Reviewerly/exHarmony huggingface dataset:
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.
Comparison of Bifacial Solar Irradiance Model Predictions With Field Validation

Models predicting rear irradiance for bifacial systems are critical to establish accurate estimates of energy yield. Here, we compare five published bifacial irradiance models, varying clearance, row spacing, tilt, and albedo to measure the sensitivity to these parameters. Bifacial energy gains (BGE) as high as 20% are predicted for some configurations. Model agreement is generally good for low ground clearance (clearance heights lower than 0.75 times the collector width), but at higher clearances, finite system size and edge effects become a significant factor in simulations, stretching assumptions of infinite system extent made in some models. In addition, rear irradiance uniformity is improved at high ground clearance, as expected. A test-bed construction and results are described for comparison between modeled and measured data in Golden, CO, USA. The investigations indicate that model agreement for BGE calculation is better than 2% (absolute) when compared with measured results, depending on the system configuration.
Bridging the Gender Digital Gap

Abstract Despite the headway the world has experienced over the last couple of years in terms of a substantial increase in digital access, there are still significant challenges to overcome in ensuring women are included in the transformation to a digital society, which in turn will enhance productivity and social development. Efforts to increase internet adoption access through broadband plans and legislative reforms have yielded improvements in use and adoption. However, there is still a stark and pervasive gender inequality in terms of access, ownership of digital devices, digital fluency as well as the capacity to make meaningful use of the access to technology. Even though affordability is a key source of exclusion, there are also significant socio-cultural norms that restrict access for women. This paper brings forward the argument that access alone is not enough, women need agency and capacity to leverage access. The authors thus highlight the need to make an assessment of the global gender gap and develop meaningful indicators that contribute to the design and implementation of effective policies that drive adoption. We need effective promotion of women´s digital adoption not only from the government but also from the private sector and civil society in order to lead the digital adoption of best practices for women around the world.
Sentiment Analysis of Chinese Microblog Based on Stacked Bidirectional LSTM

Sentiment analysis on Chinese microblogs has received extensive attention recently. Most previous studies focus on identifying sentiment orientation by encoding as many word properties as possible while they fail to consider contextual features (e.g., the long-range dependencies of words), which are, however, essentially important in the sentiment analysis. In this paper, we propose a Chinese sentiment analysis method by incorporating a word2vec model and a stacked bidirectional long short-term memory (Stacked Bi-LSTM) model. We first employ the word2vec model to capture semantic features of words and transfer words into high-dimensional word vectors. We evaluate the performance of two typical word2vec models: continuous bag-of-words (CBOW) and skip-gram. We then use the Stacked Bi-LSTM model to conduct the feature extraction of sequential word vectors. We next apply a binary softmax classifier to predict the sentiment orientation by using semantic and contextual features. Moreover, we also conduct extensive experiments on the real dataset collected from Weibo (i.e., one of the most popular Chinese microblogs). The experimental results show that our proposed approach achieves better performance than other machine-learning models.
A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services

Location-based services (LBSs) have increasingly provided by a broad range of devices and applications, but one associated risk is location disclosure. To solve this problem, a commonly method is to adopt K-anonymity in the centralized architecture based on a single trusted anonymizer. However, this strategy may compromise user privacy involving continuous LBSs. In this study, we propose a dual-K mechanism (DKM) to protect the users’ trajectory privacy for continuous LBSs. The proposed DKM method firstly inserted multiple anonymizers between the user and the location service provider (LSP), and K query locations are sent to different anonymizers to achieve K-anonymity. Simultaneously, we combined the dynamic pseudonym and the location selection mechanisms to improve user trajectory privacy. Hence, neither the LSP nor the anonymizer can obtain the user trajectory. Security analyses demonstrates that our proposed scheme can effectively enhance user trajectory privacy protection, and the simulation results prove that the DKM scheme can preserve user trajectory privacy with low overhead on a single anonymizer.
Gossip Learning as a Decentralized Alternative to Federated Learning

Federated learning is a distributed machine learning approach for computing models over data collected by edge devices. Most importantly, the data itself is not collected centrally, but a master-worker architecture is applied where a master node performs aggregation and the edge devices are the workers, not unlike the parameter server approach. Gossip learning also assumes that the data remains at the edge devices, but it requires no aggregation server or any central component. In this empirical study, we present a thorough comparison of the two approaches. We examine the aggregated cost of machine learning in both cases, considering also a compression technique applicable in both approaches. We apply a real churn trace as well collected over mobile phones, and we also experiment with different distributions of the training data over the devices. Surprisingly, gossip learning actually outperforms federated learning in all the scenarios where the training data are distributed uniformly over the nodes, and it performs comparably to federated learning overall.
Multi-view relighting using a geometry-aware network

