Reviewerly/exHarmony huggingface dataset:
SNOW-Vi

SNOW 3G is a stream cipher used as one of the standard algorithms for data confidentiality and integrity protection over the air interface in the 3G and 4G mobile communication systems. SNOW-V is a recent new version that was proposed as a candidate for inclusion in the 5G standard. In this paper, we propose a faster variant of SNOW-V, called SNOW-Vi, that can reach the targeted speeds for 5G in a software implementation on a larger variety of CPU architectures. SNOW-Vi differs in the way how the LFSR is updated and also introduces a new location of the tap T2 for stronger security, while everything else is kept the same as in SNOW-V. The throughput in a software environment is increased by around 50% in average, up to 92 Gbps. This makes the applicability of the cipher much wider and more use cases are covered. The security analyses previously done for SNOW-V are not affected in most aspects, and SNOW-Vi provides the same 256-bit security level as SNOW-V.
Mental models in the brain: On context-dependent neural correlates of mental models

In this paper, the concept of context-dependent realisation of mental models is introduced and discussed. Literature from neuroscience is discussed showing that different types of mental models can use different types of brain areas. Moreover, it is discussed that the same occurs for the formation and adaptation of mental models and the control of these processes. This makes that it is hard to claim that all mental models use the same brain mechanisms and areas. Instead, the notion of context-dependent realisation is proposed here as a better manner to relate neural correlates to mental models. It is shown in some formal detail how this context-dependent realisation approach can be related to well-known perspectives based on bridge principle realisation and interpretation mapping realisation.
A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction

Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x , y , and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose. 1
DRLPart

Workload consolidation is a commonly used approach for improving resource utilization of commodity servers. However, colocated workloads often suffer from significant performance degradations due to resource contention, which makes resource partitioning an important research problem. Partitioning multiple resources coordinately is particularly challenging due to the complex contention behaviors and huge solution space, which is not well-addressed in the literature.
Microlearner: A fine-grained Learning Optimizer for Big Data Workloads at Microsoft

Big data systems have become increasingly complex making the job of a query optimizer incredibly difficult. This is due to more complicated decision making, more complex query plans seen, and more tedious objective functions in cloud-based big data workloads. As a result, production cloud query optimizers are often far from optimal. In this paper, we describe building a learning query optimizer for big data workloads at Microsoft. We make four major contributions. First, we describe the challenges in cloud query optimizers based on our observations from the big data workloads at Microsoft. Second, we discuss what makes machine learning an attractive approach to aid the big data query optimizers in decision making. Third, we present Microlearner, a practical approach to characterize large cloud workloads into smaller subsets and build micromodels over each subset to tame the complexity of big data workloads And finally, we describe the productization of Microlearner, using learned cardinality as a concrete example, via performance results over very large production workloads and illustrating the various challenges involved in deployment.
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER

We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value function, the critic. Both functions can be deep neural networks of arbitrary complexity. Our framework allows showing convergence of the well known Proximal Policy Optimization (PPO) and of the recently introduced RUDDER. For the convergence proof we employ recently introduced techniques from the two time-scale stochastic approximation theory.Previous convergence proofs assume linear function approximation, cannot treat episodic examples, or do not consider that policies become greedy. The latter is relevant since optimal policies are typically deterministic. Our results are valid for actor-critic methods that use episodic samples and that have a policy that becomes more greedy during learning.
Multi-Camera Vehicle Tracking System Based on Spatial-Temporal Filtering

Multi-Camera multi-target tracking is essential in the research field of urban intelligence traffic. It shows that the task becomes challenging due to differences of illumination, angle, and occlusion under different cameras. In this paper, we propose an efficient multi-camera vehicle tracking system, which contains a model trained with multi-loss to extract appearance features, and a filter with spatial-temporal information between cameras. The proposed system includes three parts. Firstly, we generate tracklets in a single-camera with different views by vehicle detection and multi-target tracking. Secondly, we extract the appearance feature of each tracklet through the trained vehicle ReID model. Thirdly, we innovatively propose a matching strategy that calculates several factors, the similarity of appearance features, the time information, and the space information of target ID between adjacent cameras. The proposed system ranks the sixth place in the City-Scale Multi-Camera Vehicle Tracking of AI City 2021 Challenge (Track 3) with a score of 0.5763.
“Know What You Know”: Predicting Behavior for Learning-Enabled Systems When Facing Uncertainty

