Reviewerly/exHarmony huggingface dataset:
Dual Recursive Network for Fast Image Deraining

Recent years have witnessed the great progress on deep image deraining networks. On the one hand, deraining performance has been significantly improved by designing complex network architectures, yielding high computational cost. On the other hand, several lightweight networks try to improve computational efficiency, but at the cost of notable degrading deraining performance. In this paper, we propose a dual recursive network (DRN) for fast image deraining as well as comparable or superior deraining performance compared with state-of-the-art approaches. Specifically, our DRN utilizes a residual network (ResNet) with only 2 residual blocks (ResBlock), which is recursively unfolded to remove rain streaks in multiple stages. Meanwhile, the 2 ResBlocks can be recursively computed in one stage, forming the dual recursive network. Experimental results show that DRN is very computationally efficient and can achieve favorable deraining results on both synthetic and real rainy images. The source codes and pre-trained models are available at https://github.com/csdwren/DRN.
Quality Aware Generative Adversarial Networks

Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions. Several improvements have been made to the original GAN formulation to address some of its shortcomings like mode collapse, convergence issues, entanglement, poor visual quality etc. While a significant effort has been directed towards improving the visual quality of images generated by GANs, it is rather surprising that objective image quality metrics have neither been employed as cost functions nor as regularizers in GAN objective functions. In this work, we show how a distance metric that is a variant of the Structural SIMilarity (SSIM) index (a popular full-reference image quality assessment algorithm), and a novel quality aware discriminator gradient penalty function that is inspired by the Natural Image Quality Evaluator (NIQE, a popular no-reference image quality assessment algorithm) can each be used as excellent regularizers for GAN objective functions. Specifically, we demonstrate state-of-the-art performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over CIFAR-10, STL10 and CelebA datasets.
Visual-Textual Sentiment Analysis in Product Reviews

Sentiment analysis has attracted increasing attention recently due to its potential wide applications in opinion analysis, recommendation system, etc. Visual-textual sentiment analysis aims to improve the performance of sentiment analysis by leveraging both visual and textual signals. In this paper, we address the visual-textual sentiment analysis in product reviews. Our main contributions are two-fold. First, instead of crawling data from Flickr or Twitter with positive and negative labels in existing works, we introduce a new dataset for visual-textual sentiment analysis, termed as Product Reviews150K (PR-150K), which is collected from the product reviews of online shopping websites. Second, we propose a deep Tucker fusion method for visual-textual sentiment analysis, which efficiently combines visual and textual deep representations based on the Tucker decomposition and a bilinear pooling operation. Extensive experiments on our PR-150K, MVSO, and VSO datasets show that our method outperforms several state-of-the-art methods.
Toward a Task of Feedback Comment Generation for Writing Learning

In this paper, we introduce a novel task called feedback comment generation — a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. There has been almost no work on this task nor corpus annotated with feedback comments. We have taken the first step by creating learner corpora consisting of approximately 1,900 essays where all preposition errors are manually annotated with feedback comments. We have tested three baseline methods on the dataset, showing that a simple neural retrieval-based method sets a baseline performance with an F-measure of 0.34 to 0.41. Finally, we have looked into the results to explore what modifications we need to make to achieve better performance. We also have explored problems unaddressed in this work
The energy consumption laxity-based algorithm to perform computation processes in virtual machine environments

Server cluster systems equipped with virtual machines are widely used to realise scalable and high-performance computing systems. In order to satisfy application requirements like response time, processing loads of virtual machines have to balance with one another in a server cluster. In addition to achieve the performance objectives, the total electric energy of a server cluster to perform application processes has to be reduced. In this paper, the Energy Consumption Laxity Based (ECLB) algorithm is proposed to allocate computation type application processes to virtual machines in a server cluster so that the total electric energy of the server cluster and response time of each process can be reduced. We evaluate the ECLB algorithm compared with basic Round-Robin (RR) algorithm. Evaluation results show the average total electric energy of a server cluster and average response time of each process in the ECLB algorithm can be more reduced than in the RR algorithm.
Optimization of MFCC parameters for mobile phone recognition from audio recordings

