Reviewerly/exHarmony huggingface dataset:
How Tool-Use Shapes Body Metric Representation: Evidence From Motor Training With and Without Robotic Assistance

Previous evidence has shown that tool-use can reshape one's own body schema, extending peripersonal space and modulating the representation of related body parts. Here, we investigated the role of tool action in shaping the body metric representation, by contrasting two different views. According to a first view, the shaping would rely on the mere execution of tool action, while the second view suggests that the shaping induced by tool action on body representation would primarily depend on the representation of the voluntary motor programs. To this aim, we contrasted a condition in which participants voluntarily accomplish the movement by representing the goals and programs of tool action (i.e. active tool-use training) with a condition in which the tool-use training was produced without motor intentionality (i.e. passive tool-use training by means of robotic assistance). If the body metric representation primarily depends on coexistence between intention and action, we would expect an increase of the perceived forearm length in the post- with respect to the pre-training phase after the Active training phase only. Healthy participants were asked to estimate the midpoint of their right forearm before and after 20 minutes of tool-use training. In the Active condition, subjects performed "enfold-and-push" movements using a rake to prolong their arm. In the Passive condition, subjects were asked to be completely relaxed while the movements were performed with robotic assistance. Results showed a significant increase in the perceived arm length in the post- with respect to the pre-training phase only in the Active task. Interestingly, only in the post-training phase, a significant difference was found between Active and Passive conditions, with a higher perceived arm length in the former than in the latter. From a theoretical perspective, these findings suggest that tool-use may shape body metric representation only when action programs are motorically represented and not merely produced. From a clinical perspective, these results support the use of robots for the rehabilitation of brain-damaged hemiplegic patients, provided that robot assistance during the exercises is present only "as-needed" and that patient's motor representation is actively involved.
Security Analysis on an Optical Encryption and Authentication Scheme Based on Phase-Truncation and Phase-Retrieval Algorithm

In this paper, the security of the cryptosystem based on phase-truncation Fourier transform (PTFT) and Gerchberg-Saxton (G-S) algorithm is analyzed. In this cryptosystem, the phase key generated using phase-truncated (PT) operation is bonded with the phase key generated in G-S algorithm to form the first private key, which improves the complexity of the first private key. In addition, since the second private key is generated using the G-S algorithm, the number of known constraints decreases compared to the traditional PTFT-based cryptosystem, which will lead the non-convergence of special attacks. However, it has been found that two private keys generated in the cryptosystem based on PTFT and G-S algorithm are related to one phase key generated in the G-S algorithm, which provides an additional constraint to retrieve the other private key when one private key is disclosed. Based on this analysis, two iterative processes with different constraints are proposed to crack the cryptosystem based on PTFT and G-S algorithm. This is the first time to report the silhouette problem existing in the cryptosystem based on PTFT and G-S algorithm. Numerical simulations are carried out to validate the feasibility and effectiveness of our analysis and proposed iterative processes.
The Role of Latency and Task Complexity in Predicting Visual Search Behavior

Latency in a visualization system is widely believed to affect user behavior in measurable ways, such as requiring the user to wait for the visualization system to respond, leading to interruption of the analytic flow. While this effect is frequently observed and widely accepted, precisely how latency affects different analysis scenarios is less well understood. In this paper, we examine the role of latency in the context of visual search, an essential task in data foraging and exploration using visualization. We conduct a series of studies on Amazon Mechanical Turk and find that under certain conditions, latency is a statistically significant predictor of visual search behavior, which is consistent with previous studies. However, our results also suggest that task type, task complexity, and other factors can modulate the effect of latency, in some cases rendering latency statistically insignificant in predicting user behavior. This suggests a more nuanced view of the role of latency than previously reported. Building on these results and the findings of prior studies, we propose design guidelines for measuring and interpreting the effects of latency when evaluating performance on visual search tasks.
One-Shot Learning Hand Gesture Recognition Based on Lightweight 3D Convolutional Neural Networks for Portable Applications on Mobile Systems

