Reviewerly/exHarmony huggingface dataset:
The Explanation Game: Explaining Machine Learning Models Using Shapley Values

A number of techniques have been proposed to explain a machine learning model’s prediction by attributing it to the corresponding input features. Popular among these are techniques that apply the Shapley value method from cooperative game theory. While existing papers focus on the axiomatic motivation of Shapley values, and efficient techniques for computing them, they offer little justification for the game formulations used, and do not address the uncertainty implicit in their methods’ outputs. For instance, the popular SHAP algorithm’s formulation may give substantial attributions to features that play no role in the model. In this work, we illustrate how subtle differences in the underlying game formulations of existing methods can cause large differences in the attributions for a prediction. We then present a general game formulation that unifies existing methods, and enables straightforward confidence intervals on their attributions. Furthermore, it allows us to interpret the attributions as contrastive explanations of an input relative to a distribution of reference inputs. We tie this idea to classic research in cognitive psychology on contrastive explanations, and propose a conceptual framework for generating and interpreting explanations for ML models, called formulate, approximate, explain (FAE). We apply this framework to explain black-box models trained on two UCI datasets and a Lending Club dataset.
Gesture recognition based on surface electromyography<scp>‐feature</scp> image

Summary For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1‐dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw‐sEMG images based on the CNN and the sEMG‐feature‐images based on the CNN and sEMG based on the traditional machine learning, the multi‐sEMG‐features image based on the CNN is the highest, which coming up to 82.54%.
EVchain: An Anonymous Blockchain-Based System for Charging-Connected Electric Vehicles

Purchases of electric vehicles have been increasing in recent years. These vehicles differ from traditional fossil-fuel-based vehicles especially in the time consumed to keep them running. Electric-Vehicle-charging Service Providers (EVSPs) must arrange reasonable charging times for users in advance. Most EVSP services are based on third-party platforms, but reliance on third-party platforms creates a lack of security, leaving users vulnerable to attacks and user-privacy leakages. In this paper, we propose an anonymous blockchain-based system for charging-connected electric vehicles that eliminates third-party platforms through blockchain technology and the establishment of a multi-party security system between electric vehicles and EVSPs. In our proposed system, digital certificates are obtained by completing distributed Public Key Infrastructure (distributed-PKI) identity registration, with the user registration kept separate from the verification process, which eliminates dependence on the EVSP for information security. In the verification process, we adopt smart contracts to solve problems associated with centralized verification and opaque services. Furthermore, we utilize zero-knowledge proof and ring-signature superposition to realize completely anonymous verification, which ensures undeniability and unforgeability with no detriment to anonymity. The evaluation results show that the user anonymity, information authenticity, and system security of our system fulfill the necessary requirements.
Exploring Temporal Coherence for More General Video Face Forgery Detection

Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos. In this work, we explore to take full advantage of the temporal coherence for video face forgery detection. To achieve this, we propose a novel end-to-end framework, which consists of two major stages. The first stage is a fully temporal convolution network (FTCN). The key insight of FTCN is to reduce the spatial convolution kernel size to 1, while maintaining the temporal convolution kernel size un-changed. We surprisingly find this special design can benefit the model for extracting the temporal features as well as improve the generalization capability. The second stage is a Temporal Transformer network, which aims to explore the long-term temporal coherence. The proposed frame-work is general and flexible, which can be directly trained from scratch without any pre-training models or external datasets. Extensive experiments show that our framework outperforms existing methods and remains effective when applied to detect new sorts of face forgery videos.
A Blockchain-Based Shamir’s Threshold Cryptography Scheme for Data Protection in Industrial Internet of Things Settings

The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution, the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.
Human activity recognition based on multienvironment sensor data

