, with repeated and bundled indexes allowed as needed. An association mining algorithm plugin was designed to generate rules that adhere to the user-specified temporal order. The plugin uses temporal and sequence constraints to reduce rule permutations early in the rule generation process. The method was evaluated with a large medical claims dataset to generate clinical events. Using the plug-in algorithm, the database is scanned to calculate the support of item sequences whose sequential order conforms with the user annotated feature order. In our experiments with 20,000 medical claim data records, our method generated rules in a significantly less time than the standalone Apriori algorithm. Our approach generates dendrograms to organize the rules into meaningful hierarchies and provides a graphical interface to navigate the rules and unfold interesting clinical events. Since many associations in healthcare are of sequential nature, some of the derived rules may describe interesting clinical flows or events, while others may be contextually irrelevant. Our method exploits user-specified sequence constraints to eliminate irrelevant rules and reduce rule permutations, speeding up rule mining. This work can be the foundation for future association rule mining studies to extract sequential events based on interestingness. The work can support clinical education where the instructor defines feature sequence constraints, and students unfold and examine extracted sequential rules.
SD-CPC: SDN Controller Placement Camouflage based on Stochastic Game for Moving-target Defense

The modern paradigm of Software Defined Network (SDN) inspired the research community to develop more innovative and transformative solutions for large scale and complex crucial Cyber Physical Systems (CPS). However, the recent destructive attacks targeting such CPS applications, raised the need to initially secure and scale the core SDN network. These attacks may target the heart of SDN networks itself formed by the controllers. Therefore, recent research works discussed the Controller Placement Problem (CPP) to determine the required number of SDN controller(s) and their optimal locations in synonyms wide area networks. A few of the presented solutions discussed the network security aspect to solve this problem. In addition, none of them addressed the need to secure and dynamically change SDN controller location to confuse attackers. Therefore, in this paper, we introduce a new concept of Controller Placement Camouflage (CPC) to dynamically change the attack surface (SDN controller placements) for Moving-target Defense (MTD). The new paradigm of MTD imposes various asymmetric offensive and defensive modes against cyber-adversaries. We relied on the Zero-Sum game as a stochastic game between the system defender and the attacker to guide our MTD solution. In addition, our presented solution is adapted to consider the network vulnerabilities, evaluate the risk level of the system in real-time using Bayesian Attack Graph (BAG), to frequently shift the SDN controller(s) location. Furthermore, we used the Smart Grid as a case study for a mission critical CPS application. We conducted a scenario of UK Bulk Demand Points (BDP), 50MVA generation network capacity map to simulate a real mini-grid network topology. To the best of our knowledge, this research work may be considered the first introduction for SD-CPC for MTD.
Mutually Unbiased Equiangular Tight Frames

An equiangular tight frame (ETF) yields a type of optimal packing of lines in a Euclidean space. ETFs seem to be rare, and all known infinite families of them arise from some type of combinatorial design. In this paper, we introduce a new method for constructing ETFs. We begin by showing that it is sometimes possible to construct multiple ETFs for the same space that are “mutually unbiased” in a way that is analogous to the quantum-information-theoretic concept of mutually unbiased bases. We then show that taking certain tensor products of these mutually unbiased ETFs with other ETFs sometimes yields infinite families of new complex ETFs.
On the Defect Prediction for Large Scale Software Systems – From Defect Density to Machine Learning

As the software industry transitions to software-as-a-service (SAAS) model, there has been tremendous competitive pressure on companies to improve software quality at a much faster rate than before. The software defect prediction (SDP) plays an important role in this effort by enabling predictive quality management during the entire software development lifecycle (SDLC). The SDP has traditionally used defect density and other parametric models. However, recent advances in machine learning and artificial intelligence (ML/AI) have created a renewed interest in ML-based defect prediction among academic researchers and industry practitioners. Published studies on this subject have focused on two areas, i.e. model attributes and ML algorithms, to develop SDP models for small to medium sized software (mostly opensource). However, as we present in this paper, ML-based SDP for large scale software with hundreds of millions of lines of code (LOC) needs to address challenges in additional areas called "Data Definition" and "SDP Lifecycle." We have proposed solutions for these challenges and used the example of a large-scale software (IOS-XE) developed by Cisco Systems to show the validity of our solutions.
Poster: Combination of Blockchains to Secure Smart Home Internet of Things