Though deep convolutional neural networks (CNNs) have made great breakthroughs in the field of vision-based gesture recognition, however it is challenging to deploy these high-performance networks to resource-constrained mobile platforms and acquire large numbers of labeled samples for deep training of CNNs. Furthermore, there are some application scenarios with only a few samples or even a single one for a new gesture class so that the recognition method based on CNNs cannot achieve satisfactory classification performance. In this paper, a well-designed lightweight network based on I3D with spatial-temporal separable 3D convolutions and Fire module is proposed as an effective tool for the extraction of discriminative features. Then some effective capacity by deep training of large samples from related categories can be transferred and utilized to enhance the learning ability of the proposed network instead of training from scratch. In this way, the implementation of one-shot learning hand gesture recognition (OSLHGR) is carried out by a rational decision with distance measure. Moreover, a kind of mechanism of discrimination evolution with innovation of new sample and voting integration based on multi-classifiers is established to improve the learning and classification performance of the proposed method. Finally, a series of experiments and tests on the IsoGD and Jester datasets are conducted to demonstrate the effectiveness of our improved lightweight I3D. Meanwhile, a specific dataset of gestures with variant angles and directions, BSG 2.0, and the ChaLearn gesture dataset (CGD) are used for the test of OSLHGR. The results on different experiment platforms verify and validate the performance advantages of satisfied classification and real-time response speed.
Pattern-Based and Visual Analytics for Visitor Analysis on Websites

In this paper, We present how we combined visualization and machine learning techniques to provide an analytic tool for web log data.We designed a visualization where advertisers can observe the visits to their different pages on a site, common web analytic measures and individual user navigation on the site. In this visualization, the users can get insights of the data by looking at key elements of the graph. Additionally, we applied pattern mining techniques to observe common trends in user segments of interest.
Security and Efficiency Enhanced Revocable Access Control for Fog-Based Smart Grid System

With the popularity of smart grids, plentiful of smart devices have been put into use, such as smart meters and power assets.Due to limited computation capabilities and storage spaces of these devices, the collected data need to be ''outsourced'' towards the data server for processing and storage.The data owners, therefore, lose direct control over these ''outsourced'' data, leading to significant security issues of the users' data.In this paper, aiming at solving this problem, we propose a multi-authority Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme with revocation for the fog-based smart grid system.Specifically, in order to achieve attribute revocation without requiring users to be always online, we use the DH (Diffie-Hellman) tree to distribute the group key statelessly, which also solves the problem of collusion attack initiated by revoked user and valid user.To improve security of our proposed scheme, we remove the trusted key authority (KA) by using a secure two-party computation (2PC) protocol between the KA and the cloud service provider to generate user private key.To improve efficiency of our proposed scheme, we combine user and attribute revocation, and outsource complex calculations to fog nodes.Furthermore, our proposed scheme uses attribute group key and leaf private key together to protect user proxy key, which reduces the storage overhead of the system and improves the security.Both security analysis and experimental results demonstrate that our proposed scheme can balance the security objectives with the efficiency.
Deep Holes of Projective Reed-Solomon Codes

Projective Reed-Solomon (PRS) codes are Reed-Solomon codes of the maximum possible length q+1. The classification of deep holes-received words with maximum possible error distance- for PRS codes is an important and difficult problem. In this paper, we use algebraic methods to explicitly construct three classes of deep holes for PRS codes. We show that these three classes completely classify all deep holes of PRS codes with redundancy four. Previously, the deep hole classification was only known for PRS codes with redundancy at most three.
Stochastic polarization switching induced by optical injection in bimodal quantum-dot micropillar lasers

Mutual coupling and injection locking of semiconductor lasers is of great interest in non-linear dynamics and its applications for instance in secure data communication and photonic reservoir computing. Despite its importance, it has hardly been studied in microlasers operating at μW light levels. In this context, vertically emitting quantum dot micropillar lasers are of high interest. Usually, their light emission is bimodal, and the gain competition of the associated linearly polarized fundamental emission modes results in complex switching dynamics. We report on selective optical injection into either one of the two fundamental mode components of a bimodal micropillar laser. Both modes can lock to the master laser and influence the non-injected mode by reducing the available gain. We demonstrate that the switching dynamics can be tailored externally via optical injection in very good agreement with our theory based on semi-classical rate equations.
TSIA: A Novel Image Authentication Scheme for AMBTC-Based Compressed Images Using Turtle Shell Based Reference Matrix

