Reviewerly/exHarmony huggingface dataset:
An analytic evaluation for the impact of uncle blocks by selfish and stubborn mining in an imperfect Ethereum network

Besides supporting smart contract, another Ethereum distinct feature lies in the improvement of running efficiency achieved by the uncle block mechanism. To mitigate network congestion, an orphan block has a chance to be referenced by a regular block as an uncle block rather than being abandoned directly. In this paper, we use a Markov state machine to model selfish and stubborn mining in an imperfect Ethereum network with natural forks. The security threshold against selfish mining, which indicates how difficult an attacker could gain more than he deserves, decreases to 23.8% in an uncle block rate of 11%. Two advanced selfish mining strategies, trail stubborn mining and equal fork combined with trail stubborn mining, are also analyzed with different uncle block rates in this paper. In different feasible regions, strategies that bring about the optimal reward vary from each other. All evaluations in this paper are carried out in an analytic method, which is more flexible and scalable than a numeric method if it combines multiple attacks or extends to several attackers.
Improve the Decision-making Skill of Basketball Players by an Action-aware VR Training System

Decision-making is an essential part in basketball offenses. In this paper, we proposed a basketball offensive decision-making VR training system. During the training, the trainee can intuitively interact with the system by wearing a motion capture suit and be trained in different virtual defensive scenarios designed by professional coaches. The system will recognize the offensive action performed by the user and provide correct suggestions when he/she makes a bad offensive decision. We compared the effectiveness of the training protocols by using conventional tactics board and the proposed VR system. Furthermore, we investigated the influence of using prerecorded 360-degree panorama video and computer simulated virtual content to create immersive training environment.
IceVisionSet: lossless video dataset collected on Russian winter roads with traffic sign annotations

Ability of autonomous vehicles to operate in complex dynamic environments requires, among other things, fast and accurate perception of surroundings, which includes recognition and tracking of traffic signs.For development and testing of modern sophisticated computer vision systems large and diverse datasets are of the major importance. To test the robustness of algorithms, image data with different moving speeds, camera settings, lighting and weather conditions are especially important.In this work we present a comprehensive, lifelike dataset of traffic sign images collected on the Russian winter roads in varying conditions, which include different weather, camera exposure, illumination and moving speeds. The dataset was annotated in accordance with the Russian traffic code. Annotation results and images are published under open CC BY 4.0 license and can be downloaded from the project website: http://oscar.skoltech.ru/.
Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition

This paper introduces a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these applications, the classification problems are both numerically imbalanced - positive (object class) training and test windows are much rarer than negative (non-class) ones - and geometrically asymmetric - the positive samples typically form compact, visually-coherent groups while negatives are much more diverse, including anything at all that is not a well-centered sample from the target class. For such tasks, there is a need for discriminants whose decision regions focus on tightly circumscribing the positive class, while still taking account of negatives in zones where the two classes overlap. To this end, we propose a family of quasi-linear “polyhedral conic” discriminants whose positive regions are distorted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> or L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> balls. In addition, we also integrated the proposed classification loss into deep neural networks so that both the features and classifier can be learned simultaneously end-to-end fashion to improve the classification accuracies. The methods have properties and run-time complexities comparable to linear Support Vector Machines (SVMs), and they can be trained from either binary or positive-only samples using constrained quadratic programs related to SVMs. Our experiments show that they significantly outperform linear SVMs, deep neural networks using softmax loss function and existing one-class discriminants on a wide range of object detection, face verification, open set recognition and conventional closed-set classification tasks.
Grasping objects in immersive Virtual Reality

