Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-class Classification Based Anomaly Detection of Insider Activities

—Insider threats are the cyber attacks from within the trusted entities of an organization. Lack of real-world data and issue of data imbalance leave insider threat analysis an understudied research area. To mitigate the effect of skewed class distribution and prove the potential of multinomial classiﬁcation algorithms for insider threat detection, we propose an approach that combines generative model with supervised learning to perform multi-class classiﬁcation using deep learning. The generative adversarial network (GAN) based insider detection model introduces Conditional Generative Adversarial Network (CGAN) to enrich minority class samples to provide data for multi-class anomaly detection. The comprehensive experiments performed on the benchmark dataset demonstrates the effectiveness of introducing GAN derived synthetic data and the capability of multi-class anomaly detection in insider activity analysis. Moreover, the method is compared with other existing methods against different parameters and performance metrics.
Emergence of preferred subnetwork for correlated transport in spatial networks: On the ubiquity of force chains in dense disordered granular materials

Stress in dense granular materials and other athermal particle aggregates is transmitted through a visually striking subnetwork of interparticle contacts, the filamentary segments of which are referred to as force chains. The emergence of such preferred subnetwork in structurally disordered media with constituents interacting primarily by physical contact is not fully understood. In this work, we study locally correlated transport in Random Geometric Graphs (RGGs), and show the spontaneous emergence of preferred subnetwork. Our findings reveal that, despite structural disorder, system spanning localization of fluxes transmitted through a spatial network can emerge from short ranged correlations. The spatial and statistical features of the subnetwork are surprisingly similar to the strong force network in simulated grain assemblies, and provides insights on the structure and spatial scale of significance of the force chains.
Seeing By Haptic Glance: Reinforcement Learning Based 3d Object Recognition

Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object. This capability is defined as ‘haptic glance’ in cognitive neuroscience. Most of the existing 3D recognition models were developed based on dense 3D data. Nonetheless, in many real-life use cases, where robots are used to collect 3D data by haptic exploration, only a limited number of 3D points could be collected. In this study, we thus focus on solving the intractable problem of how to obtain cognitively representative 3D key-points of a target object with limited interactions between the robot and the object. A novel reinforcement learning based framework is proposed, where the haptic exploration procedure (the agent iteratively predicts the next position for the robot to explore) is optimized simultaneously with the objective 3D recognition with actively collected 3D points. As the model is rewarded only when the 3D object is accurately recognized, it is driven to find the sparse yet efficient haptic-perceptua13D representation of the object. Experimental results show that our proposed model outperforms the state of the art models.
LitterBox: A Linter for Scratch Programs

Creating programs with block-based programming languages like Scratch is easy and fun. Block-based programs can nevertheless contain bugs, in particular when learners have misconceptions about programming. Even when they do not, Scratch code is often of low quality and contains code smells, further inhibiting understanding, reuse, and fun. To address this problem, in this paper we introduce LitterBox, a linter for Scratch programs. Given a program or its public project ID, LitterBox checks the program against patterns of known bugs and code smells. For each issue identified, LitterBox provides not only the location in the code, but also a helpful explanation of the underlying reason and possible misconceptions. Learners can access LitterBox through an easy to use web interface with visual information about the errors in the block-code, while for researchers LitterBox provides a general, open source, and extensible framework for static analysis of Scratch programs.
Modelling Cooperation in Network Games with Spatio-Temporal Complexity

The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management of irrigation systems. Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group. Given appropriate mechanisms describing agent interaction, groups may achieve socially beneficial outcomes, even in the face of short-term selfish incentives. In many cases, collective action problems possess an underlying graph structure, whose topology crucially determines the relationship between local decisions and emergent global effects. Such scenarios have received great attention through the lens of network games. However, this abstraction typically collapses important dimensions, such as geometry and time, relevant to the design of mechanisms promoting cooperation. In parallel work, multi-agent deep reinforcement learning has shown great promise in modelling the emergence of self-organized cooperation in complex gridworld domains. Here we apply this paradigm in graph-structured collective action problems. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms, finding clear transitions between different equilibria over time. We define analytic tools inspired by related literatures to measure the social outcomes, and use these to draw conclusions about the efficacy of different environmental interventions. Our methods have implications for mechanism design in both human and artificial agent systems.
T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks

Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small switch buffer space in data-center. Such applications require a small flow completion time (FCT) to be effective. Unfortunately, TCP is myopic with respect to the composite nature of application data. In addition it tends to artificially inflate the FCT of individual flows by several orders of magnitude, because of its Internet-centric design, that fixes the retransmission timeout (RTO) to be at least hundreds of milliseconds. To better understand this problem, in this paper, we use empirical measurements in a small data center testbed to study, at a microscopic level, the effects of various types of packet losses on TCP’s performance. In particular, we single out packet losses that impact the tail end of small flows, as well as bursty losses that span a significant fraction of small TCP congestion windows, and show a non-negligible effect of such losses on the FCT. Based on this, we propose the so-called, timely-retransmitted ACKs (or T-RACKs), a simple loss recovery mechanism that conceals the drawbacks of the long RTO even in the presence of heavy packet losses. Interestingly enough, T-RACKS achieves this transparently to TCP itself as it does not require any change to TCP in the tenant’s virtual machine (VM) or container. T-RACKs can be implemented as a software shim layer in the hypervisor between the VMs and the server’s NIC or in hardware as a networking function in a SmartNIC. Simulation and real testbed results show remarkable performance improvements.
Atomic Dispacement Parameter Nomenclature. Report of a Subcommittee on Atomic Displacement Parameter Nomenclature

Modern X-ray and neutron diffraction techniques can give precise parameters that describe dynamic or static displacements of atoms in crystals. However, confusing and inconsistent terms and symbols for these quantities occur in the crystallographic literature. This report discusses various forms of these quantities, derived from probability density functions and based on Bragg diffraction data, both when the Gaussian approximation is appropriate and when it is not. The focus is especially on individual atomic anisotropic displacement parameters (ADPs), which may represent atomic motion and possible static displacive disorder. The first of the four sections gives background information, including definitions. The second concerns the kinds of parameter describing atomic displacements that have most often been used in crystal structure analysis and hence are most commonly found in the literature on the subject. It includes a discussion of graphical representations of the Gaussian mean-square displacement matrix. The third section considers the expressions used when the Gaussian approximation is not adequate. The final section gives recommendations for symbols and nomenclature.
Trace- and Improved Data-Processing Inequalities for von Neumann Algebras

We prove a version of the data-processing inequality for the relative entropy for general von Neumann algebras with an explicit lower bound involving the measured relative entropy. The inequality, which generalizes previous work by Sutter et al. on finite dimensional density matrices, yields a bound how well a quantum state can be recovered after it has been passed through a channel. The natural applications of our results are in quantum field theory where the von Neumann algebras are known to be of type III. Along the way we generalize various multi-trace inequalities to general von Neumann algebras.
Newton Method over Networks is Fast up to the Statistical Precision

We propose a distributed cubic regularization of the Newton method for solving (constrained) empirical risk minimization problems over a network of agents, modeled as undirected graph. The algorithm employs an inexact, preconditioned Newton step at each agent's side: the gradient of the centralized loss is iteratively estimated via a gradient-tracking consensus mechanism and the Hessian is subsampled over the local data sets. No Hessian matrices are thus exchanged over the network. We derive global complexity bounds for convex and strongly convex losses. Our analysis reveals an interesting interplay between sample and iteration/communication complexity: statistically accurate solutions are achievable in roughly the same number of iterations of the centralized cubic Newton method, with a communication cost per iteration of the order of $\widetilde{\mathcal{O}}\big(1/\sqrt{1-\rho}\big)$, where $\rho$ characterizes the connectivity of the network. This demonstrates a significant communication saving with respect to that of existing, statistically oblivious, distributed Newton-based methods over networks.
Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.
Impact of h-index on authors ranking: A comparative analysis of Scopus and WoS