This paper proposes a novel authentication scheme for absolute moment block truncation coding (AMBTC) of a compressed image using turtle shell based data hiding.For simplicity, we call it turtle shell based image authentication method (TSIA in short).For each block, a 3-bit authentication code (AC) is generated by combining the bitmap with a pseudo-random sequence and is concealed into the corresponding quantization levels with the use of a reference matrix.In order to solve the problem of having a high quantization level lower or equal to a low quantization level caused after the hiding operation, an iterative embedding mechanism is employed in the proposed TSIA scheme.Experimental results demonstrate that the proposed TSIA scheme outperforms previous works in the watermarked image quality with an increased PSNR of 0.16 dB and achieves high tampering detecting accuracy.
Synthesis of LTL Formulas from Natural Language Texts: State of the Art and Research Directions.

Linear temporal logic (LTL) is commonly used in model checking tasks; moreover, it is well-suited for the formalization of technical requirements. However, the correct specification and interpretation of temporal logic formulas require a strong mathematical background and can hardly be done by domain experts, who, instead, tend to rely on a natural language description of the intended system behaviour. In such situations, a system that is able to automatically translate English sentences into LTL formulas, and vice versa, would be of great help. While the task of rendering an LTL formula into a more readable English sentence may be carried out in a relatively easy way by properly parsing the formula, the converse is still an open problem, due to the inherent difficulty of interpreting free, natural language texts. Although several partial solutions have been proposed in the past, the literature still lacks a critical assessment of the work done. We address such a shortcoming, presenting the current state of the art for what concerns the English-to-LTL translation problem, and outlining some possible research directions.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification

Recent work has shown that current sentiment classification models are fragile and sensitive to simple perturbations.In this work, we propose a novel adversarial training approach, LexicalAT, to improve the robustness of current sentiment classification models.The proposed approach consists of a generator and a classifier.The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks.Considering the diversity of attacks, the generator uses a large-scale lexical knowledge base, WordNet, to generate attacking examples by replacing some words in training examples with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or supersuperior words (e.g., chair and armchair).Due to the discrete generation step in the generator, we use policy gradient, a reinforcement learning approach, to train the two modules.Experiments show LexicalAT outperforms strong baselines and reduces test errors on various neural networks, including CNN, RNN, and BERT. 1 * Equal Contribution. 1 The code will be released at https://github.com/lancopku/LexicalAT2 Adversarial examples are intentionally designed by attackers to cause the model to make a mistake.
Heterogeneous sensor data fusion for multiple object association using belief functions

In this study, the object association issue is tackled in order to ensure a correct affiliation of perceived objects with known ones. The proposed approach is based on the evidence theory and includes multiple object features in order to manage pairing issues in a complex environment. Two heterogeneous information sources are built based on kinematic features related to the objects: their position and size on one hand and their direction of motion on the other hand. A study on the estimation of the belief expressed by these independent sources is performed. The multiple features are managed through a hierarchical fusion which includes two levels of combination. The first level is a pairwise combination, fusing position and orientation data of each pair of objects and the second one processes sequentially the previously combined information over all possible associations. This paper also investigates the effectiveness of the association according to different combination operators at both levels. The performance of the proposed approach is demonstrated in the Intelligent Transportation Systems context for which environmental perception is crucial. The validation exploits a large amount of real data issued from a camera and a 3D LiDAR from the KITTI database.
Update on Triangle Counting on GPU

This work presents an update to the triangle-counting portion of the subgraph isomorphism static graph challenge. This work is motivated by a desire to understand the impact of CUDA unified memory on the triangle-counting problem. First, CUDA unified memory is used to overlap reading large graph data from disk with graph data structures in GPU memory. Second, we use CUDA unified memory hints to solve multi-GPU performance scaling challenges present in our last submission. Finally, we improve the single-GPU kernel performance from our past submission by introducing a work-stealing dynamic algorithm GPU kernel with persistent threads, which makes performance adaptive for large graphs without requiring a graph analysis phase.
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base

We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs. The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations. The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.
How to Parse Low-Resource Languages: Cross-Lingual Parsing, Target Language Annotation, or Both?

