Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training

Driving behavior monitoring plays a crucial role in managing road safety and decreasing the risk of traffic accidents. Driving behavior is affected by multiple factors like vehicle characteristics, types of roads, traffic, but, most importantly, the pattern of driving of individuals. Current work performs a robust driving pattern analysis by capturing variations in driving patterns. It forms consistent groups by learning compressed representation of time series (Auto Encoded Compact Sequence) using a multi-layer seq-2-seq autoencoder and exploiting hierarchical clustering along with recommending the choice of best distance measure. Consistent groups aid in identifying variations in driving patterns of individuals captured in the dataset. These groups are generated for both train and hidden test data. The consistent groups formed using train data, are exploited for training multiple instances of the classifier. Obtained choice of best distance measure is used to select the best train-test pair of consistent groups. We have experimented on the publicly available UAH-DriveSet dataset considering the signals captured from IMU sensors (accelerometer and gyroscope) for classifying driving behavior. We observe proposed method, significantly outperforms the benchmark performance.
Neural Program Synthesis with Query

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few inputoutput examples under the training distribution. In this work, we propose a querybased framework that trains a query neural network to generate informative inputoutput examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the querybased framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples.
Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels

Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational workflows to remote adversaries. While there exists mandates for the use of end-to-end encryption for data transmission in such settings, it is entirely possible for passive adversaries to fingerprint and reconstruct entire workflows being carried out -- establishing an understanding of how facilities operate. In this paper, we investigate whether a remote attacker can accurately fingerprint robot movements and ultimately reconstruct operational workflows. Using a neural network approach to traffic analysis, we find that one can predict TLS-encrypted movements with around ~60% accuracy, increasing to near-perfect accuracy under realistic network conditions. Further, we also find that attackers can reconstruct warehousing workflows with similar success. Ultimately, simply adopting best cybersecurity practices is clearly not enough to stop even weak (passive) adversaries.
A Comprehensive Survey on Model Quantization for Deep Neural Networks

Recent advances in machine learning by Deep Neural Networks (DNNs) are significant. DNNs accompanies with a vast array of parameters and computation, which leads to processing challenges on devices with limited hardware resources. In order to reduce parameters and computations for constructing optimized accelerators, compression techniques are suggested. A promising approach is quantization, where the full-precision values are stored in low bit-width precision, resulting in memory saving, and replacement of high-cost operations with low-cost ones. DNN quantization is flexible and efficient in hardware design, which led many methods to propose it. The necessity of an integrated report for understanding, analyzing, and comparing quantization methods cannot be overstated. Consequently, it is intended to provide a comprehensive survey of quantization concepts and categorize methods from different perspectives. In this regard, the clustering-based quantization methods are described, and it is discussed how a scale factor parameter is applied to approximate the full-precision values in quantization. For the first time, we review the training of a quantized DNN and using Straight-Through Estimator thoroughly. Replacement of the floating-point operations with low-cost bitwise ones in the quantized DNN and the sensitivity of different layers in quantization are investigated. Finally, the quantization methods are evaluated, and the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet is presented.
Bankrupting DoS Attackers Despite Uncertainty

On-demand provisioning in the cloud allows for services to remain available despite massive denial-of-service (DoS) attacks. Unfortunately, on-demand provisioning is expensive and must be weighed against the costs incurred by an adversary. This leads to a recent threat known as economic denial-of-sustainability (EDoS) , where the cost for defending a service is higher than that of attacking. A natural tool for combating EDoS is to impose costs via resource burning (RB). Here, a client must veriﬁably consume resources—for example, by solving a computational challenge— before service is rendered. However, prior RB-based defenses with security guarantees do not account for the cost of on-demand provisioning. Another common approach is the use of heuristics—such as a client’s reputation score or the geographical location—to identify and discard spurious job requests. However, these heuristics may err and existing approaches do not provide security guarantees when this occurs. Here, we propose an EDoS defense, LCharge , that uses resource burning while accounting for on-demand provisioning. LCharge leverages an estimate of the number of job requests from honest clients (i.e., good jobs) in any set S of requests to within an O ( α ) -factor, for any unknown α > 0 , but retains a strong security guarantee despite the uncertainty of this estimate. Speciﬁcally, against an adversary that expends B resources to attack, the total cost for defending is O ( α 5 / 2 (cid:112) B ( g
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher

