Reviewerly/exHarmony huggingface dataset:
Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem

In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats. When modeling these attackers, we can observe that they demonstrate different risk-seeking and risk-averse behaviors. This work explores how an attacker’s risk seeking or risk averse behavior affects their operations against detection-optimizing defenders in an Internet of Things ecosystem. Using an evaluation framework which uses real, parametrizable malware, we develop a game that is played by a defender against attackers with a suite of malware that is parameterized to be more aggressive and more stealthy. These results are evaluated under a framework of exponential utility according to their willingness to accept risk. We find that against a defender who must choose a single strategy up front, risk-seeking attackers gain more actual utility than risk-averse attackers, particularly in cases where the defender is better equipped than the two attackers anticipate. Additionally, we empirically confirm that high-risk, high-reward scenarios are more beneficial to risk-seeking attackers like cybercriminals, while low-risk, low-reward scenarios are more beneficial to risk-averse attackers like advanced persistent threats.
AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the US. Software implementing the method proposed in this paper is available in the R package BayesSpec.
Seeds of SEED: NMT-Stroke: Diverting Neural Machine Translation through Hardware-based Faults

The rapid development of deep learning has significantly bolstered the performance of natural language processing (NLP) in the form of language modeling. Recent advances in hardware security studies have demonstrated that hardware-based threats can severely jeopardize the integrity of computing systems (e.g., fault attacks for data at rest). Internal adversaries exploiting such hardware vulnerabilities are becoming a major security concern. Yet the impact of hardware faults on systems running NLP models has not been fully understood.In this paper, we perform the first investigation of hardware-based fault injections in modern neural machine translation (NMT) models. We find that compared to neural network classifiers (e.g., CNNs), fault attacks on NMT models present unique challenges. We propose a novel attack framework–NMT-Stroke–that can maliciously divert the translation of a victim NMT model by modeling memory fault injections with the rowhammer attack vector. We design a fault injection strategy to minimize bit flips needed, which would mislead the translation to an arbitrary natural output sentence. Our evaluation on state-of-the-art Transformer-based NMT models shows that NMT-Stroke can effectively induce the attacker-desired and linguistically sound translation by faulting minimal parameter bits. Our work highlights the significance of understanding the robustness of emerging NLP models with the presence of hardware vulnerabilities, which could lead to future new research directions.
Immersive Experience Prototyping: Using Mixed Reality to Integrate Real Devices in Virtual Simulated Contexts to Prototype Experiences with Mobile Apps

Interaction and augmentation strongly depend on the context of use and the respective human aspects. In this paper, we present the use of Mixed Reality (MR) for the creation of experience prototypes and the evaluation of mobile applications in a (simulated) usage context. The novelty is the integration of real mobile devices into MR according to the augmented virtuality principle and the mirroring of the screen from the real device to the virtual replica. This preserves the user experience and the full range of functions when using the mobile device in MR allowing it to be operated as in reality. In the MR environment it is possible to simulate usage contexts and to investigate the user experience with mobile applications. First, we show the technical concept and then we demonstrate the approach with a prototype smartphone app for a mobility assistant in two scenarios.
GCiM: A Near-Data Processing Accelerator for Graph Construction

Graph is widely utilized as a key data structure in many applications like social network and recommendation systems. However, real-world graph construction typically involves massive random memory accesses and distance calculation, resulting in considerable processing time and energy consumptions on CPUs and GPUs. In this work, we present GCiM, a specialized processing-in-memory architecture for efficient graph construction and update. By directly deploying the computing units on the logic layer of the 3D stacked memory, GCiM benefits from memory-level parallelism and further improves the memory access efficiency with both optimized processing ordering and data layout. According to our experiments, GCiM shows 634.64X and 53.29X speedup while consuming 1470.7X and 442.56X less energy compared to CPU and GPU respectively.
Adaptive kernel fuzzy clustering for missing data

Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.e. it discards all instances with missing data. The second approach uses the Partial Distance Strategy (PDS), in which partial distances are computed among all available resources and then re-scaled by the reciprocal of the proportion of observed values. The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. The clustering results were evaluated according to different metrics. The best performance of the clustering algorithm was achieved under the PDS and OCS strategies. Under the OCS approach, new datasets were derive and the missing values were estimated dynamically in the optimization process. The results of clustering under the OCS strategy also presented a superior performance when compared to the resulting clusters obtained by applying the VKFCM-K-LP algorithm on a version where missing values are previously imputed by the mean or the median of the observed values.
Neural Cognitive Modeling Based on The Importance of Knowledge point for Student Performance Prediction

