Reviewerly/exHarmony huggingface dataset:
A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data

Human activity recognition from multimodal body sensor data has proven to be an effective approach for the care of elderly or physically impaired people in a smart healthcare environment. However, traditional machine learning techniques are mostly focused on a single sensing modality, which is not practical for robust healthcare applications. Therefore, recently increasing attention is being given by the researchers on the development of robust machine learning techniques that can exploit multimodal body sensor data and provide important decision making in Smart healthcare. In this paper, we propose an effective multi-sensors-based framework for human activity recognition using a hybrid deep learning model, which combines the simple recurrent units (SRUs) with the gated recurrent units (GRUs) of neural networks. We use the deep SRUs to process the sequences of multimodal input data by using the capability of their internal memory states. Moreover, we use the deep GRUs to store and learn how much of the past information is passed to the future state for solving fluctuations or instability in accuracy and vanishing gradient problems. The system has been compared against the conventional approaches on a publicly available standard dataset. The experimental results show that the proposed approach outperforms the available state-of-the-art methods.
Local Binary Pattern-Based Adaptive Differential Evolution for Multimodal Optimization Problems

The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.
PDR-Net: Perception-Inspired Single Image Dehazing Network With Refinement

During recent years, we have witnessed a rapid development of wireless network technologies which have revolutionized the way people take and share multimedia content. However, images captured in the outdoor scenes usually suffer from limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite the recent progress of image dehazing methods, the visual quality of dehazed results still needs further improvement. In this paper, we propose a deep convolutional neural network (CNN) for single image dehazing called PDR-Net, which includes a perception-inspired haze removal subnetwork that reconstructs the latent dehazed image and a refinement subnetwork that further enhances the contrast and color properties of the dehazed result by joint multi-term loss optimization. Compared to the previous methods, our method combines the advantages of existing indoor and outdoor image dehazing training data, which makes the proposed PDR-Net generalized to various hazy images and effective for improving the visual quality of the dehazed results. Extensive experiments demonstrate that the proposed method achieves comparable and even better performance on both real and synthetic images in qualitative and quantitative metrics. Additionally, the potential usage of our method in high-level vision tasks is discussed.
Spatio-temporal deformable 3D ConvNets with attention for action recognition

The irregularity of human actions poses great challenges in video action recognition. Recently, 3D ConvNet methods have shown promising performance at modelling the motion and appearance information. However, the fixed geometric structure of 3D convolution filters largely limits the learning capacity for video action recognition. To address this problem, this paper proposes a spatio-temporal deformable ConvNet module with an attention mechanism, which takes into consideration the mutual correlations in both temporal and spatial domains, to effectively capture the long-range and long-distance dependencies in the video actions. Our attention based deformable module, as a generic module for 3D ConvNets, can adaptively learn more accurate spatio-temporal offsets to model the action irregularity. The experiments on two popular datasets (UCF-101 and HMDB-51) demonstrate that our module significantly outperforms the state-of-the-art methods.
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large and high-quality dataset. We then perform a second fine-tuning step to adapt the transferred model to the target domain. We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. Our approach establishes the state of the art on two well-known benchmarks, WikiQA and TREC-QA, achieving the impressive MAP scores of 92% and 94.3%, respectively, which largely outperform the the highest scores of 83.4% and 87.5% of previous work. We empirically show that TandA generates more stable and robust models reducing the effort required for selecting optimal hyper-parameters. Additionally, we show that the transfer step of TandA makes the adaptation step more robust to noise. This enables a more effective use of noisy datasets for fine-tuning. Finally, we also confirm the positive impact of TandA in an industrial setting, using domain specific datasets subject to different types of noise.
Spatio-Temporal Attention Networks for Action Recognition and Detection

