Reviewerly/exHarmony huggingface dataset:
Windows Memory Forensics: Detecting (Un)Intentionally Hidden Injected Code by Examining Page Table Entries

Malware utilizes code injection techniques to either manipulate other processes (e.g. done by banking trojans) or hide its existence. With some exceptions, such as ROP gadgets, the injected code needs to be executable by the CPU (at least at some point in time). In this work, we cover and evaluate hiding techniques that prevent executable pages (containing injected code) from being reported by current detection tools. These techniques can either be implemented by malware in order to hide its injected code (as already observed) or can, in one case, unintentionally be taken care of by the operating system through its paging mechanism. In a second step, we present an approach to reveal such pages despite the mentioned hiding techniques by examining Page Table Entries. We implement our approach in a plugin for the memory forensic framework Rekall, which automatically reports any memory region containing executable pages, and evaluate it against own implementations of different hiding techniques, as well as against real-world malware samples.
Harmonic Mean Linear Discriminant Analysis

In machine learning and data mining, dimensionality reduction is one of the main tasks. Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction algorithm and it has attracted a lot of research interests. Classical Linear Discriminant Analysis finds a subspace to minimize within-class distance and maximize between-class distance, where between-class distance is computed using arithmetic mean of all between-class distances. However, arithmetic mean between-class distance has some limitations. First, arithmetic mean gives equal weight to all between-class distances, and large between-class distance could dominate the result. Second, it does not consider pairwise between-class distance and thus some classes may overlap with each other in the subspace. In this paper, we propose two formulations of harmonic mean based Linear Discriminant Analysis: HLDA and HLDAp, to demonstrate the benefit of harmonic mean between-class distance and overcome the limitations of classical LDA. We compare our algorithm with 11 existing single-label algorithms on seven datasets and five existing multi-label algorithms on two datasets. On some single-label experiment data, the classification accuracy absolute percentage increase can reach 39 percent compared to state-of-art existing algorithms; on multi-label data, significant improvement on five evaluation metric has been achieved compared to existing algorithms.
Visual‐Interactive Preprocessing of Multivariate Time Series Data

Abstract Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.
Non-rigid registration of serial section images by blending transforms for 3D reconstruction

In this research, we propose a novel registration method for three-dimensional (3D) reconstruction from serial section images. 3D reconstructed data from serial section images provides structural information with high resolution. However, there are three problems in 3D reconstruction: non-rigid deformation, tissue discontinuity, and accumulation of scale change. To solve the non-rigid deformation, we propose a novel non-rigid registration method using blending rigid transforms. To avoid the tissue discontinuity, we propose a target image selection method using the criterion based on the blending of transforms. To solve the scale change of tissue, we propose a scale adjustment method using the tissue area before and after registration. The experimental results demonstrate that our method can represent non-rigid deformation with a small number of control points, and is robust to a variation in staining. The results also demonstrate that our target selection method avoids tissue discontinuity and our scale adjustment reduces scale change.
SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.
Interpolation With Just Two Nearest Neighboring Weighted Fuzzy Rules

Fuzzy rule interpolation (FRI) enables sparse fuzzy rule-based systems to derive an interpolated conclusion using neighboring rules, when presented with an observation that matches none of the given rules. The efficacy of FRI has been further empowered by the recent development of weighted FRI techniques, particularly the one that introduces attribute weights of rule antecedents from the given rule base, removing the conventional assumption of antecedent attributes having equal weighting or significance. However, such work was carried out within the specific transformation-based FRI mechanism. This short paper reports the results of generalizing it through enhancing two alternative representative FRI methods. The resultant weighted FRI algorithms facilitate the individual attribute weights to be integrated throughout the corresponding procedures of the conventional unweighted methods. With systematical comparative evaluations over benchmark classification problems, it is empirically demonstrated that these algorithms work effectively and efficiently using just two nearest neighboring rules.
Generation of Ball Possession Statistics in Soccer Using Minimum-Cost Flow Network

We present an automatic technique for calculating ball possession statistics from the video of a soccer match. The possession statistics is generated based on the number of valid passes made by an individual team. A valid pass is detected as a split or merge event of the ball with a player. A pass starts when the ball splits from a player. A pass ends when the ball merges with a player. We use a minimum-cost flow network to model number of valid passes in the soccer match. The ball and the players represent the nodes of the network. Each edge of the network is associated with a cost derived from the between-frame correspondences of the ball and the players. The total flow through the network is optimized to track the number of valid passes. Experimental results show that the accuracy of the proposed method is at least 4% better than that of a similar approach.
Foiling covert channels and malicious classical post-processing units in quantum key distribution

