Reviewerly/exHarmony huggingface dataset:
A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

The occurrence of fault in induction motors is dangerous in our daily life. It is significant to diagnose motor component faults accurately and quickly. In this paper, we propose an efficient and responsive motor fault diagnostic method based on Feature Incremental Broad Learning (FIBL) and Singular Value Decomposition (SVD). Firstly, we extract fault features from raw signals with Particle Swarm Optimization-Variation Model Decomposition, Sample Entropy and Time Domain Statistical Features. Secondly, these features are input into a broad learning system to train a network. Then we use FIBL to retrain the network if the diagnosis accuracy is unsatisfactory. Finally, SVD is used to further simplify the system structure to reduce diagnostic errors. In order to evaluate the performance of the diagnostic system, experiments are conducted. Experimental results show that with the proposed diagnostic method, motor component faults detection is quicker and more accurate.
Cloud-Based Collaborative 3D Modeling to Train Engineers for the Industry 4.0

In the present study, Autodesk Fusion 360 software (which includes the A360 environment) is used to train engineering students for the demands of the industry 4.0. Fusion 360 is a tool that unifies product lifecycle management (PLM) applications and 3D-modeling software (PDLM—product design and life management). The main objective of the research is to deepen the students’ perception of the use of a PDLM application and its dependence on three categorical variables: PLM previous knowledge, individual practices and collaborative engineering perception. Therefore, a collaborative graphic simulation of an engineering project is proposed in the engineering graphics subject at the University of La Laguna with 65 engineering undergraduate students. A scale to measure the perception of the use of PDLM is designed, applied and validated. Subsequently, descriptive analyses, contingency graphical analyses and non-parametric analysis of variance are performed. The results indicate a high overall reception of this type of experience and that it helps them understand how professionals work in collaborative environments. It is concluded that it is possible to respond to the demand of the industry needs in future engineers through training programs of collaborative 3D modeling environments.
Single-shot 3D multi-person pose estimation in complex images

In this paper, we propose a new single shot method for multi-person 3D human pose estimation in complex images. The model jointly learns to locate the human joints in the image, to estimate their 3D coordinates and to group these predictions into full human skeletons. The proposed method deals with a variable number of people and does not need bounding boxes to estimate the 3D poses. It leverages and extends the Stacked Hourglass Network and its multi-scale feature learning to manage multi-person situations. Thus, we exploit a robust 3D human pose formulation to fully describe several 3D human poses even in case of strong occlusions or crops. Then, joint grouping and human pose estimation for an arbitrary number of people are performed using the associative embedding method. Our approach significantly outperforms the state of the art on the challenging CMU Panoptic and a previous single shot method on the MuPoTS-3D dataset. Furthermore, it leads to good results on the complex and synthetic images from the newly proposed JTA Dataset.
Robustness to Capitalization Errors in Named Entity Recognition

Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise.Existing methods to improve robustness to the noise completely discard given orthographic information, which significantly degrades their performance on well-formed text. We propose a simple alternative approach based on data augmentation, which allows the model to learn to utilize or ignore orthographic information depending on its usefulness in the context. It achieves competitive robustness to capitalization errors while making negligible compromise to its performance on well-formed text and significantly improving generalization power on noisy user-generated text. Our experiments clearly and consistently validate our claim across different types of machine learning models, languages, and dataset sizes.
Electronic evidence in the blockchain era: New rules on authenticity and integrity

Issues regarding the authenticity and integrity of electronic evidence have plagued judges worldwide. From a statistical viewpoint, the main cause of the judicial puzzle in dealing with electronic evidence lies in its special physical properties and judges' lack of necessary expertise. With its technical development and widespread application, blockchain technology is showing significant power and is gradually supplanting judges and expert witnesses, who have played a central role in the identification of electronic evidence's authenticity and integrity. In two recent cases, Chinese courts have confirmed for the first time that electronic data stored on or generated by blockchain met the requirements of authenticity and integrity and proposed a specific procedural test to identify blockchain evidence. Although blockchain technology may revolutionise the rules of evidence, it contains inherent risks. Its application in electronic evidence may also indicate the fusion between decentralised technology and the traditional, centralised juridical mechanisms.
Multi-view clustering by joint manifold learning and tensor nuclear norm