Since deep learning systems do not generalize well when training data is incomplete and missing coverage of corner cases, it is difficult to ensure the robustness of safety-critical self-adaptive systems with deep learning components. Stakeholders require a reasonable level of confidence that a safety-critical system will behave as expected in all contexts. However, uncertainty in the behavior of safety-critical Learning-Enabled Systems (LESs) arises when run-time contexts deviate from training and validation data. To this end, this paper proposes an approach to develop a more robust safety-critical LES by predicting its learned behavior when exposed to uncertainty and thereby enabling mitigating countermeasures for predicted failures. By combining evolutionary computation with machine learning, an automated method is introduced to assess and predict the behavior of an LES when faced with previously unseen environmental conditions. By experimenting with Deep Neural Networks (DNNs) under a variety of adverse environmental changes, the proposed method is compared to a Monte Carlo (i.e., random sampling) method. Results indicate that when Monte Carlo sampling fails to capture uncommon system behavior, the proposed method is better at training behavior models with fewer training examples required.
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset with Limited Computational Resources

Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to train a fully-connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities. This requires many computational resources, such as GPU memory. The metric learning paradigm is an economical computation method, but its performance is greatly inferior to that of the classification paradigm. To address this challenge, we propose a simple but effective CNN layer called the Virtual fully-connected (Virtual FC) layer to reduce the computational consumption of the classification paradigm. Without bells and whistles, the proposed Virtual FC reduces the parameters by more than 100 times with respect to the fully-connected layer and achieves competitive performance on mainstream face recognition evaluation datasets. Moreover, the performance of our Virtual FC layer on the evaluation datasets is superior to that of the metric learning paradigm by a significant margin. Our code will be released in hopes of disseminating our idea to other domains <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Image compression based on octave convolution and semantic segmentation

Lossy image compression based on deep learning usually contains stacking convolutional layers, pooling layers, and nonlinear functions. However, the feature map is obtained by the convolutional layer, which has a lot of redundancy, so we use octave convolution instead of vanilla convolution to improve compression efficiency. The feature map can be divided into high-frequency and low-frequency information. We use octave convolution to design an automatic codec to decompose the feature map into high-frequency and low-frequency information, which effectively improves the quality of the generated image. First, the semantic segmentation map of the input image is obtained by pre-training S e g N e t . The C o m N e t uses the original image and the semantic segmentation map to generate a low-dimensional representation, and the G e n N e t network utilizes the low-dimensional representation and the semantic segmentation map to estimate images. Then, the residuals between the reconstructed image and the original image are encoded. Finally, the reconstructed image and the decoded residual image are used to obtain the final high-quality reconstruction. Experimental results show that our method outperforms the existing image coding standards in terms of PSNR and MS-SSIM at different bit rates, and the reconstruction of images with complex textures and semantics has more obvious advantages.
A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy Under a Cloud Environment

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.
Towards Benchmarking Feature Type Inference for AutoML Platforms

The paradigm of AutoML has created an opportunity to enable ML for the masses. Emerging industrial-scale cloud AutoML platforms aim to automate the end-to-end ML workflow. While many works have looked into automated feature engineering, model selection, or hyper-parameter search in AutoML, little work has studied a crucial step that serves as an entry point to this workflow: ML feature type inference. The semantic gap between attribute types (e.g., strings, numbers) in databases/files and ML feature types (e.g., Numeric, Categorical) necessitates type inference. In this work, we formalize and standardize this task by creating the first ever benchmark labeled dataset, which we use to objectively evaluate existing AutoML tools. Our dataset has 9921 examples and a 9-class label vocabulary. Our labeled data also offers an alternative approach to automate this task than existing rule-based or syntax-based approaches: use ML itself to predict feature types. We collate a benchmark suite of 30 classification and regression tasks to assess the importance of type inference for downstream models. Empirical comparison on our labeled data shows that an ML-based approach delivers a lift of an average 14% and up to 38% in accuracy for identifying feature types compared to prominent industrial tools. Our downstream benchmark suite reveals that the ML-based approach outperforms existing industrial-strength tools for 47 out of 60 downstream models. We release our labeled dataset, models, and downstream benchmarks in a public repository with a leaderboard.
Performing social engineering: A qualitative study of information security deceptions

Contemporary computer and networking technologies have expanded the scope and scale of deceptions worldwide. Among hackers, security practitioners, and other technologists, ruses designed to gain access to otherwise secure information or computer systems are often referred to as “social engineering.” To date, little research has explored the creation of fabrications from the perspective of social engineers. The current study addresses this gap by examining the attributes that social engineers ascribe to successful and effective social engineering deceptions through a grounded theory analysis of interviews with social engineers (n = 37). Results reveal twelve characteristics of effective social engineers according to participants–findings which indicate that perpetrators consider social context, assumptions about human nature, the complexities of social networks, the role of social conventions, and the limitations of human processing and reasoning in the execution of their deceptions. The study concludes by considering the theoretical implications of the results and advancing propositions to guide future criminological research on social engineering, fraud, and deception more generally.
SKR-QA: Semantic ranking and knowledge revise for multi-choice question answering