At present, cognitive diagnosis has become one of the important research issues of current intelligent education. Existing cognitive diagnosis models generally use a manually designed function to mine the students&#x0027; practice process. These functions are usually liner and not enough to capture the complex relationship between students and exercises. In this paper, based on the Neural Cognitive Diagnosis (NeuralCD) framework, we propose an importance of knowledge point-based neural cognitive diagnosis model (IK-NeuralCD). It introduces the importance factor of knowledge point and uses the frequency of knowledge points examined to express the importance of knowledge point, which improves the degree of fitting of the complex relationship between students and exercises, thereby improving the diagnosis and prediction effect. Based on comparison between IK-NeuralCD and the existing typical model on the real dataset, this paper proves the effectiveness of the IK-NeuralCD model.
New chaotic satellite image encryption by using some or all the rounds of the AES algorithm

This study suggests new satellite chaotic image encryption by using some or all the rounds of the AES algorithm to improve security techniques to protect sensitive data and images during transmission between satellites and ground stations. First, the 7D hyperchaotic system generates the initial key of the pseudo-AES algorithm by using the first and the second variables of the hyperchaotic system. Second, the pseudo-AES algorithm consists of several rounds, where two of them are fixed rounds (the first and the last rounds of the classical AES algorithm), while the number of the remaining rounds depends on the value of the third variable of the hyperchaotic system which considered as a determiner. Third, the permutation operation is applied to the output of the pseudo-AES algorithm by an arrangement permutation on the rows and the columns using the fourth and fifth variables of the hyperchaotic system. Finally, the remaining two variables of the hyperchaotic system are used to generate a mask matrix to be XORed with the output of the previous step to obtain the final cipher image. Moreover, the experimental results show that the proposed technique of encryption for satellite images has extraordinary resistance against well-known attacks.
Pedestrian Tracking through Coordinated Mining of Multiple Moving Cameras

Multiple object tracking has attracted great interest in the computer vision community. Most researchers focus on the applications under a single static or moving camera. More recently, tracking across multiple static cameras is also investigated due to the need for surveillance purposes. With the growing development of autonomous driving, it is critical to correlate all the vehicles’ vision systems on the road to achieve a global perception. However, tracking across multiple moving cameras has not been well studied yet. We observe a lack of such a publicly available dataset for coordinated mining of multiple moving cameras. In this paper, we aim to bridge the gap and propose a new dataset of multiple moving cameras, called "DHU-MTMMC", in which the videos are collected from several cameras mounted on the moving cars. The dataset contains fourteen sequences in different scenarios with annotated pedestrians. We propose a baseline MTMMC workflow to deal with tracking pedestrians across cameras. When the joint detection and embedding are performed, the association algorithm can run online under single-camera settings. We treat multi-camera tracking as a linear assignment problem that can be solved efficiently. The overall IDF1 of the proposed MTMMC tracking on the dataset is 57.8%.
A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.
AutoFAIR-A portal for automating FAIR assessments for bioinformatics resources

Research in Bioinformatics generates tools and datasets in Bioinformatics at a very fast rate. Meanwhile, a lot of effort is going into making these resources findable and reusable to improve resource discovery by researchers in the course of their work.This paper proposes a semi-automated tool to assess a resource according to the Findability, Accessibility, Interoperability and Reusability (FAIR) criteria. The aim is to create a portal that presents the assessment score together with a report that researchers can use to gauge a resource.Our system uses internet searches to automate the process of generating FAIR scores. The process is semi-automated in that if a particular property of the FAIR scores has not been captured by AutoFAIR, a user is able to amend and supply the information to complete the assessment.We compare our results against FAIRshake that was used as the benchmark tool for comparing the assessments. The results show that AutoFAIR was able to match the FAIR criteria in FAIRshake with minimal intervention from the user.We show that AutoFAIR can be a good repository for storing metadata about tools and datasets, together with comprehensive reports detailing the assessments of the resources. Moreover, AutoFAIR is also able to score workflows, giving an overall indication of the FAIRness of the resources used in a scientific study.
Benchmarking the Operation Times of NoSQL and MySQL Databases for Python Clients

Databases have a huge impact on the functionality of a running system, especially in the context of the Internet of Things (IoT). In the IoT, smart products are created with the usage of neural networks (NN) that require huge amount of stored data for training. The programming language Python is the common way for creating and working with NNs and its data preparation. Therefore, it is useful to use the same language for both, database driver and NN. For this reason, four well-known databases (DBs) are examined for handling different operations. In this paper, the performance of different not only Structured Query Language (NoSQL) DBs (Redis, Cassandra, mongoDB) and MySQL are compared to each other, using most popular Python clients. Important for the performance measurements are not only the actual operation time but also the preparation time that Python or the DB requires to access the data. The results of the paper show that Redis is efficient with small data sets, whereas mongoDB and MySQL are usable for all kinds of data sets, whereas Cassandra handles reading operations well.
Combining quantitative and qualitative reasoning in concurrent multi-player games