Recent researches in the domain of electronics and telecommunication have made available various low price chips into a single smaller device. In the similar fashion, mobile phones are now equipped with efficient audio, video and image recorders. With the extended facilities and ease of use, it is frequently used to take photographs, videos and audio recordings. As the use of these devices increased, its use in criminal activities also increased. In such a scenario, the available recording may help in detection of its source device from which it has originated. Source device identification from audio recording is one of the major problems in the multimedia forensics. In this paper, we perform a study for optimization of MFCC (Mel Frequency Cepstral Coefficient) parameters in device identification from audio recording.
A New Threat for Pseudorange-Based RAIM: Adversarial Attacks on GNSS Positioning

Global Navigation Satellite System (GNSS) signals are very vulnerable to spoofing due to the low power level and opening service mode.Although pseudorange-based Receiver Autonomous Integrity Monitoring (RAIM) method performances effectively in spoofing detection and exclusion through a consistency check method, it might still suffer from threats which are deliberately designed.Besides an exhaustive research on the defense method, a deep understanding of the possible attack modes of the spoofing methods is also crucial for positioning security.This paper proposes a new threat for pseudorange-based RAIM, named adversarial attacks on GNSS positioning, which can mislead the victim receiver deducing any targeted position within a certain range without triggering the RAIM alarm.First, the adversarial attack is formulated as an optimization problem that minimizes the difference between the misled GNSS positioning results and the targeted position with RAIM missing detection.Then, the Fast Gradient Sign Method (FGSM) is applied to solve the optimization problem and obtain the adversarial examples for GNSS spoofing.Simulated and real GNSS data experiments are designed to verify the threat range and efficiency of the adversarial attacks on GNSS positioning.
A Certificateless Verifiable Strong Designated Verifier Signature Scheme

Certificateless strong designated verifier signature schemes have realized the merit of CL-PKC against the traditional strong designated verifier signatures. However, when the signer and the designated verifier disagree with the signature, existing schemes cannot distinguish the original signature from the signature transcript. In addition, errors or malicious actions introduced by the designated verifier may lead to the failure of signature verification. To solve these issues, we propose a certificateless verifiable strong designated verifier signature scheme. When disputes arise between the signer and the verifier, the scheme can effectively prevent the signer from denying the signature generated by the signer, as well as the designated verifier from denying valid signatures or invalid ones. Our scheme does not rely on bilinear pairings. The proposed scheme satisfies the requirements of verifiability, unforgeability, non-delegability, non-transferability and signer ambiguity. We also provide formal security proof in the random oracle model for the proposed scheme.
Virtual training leads to real acute physical, cognitive, and neural benefits on healthy adults: study protocol for a randomized controlled trial

Keeping a certain level of physical activity has beneficial effects on the body itself but also, surprisingly, on cognition: specifically, physical high-intensity intermittent aerobic exercise (HIE) can show improvement on cognitive executive functions. Although, in some cases performing strength or aerobic training is problematic or not feasible. Immersive virtual reality (IVR) can induce the illusory feeling of ownership and agency over a moving virtual body, therefore showing comparable physiological reactions: for example, if an individual is sitting on a chair but his virtual body climbs a hill, the individual's heart rate increases coherently, as if he is actually walking. In this study, we investigate whether this same illusion can show beneficial consequences on the body as well as on executive functions (using the color-word matching Stroop task) and on its neural substrates (using functional near-infrared spectroscopy [fNIRS]).In a cross-over randomized controlled trial, 30 healthy young adults will experience HIE training in IVR (i.e. the virtual body will perform eight sets of 30 s of running followed by 30 s of slow walking, while the participant is completely still) according to two random-ordered conditions: during the experimental condition, the virtual body is displayed in first-person perspective (1PP), while in the control condition, the virtual body is displayed in third-person perspective (3PP). To confirm that individuals have the illusion of ownership and agency over the virtual body in 1PP (and not in 3PP), we will record the heart rate, in addition to subjective questionnaires. Before and after every IVR sessions (one week apart), we will measure cortical hemodynamic changes in the participants' prefrontal cortex using the fNIRS device during the Stroop task's execution.From a theoretical perspective, we could prove that the sense of body ownership and agency can modulate physical and cognitive parameters, even in the absence of actual movements; from a clinical perspective, these results could be useful to train cognition and body simultaneously, in a completely safe environment.University Hospital Medical Information Network Clinical Trial Registry, UMIN000034255 . Registered on 1 October 2018.
ProCSA: Protecting Privacy in Crowdsourced Spectrum Allocation