Abstract The existing paradigm for the security of quantum key distribution (QKD) suffers from two fundamental weaknesses. First, covert channels have emerged as an important threat and have attracted a lot of attention in security research in conventional information and communication systems. Covert channels (e.g. memory attacks) can fatally break the security of even device-independent quantum key distribution (DI-QKD), whenever QKD devices are re-used. Second, it is often implicitly assumed that the classical post-processing units of a QKD system are trusted. This is a rather strong assumption and is very hard to justify in practice. Here, we propose a new paradigm for the security of QKD that addresses these two fundamental problems. Specifically, we show that by using verifiable secret sharing and multiple optical devices and classical post-processing units, one could re-establish the security of QKD. Our techniques are rather general and they apply to both DI-QKD and non-DI-QKD.
Integrated Coherent Ising Machines Based on Self-Phase Modulation in Microring Resonators

We propose an integrated photonic circuit that acts as an optical coherent Ising machine and simulates its performance on the basis of some example problems. In contrast to previous all-optical approaches, the proposed integrated Ising machine does not require an optical parametric oscillator and can, hence, operate at a single wavelength, reducing the overall design complexity. We present a symmetric nonlinear photonic device as the fundamental building block that can use self-phase modulation in two microring resonators to emulate a continuously tunable, symmetrically bistable Ising node. We derive and verify using numerical simulation the key properties of the single Ising node device and the full Ising machine circuit. We estimate the full Ising machine's tolerance to realistic fabrication errors on the basis of randomly sampled example problems, and we discuss which technologies are required to obtain large-scale systems.
ERIDANUS: Efficiently Running Inference of DNNs Using Systolic Arrays

Systolic arrays with promising attributes, such as high degree of concurrent computation and high data-reuse rate, are attractive solutions for dense linear algebra. Recently, systolic arrays have been used for accelerating the inference of deep neural networks (DNNs). However, as sparsification mechanisms are applied to DNNs during or after training, DNN inference is usually a sparse problem. Therefore, it cannot fully benefit from the fundamental advantages offered by systolic arrays. To solve this challenge, we propose Eridanus, an approach to structured pruning that produces DNNs compatible with the synchronous and rhythmic flow of data from memory to systolic arrays.
DeepFeat: A Bottom-Up and Top-Down Saliency Model Based on Deep Features of Convolutional Neural Networks

A deep feature-based saliency model (DeepFeat) is developed to leverage understanding of the prediction of human fixations. Conventional saliency models often predict the human visual attention relying on few image cues. Although such models predict fixations on a variety of image complexities, their approaches are limited to the incorporated features. In this paper, we aim to utilize the deep features of convolutional neural networks by combining bottom-up (BU) and top-down (TD) saliency maps. The proposed framework is applied on deep features of three popular deep convolutional neural networks (DCNNs). We exploit four evaluation metrics to evaluate the correspondence between the proposed saliency model and the ground-truth fixations over two datasets. The results demonstrate that the deep features of pretrained DCNNs over the ImageNet dataset are strong predictors of the human fixations. The incorporation of BU and TD saliency maps outperforms the individual BU or TD implementations. Moreover, in comparison to nine saliency models, including four state-of-the-art and five conventional saliency models, our proposed DeepFeat model outperforms the conventional saliency models over all four evaluation metrics.
Optimal Scheduling of Tasks in Cloud Computing Using Hybrid Firefly-Genetic Algorithm

Today cloud computing is an evolved form of utility computing which is widely used for commercial computing needs. The Cloud service provider’s success, profit, and efficiency lie in optimally allocating the computing resources to users from a vast pool of resources. The ability to allocate resources in a ubiquitous, seamless and on-demand connection involves serious challenges. Task scheduling is a variant of job-shop scheduling problem which is categorized as NP-COMPLETE. In this paper, a novel meta-heuristic algorithm of hybrid Firefly-Genetic combination is propounded for scheduling tasks. The proposed algorithm blends benefits of a mathematical optimization algorithm like Firefly with an evolutionary algorithm like Genetic algorithm to form a powerful metaheuristic search algorithm. The proposed hybrid Firefly-Genetic algorithm was able to schedule the tasks with the objective of minimal execution time for all tasks and a swift convergence to the near optimal solution. The proposed algorithm was tested in CloudSim which is a simulator toolkit for testing cloud-based algorithms. The experimental results showed that the proposed algorithm outweighed the performances of traditional First In First Out (FIFO) and Genetic algorithms.
Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics

The delineation of contamination at a co-contaminated site is vital for designing remedial strategy and estimating costs. Backpropagation (BP) neural networks and the Nemerow pollution index (NPI) with 3-D kriging were combined in this study to delineate contaminant plumes, analyze the spatial distribution of pollutants in different layers and visualize them in three dimensions, quantify polluted areas and pollution levels, and identify hotspots of the contaminants of concern (COCs). The results of a comprehensive assessment performed using BP networks and NPI were compared. The analysis of the volumes of soil contaminated with specific COCs in different soil layers showed that arsenic (As) hardly migrated downgradient whereas benzo[a]pyrene (BaP) had a strong tendency to migrate. The tendency of fluorene (FLE), naphthalene (NAP), and phenanthrene (PHE) to migrate was between that for As and BaP. The volumes of earth contaminated with of all five of these COCs generally decreased with increasing pollution levels. The volumes of PHE, FLE, and NAP at different pollution levels exhibited similar trends, and most contaminated areas were safe. The volume of BaP at high pollution levels was markedly greater than that for the other four COCs, and the volume of As at low pollution levels was also notably greater than that for the other four pollutants. (3) The spatial patterns performed by the NPI and BP network comprehensive assessment methods were similar. However, BP networks can overcome the deficiencies of NPI, which are amplification of the effect of heavily polluted elements and narrowing the separability of polluted and unpolluted areas. After comparing performance and performing a cost-benefit analysis, we propose a model that integrates BP networks and geostatistics to delineate soil contaminant plumes at co-contaminated sites.
Scalable $M$-Channel Critically Sampled Filter Banks for Graph Signals