Recently, 3D Convolutional Neural Network (3D CNN) models have been widely studied for video sequences and achieved satisfying performance in action recognition and detection tasks. However, most of the existing 3D CNNs treat all input video frames equally, thus ignoring the spatial and temporal differences across the video frames. To address the problem, we propose a spatio-temporal attention (STA) network that is able to learn the discriminative feature representation for actions, by respectively characterizing the beneficial information at both the frame level and the channel level. By simultaneously exploiting the differences in spatial and temporal dimensions, our STA module enhances the learning capability of the 3D convolutions when handling the complex videos. The proposed STA method can be wrapped as a generic module easily plugged into the state-of-the-art 3D CNN architectures for video action detection and recognition. We extensively evaluate our method on action recognition and detection tasks over three popular datasets (UCF-101, HMDB-51 and THUMOS 2014), and the experimental results demonstrate that adding our STA network module can obtain the state-of-the-art performance on UCF-101 and HMDB-51, which has the top-1 accuracies of 98.4% and 81.4% respectively, and achieve significant improvement on THUMOS 2014 dataset compared against original models.
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks.However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples.In many real scenarios, obtaining highquality annotated data is expensive and timeconsuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected.One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi-Supervised Generative Adversarial Networks.In this paper, we propose GAN-BERT that extends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting.Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks.
A Survey of Blockchain Applications in the Energy Sector

As our fossil fuel reserves are rapidly depleting, there has been an increased focus to explore the utility of renewable energy (e.g., solar energy and wind energy) in replacing fossil fuel. One resulting trend is the energy market gradually shifting toward a distributed market, where renewable energy can be traded, partly evidenced by the number of blockchain-based solutions designed for the (distributed) energy sector. The interest in blockchain is also due to blockchain's underpinning characteristics such as anonymity, decentralized, and transparency. Therefore, in this article, we perform a comprehensive review of how blockchain technology has been, and can be, deployed in energy applications, ranging from energy management to peer-to-peer trading to electric vehicle-related applications to carbon emissions trading, and others. We also study the existing architectures and solutions, and existing and emerging security and privacy challenges, as well as exploring other potential applications of blockchain in the energy sector.
Reformulating Unsupervised Style Transfer as Paraphrase Generation

Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs.However, many existing systems purportedly designed for style transfer inherently warp the input's meaning through attribute transfer, which changes semantic properties such as sentiment.In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data.Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations.We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants.Finally, we pivot to a more real-world style transfer setting by collecting a large dataset of 15M sentences in 11 diverse styles, which we use for an in-depth analysis of our system.
Structure optimization for parameterized quantum circuits

We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
Local Relation Learning for Face Forgery Detection

With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern. Moreover, we propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern. Extensive experiments show that the proposed method consistently outperforms the state-of-the-arts on widely-used benchmarks. Furthermore, detailed visualization shows the robustness and interpretability of our method.
Low-Light Image Enhancement with Normalizing Flow

To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. In this way, the conditional distribution of the normally exposed images can be well modeled, and the enhancement process, i.e., the other inference direction of the invertible network, is equivalent to being constrained by a loss function that better describes the manifold structure of natural images during the training. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
Fault-Tolerant Logical Gates in the IBM Quantum Experience

Quantum computers will require encoding of quantum information to protect them from noise. Fault-tolerant quantum computing architectures illustrate how this might be done but have not yet shown a conclusive practical advantage. Here we demonstrate that a small but useful error detecting code improves the fidelity of the fault-tolerant gates implemented in the code space as compared to the fidelity of physically equivalent gates implemented on physical qubits. By running a randomized benchmarking protocol in the logical code space of the [4,2,2] code, we observe an order of magnitude improvement in the infidelity of the gates, with the two-qubit infidelity dropping from 5.8(2)% to 0.60(3)%. Our results are consistent with fault-tolerance theory and conclusively demonstrate the benefit of carrying out computation in a code space that can detect errors. Although the fault-tolerant gates offer an impressive improvement in fidelity, the computation as a whole is not below the fault-tolerance threshold because of noise associated with state preparation and measurement on this device.
Partially policy-hidden attribute-based broadcast encryption with secure delegation in edge computing