To develop a parser for a language with no syntactically annotated data, we either have to develop a (small) treebank for the target language or rely on cross-lingual learning or projection, or possibly use some combination of these methods.In this paper, we compare the usefulness of cross-lingual model transfer and target language annotation for three different languages, with varying support from closely related high-resource languages.The results show that annotating even a very small amount of data in the target language is superior to any cross-lingual setup and that accuracy can be further improved by adding training data from related languages in a multilingual model.
Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages

We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to provide a much wider range of possible linkages. We analyse various families of weighting functions on numerous benchmark data sets in order to assess their influence on the resulting cluster structure. Moreover, we inspect the correction for the inequality of cluster size distribution – similar to the one in the Genie algorithm. Our results demonstrate that by robustifying the procedure with the Genie correction, we can obtain a significant performance boost in terms of clustering quality. This is particularly beneficial in the case of the linkages based on the closest distances between clusters, including the single linkage and its “smoothed” counterparts. To explain this behaviour, we propose a new linkage process called three-stage OWA which yields further improvements. This way we confirm the intuition that hierarchical cluster analysis should rather take into account a few nearest neighbours of each point, instead of trying to adapt to their non-local neighbourhood.
A Hybrid Multiple Access Scheme via Deep Learning-Based Detection

In this article, we propose an uplink hybrid multiple access scheme (HMAS) in order to support a highly overloaded multiuser system. In HMAS, for a fixed K-orthogonal resources, there are K-near users (NUs) adopting orthogonal frequency division multiple access and J > K far users (FUs) adopting sparse code multiple access for uplink transmission. To improve the performance of HMAS, we propose two deep learning-based detectors via deep neural network (DNN) models, one for NUs symbol detection, and the other for FUs symbol detection. Both DNN models are trained offline via simulated data and-then-applied for online symbol detection. Simulation results demonstrate the effectiveness of HMAS in terms of symbol error rate performance over Rayleigh fading channels. In particular, it shows that the HMAS with DNN-based detections outperforms significantly the one using conventional message passing algorithm and successive interference cancellation-based detection.
Towards Ground Truth Evaluation of Visual Explanations

Several methods have been proposed to explain the decisions of neural networks in the visual domain via saliency heatmaps (aka relevances/feature importance scores). Thus far, these methods were mainly validated on real-world images, using either pixel perturbation experiments or bounding box localization accuracies. In the present work, we propose instead to evaluate explanations in a restricted and controlled setup using a synthetic dataset of rendered 3D shapes. To this end, we generate a CLEVR-alike visual question answering benchmark with around 40,000 questions, where the ground truth pixel coordinates of relevant objects are known, which allows us to validate explanations in a fair and transparent way. We further introduce two straightforward metrics to evaluate explanations in this setup, and compare their outcomes to standard pixel perturbation using a Relation Network model and three decomposition-based explanation methods: Gradient x Input, Integrated Gradients and Layer-wise Relevance Propagation. Among the tested methods, Layer-wise Relevance Propagation was shown to perform best, followed by Integrated Gradients. More generally, we expect the release of our dataset and code to support the development and comparison of methods on a well-defined common ground.
BDARS_CapsNet: Bi-Directional Attention Routing Sausage Capsule Network

In order to improve the accuracy of capsule network in disentangled representation, and further expand its application in computer vision, a novel BDARS_CapsNet (bi-directional attention routing sausage capsule network) architecture is proposed in this paper. Firstly, the bi-directional routing, namely bottom-up and top-down attention is used to achieve information feed-forward and feedback mechanism, which contributes to describing the attributes of object entity more accurately and completely. Secondly, inspired by the concept of covering learning, the sausage measure model is introduced into the network. The sausage model measures both the similarities and differences of the capsules and projects them into a more complex curved surface, which makes it possible to approximate any nonlinear function with arbitrary precision and preserving the local responsiveness of capsule entity to the maximum. Finally, the BDARS_CapsNet combines the CNN (Convolutional Neural Network), bi-directional attention routing, and sausage measure into capsule network modeling, and makes full use of high-level category information and low-level vision information; as a result, the reconstruction and classification accuracy is accordingly improved. Experiments demonstrate the effectiveness of proposed information routing, sausage measure, and new framework. Furthermore, the proposed BDARS_CapsNet provides a foundation for future research on disentangled representation learning.