In academia, the research performance of the faculty members is either evaluated by the number of publications or the number of citations. Most of the time h-index is widely used during the hiring process or the faculty performance evaluation. The calculation of the h-index is shown in various databases; however, there is no recent or systematic evidence about the differences between them. In this study, we compare the difference in the h-index compiled with Scopus and Web of Science (WoS) with the aim of analyzing the ranking of the authors within a university. We analyze the publication records of 350 authors from Monash University (Australia). We also investigate the discipline wise variation in the authors ranking. 31% of the author's profiles show no variation in the two datasets whereas 55% of the author's profiles show a higher count in Scopus and 9% in WoS. The maximum difference in h-index count among Scopus and WoS is 3. On average 12.4% of publications per author are unique in Scopus and 4.1% in WoS. 53.5% of publications are common in both Scopus and WoS. Despite larger unique publications in Scopus, there is no difference shown in the Spearman correlation coefficient between WoS and Scopus citation counts and h-index.
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification

From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in learning the distribution and generating images of a particular class, they can be used for semi-supervised classification tasks. However, the problem is that if two classes of images share similar characteristics, the GAN might learn to generalize and hinder the classification of the two classes. In this paper, we use various images from MNIST and Fashion-MNIST datasets to illustrate how similar images cause the GAN to generalize, leading to the poor classification of images. We propose a modification to the traditional training of GANs that allows for improved multi-class classification in similar classes of images in a semi-supervised learning framework.
Context-Responsive Labeling in Augmented Reality

Route planning and navigation are common tasks that often require additional information on points of interest. Augmented Reality (AR) enables mobile users to utilize text labels, in order to provide a composite view associated with additional information in a real-world environment. Nonetheless, displaying all labels for points of interest on a mobile device will lead to unwanted overlaps between information, and thus a context-responsive strategy to properly arrange labels is expected. The technique should remove overlaps, show the right level-of-detail, and maintain label coherence. This is necessary as the viewing angle in an AR system may change rapidly due to users’ behaviors. Coherence plays an essential role in retaining user experience and knowledge, as well as avoiding motion sickness. In this paper, we develop an approach that systematically manages label visibility and levels-of-detail, as well as eliminates unexpected incoherent movement. We introduce three label management strategies, including (1) occlusion management, (2) level-of-detail management, and (3) coherence management by balancing the usage of the mobile phone screen. A greedy approach is developed for fast occlusion handling in AR. A level-of-detail scheme is adopted to arrange various types of labels. A 3D scene manipulation is then built to simultaneously suppress the incoherent behaviors induced by viewing angle changes. Finally, we present the feasibility and applicability of our approach through one synthetic and two real-world scenarios, followed by a qualitative user study.
CHARET: Character-centered Approach to Emotion Tracking in Stories

Autonomous agents that can engage in social interactions witha human is the ultimate goal of a myriad of applications. A keychallenge in the design of these applications is to define the socialbehavior of the agent, which requires extensive content creation.In this research, we explore how we can leverage current state-of-the-art tools to make inferences about the emotional state ofa character in a story as events unfold, in a coherent way. Wepropose a character role-labelling approach to emotion tracking thataccounts for the semantics of emotions. We show that by identifyingactors and objects of events and considering the emotional stateof the characters, we can achieve better performance in this task,when compared to end-to-end approaches.
Responsibility Management through Responsibility Networks

The safety management is critically important in the workplace. Unfortunately, responsibility issues therein such as inefficient supervision, poor evaluation and inadequate perception have not been properly addressed. To this end, in this paper, we deploy the Internet of Responsibilities (IoR) for responsibility management. Through the building of IoR framework, hierarchical responsibility management, automated responsibility evaluation at all level and efficient responsibility perception are achieved. The practical deployment of IoR system showed its effective responsibility management capability in various workplaces.
A Reference Model for IoT Embodied Agents Controlled by Neural Networks