This study focuses on addressing Smart home (internet of things) IoT system devises security risks and suggests solution integrating combination of Blockchains using two approaches: 1) analysis outcome of survey target 270 random people and three interviews of Saudi smart home IoT users, to identify common security concerns in smart home I0T's, 2) State-of-the-art analysis is motivated by comparing existing Blockchain solutions implemented in smart home IoT's, which resolve the current IoT security problems. Finally, we propose a new Smart home IoT system design combined with private and public Blockchain mechanisms which improves users' security concerns.
Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test study validity based on the presence of the expected proliferative lesions in the transgenic mice. We hypothesized that artificial intelligence–based deep learning (DL) could provide decision support for the toxicologic pathologist by screening for the proliferative changes, verifying the expected pattern for the positive control groups. Whole slide images (WSIs) of the lungs, thymus, and stomach from positive control groups were used for supervised training of a convolutional neural network (CNN). A single pathologist annotated WSIs of normal and abnormal tissue regions for training the CNN-based supervised classifier using INHAND criteria. The algorithm was evaluated using a subset of tissue regions that were not used for training and then additional tissues were evaluated blindly by 2 independent pathologists. A binary output (proliferative classes present or not) from the pathologists was compared to that of the CNN classifier. The CNN model grouped proliferative lesion positive and negative animals at high concordance with the pathologists. This process simulated a workflow for review of these studies, whereby a DL algorithm could provide decision support for the pathologists in a nonclinical study.
Resampling with in situ field portable X-ray fluorescence spectrometry (FPXRF) to reduce the uncertainty in delineating the remediation area of soil heavy metals

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (PTE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI = 71.5%) than GWR (RI = 59.68%) and OLSR (RI = 25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G = 0.97) than CoSGS-CorFPXRF (G = 0.95) and SGS (G = 0.91) in the spatial simulation; (iii) High-uncertainty area (30%<PTE<70%) was reduced from 36.55% to 8.7% of the whole study area. It is concluded that the recommended methods are cost-effective to reduce the uncertainty in delineating the remediation areas of soil heavy metals.
Adaptive Context Reading Network for Movie Scene Detection

Video scene detection is the task of temporally segmenting a video into its basic story units called scenes. We propose a temporal context aware scene detection method. For each shot in a video, we store the time-indexed features of its surrounding shots as its context memory. A context-reading operation is performed to read the most relevant information from the memory which is used to update the feature of the query shot. To adaptively determine the temporal scale of context memory for different queries, we apply a bank of context memories of different temporal scales to generate multiple context reads, and adaptively aggregate them according to their confidence scores. The adaptive context-reading is guided by a structure learning objective which encourages each shot to read the most appropriate context such that the global structure of scene can be revealed in the feature space. With the context-aware shot features learned by our method, we perform clustering to find the scene boundaries. Our experiments demonstrate that adaptively modeling temporal context yields the state-of-the-art results on the existing video scene detection datasets. We also construct a large-scale dataset for the task and our ablation studies on it show that the performance gains owe to the proposed adaptive context reading.
Activity recognition in wearables using adversarial multi-source domain adaptation

Human activity recognition (HAR) from wearable sensors data has become ubiquitous due to the widespread proliferation of IoT and wearable devices. However, recognizing human activity in heterogeneous environments, for example, with sensors of different models and make, across different persons and their on-body sensor placements introduces wide range discrepancies in the data distributions, and therefore, leads to an increased error margin. Transductive transfer learning techniques such as domain adaptation have been quite successful in mitigating the domain discrepancies between the source and target domain distributions without the costly target domain data annotations. However, little exploration has been done when multiple distinct source domains are present, and the optimum mapping to the target domain from each source is not apparent. In this paper, we propose a deep Multi-Source Adversarial Domain Adaptation (MSADA) framework that opportunistically helps select the most relevant feature representations from multiple source domains and establish such mappings to the target domain by learning the perplexity scores. We showcase that the learned mappings can actually reflect our prior knowledge on the semantic relationships between the domains, indicating that MSADA can be employed as a powerful tool for exploratory activity data analysis. We empirically demonstrate that our proposed multi-source domain adaptation approach achieves 2% improvement with OPPORTUNITY dataset (cross-person heterogeneity, 4 ADLs), whereas 13% improvement on DSADS dataset (cross-position heterogeneity, 10 ADLs and sports activities).
Underwater image recovery method based on hyperspectral polarization imaging