Sharing a spectrum is an emerging paradigm to increase spectrum utilization and thus address the unabated increase in mobile data consumption. The paradigm allows the “unused” spectrum bands of licensed primary users to be shared with secondary users, as long as the allocated spectrum to the secondary users does not cause any harmful interference to the primary users. However, such shared spectrum paradigms pose serious privacy risks to the participating entities, e.g., the secondary users may be sensitive about their locations and usage patterns. This paper presents a privacy-preserving protocol for the shared spectrum allocation problem in a crowdsourced architecture, wherein spectrum allocation to secondary users is done based on real-time sensing reports from geographically distributed and crowdsourced spectrum sensors. Such an architecture is highly desirable since it obviates the need to assume a propagation model, and facilitates estimation based on real-time propagation conditions and high granularity data via inexpensive means. We design our protocol by leveraging the efficiency and generality of recently developed fast and secure two-party computation ( $$\mathrm {S2PC}$$ ) protocols. We show that this approach leads to practical solutions that outperform the state-of-the-art in terms of both efficiency as well as functionality. To achieve the desired computational efficiency, we optimize the spectrum allocation algorithm to select a small number of relevant reports based on certain parameters. This results in a faster RAM program for power allocation which, under suitable adjustments to underlying arithmetic operations, can be efficiently implemented using $$\mathrm {S2PC}$$ . We use the standard “ideal/real paradigm” to define the security of spectrum allocation and prove security of our protocol (in the semi-honest model). We also provide data from extensive simulations to demonstrate the accuracy, as well as computational and communication efficiency of our schemes.
Knowledge Enhanced Contextual Word Representations

Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance their representations with structured, human-curated knowledge. For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. In contrast to previous approaches, the entity linkers and self-supervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. KnowBert's runtime is comparable to BERT's and it scales to large KBs.
Probing Natural Language Inference Models through Semantic Fragments.

Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena are involved in natural language inference (NLI) and go beyond basic linguistic understanding, it is unclear the extent to which they are captured in existing NLI benchmarks and effectively learned by models. To investigate this, we propose the use of semantic fragments---systematically generated datasets that each target a different semantic phenomenon---for probing, and efficiently improving, such capabilities of linguistic models. This approach to creating challenge datasets allows direct control over the semantic diversity and complexity of the targeted linguistic phenomena, and results in a more precise characterization of a model's linguistic behavior. Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task. (b) On the other hand, with only a few minutes of additional fine-tuning---with a carefully selected learning rate and a novel variation of inoculation---a BERT-based model can master all of these logic and monotonicity fragments while retaining its performance on established NLI benchmarks.
Non-Invasive Forehead Segmentation in Thermographic Imaging

The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions.
FAIR adoption, assessment and challenges at UniProt

UniProt continues to support the ongoing process of making scientific data FAIR. Here we contribute to this process with a FAIRness assessment of our UniProtKB dataset followed by a critical reflection on the challenges and future directions of the adoption and validation of the FAIR principles and metrics.
Visualization of the Relevance