With the development of artificial intelligence and the broad application of sensors, human activity recognition (HAR) technologies based on noninvasive environmental sensors have received extensive attention and have shown great application value. Owing to the initiative of human activities and machine learning-based methods relying on domain knowledge, obtaining a uniform model to understand the daily behaviors of different residents is difficult. From the perspective of data feature constraints to recognition methods, we constructed a methodology for single user's daily behavior recognition that can adaptively constrain the sensor noise during human activities in multitenant smart home scenarios. We propose a sensor data contribution significance analysis (CSA) method based on the sensor status frequency-inverse type frequency for HAR. This method is employed to measure the contribution of a particular type of sensor to a certain type of behavior recognition. We then build a spatial distance matrix based on the layout of environmental sensors for context-awareness and reducing data noise. Finally, we propose a HAR algorithm based on wide time-domain convolutional neural network and multienvironment sensor data (HAR_WCNN) for daily behavior recognition. Comparative experiment results on the CASAS dataset show that the proposed HAR_WCNN outperforms the compared state-of-the-art methods in terms of HAR accuracy and time consumption.
Spatio-temporal fall event detection in complex scenes using attention guided LSTM

Fall events are one of the greatest risks for public safety, especially in some complex scenes with large number of people. Nevertheless, there are few researches on fall detection in complex scenes, and even no public datasets. A fall event dataset in crowded and complex scenes is constructed. Aiming at detecting fall events in complex scenes, we further propose an attention guided LSTM model. Our method provides the spatial and temporal locations of fall events, which are indispensable information for danger alarm in complex public scenes. Specifically, the effective YOLO v3 is employed to detect pedestrian in videos, and followed by a tracking module. CNN features are extracted for each tracked bounding boxes. Fall events are detected by the attention guided LSTM. Experimental results show that our method achieves good performance, outperforming the state-of-the-art methods.
Deep Alignment Network Based Multi-Person Tracking With Occlusion and Motion Reasoning

Tracking-by-detection is one of the typical paradigms for multi-person tracking, due to the availability of automatic pedestrian detectors. However, existing multi-person trackers are greatly challenged by misalignment in the pedestrian detectors (i.e., excessive background and part missing) and occlusion. To effectively handle these problems, we propose a deep alignment network-based multi-person tracking method with occlusion and motion reasoning. Specifically, the inaccurate detections are first corrected via a deep alignment network, in which an alignment estimation module is used to automatically learn the spatial transformation of these detections. As a result, the deep features from our alignment network will have better representation power and, thus, lead to more consistent tracks. Then, a coarse-to-fine schema is designed for construing a discriminative association cost matrix with spatial, motion, and appearance information. Meanwhile, a principled approach is developed to allow our method to handle occlusion with motion reasoning and the reidentification ability of the pedestrian alignment network. Finally, the association problem is solved via a simple yet real-time Hungarian algorithm. Comprehensive experiments on MOT16, ISSIA soccer, PETS09, and TUD datasets validate the effectiveness and robustness of our proposed tracker.
Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers

The current generation of experimental quantum annealers can shed light on the effects of noise and thermalization present in superconducting quantum circuits. Recent advances in this technology allow for direct probing of intermediate dynamics during the annealing process. By pausing the annealing over spans ranging from micro- to milliseconds and allowing the system to equilibrate, the authors identify several regimes of interest, characterized by the time scales of the dominant processes. It is shown that alternative annealing schedules inspired by physics may prove a fruitful avenue for designing more efficient solvers for optimization problems in quantum computing.
SANCTUARY: ARMing TrustZone with User-space Enclaves

