Reviewerly/exHarmony huggingface dataset:
Performance Analysis of the Libra Blockchain: An Experimental Study

Since Bitcoin was first introduced i n 2008, many types of cryptocurrencies have been proposed based on blockchain. However, the performance of permissionless blockchains restricts the widespread of cryptocurrency. Recently, Libra was proposed by Facebook based on a permissioned blockchain, i.e. the Libra blockchain. The vision of Libra is to become a global currency supporting financial a pplications, but it is doubted whether the performance of the Libra blockchain is able to support frequent micropayment scenarios. In this paper, we propose a methodology to evaluate the performance of blockchain platforms and conducted an experimental study on the Libra blockchain. The results show that the Libra blockchain can only process about one thousand transactions per second at most, and the performance drops significantly a s t he number of validators increases. Although it outperforms permissionless blockchain platforms, the performance of the Libra blockchain is still unsatisfactory compared to other permissioned blockchains like Hyperledger Fabric and needs to make effective improvements in order to support global micropayment in the future.
Semantic-spatial fusion network for human parsing

Recently, many methods have united low-level and high-level features to generate the desired accurate high-resolution prediction for human parsing. Nevertheless, there exists a semantic-spatial gap between low-level and high-level features in some methods, i.e., high-level features represent more semantics and less spatial details, while low-level ones have less semantics and more spatial details. In this paper, we propose a Semantic-Spatial Fusion Network (SSFNet) for human parsing to shrink the gap, which generates the accurate high-resolution prediction by aggregating multi-resolution features. SSFNet includes two models, a semantic modulation model and a resolution-aware model. The semantic modulation model guides spatial details with semantics and then effectively facilitates the feature fusion, narrowing the gap. The resolution-aware model sufficiently boosts the feature fusion and obtains multi-receptive-fields, which generates reliable and fine-grained high-resolution features for each branch, in bottom-up and top-down processes. Extensive experiments on three public datasets, PASCAL-Person-Part, LIP and PPSS, show that SSFNet achieves significant improvements over state-of-the-art methods.
Temporal Aggregation with Clip-level Attention for Video-based Person Re-identification

Video-based person re-identification (Re-ID) methods can extract richer features than image-based ones from short video clips. The existing methods usually apply simple strategies, such as average/max pooling, to obtain the tracklet-level features, which has been proved hard to aggregate the information from all video frames. In this paper, we propose a simple yet effective Temporal Aggregation with Clip-level Attention Network (TACAN) to solve the temporal aggregation problem in a hierarchal way. Specifically, a tracklet is firstly broken into different numbers of clips, through a two-stage temporal aggregation network we can get the tracklet-level feature representation. A novel min-max loss is introduced to learn both a clip-level attention extractor and a clip-level feature representer in the training process. Afterwards, the resulting clip-level weights are further taken to average the clip-level features, which can generate a robust tracklet-level feature representation at the testing stage. Experimental results on four benchmark datasets, including the MARS, iLIDS-VID, PRID-2011 and DukeMTMC-VideoReID, show that our TACAN has achieved significant improvements as compared with the state-of-the-art approaches.
Towards Multilingual Sign Language Recognition

Sign language recognition involves modeling of multichannel information such as, hand shapes, hand movements.This requires also sufficient sign language specific data.This is a challenge as sign languages are inherently under-resourced.In the literature, it has been shown that hand shape information can be estimated by pooling resources from multiple sign languages.Such a capability does not exist yet for modeling hand movement information.In this paper, we develop a multilingual sign language approach, where hand movement modeling is also done with target sign language independent data by derivation of hand movement subunits.We validate the proposed approach through an investigation on Swiss German Sign Language, German Sign Language and Turkish Sign Language, and demonstrate that sign language recognition systems can be effectively developed by using multilingual sign language resources.
Generating Multilingual Voices Using Speaker Space Translation Based on Bilingual Speaker Data

We present progress towards bilingual Text-to-Speech which is able to transform a monolingual voice to speak a second language while preserving speaker voice quality. We demonstrate that a bilingual speaker embedding space contains a separate distribution for each language and that a simple transform in speaker space generated by the speaker embedding can be used to control the degree of accent of a synthetic voice in a language. The same transform can be applied even to monolingual speakers.In our experiments speaker data from an English-Spanish (Mexican) bilingual speaker was used, and the goal was to enable English speakers to speak Spanish and Spanish speakers to speak English. We found that the simple transform was sufficient to convert a voice from one language to the other with a high degree of naturalness. In one case the transformed voice outperformed a native language voice in listening tests. Experiments further indicated that the transform preserved many of the characteristics of the original voice. The degree of accent present can be controlled and naturalness is relatively consistent across a range of accent values.
Instance-based Model Adaptation for Direct Speech Translation

Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora. We tackle this limitation with a method to improve data exploitation and boost the system's performance at inference time. Our approach allows us to customize "on the fly" an existing model to each incoming translation request. At its core, it exploits an instance selection procedure to retrieve, from a given pool of data, a small set of samples similar to the input query in terms of latent properties of its audio signal. The retrieved samples are then used for an instance-specific fine-tuning of the model. We evaluate our approach in three different scenarios. In all data conditions (different languages, in/out-of-domain adaptation), our instance-based adaptation yields coherent performance gains over static models.
EyeNet: An Improved Eye States Classification System using Convolutional Neural Network

The classification of eye states (open or closed) has numerous potential applications such as fatigue detection, psychological state analysis, smart home devices controlling, etc. Due to its importance, there are a number of works already reported in the literature using traditional shallow neural networks or support vector machines, which have reported good accuracy (about 96%). However, there is still enough space to improve the accuracy of existing systems using proper classification methods. The major problem with traditional classifiers is that they depend on manual selection of features and that is very challenging to select meaningful features for such classifiers. Convolutional neural networks (CNNs) have become popular for computer vision and pattern recognition problems with better performance than traditional methods. In this study, we proposed a model of CNN (EyeNet) for eye states classification and tested it on three datasets (CEW, ZJU, and MRL Eye). The quality (or diversity) of a recently proposed larger dataset (MRL Eye) has been compared with two other existing datasets with respect to the sufficient training of the model. The model shows very high performance (about 99% accuracy for classification of eye states on the test set of data when it is trained by the training samples of the same dataset. The proposed model improves the accuracy of the best existing method by about 3%. The performance of the model for classification of samples coming from different datasets is reduced when it is trained with the MRL Eye dataset. This concludes that even though, the MRL Eye has a large number of samples compared to other datasets, but diversity still lacks in the MRL Eye samples to sufficiently train the model.
IoT-based Google Duplex Artificial Intelligence Solution for Elderly Care

The advancements in medical science and technology has resulted in an increased life span thus the mortality rate of the elderly has greatly decreased. The elderly often gets cognitively impaired and require urgent medical services which when left unnoticed may lead to fatal consequences. Due to lack of social care support for these adults, there arises the need to develop cost-effective assistive healthcare technological solutions for taking care of the elders and giving them the best tech-friendly experience. Intelligent homes, an environment of sensors with artificial intelligence integrated with home appliances, can provide the best solution for continuous and remote monitoring of the health of the persons. This helps elders to control various devices, also get immediate attention from the family members, healthcare assistants and/or have frequent visit to hospitals. Basically, an intelligent bed can help elders to prevent the occurrence of bedsores and falling off from bed by monitoring the position of the person while they are in bed. The latest Google Duplex Artificial Intelligence (AI) will be used as a voice-controlled speech recognition system, a personal assistant, to respond to the commands given by the person. We use Internet of Things (IoT) to establish the connectivity between the appliances, the user and his/her network. The major element of this system is a Raspberry Pi which will collect the data from the sensors and interprets them to manage the home appliances like light, fan, door, alarm (in case of emergency), manage phone calls, television, and other home appliances. There is also an emergency module which has sensors attached to the body of the person which helps the caretakers, relatives or friends to know about the health of the person. It continuously monitors the body conditions of the person and alerts his/her network in case of emergency.
Digital video intrusion intelligent detection method based on narrowband Internet of Things and its application

This paper proposes a digital video intrusion detection method based on Narrow Band Internet of Things (NB-IoT), and establishes a digital video intrusion detection system based on NB-IoT network and SVM intelligent classification algorithm. Firstly, the image is preprocessed by gradation processing and threshold transformation to extract the HOG feature extraction of human intrusion behavior in digital video frame images. Then, combined with the human intrusion HOG feature data, the SVM intelligent algorithm is used to classify the human intrusion behavior, so as to accurately classify the movements of walking, jumping, running and waving in video surveillance. Finally, the performance analysis of the algorithm finds that the classification time, classification accuracy and classification false positive rate of the model are tested. The classification time is 40.8 s, the shortest is 27 s, the classification accuracy is 87.65%, and the lowest is 83.7%. The false detection rate is up to 15%, both of which are less than 20%, indicating that the classification method has good accuracy and stability. Comparing the algorithm with other algorithms, the intrusion sensitivity, intrusion specificity and training speed of the model are 93.6%, 94.3%, and 19 s, respectively, which are better than other methods, which indicates that the model has good detection performance in the experimental stage.
Textual Entailment--Based Figure Summarization for Biomedical Articles