Grasping is one of the fundamental actions we perform to interact with objects in real environments, and in the real world we rarely experience difficulty picking up objects. Grasping plays a fundamental role for interactive virtual reality (VR) systems that are increasingly employed not only for recreational purposes, but also for training in industrial contexts, in medical tasks, and for rehabilitation protocols. To ensure the effectiveness of such VR applications, we must understand whether the same grasping behaviors and strategies employed in the real world are adopted when interacting with objects in VR. To this aim, we replicated in VR an experimental paradigm employed to investigate grasping behavior in the real world. We tracked participants' forefinger and thumb as they picked up, in a VR environment, unfamiliar objects presented at different orientations, and exhibiting the same physics behavior of their real counterparts. We compared grasping behavior within and across participants, in VR and in the corresponding real world situation. Our findings highlight the similarities and differences in grasping behavior in real and virtual environments.
Semantic Predictive Control for Explainable and Efficient Policy Learning

Visual anticipation of ego and object motion over a short time horizons is a key feature of human-level performance in complex environments. We propose a driving policy learning framework that predicts feature representations of future visual inputs; our predictive model infers not only future events but also semantics, which provide a visual explanation of policy decisions. Our Semantic Predictive Control (SPC) framework predicts future semantic segmentation and events by aggregating multi-scale feature maps. A guidance model assists action selection and enables efficient sampling-based optimization. Experiments on multiple simulation environments show that networks which implement SPC can outperform existing model-based reinforcement learning algorithms in terms of data efficiency and total rewards while providing clear explanations for the policy's behavior.
Public-Key Cryptography in the Fine-Grained Setting

Cryptography is largely based on unproven assumptions, which, while believable, might fail. Notably if $$P = NP$$ , or if we live in Pessiland, then all current cryptographic assumptions will be broken. A compelling question is if any interesting cryptography might exist in Pessiland. A natural approach to tackle this question is to base cryptography on an assumption from fine-grained complexity. Ball, Rosen, Sabin, and Vasudevan [BRSV'17] attempted this, starting from popular hardness assumptions, such as the Orthogonal Vectors (OV) Conjecture. They obtained problems that are hard on average, assuming that OV and other problems are hard in the worst case. They obtained proofs of work, and hoped to use their average-case hard problems to build a fine-grained one-way function. Unfortunately, they proved that constructing one using their approach would violate a popular hardness hypothesis. This motivates the search for other fine-grained average-case hard problems. The main goal of this paper is to identify sufficient properties for a fine-grained average-case assumption that imply cryptographic primitives such as fine-grained public key cryptography (PKC). Our main contribution is a novel construction of a cryptographic key exchange, together with the definition of a small number of relatively weak structural properties, such that if a computational problem satisfies them, our key exchange has provable fine-grained security guarantees, based on the hardness of this problem. We then show that a natural and plausible average-case assumption for the key problem Zero-k-Clique from fine-grained complexity satisfies our properties. We also develop fine-grained one-way functions and hardcore bits even under these weaker assumptions. Where previous works had to assume random oracles or the existence of strong one-way functions to get a key-exchange computable in O(n) time secure against $$O(n^2)$$ adversaries (see [Merkle'78] and [BGI'08]), our assumptions seem much weaker. Our key exchange has a similar gap between the computation of the honest party and the adversary as prior work, while being non-interactive, implying fine-grained PKC.
Exploiting Ground and Ceiling Effects on Autonomous UAV Motion Planning

Micro Aerial Vehicles (MAVs) and in particular quadrotors have gained a lot of attention because of their small size, stable, robust, and diverse sensing capabilities that make them perfect test beds in several safety critical operations. Shrinking these vehicles is desirable since agility increases. However, it entails smaller power sources and hence less flight time. Adding sensors on these systems also implies more energy consumption due to both the added weight and the supplied energy to the sensors. In this work, we build a framework to leverage the flow dynamic effects near surfaces to recognize grounds and ceilings during operations and to plan a trajectory while minimizing energy consumption. Our proposed framework leverages data from real experiments to model the behavior of the system near surfaces and graph theoretical approaches for energy efficient motion planning. As a result, this study indicates that i) we can detect surfaces during operations without the need of extra onboard sensors and ii) we can minimize energy consumption up to 15% when the system can fly near ground or ceiling surfaces. The proposed framework is validated with experimental results on a quadrotor UAV.
AnswerBot: an answer summary generation tool based on stack overflow