Machine unlearning has become an important area of research due to an increasing need for machine learning (ML) applications to comply with the emerging data privacy regulations. It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch. Recently, several efforts have been put in to make unlearning to be effective and efficient. We propose a novel machine unlearning method by exploring the utility of competent and incompetent teachers in a student-teacher framework to induce forgetfulness. The knowledge from the competent and incompetent teachers is selectively transferred to the student to obtain a model that doesn't contain any information about the forget data. We experimentally show that this method generalizes well, is fast and effective. Furthermore, we introduce the zero retrain forgetting (ZRF) metric to evaluate any unlearning method. Unlike the existing unlearning metrics, the ZRF score does not depend on the availability of the expensive retrained model. This makes it useful for analysis of the unlearned model after deployment as well. We present results of experiments conducted for random subset forgetting and class forgetting on various deep networks and across different application domains. Code is at: https://github.com/vikram2000b/bad-teaching- unlearning
Static analysis tools in the era of cloud-native systems

Microservices fuel cloud-native systems with small service sets developed and deployed independently. The independent nature of this modular architecture also leads to challenges and gaps. The intended system design might deviate far from what is eventually produced and maintained as the architecture tends to degrade over time. This paper challenges the audience on how static analysis could contribute to microservice system development and management, particularly managing architectural degradation. It elaborates on challenges and needed changes in the traditional code analysis to better fit these systems and discusses implications for practitioners once robust static analysis tools become available
DynPL-SVO: A New Method Using Point and Line Features for Stereo Visual Odometry in Dynamic Scenes

—Stereo visual odometry is widely used in which a robot tracks its position and orientation using stereo cameras. Most of the approaches recovered mobile robotics motion based on the matching and tracking of point features along a sequence of stereo images. But in low-textured and dynamic scenes, there are no sufﬁcient robust static point features for motion estimation, causing lots of previous work failed to reconstruct robotic motion. While line features can be detected in low-textured and dynamic scenes.Inthis paper, we mainly proposed a new cost function and dynamics grids containing both vertical and horizontal information of features, which can be used in frameworks like stvo pl. The method can work robustly in a wide variety of low-textured and dynamic scenes. Stereo camera motion was obtained through Levenberg-Marquard minimization of re-projection error of point and line features. The experimental results on the KITTI and EuRoC MAV datasets showed that the proposed method had a competitive performance when compared with other state-of-the-art systems by producing more robust and accurate motion estimation in dynamic scenes.
Distributed Feature Selection for High-dimensional Additive Models

Distributed statistical learning is a common strategy for handling massive data where we divide the learning task into multiple local machines and aggregate the results afterward. However, most existing work considers the case where the samples are divided. In this work, we propose a new algorithm, DDAC-SpAM, that divides features under the high-dimensional sparse additive model. The new algorithm contains three steps: divide, decorrelate, and conquer. We show that after the decorrelation operation, every local estimator can recover the sparsity pattern for each additive component consistently without imposing strict constraints to the correlation structure among variables. Theoretical analysis of the aggregated estimator and empirical results on synthetic and real data illustrate that the DDAC-SpAM algorithm is effective and competitive in ﬁtting sparse additive models.
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization

Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-stationarity could harm performances. Another prevalent human behavior is social interaction which has become available in a form of a social network or graph structure. As a result, graph-based contextual bandits have received much attention. In this paper, we propose"SemiGraphTS,"a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric reward model. Our algorithm is the first to be proposed in this setting. We derive an upper bound of the cumulative regret that can be expressed as a multiple of a factor depending on the graph structure and the order for the semi-parametric model without a graph. We evaluate the proposed and existing algorithms via simulation and real data example.
A Framework for Comparing Engineering Methods

This article proposes a comprehensive framework for comparing the current methodologies and tools for information engineering and using these methods for applying information technology to construct the overall information systems architecture for the organization. The proposed framework consists of two dimensions: (1) expanded sequence of the traditional system life cycle; and (2) the conceptual depth of the methods. The article shows that information engineering is the key to effective information management. Using the proposed framework, 26 widelycited methods for information engineering are compared. Evolution to more effective methods of information engineering are needed to align future information systems requirements to strategic goals and objectives of an organization and to exploit the current information systems technologies for competitive advantage.
End-to-end Argument Mining with Cross-corpora Multi-task Learning

Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.
Centralized gaze as an adaptive component of defensive states in humans

Adequate defensive responding is crucial for mental health but scientifically not well understood. Specifically, it seems difficult to dissociate defense and approach states based on autonomic response patterns. We thus explored the robustness and threat-specificity of recently described oculomotor dynamics upon threat in anticipation of either threatening or rewarding stimuli in humans. While visually exploring naturalistic images, participants (50 per experiment) expected an inevitable, no, or avoidable shock (Experiment 1) or a guaranteed, no, or achievable reward (Experiment 2) that could be averted or gained by a quick behavioural response. We observed reduced heart rate (bradycardia), increased skin conductance, pupil dilation and globally centralized gaze when shocks were inevitable but, more pronouncedly, when they were avoidable. Reward trials were not associated with globally narrowed visual exploration, but autonomic responses resembled characteristics of the threat condition. While bradycardia and concomitant sympathetic activation reflect not only threat-related but also action-preparatory states independent of valence, global centralization of gaze seems a robust phenomenon during the anticipation of avoidable threat. Thus, instead of relying on single readouts, translational research in animals and humans should consider the multi-dimensionality of states in aversive and rewarding contexts, especially when investigating ambivalent, conflicting situations.
Collectivist Cultures and the Emergence of Family Firms

Using a sample of 1,103 Chinese private-sector firms that went public during 2004–16, we find that founders of firms from regions with stronger collectivist cultures engage more family members as managers, retain more ownership in the family, and share the controlling ownership with more family members. These findings are robust to a battery of diagnostic tests to account for alternative institutional factors that may induce the relationships. The results are consistent with the hypothesis that because the collectivist culture reduces information asymmetry, shirking problems, and associated monitoring costs among family members, more family ownership and management are expected in firms when founders are from collectivist regions. The overall evidence supports the theory of the firm pioneered by Harold Demsetz and his coauthors.
Overcoming a “forbidden phenotype”: The parrot’s head supports, propels, and powers tripedal locomotion

No vertebrate, living or extinct, is known to have possessed an odd number of limbs. Despite this “forbidden phenotype", gaits that utilize odd numbers of limbs (e.g., tripedalism or pentapedalism) have evolved in both avian and mammalian lineages. Tripedal locomotion is commonly employed by parrots during climbing, who utilize their beaks as an additional support. However, it is unclear whether the beak functions simply as a stabilizing hook, or as a propulsive limb. Here, we present data on kinetics of tripedal climbing in six rosy-faced lovebirds (Agapornis rosiecollis). Our findings demonstrate that parrots utilize cyclical tripedal gaits when climbing and the beak and hindlimbs generate comparable propulsive and tangential substrate reaction forces and power. Propulsive and tangential forces generated by the beak are of equal or greater magnitudes to those forces generated by the forelimbs of humans and non-human primates during vertical climbing. We conclude that the feeding apparatus and neck musculature of parrots has been co-opted to function biomechanically as a third limb during vertical climbing. We hypothesize that this exaptation required substantive alterations to the neuromuscular system including enhanced force-generating capabilities of the neck musculature and modifications to limb central pattern generators.
Efficient frequency conversion of low-intensity light in AlGaAs-on-insulator waveguides

A design study is presented for difference-frequency generation (DFG) in aluminum gallium arsenide (AlGaAs)- on-insulator waveguides. AlGaAs is a mature technology platform with large optical nonlinearities, a high refractive index contrast, and the presence of second-order susceptibility, making it interesting for chip-based frequency conversion. This work targets to efficiently down-convert single-photons to the telecom C-band by DFG. Modal phase-matching (PhM) is used, where the waveguide dimensions are optimized for an efficient and robust conversion of single-photons at 930 nm to around 1550 nm. This paves the way for a single-photon converter that can be integrated on a chip-platform with a single-photon emitters, along other photonic components with standard fabrication techniques. Furthermore, a thorough revision of the DFG theory provides insight into the particular case of a low-powered pump, which is relevant for quantum applications. Finally, a comparison is made with state of the art devices in periodically poled thin film lithium niobate (PPLN). This is, to the best of our knowledge, the first design of a single-photon converter operating in the telecom band that is realized with a III-V material.
Design of Y stiffened panels in double hull tanker under axial compressive loads