The quality of underwater imaging can be severely degraded by particles in the turbid water due to absorption and backscatter effects. This study proposes an underwater image recovery method based on hyperspectral polarization imaging. The proposed method is based on a physical degradation model, and combines polarization recovery and spectral fusion methods. Thus, the optimal spectral fusion is realized according to the spectral selectivity of the polarization recovery method, leading to underwater imaging with improved clarity as well as effective colour information restoration. The experimental results show that, compared with other methods, the comprehensive restorative abilities of the method proposed in this study are better with respect to subjective vision and various image quality evaluation indexes. The proposed method is effective for enhancing the visibility and contrast of underwater images for objects comprising different materials and at different imaging distances. Consequently, the proposed method has potential application value in many underwater complex environments.
Adaptive confidence thresholding for monocular depth estimation

Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results across object boundaries and is incapable of handling occlusion explicitly. In this paper, we propose a new approach to leverage pseudo ground truth depth maps of stereo images generated from self-supervised stereo matching methods. The confidence map of the pseudo ground truth depth map is estimated to mitigate performance degeneration by inaccurate pseudo depth maps. To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps. The pseudo depth labels filtered out by the thresholded confidence map are used to supervise the monocular depth network. Furthermore, we propose the probabilistic framework that refines the monocular depth map with the help of its uncertainty map through the pixel-adaptive convolution (PAC) layer. Experimental results demonstrate superior performance to state-of-the-art monocular depth estimation methods. Lastly, we exhibit that the proposed threshold learning can also be used to improve the performance of existing confidence estimation approaches.
A new scalable distributed k-means algorithm based on Cloud micro-services for High-performance computing

The paper aims to propose a distributed clustering method for High performance computing (HPC) models and, its application for medical image processing. The communication cost is one of the great challenges, which minimizes the scalability of parallel and distributed computing models. Indeed, it reduces significantly the performance of HPC systems where these models are assigned to be implemented. In this paper, we present a new distributed k-means method which integrates virtual parallel distributed computing model with a low communication cost mechanism. The k-means method is performed as a distributed service within a cooperative micro-services team which uses asynchronous communication mechanism based on AMQP protocol. We design and implement a parallel and distributed HPC application for MRI image segmentation assigned to be deployed on cloud. Experimental results show that the proposed method (DSCM) and its assigned model reach high degree of scalability. We expect this clustering approach to provide scalable HPC applications for big data clustering.
Optical flow estimation combining with illumination adjustment and edge refinement in livestock UAV videos

The performance of optical flow estimation is affected by many factors. The impact of illumination changes and video quality degradation in unmanned aerial vehicles videos on optical flow estimation cannot be ignored. Inspired by the human retina's visual adaptation mechanism, we propose a mechanism for illumination adjustment that imitates retinal processing in order to reduce illumination variation. We further introduce an edge refinement mechanism into optical flow estimation that is based on a weighted neighborhood filtering. The experimental results on public benchmarks inlcuding KITTI 2012, KITTI 2015, MPI Sintel and Middlebury show that the proposed approach is robust on illumination and preserves accurate motion details. Further, experiments on outdoor livestock UAV videos show that the proposed approach implements illumination robustness and preserves the accurate detection of motion edges in other types of video. The performance of the proposed method on public benchmarks and livestock UAV videos demonstrates that the proposed approach improves motion edge accuracy of optical flow fields in varying illumination.
Face Recognition Using Eigenface Algorithm on Laptop Camera

The eigenface algorithm is a collection of eigenvectors used for face recognition through computers. The face recognition system is part of image processing that recognizes faces based on imagery that is stamped and stored in an image file in JPEG format. Face recognition problems can be solved through the implementation of an algorithm. The algorithm used in this study is the eigenface algorithm. The input image is stamped through a laptop camera with a size of 320 pixels x 240 pixels and reduced to 100 pixels x 100 pixels to be saved as a master file of the face with various facial expressions is a forward-facing position without a smile, facing forward a thin smile, facing forward with a big smile, head tilted to the left, and head tilted to the right. The purpose of this research is to build face recognition software using eigenface algorithms. The results showed that faces could be recognized using the eigenface algorithm with an average accuracy rate of 85%.
A distributed biometric authentication scheme based on blockchain

Biometric authentication is the preferred authentication scheme in modern computing systems. While it offers enhanced usability, it also requires cautious handling of sensitive users' biometric templates. In this paper, a distributed scheme that eliminates the requirement for a central node that holds users' biometric templates is presented. This is replaced by an Ethereum/IPFS combination to which the templates of the users are stored in a homomorphically encrypted form. The scheme enables the biometric authentication of the users by any third party service, while the actual biometric templates of the user never leave his device in non encrypted form. Secure authentication of users in enabled, while sensitive biometric data are not exposed to anyone. Experiments show that the scheme can be applied as an authentication mechanism with minimal time overhead.