Abstract We propose a general framework for modelling and formal reasoning about multi-agent systems and, in particular, multi-stage games where both quantitative and qualitative objectives and constraints are involved. Our models enrich concurrent game models with payoffs and guards on actions associated with each state of the model and propose a quantitative extension of the logic $${\textsf {ATL}}^{*}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>ATL</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:mrow> </mml:msup> </mml:math> that enables the combination of quantitative and qualitative reasoning. We illustrate the framework with some detailed examples. Finally, we consider the model-checking problems arising in our framework and establish some general undecidability and decidability results for them.
Arrival-time picking of microseismic events based on MSNet

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, because field monitoring data contain many microseismic events with low signal-to-noise ratios (S/Ns), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-S/N environments. Therefore, we have adopted a deep convolutional network that combines the U-Net and DenseNet approaches to pick arrival times automatically. This novel network called MSNet not only retains the spatial information of any input signal or profile based on the U-Net, but also extracts and integrates more essential features of events and nonevents through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of our method. First, we describe the structure of MSNet and the workflow of our picking method. Then, data sets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different S/Ns and field microseismic data from different monitoring areas. By comparing the picking results of our method with the results of U-Net and short-term average and long-term average methods, the effectiveness of our method is verified. The arrival-picking results of synthetic data and microseismic field data indicate that our network has increased adaptability and can achieve high accuracy for picking the arrival time of microseismic events.
PSM: Parametric Saliency Maps for Autonomous Pedestrians

Modeling visual attention is an important aspect of simulating realistic virtual humans. This work proposes a parametric model and method for generating real-time saliency maps from the perspective of virtual agents which approximate those of vision-based saliency approaches. The model aggregates a saliency score from user-defined parameters for objects and characters in an agent’s view and uses that to output a 2D saliency map which can be modulated by an attention field to incorporate 3D information as well as a character’s state of attentiveness. The aggregate and parameterized structure of the method allows the user to model a range of diverse agents. The user may also expand the model with additional layers and parameters. The proposed method can be combined with normative and pathological models of the human visual field and gaze controllers, such as the recently proposed model of egocentric distractions for casual pedestrians that we use in our results.
Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data

A large-scale system for obtaining fine-grained vehicle trajectories is becoming increasingly important because it lays a solid foundation for a wide range of downstream applications, such as urban traffic optimization, road network profiling, route planning, etc. Traditional methods recover the trajectories from GPS data from apps or coarse-grained traces collected from base stations, which are costly and, more importantly, only cover limited vehicles on the road. Thus, they are not applicable to downstream tasks. To fill this gap, we explore the possibility of recovering vehicle trajectories from the video data recorded by widely deployed traffic cameras. The major challenges lie in the quality of the captured image, low sampling rate, and unbalanced temporal and spatial distribution. To address these challenges, we propose a general system to recover vehicle trajectories at the level of the road intersection, where a novel iterative framework is developed to combine both vehicle clustering and trajectory recovery tasks, which improve their performance simultaneously. The key motivation is that vehicle clustering based on visual features can provide essential discrete points for trajectory recovery, while the recovered routes can introduce spatial-temporal constraints to the initial vehicle clusters for de-noising the false results and complement the missing results. To prove the feasibility of our framework, we collect and plan to release a city-scale traffic camera dataset consisting of 24 hours of videos from 673 cameras across 1,106 intersections. To the best of our knowledge, this benchmark is the first to contain the ground truth of vehicle trajectories with a wide range of spatial and temporal coverage in an urban environment. We conduct extensive experiments and analysis on datasets of different scales to demonstrate the robustness of our framework. Last but not least, we have already deployed the whole system in the business applications of SenseTime, China, including traffic signal control and traffic flow analysis. We highly expect this dataset to further facilitate the research in this field and contribute more to traffic optimization systems in the real world.
Efficient nucleus segmentation of white blood cells mimicking the human perception of color

Abstract In this study, we present a nucleus segmentation proposal of white blood cells (WBCs) using chromatic features. It is human inspired on perception of color: a person locates the nucleus of the WBCs by the chromatic contrast between the nucleus and the other elements of the blood smear. To implement that, we segment the nucleus by selecting the pixels with high chromatic variance. First, an unsupervised neural network, which was trained offline to recognize different colors is applied to the images. Thereby, the hue of the pixels is normalized, and the chromatic variance is accurately computed. Processing the hue and using the unsupervised neural network the brightness and staining variations are robustly estimated. In previous related works, the color components are processed separately as uncorrelated intensity channels, and the mathematical operations are selected intuitively. Unlike that, we use color as a feature without separating the hue components, keeping their correlation, so the formal treat becomes systematic. Experiments use the RGB and HSV spaces with three public image databases: ALL‐IDB2, CellaVision, and JTSC. A pixel‐level segmentation evaluation is performed by comparing the segmented images with the ground truth. Our proposal competes with current methods since the values in accuracy, specificity, precision, sensitivity, dice coefficient, kappa index, and true positive rate all are similar to or improved upon the state of the art. The performance of our approach is classified as excellent regarding the kappa index value, and it detects at least 80% of the cells with an average dice coefficient larger than 0.9.