In real-world applications, the variation between multi-view data points, which should belong to the same cluster, is larger than the variation between data points belonging to different clusters. This results in instability of most existing clustering algorithms, because they mainly employ the original data as input to learn the latent similarity matrix. To address these problems, we propose a novel latent similarity learning for multi-view clustering(LSLMC) by integrating manifold learning and tensor Singular Value Decomposition (t-SVD) into a uniform framework. LSLMC utilizes the similarity matrices calculated from each view to recover a latent representation matrix by local manifold learning coupled with spectral clustering. Thus, the adaptively recovered similarity matrix, which is shared by all views, can well characterize both the clustering structure and local manifold structure underlying multi-view data. In addition, by simultaneously using low-rank tensor constraint on the error matrix, the view-specific information and the noise can be well explored. Therefore, the recovered similarity matrix become more robust and accurate, leading to better clustering performance. Extensive experiments on five widely used multi-view datasets have demonstrated the superiority of the proposed method.
Joint metric and feature representation learning for unsupervised domain adaptation

Domain adaptation algorithms leverage the knowledge from a well-labeled source domain to facilitate the learning of an unlabeled target domain, in which the source domain and the target domain are related but drawn from different data distributions. Existing domain adaptation approaches are either trying to explicitly mitigate the data distribution gaps by minimizing some distance metrics, or attempting to learn a new feature representation by revealing the shared factors and use the learned representation as a bridge of knowledge transfer. Recently, several researchers claim that jointly optimizing the distribution gaps and latent factors can learn a better transfer model. In this paper, therefore, we propose a novel approach which simultaneously mitigates the data distribution and learns a feature representation via a common objective. Specifically, we present joint metric and feature representation learning (JMFL) for unsupervised domain adaptation. JMFL, on the one hand, minimizes the domain discrepancy between the source domain and the target domain. On the other hand, JMFL reveals the shared underlying factors between the two domains to learn a new feature representation. We smoothly incorporate the two aspects into a unified objective and present a detailed optimization method. Extensive experiments on several open benchmarks verify that our approach achieves state-of-the-art results with significant improvements.
Social MIL: Interaction-Aware for Crowd Anomaly Detection

Crowd anomaly detection under surveillance scene is a quite challenging task, which often companies with not rare objects, unexpected bursts in activity and complex dynamic patterns. In this paper, we propose a social multiple-instance learning(MIL) framework with a dual-branch network by considering dynamic interaction among groups, individuals and environment to obtain attentive spatial-temporal feature representation. First, MIL is employed to overcome the challenge of rare training abnormal samples and video-based labels. The social force map is utilized for modeling behavior interaction to supply the prior knowledge. In addition, we introduce the self-attention module, which represents a more discriminative spatial-temporal feature based on C3D network through implementing weight redistribution inside the feature. The results of the experiments conducted on UCF-Crime dataset show that the proposed dual-branch social multiple-instance learning (MIL) anomaly detection framework with the dual-branch network outperforms than existing approaches and obtains the state-of-the-art performance.
CrowdVAS-Net: A Deep-CNN Based Framework to Detect Abnormal Crowd-Motion Behavior in Videos for Predicting Crowd Disaster

With the increased occurrences of crowd disasters like human stampedes, crowd management and their safety during mass gathering events like concerts, congregation or political rally, etc., are vital tasks for the security personnel. In this paper, we propose a framework named as CrowdVAS-Net for crowd-motion analysis that considers velocity, acceleration and saliency features in the video frames of a moving crowd. CrowdVAS-Net relies on a deep convolutional neural network (DCNN) for extracting motion and appearance feature representations from the video frames that help us in classifying the crowd-motion behavior as abnormal or normal from a short video clip. These feature representations are then trained with a random forest classifier. Furthermore, a dataset having 704 video clips having dense crowded scenes have been created for performance evaluation of the proposed method. Simulation results confirm that the proposed CrowdVAS-Net achieves the classification accuracy of 77.8% outperforming the state-of-the-art machine learning models. Moreover, this framework can reduce the video processing and analyzing time up to 96.8% compared to state-of-the-art techniques on the larger dataset. Based on our results, we believe that our work will help security personnel and crowd managers in ensuring the public safety during mass gatherings with better accuracy.
Forkcipher: A New Primitive for Authenticated Encryption of Very Short Messages