TrustZone is one of the most widely deployed security architecture providing Trusted Execution Environments (TEEs).Unfortunately, its usage and potential benefits for application developers and end users are largely limited due to restricted deployment policies imposed by device vendors.Restriction is enforced since every Trusted App (TA) increases the TEE's attack surface: any vulnerable or malicious TA can compromise the system's security.Hence, deploying a TA requires mutual trust between device vendor and application developer, incurring high costs for both.Vendors work around this by offering interfaces to selected TEE functionalities, however, these are not sufficient to securely implement advanced mobile services like banking.Extensive discussion of Intel's SGX technology in academia and industry has unveiled the demand for an unrestricted use of TEEs, yet no comparable security architecture for mobile devices exists to this day.We propose SANCTUARY, the first security architecture which allows unconstrained use of TEEs in the TrustZone ecosystem without relying on virtualization.SANCTUARY enables execution of security-sensitive apps within strongly isolated compartments in TrustZone's normal world comparable to SGX's user-space enclaves.In particular, we leverage TrustZone's versatile Address-Space Controller available in current ARM System-on-Chip reference designs, to enforce two-way hardware-level isolation: (i) security-sensitive apps are shielded against a compromised normal-world OS, while (ii) the system is also protected from potentially malicious apps in isolated compartments.Moreover, moving security-sensitive apps from the TrustZone's secure world to isolated compartments minimizes the TEE's attack surface.Thus, mutual trust relationships between device vendors and developers become obsolete: the full potential of TEEs can be leveraged.We demonstrate practicality and real-world benefits of SANC-TUARY by thoroughly evaluating our prototype on a HiKey 960 development board with microbenchmarks and a use case for one-time password generation in two-factor authentication.
Cybercriminal Minds: An investigative study of cryptocurrency abuses in the Dark Web

The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods.Perpetrators have also used cryptocurrencies to conduct illicit financial transactions while hiding their identities.The limited coverage and outdated data of the Dark Web in previous studies motivated us to conduct an in-depth investigative study to understand how perpetrators abuse cryptocurrencies in the Dark Web.We designed and implemented MFScope, a new framework which collects Dark Web data, extracts cryptocurrency information, and analyzes their usage characteristics on the Dark Web.Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero.It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web.In total, using MFScope we discovered that more than 80% of Bitcoin addresses on the Dark Web were used with malicious intent; their monetary volume was around 180 million USD, and they sent a large sum of their money to several popular cryptocurrency services (e.g., exchange services).Furthermore, we present two real-world unlawful services and demonstrate their Bitcoin transaction traces, which helps in understanding their marketing strategy as well as black money operations.
Component Fusion: Learning Replaceable Language Model Component for End-to-end Speech Recognition System

Recently, attention-based end-to-end automatic speech recognition system (ASR) has shown promising results. One of the limitations of an attention-based ASR system is that its language model (LM) component has to be implicitly learned from transcribed speech data which prevents one from uti-lizing plenty of text corpora to improve language modeling. In this work, the Component Fusion method is proposed to incorporate externally trained neural network (NN) LM into an attention-based ASR system. During training stage we equip the attention-based system with an additional LM component which is replaced by an externally trained NN LM at decoding stage. Experimental results show that the proposed Component Fusion outperforms two prior LM fusion approaches, i.e., Shallow Fusion and Cold Fusion, in both out-of-domain and in-domain scenarios. Further improvements can be achieved when combining Component and Shallow Fusion.
Quantum Interference between Light Sources Separated by 150 Million Kilometers

We report an experiment to test quantum interference, entanglement and nonlocality using two dissimilar photon sources, the Sun and a semiconductor quantum dot on the Earth, which are separated by 150 million kilometers. By making the otherwise vastly distinct photons indistinguishable all degrees of freedom, we observe time-resolved two-photon quantum interference with a raw visibility of 0.796(17), well above the 0.5 classical limit, providing the first evidence of quantum nature of thermal light. Further, using the photons with no common history, we demonstrate post-selected two-photon entanglement with a state fidelity of 0.826(24), and a violation of Bell's inequality by 2.20(6). The experiment can be further extended to a larger scale using photons from distant stars, and open a new route to quantum optics experiments at an astronomical scale.
Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments

The fusion of different technologies is the base of the fourth industrial revolution. Companies are encouraged to integrate new tools in their production processes in order to improve working conditions and increase productivity and production quality. The integration between information, communication technologies and industrial automation can create highly flexible production models for products and services that can be customized through real-time interactions between consumer, production and machinery throughout the production process. The future of production, therefore, depends on increasingly intelligent machinery through the use of digital systems. The key elements for future integrated devices are intelligent systems and machines, based on human–machine interaction and information sharing. To do so, the implementation of shared languages that allow different systems to dialogue in a simple way is necessary. In this perspective, the use of advanced prototyping tools like Open-Source programming systems, the development of more detailed multibody models through the use of CAD software and the use of self-learning techniques will allow for developing a new class of machines capable of revolutionizing our companies. The purpose of this paper is to present a waypoint navigation activity of a custom Wheeled Mobile Robot (WMR) in an available simulated 3D indoor environment by using the Gazebo simulator. Gazebo was developed in 2002 at the University of Southern California. The idea was to create a high-fidelity simulator that gave the possibility to simulate robots in outdoor environments under various conditions. In particular, we wanted to test the high-performance physics Open Dynamics Engine (ODE) and the sensors feature present in Gazebo for prototype development activities. This choice was made for the possibility of emulating not only the system under analysis, but also the world in which the robot will operate. Furthermore, the integration tools available with Solidworks and Matlab-Simulink, well known commercial platforms of modelling and robotics control respectively, are also explored.
Radial-Based Oversampling for Multiclass Imbalanced Data Classification

Learning from imbalanced data is among the most popular topics in the contemporary machine learning. However, the vast majority of attention in this field is given to binary problems, while their much more difficult multiclass counterparts are relatively unexplored. Handling data sets with multiple skewed classes poses various challenges and calls for a better understanding of the relationship among classes. In this paper, we propose multiclass radial-based oversampling (MC-RBO), a novel data-sampling algorithm dedicated to multiclass problems. The main novelty of our method lies in using potential functions for generating artificial instances. We take into account information coming from all of the classes, contrary to existing multiclass oversampling approaches that use only minority class characteristics. The process of artificial instance generation is guided by exploring areas where the value of the mutual class distribution is very small. This way, we ensure a smart oversampling procedure that can cope with difficult data distributions and alleviate the shortcomings of existing methods. The usefulness of the MC-RBO algorithm is evaluated on the basis of extensive experimental study and backed-up with a thorough statistical analysis. Obtained results show that by taking into account information coming from all of the classes and conducting a smart oversampling, we can significantly improve the process of learning from multiclass imbalanced data.
Experimental Engineering of Arbitrary Qudit States with Discrete-Time Quantum Walks

The capability to generate and manipulate quantum states in high-dimensional Hilbert spaces is a crucial step for the development of quantum technologies, from quantum communication to quantum computation. One-dimensional quantum walk dynamics represents a valid tool in the task of engineering arbitrary quantum states. Here we affirm such potential in a linear-optics platform that realizes discrete-time quantum walks in the orbital angular momentum degree of freedom of photons. Different classes of relevant qudit states in a six-dimensional space are prepared and measured, confirming the feasibility of the protocol. Our results represent a further investigation of quantum walk dynamics in photonics platforms, paving the way for the use of such a quantum state-engineering toolbox for a large range of applications.
Stereo Matching Using Multi-Level Cost Volume and Multi-Scale Feature Constancy

For CNNs based stereo matching methods, cost volumes play an important role in achieving good matching accuracy. In this paper, we present an end-to-end trainable convolution neural network to fully use cost volumes for stereo matching. Our network consists of three sub-modules, i.e., shared feature extraction, initial disparity estimation, and disparity refinement. Cost volumes are calculated at multiple levels using the shared features, and are used in both initial disparity estimation and disparity refinement sub-modules. To improve the efficiency of disparity refinement, multi-scale feature constancy is introduced to measure the correctness of the initial disparity in feature space. These sub-modules of our network are tightly-coupled, making it compact and easy to train. Moreover, we investigate the problem of developing a robust model to perform well across multiple datasets with different characteristics. We achieve this by introducing a two-stage finetuning scheme to gently transfer the model to target datasets. Specifically, in the first stage, the model is finetuned using both a large synthetic dataset and the target datasets with a relatively large learning rate, while in the second stage the model is trained using only the target datasets with a small learning rate. The proposed method is tested on several benchmarks including the Middlebury 2014, KITTI 2015, ETH3D 2017, and SceneFlow datasets. Experimental results show that our method achieves the state-of-the-art performance on all the datasets. The proposed method also won the 1st prize on the Stereo task of Robust Vision Challenge 2018.