The rapid growth of data has successfully promoted the development of edge computing, which is used for processing the data at the edge of network. The emergence of edge computing compensates for the network delay caused by massive data uploads to the cloud. However, the issues of data security and privacy protection still need to be resolved. In this paper, we propose an efficient ciphertext-policy attribute-based encryption (CP-ABE) scheme that for the first time simultaneously achieves partially hidden policy, direct revocation, and verifiable outsourced decryption. Specifically, in our scheme, the concept of partially hidden policy is introduced to protect private information in an access policy. In addition, after a revocation is successfully executed, the revoked users will not be able to access the message without affecting any other non-revoked users. Our new scheme leverages the outsourcing technique to minimize the overhead required of the user. We demonstrate that our scheme is secure under the Decisional (q−1) Diffie–Hellman assumption and the Decisional Bilinear Diffie–Hellman assumption, as well as evaluating its performance using simulations.
Detector tomography on IBM quantum computers and mitigation of an imperfect measurement

We use quantum detector tomography to characterize the qubit readout in terms of measurement positive operator-valued measures (POVMs) on IBM quantum computers IBM Q 5 Tenerife and IBM Q 5 Yorktown. Our results suggest that the characterized detector model deviates from the ideal projectors, ranging from 10 to 40%. This is mostly dominated by classical errors, evident from the shrinkage of arrows from the poles in the corresponding Bloch-vector representations. There are also small deviations that are not ``classical,'' of order 3% or less, represented by the tilt of the arrows from the $z$ axis. Further improvement on this characterization can be made by adopting two- or more-qubit detector models instead of independent single-qubit detectors for all the qubits in one device. We also find evidence indicating correlations in the detector behavior, i.e., the detector characterization is slightly altered (to a few percent) when other qubits and their detectors are in operation. Such peculiar behavior is consistent with characterization from the more sophisticated approach of the gate set tomography. We also discuss how the characterized detectors' POVMs, despite deviation from the ideal projectors, can be used to estimate the ideal detection distribution.
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts

For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming.The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic similarity.We introduce methods based on sentence mover's similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings.We find that sentence-based metrics correlate with human judgments significantly better than ROUGE, both on machine-generated summaries (average length of 3.4 sentences) and human-authored essays (average length of 7.5).We also show that sentence mover's similarity can be used as a reward when learning a generation model via reinforcement learning; we present both automatic and human evaluations of summaries learned in this way, finding that our approach outperforms ROUGE.
The platform governance triangle: conceptualising the informal regulation of online content

From the new Facebook 'Oversight Body' for content moderation to the 'Christchurch Call to eliminate terrorism and violent extremism online,' a growing number of voluntary and non-binding informal governance initiatives have recently been proposed as attractive ways to rein in Facebook, Google, and other platform companies hosting user-generated content.Drawing on the literature on transnational corporate governance, this article reviews a number of informal arrangements governing online content on platforms in Europe, mapping them onto Abbott and Snidal's (2009) 'governance triangle' model.I discuss three key dynamics shaping the success of informal governance arrangements: actor competencies, 'legitimation politics,' and inter-actor relationships of power and coercion.
Large-Scale Visual Relationship Understanding

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of 〈subject, relation, object〉 triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories.
IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces

We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing HGR algorithms do not consider this characteristic, which results in the degradation of recognition performance. Because the dynamic time warping (DTW) technique considers the time-dependent characteristic of IMU sensor data, the recognition performance of DTW-based algorithms is better than that of others. However, the DTW technique requires a very complex learning algorithm, which makes it difficult to support real-time learning. To solve this issue, the proposed HGR algorithm is based on a restricted column energy (RCE) neural network, which has a very simple learning scheme in which neurons are activated when necessary. By replacing the metric calculation of the RCE neural network with DTW distance, the proposed algorithm exhibits superior recognition performance for time-dependent sensor data while supporting real-time learning. Our verification results on a field-programmable gate array (FPGA)-based test platform show that the proposed HGR algorithm can achieve a recognition accuracy of 98.6% and supports real-time learning and recognition at an operating frequency of 150 MHz.