Software Q&A sites (like Stack Overflow) play an essential role in developers’ day-to-day work for problem-solving. Although search engines (like Google) are widely used to obtain a list of relevant posts for technical problems, we observed that the redundant relevant posts and sheer amount of information barriers developers to digest and identify the useful answers. In this paper, we propose a tool AnswerBot which enables to automatically generate an answer summary for a technical problem. AnswerBot consists of three main stages, (1) relevant question retrieval, (2) useful answer paragraph selection, (3) diverse answer summary generation. We implement it in the form of a search engine website. To evaluate AnswerBot, we first build a repository includes a large number of Java questions and their corresponding answers from Stack Overflow. Then, we conduct a user study that evaluates the answer summary generated by AnswerBot and two baselines (based on Google and Stack Overflow search engine) for 100 queries. The results show that the answer summaries generated by AnswerBot are more relevant, useful, and diverse. Moreover, we also substantially improved the efficiency of AnswerBot (from 309 to 8 seconds per query).
WeChat-based teaching for an immersion cultural exchange program – a case study in CFL

This paper reports on an exploratory study investigating WeChat-based learning efficiency in teaching Chinese as a foreign language (CFL). Generated from the Connectivism theory, this study aims at exploring the features of mobile-assisted teaching in an immersion CFL exchange program with the social communication app WeChat as a case in point. WeChat is a multimedia instant messenger application in China. In this two-year-long study, an action study was applied. A total of ten learners of elementary Chinese were involved. The participants received and submitted the assignment via WeChat and were asked to review language points on the weekly course posts. Their posts on WeChat Moments were recorded and then reconstructed into language learning content. This study adopted a mixed-method design. Data were collected through the course learning outcomes, an end-of-semester questionnaire, and follow-up interviews. The results show a positive reception from the learners on WeChat-based learning. This study observes (1) students’ evident linguistic development in the spontaneous expression on WeChat; (2) a substantial change toward learning with social media; (3) conscious and unconscious language input and output. Language learners reported that using WeChat (1) provided them with a better opportunity to communicate with native speakers; (2) enabled them to integrate knowledge into life; (3) helped them review class content and share thoughts more efficiently; (4) offered them a long-term supportive Chinese language community. However, the results also indicate that (1) personal preference to social media should be taken into consideration; (2) WeChat fails to foster autonomous learning in the students; (3) students’ participation and motivation declined towards the end of the program. In retrospect, monitoring and supervising WeChat usage when necessary, assigning the topics of the posts, and inspiring interest in learning language by solving real-life problems are recommended.
Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. However, existing methods still attempt to reason over a representation of the environment that is flat and unnecessarily detailed, which limits scalability. An open problem is then to develop methods capable of producing the most compact environment model sufficient for accurate and efficient natural language understanding. We propose a model that leverages environment-related information encoded within instructions to identify the subset of observations and perceptual classifiers necessary to perceive a succinct, instruction-specific environment representation. The framework uses three probabilistic graphical models trained from a corpus of annotated instructions to infer salient scene semantics, perceptual classifiers, and grounded symbols. Experimental results on two robots operating in different environments demonstrate that by exploiting the content and the structure of the instructions, our method learns compact environment representations that significantly improve the efficiency of natural language symbol grounding.
Least Square Policy Iteration for IBVS based Dynamic Target Tracking

This paper delves into the problem of tracking a maneuvering target based on only vision based feedback namely Image Based Visual Servoing (IBVS). In the absence of a GPS or any other external sensor, vision proves to be a valuable source of information about the environment. However it is difficult to perform the tracking using only a monocular vision due to the absence of depth measurement. This restricts the use of traditional IBVS methods as they rely on the interaction matrix which is sensitive to various camera parameters and depth estimate. We thus solve this problem through a learning based approach and model it as Markov decision process, on which we apply a Reinforcement Learning technique. Least Square Policy Iteration (LSPI) learns the optimal control policies required to keep following the target drone while maintaining a fixed distance from it. The performance of the proposed algorithm is tested and simulated in Gazebo environment.
Motion Planning Templates: A Motion Planning Framework for Robots with Low-power CPUs