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors and actuators to interact with the environment, and with a ‘controller’ that usually contains an artificial neural network (ANN). In previous publications, we introduced three software approaches to design, implement and test IoT embodied agents. In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications. The model represents embodied agents that are controlled by neural networks. Our model includes the ANN training process, represented as a reconfiguration step such as changing agent features or neural net connections. Our contributions include the identification of the main characteristics of IoT embodied agents, a reference model specification based on statecharts, and an illustrative application of the model to support autonomous street lights. The proposal aims to support the design and implementation of IoT applications by providing high-level design abstractions and models, thus enabling the designer to have a uniform approach to conceiving, designing and explaining such applications.
Euclidean Frustrated Ribbons

Geometrical frustration in thin sheets is ubiquitous across scales in biology and becomes increasingly relevant in technology. Previous research identified the origin of the frustration as the violation of Gauss's \emph{Theorema Egregium}. Such "Gauss frustration" exhibits rich phenomenology; it may lead to mechanical instabilities, anomalous mechanics and shape-morphing abilities that can be harnessed in engineering systems. Here we report a new type of geometrical frustration, one that is as general as Gauss frustration. We show that its origin is the violation of Mainardi-Codazzi-Peterson compatibility equations and that it appears in Euclidean sheets. Combining experiments, simulations and theory, we study the specific case of a Euclidean ribbon with radial and geodesic curvatures. Experiments, conducted using different materials and techniques, reveal shape transitions, symmetry breaking and spontaneous stress focusing. These observations are quantitatively rationalized using analytic solutions and geometrical arguments. We expect this frustration to play a significant role in natural and engineering systems, specifically in slender 3D printed sheets.
Multimodal data visualization, denoising and clustering with integrated diffusion

We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that re-ﬂects the combined information from both modalities. We show the utility of this joint operator in data denoising, visualization and clustering, performing better than other methods when applied to multi-omic data generated from peripheral blood mononuclear cells. Our approach better visualizes the geometry of the joint data, captures known cross-modality associations and identiﬁes known cellular populations. More generally, integrated diffusion is broadly applicable to multi-modal datasets generated in many medical and biological systems.
On the role of vorticity stretching and strain self-amplification in the turbulence energy cascade

Abstract The tendency of turbulent flows to produce fine-scale motions from large-scale energy injection is often viewed as a scale-wise cascade of kinetic energy driven by vorticity stretching. This has been recently evaluated by an exact, spatially local relationship (Johnson, P.L. Phys. Rev. Lett., vol. 124, 2020, p. 104501), which also highlights the contribution of strain self-amplification. In this paper, the role of these two mechanisms is explored in more detail. Vorticity stretching and strain amplification interactions between velocity gradients filtered at the same scale account for approximately half of the energy cascade rate, directly connecting the restricted Euler dynamics to the energy cascade. Multiscale strain amplification and vorticity stretching are equally important, however, and more closely resemble eddy viscosity physics. Moreover, ensuing evidence of a power-law decay of energy transfer contributions from disparate scales supports the notion of an energy cascade, albeit a ‘leaky’ one. Besides vorticity stretching and strain self-amplification, a third mechanism of energy transfer is introduced and related to the vortex thinning mechanism important for the inverse cascade in two dimensions. Simulation results indicate this mechanism also provides a net source of backscatter in three-dimensional turbulence, in the range of scales associated with the bottleneck effect. Taken together, these results provide a rich set of implications for large-eddy simulation modelling.
Model Synthesis for Communication Traces of System-on-Chip Designs

Concise and abstract models of system-level behaviors are invaluable in design analysis, testing, and validation. In this paper, we consider the problem of inferring models from communication traces of system-on-chip~(SoC) designs. The traces capture communications among different blocks of a SoC design in terms of messages exchanged. The extracted models characterize the system-level communication protocols governing how blocks exchange messages, and coordinate with each other to realize various system functions. In this paper, the above problem is formulated as a constraint satisfaction problem, which is then fed to a SMT solver. The solutions returned by the SMT solver are used to extract the models that accept the input traces. In the experiments, we demonstrate the proposed approach with traces collected from a transaction-level simulation model of a multicore SoC design and traces of a more detailed multicore SoC design developed in GEM5 environment.