This article proposes a novel unsupervised approach (FigSum++) for automatic figure summarization in biomedical scientific articles using a multi-objective evolutionary algorithm. The problem is treated as an optimization problem where relevant sentences in the summary for a given figure are selected based on various sentence scoring features (or objective functions), such as the textual entailment score between sentences in the summary and a figure’s caption, the number of sentences referring to that figure, semantic similarity between sentences and a figure’s caption, and the number of overlapping words between sentences and a figure’s caption. These objective functions are optimized simultaneously using multi-objective binary differential evolution (MBDE). MBDE consists of a set of solutions, and each solution represents a subset of sentences to be selected in the summary. MBDE generally uses a single differential evolution variant, but in the current study, an ensemble of two different differential evolution variants measuring diversity among solutions and convergence toward global optimal solution, respectively, is employed for efficient search. Usually, in any summarization system, diversity among sentences (called anti-redundancy ) in the summary is a very critical feature, and it is calculated in terms of similarity (like cosine similarity) among sentences. In this article, a new way of measuring diversity in terms of textual entailment is proposed. To represent the sentences of the article in the form of numeric vectors, the recently proposed BioBERT pre-trained language model in biomedical text mining is utilized. An ablation study has also been presented to determine the importance of different objective functions. For evaluation of the proposed technique, two benchmark biomedical datasets containing 91 and 84 figures are considered. Our proposed system obtains 5% and 11% improvements in terms of the F -measure metric over two datasets, compared to the state-of-the-art unsupervised methods.
Covid-19: UK pledges to reintroduce contact tracing to fight virus

The government is to reintroduce contact tracing of people who have had symptoms of covid-19, England’s health secretary, Matt Hancock, said last week.Hancock told MPs on the health select committee that the planned increase in testing capacity (the government has promised to conduct 100 000 tests a day by the end of April) would enable it to restart a policy that it stopped in March when the prevalence of the virus began to increase in the UK.He said, “Community testing is part of the strategy, and we will be introducing it when we can. It wasn’t possible when we had small numbers of tests, but as we have expanding numbers of tests it will be possible.”Hancock said that a central team would be set up to implement the strategy, which includes an app for people to trace and report their contacts themselves, alongside more traditional methods such as using health professionals to proactively contact and trace people.“That [central team] brings together teams from NHSX, who are leading on the app, with a huge array of partners that are working within that, [and] from Public Health England, who are the experts on what we refer to as external contact tracing,” Hancock said.Hancock said the app …
Deep Neighborhood Component Analysis for Visual Similarity Modeling

Learning effective visual similarity is an essential problem in multimedia research. Despite the promising progress made in recent years, most existing approaches learn visual features and similarities in two separate stages, which inevitably limits their performance. Once useful information has been lost in the feature extraction stage, it can hardly be recovered later. This article proposes a novel end-to-end approach for visual similarity modeling, called deep neighborhood component analysis , which discriminatively trains deep neural networks to jointly learn visual features and similarities. Specifically, we first formulate a metric learning objective that maximizes the intra-class correlations and minimizes the inter-class correlations under the neighborhood component analysis criterion, and then train deep convolutional neural networks to learn a nonlinear mapping that projects visual instances from original feature space to a discriminative and neighborhood-structure-preserving embedding space, thus resulting in better performance. We conducted extensive evaluations on several widely used and challenging datasets, and the impressive results demonstrate the effectiveness of our proposed approach.
Category-based induction in conceptual spaces

Category-based induction is an inferential mechanism that uses knowledge of conceptual relations in order to estimate how likely is for a property to be projected from one category to another. During the last decades, psychologists have identified several features of this mechanism, and they have proposed different formal models of it. In this article; we propose a new mathematical model for category-based induction based on distances on conceptual spaces. We show how this model can predict most of the properties of this kind of reasoning while providing a solid theoretical foundation for it. We also show that it subsumes some of the previous models proposed in the literature and that it generates new predictions.
Becoming JUDAS: Correlating Users and Devices During a Digital Investigation