Knowledge has long been cosnsidered a crucial part of natural language understanding. Many knowledge bases have been constructed, but none of them will ever be complete. Nevertheless, we argue that complete knowledge already exists in natural language. Most previous work on question answering retrieved such knowledge using traditional statistical methods, and consequently, the knowledge retrieved contained co-occurring phrases and could not provide guidance for model understanding and reasoning. Therefore, in addition to demonstrating the effectiveness of natural language knowledge in machine understanding, this study presents a novel knowledge retrieval approach that evaluates the importance of knowledge from a semantic perspective. Furthermore, we propose a knowledge revise mechanism that allows the model to revise the retrieved knowledge from local and global perspectives. We demonstrate our Semantic-rank-and-Knowledge-Revise-based Question Answering (SKR-QA) approach on two challenging multi-choice question and answering tasks: ARC–Challenge and OpenbookQA. Compared with the previous State-of-the-Art (SOTA) models, our work achieves consistent improvements. Moreover, the knowledge obtained by our method is more conducive to machine understanding, thus providing certain interpretability.
Technology of application of competence-based educational simulators in the informational and educational environment for learning general technical disciplines

Abstract The article presents the technology of application of competence-based educational simulators in the informational and educational environment for learning general technical disciplines. It was designed the classification of competence-based educational simulators for learning general technical disciplines. There are presented types of educational simulators and outlined professional competencies of general technical disciplines that provide the developed types of simulators. On the basis of passing educational simulators it is formed not only a qualitative indicator of the educational results, but also an indicator of the formation of competencies in the course and curriculum. The method was tested using experimental group and control group (total 1301 students of specialties ‘Agricultural Engineering’, ‘Electrical Power, Electrical Engineering and Electrical Mechanics’, ‘Professional Education’ that studying general technical disciplines) by systematically measuring achievement of professional competencies in the conditions of informational and educational environment by using educational simulators. The results show that higher education applicants in the experimental group achieve better results of acquiring professional competencies.
Learning monocular 3D reconstruction of articulated categories from motion

Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem. In this work we use video self-supervision, forcing the consistency of consecutive 3D reconstructions by a motion-based cycle loss. This largely improves both optimization-based and learning-based 3D mesh reconstruction. We further introduce an interpretable model of 3D template deformations that controls a 3D surface through the displacement of a small number of local, learnable handles. We formulate this operation as a structured layer relying on meshlaplacian regularization and show that it can be trained in an end-to-end manner. We finally introduce a per-sample numerical optimisation approach that jointly optimises over mesh displacements and cameras within a video, boosting accuracy both for training and also as test time post-processing.While relying exclusively on a small set of videos collected per category for supervision, we obtain state-of-the-art reconstructions with diverse shapes, viewpoints and textures for multiple articulated object categories. Supplementary materials, code, and videos are provided on the project page: https://fkokkinos.github.io/video_3d_reconstruction/.
Fully device-independent model on quantum networks

The Bell inequality can provide a useful witness for device-independent applications with quantum (or postquantum) eavesdroppers. This feature holds only for single entangled systems. Our goal is to explore a device-independent model for quantum networks. We first propose a Bell inequality to verify the genuinely multipartite nonlocality of connected quantum networks including cyclic networks and universal quantum computational resources for a measurement-based computation model. This is further used to construct a monogamy relation in a fully device-independent model with multisource quantum resources. It is finally applied for multiparty quantum key distribution, blind quantum computation, and quantum secret sharing. The present model can inspire various large-scale applications on quantum networks in a device-independent manner.
Multimodal Surprise Adequacy Analysis of Inputs for Natural Language Processing DNN Models

As Deep Neural Networks (DNNs) are rapidly adopted in various domains, many test adequacy metrics for DNN inputs have been introduced to help evaluating, and validating, trained DNN models. Surprise Adequacy (SA) is one such metric that aims to quantitatively measure how surprising a new input is with respect to the data used to train the given model. While SA has been shown to be effective for computer vision tasks such as image classification or object segmentation, its efficacy for DNN based Natural Language Processing has not been thoroughly studied. This paper evaluates whether it is feasible to apply SA analysis to DNN models trained for NLP tasks. We also show that the input distribution captured in the latent embedding space can be multimodal <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for some NLP tasks, unlike those observed in computer vision tasks, and investigate if catering for the multimodal property of NLP models can improve SA analysis. An empirical evaluation of extended SA metrics with three NLP tasks and nine DNN models shows that, while unimodal SAs perform sufficiently well for text classification, multimodal SA can outperform unimodal metrics.