Highly efficient encryption and authentication of short messages is an essential requirement for enabling security in constrained scenarios such as the CAN FD in automotive systems (max. message size 64 bytes), massive IoT, critical communication domains of 5G, and Narrowband IoT, to mention a few. In addition, one of the NIST lightweight cryptography project requirements is that AEAD schemes shall be “optimized to be efficient for short messages (e.g., as short as 8 bytes)”. In this work we introduce and formalize a novel primitive in symmetric cryptography called forkcipher. A forkcipher is a keyed primitive expanding a fixed-lenght input to a fixed-length output. We define its security as indistinguishability under a chosen ciphertext attack (for n-bit inputs to 2n-bit outputs). We give a generic construction validation via the new iterate-fork-iterate design paradigm. We then propose $$ {\mathsf {ForkSkinny}} $$ as a concrete forkcipher instance with a public tweak and based on SKINNY: a tweakable lightweight cipher following the TWEAKEY framework. We conduct extensive cryptanalysis of $$ {\mathsf {ForkSkinny}} $$ against classical and structure-specific attacks. We demonstrate the applicability of forkciphers by designing three new provably-secure nonce-based AEAD modes which offer performance and security tradeoffs and are optimized for efficiency of very short messages. Considering a reference block size of 16 bytes, and ignoring possible hardware optimizations, our new AEAD schemes beat the best SKINNY-based AEAD modes. More generally, we show forkciphers are suited for lightweight applications dealing with predominantly short messages, while at the same time allowing handling arbitrary messages sizes. Furthermore, our hardware implementation results show that when we exploit the inherent parallelism of $$ {\mathsf {ForkSkinny}} $$ we achieve the best performance when directly compared with the most efficient mode instantiated with SKINNY.
Deep Generative Video Compression

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this representation according to predictions of the sequential VAE. Rate-distortion evaluations on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved when training the model on specialized content.
The fire triangle: how to mix substitution, dependent elimination, and effects

There is a critical tension between substitution, dependent elimination and effects in type theory. In this paper, we crystallize this tension in the form of a no-go theorem that constitutes the fire triangle of type theory. To release this tension, we propose ∂CBPV, an extension of call-by-push-value (CBPV) —a general calculus of effects—to dependent types. Then, by extending to ∂CBPV the well-known decompositions of call-by-name and call-by-value into CBPV, we show why, in presence of effects, dependent elimination must be restricted in call-by-name, and substitution must be restricted in call-by-value. To justify ∂CBPV and show that it is general enough to interpret many kinds of effects, we define various effectful syntactic translations from ∂CBPV to Martin-Löf type theory: the reader, weaning and forcing translations.
An empirical study of SMS one-time password authentication in Android apps

A great quantity of user passwords nowadays has been leaked through security breaches of user accounts. To enhance the security of the Password Authentication Protocol (PAP) in such circumstance, Android app developers often implement a complementary One-Time Password (OTP) authentication by utilizing the short message service (SMS). Unfortunately, SMS is not specially designed as a secure service and thus an SMS One-Time Password is vulnerable to many attacks. To check whether a wide variety of currently used SMS OTP authentication protocols in Android apps are properly implemented, this paper presents an empirical study against them. We first derive a set of rules from RFC documents as the guide to implement secure SMS OTP authentication protocol. Then we implement an automated analysis system, AUTH-EYE, to check whether a real-world OTP authentication scheme violates any of these rules. Without accessing server source code, AUTH-EYE executes Android apps to trigger the OTP-relevant functionalities and then analyzes the OTP implementations including those proprietary ones. By only analyzing SMS responses, AUTH-EYE is able to assess the conformance of those implementations to our recommended rules and identify the potentially insecure apps. In our empirical study, AUTH-EYE analyzed 3,303 popular Android apps and found that 544 of them adopt SMS OTP authentication. The further analysis of AUTH-EYE demonstrated a far-from-optimistic status: the implementations of 536 (98.5%) out of the 544 apps violate at least one of our defined rules. The results indicate that Android app developers should seriously consider our discussed security rules and violations so as to implement SMS OTP properly.
VisDrone-MOT2019: The Vision Meets Drone Multiple Object Tracking Challenge Results