One of the biggest challenges in IoT-forensics is the analysis and correlation of heterogeneous digital evidence, to enable an effective understanding of complex scenarios. This paper defines a methodology for extracting unique objects (e.g., representing users or devices) from the files of a case, defining the context of the digital investigation and increasing the knowledge progressively, using additional files from the case (e.g. network captures). The solution includes external searches using open source intelligence (OSINT) sources when needed. In order to illustrate this approach, the proposed methodology is implemented in the JSON Users and Devices analysis (JUDAS) tool, which is able to generate the context from JSON files, complete it, and show the whole context using dynamic graphs. The approach is validated using the files in an IoT-Forensic digital investigation where an important set of potential digital evidence extracted from Amazon's Alexa Cloud is analysed.
What would a feminist open source investigation look like?

Use of publicly available information to offer radical retellings of violence has powerful democratising potential, both in terms of who contributes to open source investigations and whose stories they centre. At a time when trust in government, media institutions and non-government organisations as fact bearers has been eroded, emergent open source methods have become “an alternative set of truth practices” (Weizman in Open Verification, e-flux, 2019). Yet there are few accepted guidelines on what is legally, morally, or ethically permissible in such investigations. A growing question among practitioners using open source techniques in human rights investigations is not “Can we do this?” but “Should we be doing this?” Here, we set out why intersectional feminist thought should be considered when grappling with the radical possibilities and serious ethical challenges of open source investigations. To this end, we offer practical examples of how an investigator might better situate their findings, show their workings, design for ambiguity, practice equity in attribution, and find new ways to care for themselves and others.
Risks and Opportunities of Blockchain Based on E-Voting Systems

Elections are one of a democratic society's primary pillars, but the Internet's emerging influence is continually challenging the voting process. Recently, E-voting based-Blockchain has already taken place in some countries. However, there are vulnerabilities issues around the E-voting system based- blockchain. This paper aims to highlight some of the risks and opportunities of the e-voting systems based blockchain. As well, we believe that this study can bring a valuable contribution as it illustrates some of the risks and opportunities of e-voting systems based-blockchain. That to offer users and developers a broad view of the potential risks and opportunities associated with the adoption of blockchain in the e-voting system.
To See in the Dark: N2DGAN for Background Modeling in Nighttime Scene

Due to the deteriorated conditions of illumination lack and uneven lighting, the performance of traditional background modeling methods is greatly limited for the surveillance of nighttime video. To make background modeling under nighttime scene performs as well as in daytime condition, we put forward a promising generation-based background modeling framework for foreground surveillance. With a pre-specified daytime reference image as background frame, the GAN based generation model, called N2DGAN, is trained to transfer each frame of nighttime video to a virtual daytime image with the same scene to the reference image except for the foreground part. Specifically, to balance the preservation of background scene and the foreground object(s) in generating the virtual daytime image, we presented a two-pathway generation model, in which the global and local sub-networks were well combined with spatial and temporal consistency constraints. For the sequence of generated virtual daytime images, a multi-scale Bayes model was further proposed to characterize pertinently the temporal variation of background. We manually labeled ground truth on the collected nightime video datasets for performance evaluation. The impressive results illustrated in both the main paper and supplementary show the effectiveness of our proposed approach.
Similarity preserving feature generating networks for zero-shot learning

Traditional zero-shot learning methods usually solve the problem through visual semantic embedding. Recently, considering the power of generating adversarial network, some methods propose to use GAN to synthesize visual representation by using class attribute or semantic embedding. In this paper, we propose a novel method, named similarity preserving GAN (SPGAN), which can generate unseen visual features from random noises concatenating with semantic descriptions. Specifically, we train a conditional Wasserstein GAN that takes semantic descriptions about an unseen class as input and generates synthesized visual features for this class. For the GAN-based approaches, we hope that the generator can generate as real features as possible. However, the unconstrained training process makes the sample generated by the model deviate greatly from the real sample. To avoid this problem, we propose a similarity preserving loss to regularize the generative network, which helps to minimize the distance between synthesized samples and real samples. Furthermore, we use an ensemble method in the test stage which integrates nearest neighbor classifier and linear softmax classifier. Concretely, in the first stage, we use the nearest neighbor method to pick out the real features that are highly similar to the generated ones, then combine the selected real features with generated features to train the final softmax classifier. Experiments on four popular datasets show that our method can surpass all the most state-of-the-art methods.