In typical tanker ship structures, deck and bottom panels are reinforced by longitudinals (stiffeners) in the longitudinal direction and transversely supported by widely spaced transverse structures (such as transverse bulkheads, deck beams and bottom floors). The conventional longitudinals are usually Tee, angle, bulb or flat bar profiles, while the transverses are typically T-beam sections. The objective of the present work is to replace the longitudinal conventional stiffener profiles with new longitudinal Y (Hat + Tee/Angle) stiffener profiles in order to obtain more strength/safety margin to weight ratio based on the advantage of using Hat section (closed section) to give more effective plate allowing an increase of the stiffener spacing, hence a reduction of the number of stiffeners. The weight of the stiffened deck and bottom panels and the unstiffened panel width using the new Y stiffener profiles are less than those panels with the as-built conventional stiffener profiles. The section modulus of the new Y stiffener profiles with the effective plate and the safety margin (ultimate strength minus applied compression stress) of deck and bottom panels with the new Y stiffener profiles are larger than for panels with the as-built conventional stiffener profiles.
A broadband and high-efficiency polarization conversion metasurface

ABSTRACT In this paper, a broadband and high-efficiency polarization conversion metasurface (PCM) is proposed. It is composed of the patch layer, two layers of dielectric substrates and the ground plane. The patch layer is formed by hexagonal patches etched with open-loop gaps. Also, the hexagonal patches are printed on the diagonal of the unit of PCM that the polarization direction of the incident wave is rotated 90°. In order to improve the efficiency of polarization conversion, two different dielectric substrates are used to achieve impedance matching between the PCM and the free space. In addition, through introducing the open-loop gap in the center of the hexagonal patch, multiple resonant frequencies are generated. Therefore, the conversion bandwidth is further expanded. Simulated and measured results show that the polarization conversion ratio (PCR) of the proposed metasurface is higher than 90% from 7.4 to 17.0 GHz. Especially, the PCR can reach 100% at the resonant frequencies.
‘Such a Thing Does Not Have a Name in his Country’: Entanglements of Diaspora and ‘Home’ Homes in the Zimbabwean Short Story of Crisis

ABSTRACT Home, crisis and migration have defined the experience and concept of being post-colonial Zimbabwe(an) for the past two decades. Much has been written about the post-coloniality of this entangled experience and about how, in particular, literary fiction re-discourses normative perspectives of the Zimbabwean crisis, the home, the unhomely and trans-national out-migration. Rarely considered a serious discursive site from which to (re)know the intricacies inhabiting versions, configurations and symbolisms of the concept of home (especially in the context of crisis and mobility), the Zimbabwean short story has largely remained underexplored. This article recentres the short story of migration (Farai Mpofu’s ‘The Letter’ and NoViolet Mkha’s ‘Shamisos’) in examining how, as socio-cultural and geo-political constructs, diaspora and ‘home’ homes manifest and orchestrate temporalities, processes, relations, attitudes, places, people, and discourses that shape a certain understanding of Zimbabwe as a contested post-colonial ‘home’. On the one hand, the protagonists in the stories live precariously in ‘refuge’ new homes (Botswana and South Africa respectively), and on the other, they attempt to make sense of their precarity through traumatic re-memories of their haunting ‘home’ home (Zimbabwe). We interpret this connection between these unstable ‘homes’ using a conceptual frame that we term ‘ambivalent continuum of precarity’, a concept we coined from the notions of ‘precarity of place’ and ‘continuum of precarity’ advanced by Susan Banki and Julia Ann McWilliams and Sally Wesley Bonet respectively. Our analysis of literary representations of the home(s) therefore focuses on their complex, multiple and shifting layers, signs, symbolisms and ontologies as constructs that reflect on the crisis of post-coloniality manifest in precarious mobilities and ambivalent homes.