Motion Planning Templates (MPT) is a C++ template-based library that uses compile-time polymorphism to generate robot-specific motion planning code and is geared towards eking out as much performance as possible when running on the low-power CPU of a battery-powered small robot. To use MPT, developers of robot software write or leverage code specific to their robot platform and motion planning problem, and then have MPT generate a robot-specific motion planner and its associated data-structures. The resulting motion planner implementation is faster and uses less memory than general motion planning implementations based upon runtime polymorphism. While MPT loses runtime flexibility, it gains advantages associated with compile-time polymorphism- including the ability to change scalar precision, generate tightly-packed data structures, and store robot-specific data in the motion planning graph. MPT also uses compile-time algorithms to resolve the algorithm implementation, and select the best nearest neighbor algorithm to integrate into it. We demonstrate MPT's performance, lower memory footprint, and ability to adapt to varying robots in motion planning scenarios on a small humanoid robot and on 3D rigid-body motions.
Turn-minimizing multirobot coverage

Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot's coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots' mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1-5 robots.
The Diversity Is in the Details: Unintentional Language Discrimination in the Practice of Applied Behavior Analysis

Individuals with limited English proficiency face more challenges accessing applied behavior analysis (ABA) than their English-speaking counterparts. Many federal and state laws have been enacted to ensure the civil rights of protected classes, and Section 1557 of the Affordable Care Act (ACA, 2010) builds on those laws and explicitly establishes a cause of action (i.e., a basis to sue) against health care providers, including ABA providers, who discriminate against patients on the basis of race, color, national origin, sex, age, or disability. A patient's language falls under the scope of national origin, and most health care providers, including behavior analysts who deliver ABA as medically necessary treatment, have a duty to ensure that patients who are Limited English Proficient (LEP) have the same access to the provider's services as English-speaking patients. Knowledge of this provision of the ACA is critical to its compliance and, more importantly, to ensuring that behavior analysts rise to the challenge that the goal of true diversity represents. Note: Many terms are used interchangeably to describe insurance carriers, insurance issuers, health plans, and managed care organizations, as well as practitioners of applied behavior analysis. In this article, insurance carriers, insurance issuers, health plans, and managed care organizations are referred to as payors, and practitioners of applied behavior analysis are referred to as behavior analysts or ABA providers.
Exploring Social Recommendations with Visual Diversity-Promoting Interfaces

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this article, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users’ subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs.
ACDAS: Authenticated controlled data access and sharing scheme for cloud storage

Summary Cloud storage services require cost‐effective, scalable, and self‐managed secure data management functionality. Public cloud storage always enforces users to adopt the restricted generic security consideration provided by the cloud service provider. On the contrary, private cloud storage gives users the opportunity to configure a self‐managed and controlled authenticated data security model to control the accessing and sharing of data in a private cloud. However, this introduces several new challenges to data security. One critical issue is how to enable a secure, authenticated data storage model for data access with controlled data accessibility. In this paper, we propose an authenticated controlled data access and sharing scheme called ACDAS to address this issue. In our proposed scheme, we employ a biometric‐based authentication model for secure access to data storage and sharing. To provide flexible data sharing under the control of a data owner, we propose a variant of a proxy reencryption scheme where the cloud server uses a proxy reencryption key and the data owner generates a credential token during decryption to control the accessibility of the users. The security analysis shows that our proposed scheme is resistant to various attacks, including a stolen verifier attack, a replay attack, a password guessing attack, and a stolen mobile device attack. Further, our proposed scheme satisfies the considered security requirements of a data storage and sharing system. The experimental results demonstrate that ACDAS can achieve the security goals together with the practical efficiency of storage, computation, and communication compared with other related schemes.