We propose the first learning-based algorithm that can relight images in a plausible and controllable manner given multiple views of an outdoor scene. In particular, we introduce a geometry-aware neural network that utilizes multiple geometry cues (normal maps, specular direction, etc.) and source and target shadow masks computed from a noisy proxy geometry obtained by multi-view stereo. Our model is a three-stage pipeline: two subnetworks refine the source and target shadow masks, and a third performs the final relighting. Furthermore, we introduce a novel representation for the shadow masks, which we call RGB shadow images. They reproject the colors from all views into the shadowed pixels and enable our network to cope with inacuraccies in the proxy and the non-locality of the shadow casting interactions. Acquiring large-scale multi-view relighting datasets for real scenes is challenging, so we train our network on photorealistic synthetic data. At train time, we also compute a noisy stereo-based geometric proxy, this time from the synthetic renderings. This allows us to bridge the gap between the real and synthetic domains. Our model generalizes well to real scenes. It can alter the illumination of drone footage, image-based renderings, textured mesh reconstructions, and even internet photo collections.
FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System

Dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality. However learning dataflow diagram usage presents overhead that often discourages the user. In this work we design FlowSense, a natural language interface for dataflow visualization systems that utilizes state-of-the-art natural language processing techniques to assist dataflow diagram construction. FlowSense employs a semantic parser with special utterance tagging and special utterance placeholders to generalize to different datasets and dataflow diagrams. It explicitly presents recognized dataset and diagram special utterances to the user for dataflow context awareness. With FlowSense the user can expand and adjust dataflow diagrams more conveniently via plain English. We apply FlowSense to the VisFlow subset-flow visualization system to enhance its usability. We evaluate FlowSense by one case study with domain experts on a real-world data analysis problem and a formal user study.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn’t contain a fraud (ii) The sequence is obtained by fixing the card-holder or the payment terminal (iii) It is a sequence of spent amount or of elapsed time between the current and previous transactions. Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sequences is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection. In extension to previous works, we show that this approach goes beyond ecommerce transactions and provides a robust feature engineering over different datasets, hyperparameters and classifiers. Moreover, we compare strategies to deal with structural missing values.
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding

Understanding time is crucial for understanding events expressed in natural language.Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order.However, this important problem has so far received limited attention.This paper systematically studies this temporal commonsense problem.Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO , that serves as a test set for this task.We find that the best current methods used on MCTACO are still far behind human performance, by about 20%, and discuss several directions for improvement.We hope that the new dataset and our study here can foster more future research on this topic. 1
A Hierarchically-Labeled Portuguese Hate Speech Dataset

Over the past years, the amount of online offensive speech has been growing steadily.To successfully cope with it, machine learning is applied.However, ML-based techniques require sufficiently large annotated datasets.In the last years, different datasets were published, mainly for English.In this paper, we present a new dataset for Portuguese, which has not been in focus so far.The dataset is composed of 5,668 tweets.For its annotation, we defined two different schemes used by annotators with different levels of expertise.First, non-experts annotated the tweets with binary labels ('hate' vs. 'no-hate').Then, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total.The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception.The hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections.To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.
The Application of Improved YOLO V3 in Multi-Scale Target Detection

Target detection is one of the most important research directions in computer vision. Recently, a variety of target detection algorithms have been proposed. Since the targets have varying sizes in a scene, it is essential to be able to detect the targets at different scales. To improve the detection performance of targets with different sizes, a multi-scale target detection algorithm was proposed involving improved YOLO (You Only Look Once) V3. The main contributions of our work include: (1) a mathematical derivation method based on Intersection over Union (IOU) was proposed to select the number and the aspect ratio dimensions of the candidate anchor boxes for each scale of the improved YOLO V3; (2) To further improve the detection performance of the network, the detection scales of YOLO V3 have been extended from 3 to 4 and the feature fusion target detection layer downsampled by 4× is established to detect the small targets; (3) To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection layer are transformed into two residual units. The experimental results upon PASCAL VOC dataset and KITTI dataset show that the proposed method has obtained better performance than other state-of-the-art target detection algorithms.
Flexible Online Multi-modal Hashing for Large-scale Multimedia Retrieval

Multi-modal hashing fuses multi-modal features at both offline training and online query stage for compact binary hash learning. It has aroused extensive attention in research filed of efficient large-scale multimedia retrieval. However, existing methods adopt batch-based learning scheme or unsupervised learning paradigm. They cannot efficiently handle the very common online streaming multi-modal data (for batch-learning methods), or learn the hash codes suffering from limited discriminative capability and less flexibility for varied streaming data (for existing online multi-modal hashing methods). In this paper, we develop a supervised Flexible Online Multi-modal Hashing (FOMH) method to adaptively fuse heterogeneous modalities and flexibly learn the discriminative hash code for the newly coming data, even if part of the modalities is missing. Specifically, instead of adopting the fixed weights, the modalities weights in FOMH are automatically learned with the proposed flexible multi-modal binary projection to timely capture the variations of streaming samples. Further, we design an efficient asymmetric online supervised hashing strategy to enhance the discriminative capability of the hash codes, while avoiding the challenging symmetric semantic matrix decomposition and storage cost. Moreover, to support fast hash updating and avoid the propagation of binary quantization errors in online learning process, we propose to directly update the hash codes with an efficient discrete online optimization. Experiments on several public multimedia retrieval datasets validate the superiority of the proposed method from various aspects.
An improved two-hidden-layer extreme learning machine for malware hunting