The Vision Meets Drone Multiple Object Tracking (MOT) Challenge 2019 is the second annual activity focusing on evaluating multi-object tracking algorithms on drones, held in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019). Results of 12 submitted MOT algorithms on the collected drone-based dataset are presented. Meanwhile, we also report the results of 6 state-of-the-art MOT algorithms, and provide a comprehensive analysis and discussion of the results. The results of all submissions are publicly available at the website: http://www.aiskyeye.com/. The challenge results show that MOT on drones is far from being solved. We believe the challenge can largely boost the research and development in MOT on drone platforms.
Unicode from a Linguistic Point of View

In this paper we describe and comment, from a linguistic point of view, Unicode notions pertaining to writing. After comparing characters with graphemes, glyphs with graphs and basic shapes, character general categories with grapheme classes, and character strings with graphemic sequences, we discuss two issues: the phenomenon of ligatures that stand at the boundary between graphemics and graphetics, and the proposal for the introduction of QID emojis which may end up being a turning point in human communication.
TECCD: A Tree Embedding Approach for Code Clone Detection

Clone detection techniques have been explored for decades. Recently, deep learning techniques has been adopted to improve the code representation capability, and improve the state-of-the-art in code clone detection. These approaches usually require a transformation from AST to binary tree to incorporate syntactical information, which introduces overheads. Moreover, these approaches conduct term-embedding, which requires large training datasets. In this paper, we introduce a tree embedding technique to conduct clone detection. Our approach first conducts tree embedding to obtain a node vector for each intermediate node in the AST, which captures the structure information of ASTs. Then we compose a tree vector from its involving node vectors using a lightweight method. Lastly Euclidean distances between tree vectors are measured to determine code clones. We implement our approach in a tool called TECCD and conduct an evaluation using the BigCloneBench (BCB) and 7 other large scale Java projects. The results show that our approach achieves good accuracy and recall and outperforms existing approaches.
Optimized fast gates for quantum computing with trapped ions

We present an efficient approach to optimizing pulse sequences for implementing fast entangling two-qubit gates on trapped ion quantum information processors. We employ a two-phase procedure for optimizing gate fidelity, which we demonstrate for multi-ion systems in linear Paul trap and microtrap architectures. The first phase involves a global optimization over a computationally inexpensive cost function constructed under strong approximations of the gate dynamics. The second phase involves local optimizations that utilize a more precise ordinary differential equation description of the gate dynamics, which captures the nonlinearity of the Coulomb interaction and the effects of finite laser repetition rate. We propose two gate schemes that are compatible with this approach, and we demonstrate that they outperform existing schemes in terms of achievable gate speed and fidelity for feasible laser repetition rates. In optimizing sub-microsecond gates in microtrap architectures, the proposed schemes achieve orders-of-magnitude-higher fidelities than previous proposals. Finally, we investigate the impact of pulse imperfections on gate fidelity and evaluate error bounds for a range of gate speeds.
Informing, simulating experience, or both: A field experiment on phishing risks

Cybersecurity cannot be ensured with mere technical solutions. Hackers often use fraudulent emails to simply ask people for their password to breach into organizations. This technique, called phishing, is a major threat for many organizations. A typical prevention measure is to inform employees but is there a better way to reduce phishing risks? Experience and feedback have often been claimed to be effective in helping people make better decisions. In a large field experiment involving more than 10,000 employees of a Dutch ministry, we tested the effect of information provision, simulated experience, and their combination to reduce the risks of falling into a phishing attack. Both approaches substantially reduced the proportion of employees giving away their password. Combining both interventions did not have a larger impact.
Towards Generating Math Word Problems from Equations and Topics

A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein.In this paper, we focus on the task of generating math word problems.Previous works are mainly templatebased with pre-defined rules.We propose a novel neural network model to generate math word problems from the given equations and topics.First, we design a fusion mechanism to incorporate the information of both equations and topics.Second, an entity-enforced loss is introduced to ensure the relevance between the generated math problem and the equation.Automatic evaluation results show that the proposed model significantly outperforms the baseline models.In human evaluations, the math word problems generated by our model are rated as being more relevant (in terms of solvability of the given equations and relevance to topics) and natural (i.e., grammaticality, fluency) than the baseline models.
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics

Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems. The discretization error becomes even larger when the sparse data are irregularly distributed so that the data defined on an unstructured grid, making it hard to build deep learning models to handle physics-governing observations on the unstructured grid. In this paper, we propose a novel architecture named Physics-aware Difference Graph Networks (PA-DGN) that exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN further leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given observations. We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from weather stations.