We investigate a scalable M-channel critically sampled filter bank for graph signals, where each of the M filters is supported on a different subband of the graph Laplacian spectrum. For analysis, the graph signal is filtered on each subband and downsampled on a corresponding set of vertices. However, the classical synthesis filters are replaced with interpolation operators. For small graphs, we use a full eigendecomposition of the graph Laplacian to partition the graph vertices such that the mth set comprises a uniqueness set for signals supported on the mth subband. The resulting transform is critically sampled, the dictionary atoms are orthogonal to those supported on different bands, and graph signals are perfectly reconstructable from their analysis coefficients. We also investigate fast versions of the proposed transform that scale efficiently for large, sparse graphs. Issues that arise in this context include designing the filter bank to be more amenable to polynomial approximation, estimating the number of samples required for each band, performing nonuniform random sampling for the filtered signals on each band, and using efficient reconstruction methods. We empirically explore the joint vertex-frequency localization of the dictionary atoms, the sparsity of the analysis coefficients for different classes of signals, the reconstruction error resulting from the numerical approximations, and the ability of the proposed transform to compress piecewise-smooth graph signals. The proposed filter bank also yields a fast, approximate graph Fourier transform with a coarse resolution in the spectral domain.
Three skewed matrix variate distributions

Three-way data can be conveniently modelled by using matrix variate distributions. Although there has been a lot of work for the matrix variate normal distribution, there is little work in the area of matrix skew distributions. Three matrix variate distributions that incorporate skewness, as well as other flexible properties such as concentration, are discussed. Equivalences to multivariate analogues are presented, and moment generating functions are derived. Maximum likelihood parameter estimation is discussed, and simulated data is used for illustration.
Challenges in Search on Streaming Services

We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search context to aid content discovery and support searches for unavailable (out-of-catalog) entities. We also stress the importance of keystroke-level Instant Search experience, and the technical challenges associated with implementing it across different devices and languages for a global audience.
Trust and Trustworthiness in Social Recommender Systems

The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions about network structure (like homophily) and group cognition (like, social action is largely imitative). Assumptions like these can be naïve and exclusionary in the era of fake news and ideological uniformity towards the political poles. We examine these assumptions with aid from the user-centric framework of trustworthiness in social recommendation. The constituent dimensions of trustworthiness (diversity, transparency, explainability, disruption) highlight new opportunities for discouraging dogmatization and building decision-aware, transparent news recommender systems.
Distributed Kalman Filtering Under Model Uncertainty

We study the problem of distributed Kalman filtering for sensor networks in the presence of model uncertainty. More precisely, we assume that the actual state-space model belongs to a ball, in the Kullback-Leibler topology, about the nominal state-space model and whose radius reflects the mismatch modeling budget allowed for each time step. We propose a distributed Kalman filter with a diffusion step which is robust with respect to the aforementioned model uncertainty. Moreover, we derive the corresponding least favorable performance. Finally, we check the effectiveness of the proposed algorithm in the presence of uncertainty through a numerical example.
Rescue Boat Path Planning in Flooded Urban Environments

Mapping and path planning in disaster scenarios is an area that has benefited from aerial imaging and unmanned aerial (UAV) and surface vehicles (USV). Nowadays, there are many application areas of UAV and USV which consist of environmental monitoring, victim recovery and river mapping in the purpose of rescue operations. There are many challenges for flood rescue operations with existing systems where the time response is critical. The environment is completely changing under flood conditions and it causes a dangerous environment for victims and rescue operators. Rescue boats are widely used for search and rescue in flooded areas. However, rescue boats have a limited view while searching and rescuing the victims. By using UAVs, an aerial image can be taken off the flooded environment and this aerial image can provide global information such as the location of victims and landmarks. The rescue operations can be organized based on a generated flood environment map by using this global information. In this work, we analyze ground map generation and path planning algorithms which make use of aerial imaging provided by a UAV in a flooded urban environment. Based on the generated ground map, we used the concept of mobile robot path planning to represent our proposed approach in rescue boat path planning. In this purpose, A*, GA and PRM path planning algorithms are used and analyzed to find near-optimal paths for rescue boat between initial and target locations. Simulations are performed to evaluate the performance of the system algorithms to find out the most suitable algorithm in flooded urban environments.