Detecting unknown malware and their variants remains both an operational challenge and a research challenge. In recent years, there have been attempts to design machine learning techniques to increase the success of existing automated malware detection and analysis. In this paper, we build a modified Two-hidden-layered Extreme Learning Machine (TELM), which uses the dependency of malware sequence elements in addition to having the advantage of avoiding backpropagation when training neural networks. We achieve this goal by using partially connected networks between the input and the first hidden layer. These are then aggregated with a fully connected network in the second layer. Finally, we utilize an ensemble to improve the accuracy and robustness of the system for malware threat hunting. The proposed method speeds up the training and detection steps of malware hunting, in comparison to stacked Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). Specifically, this is achieved by avoiding the backpropagation method and using a more simple architecture. Hence, the complexity of our final method is reduced, which leads to better accuracy, higher Matthews Correlation Coefficients (MCC), and Area Under the Curve (AUC), in comparison to a standard LSTM with reduced detection time. Our proposed method is especially useful for malware threat hunting in safety-critical systems, such as electronic health or Internet of Battlefield / Military of Things, since the enormous size of the training data makes it impractical to use complex models (e.g., deep neural networks). In addition in safety-critical systems, both training and detection speeds, as well as the detection rate, are equally important. Our research results in a powerful network that can be used for all platforms with a range of malware analysis. The proposed approach is tested on Windows, Ransomware, Internet of Things (IoT) and a mix of different malware samples datasets. For example, our evaluation using an IoT-specific dataset reports an accuracy of 99.65% in detecting IoT malware samples with an AUC of 0.99, and an MCC of 0.992; thus, outperforming standard LSTM based methods for IoT malware detection in all metrics.
Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification

Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention and Fusion Network (ESAFN) for this task. First, to capture the intra-modality dynamics, ESAFN leverages an effective attention mechanism to generate entity-sensitive textual representations, followed by aggregating them with a textual fusion layer. Next, ESAFN learns the entity-sensitive visual representation with an entity-oriented visual attention mechanism, followed by a gated mechanism to eliminate the noisy visual context. Moreover, to capture the inter-modality dynamics, ESAFN further fuses the textual and visual representations with a bilinear interaction layer. To evaluate the effectiveness of ESAFN, we manually annotate the sentiment orientation over each given entity based on two recently released multimodal NER datasets, and show that ESAFN can significantly outperform several highly competitive unimodal and multimodal methods.
Deep Hough Transform for Semantic Line Detection

We focus on a fundamental task of detecting meaningful line structures, a.k.a., semantic line, in natural scenes. Many previous methods regard this problem as a special case of object detection and adjust existing object detectors for semantic line detection. However, these methods neglect the inherent characteristics of lines, leading to sub-optimal performance. Lines enjoy much simpler geometric property than complex objects and thus can be compactly parameterized by a few arguments. In this paper, we incorporate the classical Hough transform technique into deeply learned representations and propose a one-shot end-to-end learning framework for line detection. By parameterizing lines with slopes and biases, we perform Hough transform to translate deep representations into the parametric domain, in which we perform line detection. Specifically, we aggregate features along candidate lines on the feature map plane and then assign the aggregated features to corresponding locations in the parametric domain. The problem of detecting semantic lines in the spatial domain is transformed into spotting individual points in the parametric domain, making the post-processing steps, i.e., non-maximal suppression, more efficient. Experimental results on our proposed dataset and another public dataset demonstrate the advantages of our method over previous state-of-the-art alternatives
Digital signature scheme for information non-repudiation in blockchain: a state of the art review

Abstract Blockchain, as one of the most promising technology, has attracted tremendous attention. The interesting characteristics of blockchain are decentralized ledger and strong security, while non-repudiation is the important property of information security in blockchain. A digital signature scheme is an effective approach to achieve non-repudiation. In this paper, the characteristics of blockchain and the digital signature to guarantee information non-repudiation are firstly discussed. Secondly, the typical digital signature schemes in blockchain are classified and analyzed, and then the state-of-the-art digital signatures are investigated and compared in terms of application fields, methods, security, and performance. Lastly, the conclusions are given, and some future works are suggested to stir research efforts in this field. Our works will facilitate to design efficient and secure digital signature algorithms in blockchain.
Three-dimensional waveguide interconnects for scalable integration of photonic neural networks

Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example by lithography, are strongly limited in size due to disadvantageous scaling. We use three dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical-couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's footprint area scales linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.