The task of organizing and retrieving knowledge is often elaborative and involves different types of media including digital or analog. In this paper we describe a system that is based on related research in the fields of spatial hypertext, information retrieval, and visualization. It utilizes a 2D space on which users can add, remove, or manipulate information entities (so-called user nodes) visually. A spatial parser recognizes the evolving structure and queries a knowledge base for helpful other information entities (so-called suggestions nodes). Similar to user nodes, those suggestions are presented as visual objects in the space. We propose a physics model to simulate their behavior. Their characteristics encode the relevance of suggestions to user nodes and to each other. This enables human recipients to interpret the given visual clues and, thus, identify information of interest. The way users organize nodes spatially influences the parsed spatial structures, i.e., the placement of suggestion nodes. This allows the creation of complex queries without any prior knowledge, yet the users do not have to be aware of that, because they can express their thoughts implicitly by manipulating their nodes. We discuss the strengths of a physics based simulation to encode context visually and point to open issues and potential solutions. On the basis of an implemented demonstrator we show the benefits compared to similar and related applications in the field of information visualization, especially when it comes to tasks where a high portion of creativity is involved and the information space is not well known.
Coherent router for quantum networks with superconducting qubits

This work presents a general idea and specific realization of a coherent router for quantum networks. The router is composed of a small network in which the path taken by a quantum state between the input and output qubits is controlled by an ancilla qubit.
Black-box Adversarial Attacks with Bayesian Optimization

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to $80\%$ with respect to the state of the art methods.
Google claims quantum computing milestone

Leaked paper says device achieved "quantum supremacy," besting conventional computers.
Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services

Nowadays, more and more online content providers are offering multiple types of data services. To provide users with a better service experience, Quality of Experience (QoE) has been widely used in the delivery quality measurement of network services. How to accurately measure the QoE score for all types of network services has become a meaningful but difficult problem. To solve this problem, we proposed a unified QoE scoring framework that measures the user experience of almost all types of network services. The framework first uses a machine learning model (random forest) to classify network services, then selects different nonlinear expressions based on the type of service and comprehensively calculates the QoE score through the Quality of Service (QOS) metrics including transmission delay, packet loss rate, and throughput rate. Experiment results show that the proposed method has the ability to be applied on almost all the types of network traffic, and it achieves better QoE assessment accuracy than other works.
Waqfintech and Sustainable Socio-Economic Development

This paper aims to discuss the use of financial technology in waqf (Islamic endowment) for funding religious and social causes. It is believed that waqfintech could reach a wider audience and benefit more people in need through the use of innovative financial technology like crowdfunding and blockchain. The main objective of this paper is to introduce the concept of waqfcrowd and waqfcoin as a means of collecting and managing waqf in the digital age. Waqf institutions can thus take advantage of technological innovation to utilise the convenience and efficiency offered by financial technology. This is a conceptual paper which reviews the relevant literature regarding waqf and financial technology. One major limitation of this paper is that it does not discuss the regulatory and operational details of how to institutionalise blockchain technology in different regions. This paper intends to inspire Islamic financial institutions and waqf institutions to harness the potential of waqfintech to raise funds in an effective way.
Structural constraints for dynamic operators in abstract argumentation

Many recent studies of dynamics in formal argumentation within AI focus on the well-known formalism of Dung’s argumentation frameworks (AFs). Despite the usefulness of AFs in many areas of argumentation, their abstract notion of arguments creates a b
Layout Analysis and Text Column Segmentation for Historical Vietnamese Steles

Stone engravings in Historical Vietnamese steles allow historians to study the life of common people in the villages. Only recently, a large amount of images of such engravings have become available. For supporting the historians, automatic document analysis systems are needed for reading the ancient Chu Nôm characters that are written in columns from top to bottom. In this paper, we study the problem of layout analysis, which is the first step of automatic reading. Semantic segmentation is applied at pixel-level to find the title, main text, label, and reference number on the page using deep convolutional neural networks. Afterwards, seam carving is used to segment the text columns within the main text. We present baseline results for hundred exemplary pages, discuss error cases, and outline lines of future research.