Reviewerly/exHarmony huggingface dataset:
Comparative Weibull distribution methods for reliable global solar irradiance assessment in France areas

This paper investigates the Weibull distribution analysis for an accuracy global solar irradiance assessment considering period measurements based on several and grouping years. The problem study in this paper is to find a global solar irradiance model in order to provide accurate estimation of PV energy output allowing better sizing of PV installation. The aim is to select the best Weibull fit procedure for obtaining reliable global solar irradiance from sun that incident in a place during time periods to estimate its yearly energy generation from PV plant. Comparisons are carried out between Graphic, Moments and Maximum Likelihood methods with two different databases (real data on-site measurements and SODA website database). These comparisons are made on global solar irradiance frequency distributions and annual solar irradiance assessment for different French locations. The originality of the paper is that the obtained results prove that the Maximum Likelihood method fits better with the global solar irradiance distribution while the Moment method provides an annual solar irradiance prediction. Thereby, we exploit the obtained results from the Moment method to achieve a more accurate solar energy forecasting model. This resulting model can be implemented for providing one solar energy estimation tool for PV plant sites.
A W2VV++ Case Study with Automated and Interactive Text-to-Video Retrieval

As reported by respected evaluation campaigns focusing both on automated and interactive video search approaches, deep learning started to dominate the video retrieval area. However, the results are still not satisfactory for many types of search tasks focusing on high recall. To report on this challenging problem, we present two orthogonal task-based performance studies centered around the state-of-the-art W2VV++ query representation learning model for video retrieval. First, an ablation study is presented to investigate which components of the model are effective in two types of benchmark tasks focusing on high recall. Second, interactive search scenarios from the Video Browser Showdown are analyzed for two winning prototype systems implementing a selected variant of the model and providing additional querying and visualization components. The analysis of collected logs demonstrates that even with the state-of-the-art text search video retrieval model, it is still auspicious to integrate users into the search process for task types, where high recall is essential.
Biomedical Event Extraction as Multi-turn Question Answering

Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components.It usually is implemented by a complex pipeline of individual tools to solve the different relation extraction subtasks.We present an alternative approach where the detection of relationships between entities is described uniformly as questions, which are iteratively answered by a question answering (QA) system based on the domain-specific language model SciBERT.This model outperforms two strong baselines in two biomedical event extraction corpora in a Knowledge Base Population setting, and also achieves competitive performance in BioNLP challenge evaluation settings.
Investigating African-American Vernacular English in Transformer-Based Text Generation

The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts.However, NLP models have historically been developed using dominant English varieties, such as Standard American English (SAE), due to text corpora availability.We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE-or SAE-specific language for each pair.We evaluate each sample and its GPT-2 generated text with pretrained sentiment classifiers and find that while AAVE text results in more classifications of negative sentiment than SAE, the use of GPT-2 generally increases occurrences of positive sentiment for both.Additionally, we conduct human evaluation of AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall quality.
Unsupervised stance detection for arguments from consequences

Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions.In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic.Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic.To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences.We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence.Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT.Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.
Broadband pumped polarization entangled photon-pair source in a linear beam displacement interferometer

We experimentally demonstrate a source of polarization entangled photon-pairs based on a single periodically-poled potassium titanyl phosphate (PPKTP) crystal pumped with a broadband, free running laser diode.The crystal is placed within a linear beam-displacement interferometer, and emits photon-pairs based ontype-0 spontaneous parametric downconversion (SPDC). We observe pair rates of 0.56 Mpairs/s/mW in a single spatial mode with a polarization visibility of 97.7% over a spectral range of 100 nm. This experiment demonstrates a pathway towards observing Gigacount rates of polarization entangled photon pairs by using high-power free-running laser diodes with fast multiplexed detectors.
Distance learning in Russia during the coronavirus pandemic

Abstract This paper shows the distance learning in Russian during the coronavirus period in schools, technical schools and institutions. School and university students should keep up with the curriculum and continue to study even under quarantine. Distance learning has become a new form of remote educational process. Students can use publicly available electronic resources to continue their distance education. Currently, the distance learning technologies has helped schools, technical schools and universities to switch to distance learning, which allows them to receive effective feedback from the teacher.
<i>DRL</i>-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications

Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this article, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on low-frequency (LF) components and high-frequency (HF) components. To address this LF-HF inconsistency problem, we propose a dual-stream representation learning generative adversarial network (DRL-GAN). The core idea is to produce enhanced image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected `WIDER-SHIP' dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.
A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices

Federated learning is a revolutionary machine learning approach whose main idea is to train the machine learning model in a distributed fashion over a large number of edge/end devices without having to share the raw data. We consider in this work a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is scheduling, i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we design a Double Deep Q Learning (DDQN)-based scheduling algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Experiments conducted using a real-world dataset ( https://www.cs.toronto.edu/~kriz/cifar.html ) show that our DDQN solution always achieves better performance compared to the DQN and random scheduling algorithms.
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet

Word sense disambiguation (WSD) is a fundamental natural language processing task.Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of sense-annotated data.HowNet is the most widely used lexical knowledge base in Chinese WSD.Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNetbased WSD, and the tailor-made methods have not obtained satisfying results.In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models.In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset.Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods.
A Novel Hash Function Based on a Chaotic Sponge and DNA Sequence

Many chaos-based hash functions have convoluted designs that are not based on proper design principles, complicating the verification of security claims.We address this problem by proposing a hash function based on a chaotic sponge construction and DNA sequence.DNA sequence is used to design state transition rules of a deterministic chaotic finite-state automata (DCFSA), a chaotic structure that enhances the chaoticity of digital chaotic maps.We use a DCFSA configuration consisting of four states associated with logistic maps.Analysis shows that the DCFSA configuration is efficient and has high chaotic complexity.Both DCFSA and DNA are used to design the sponge-based hash function.An input message is transformed into a DNA sequence and divided into fixed-length blocks.Each block is absorbed into a sponge structure via DNA-XOR.The sponge state is then updated with a new DNA sequence by iterating DCFSA as a compression function.Statistical evaluations indicate that the proposed hash function has near-ideal diffusion, confusion, collision resistance and distribution properties.The proposed hash can also extend the provably secure notions of the sponge construction, specifically the indifferentiability from random oracles.Furthermore, the hash function has a high level of performance as compared to other chaos-based hash functions.
Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm

Fuzzy logic is widely known as a value-based technique. Whale optimization algorithm (WOA), on the other hand, is a nature-inspired optimization technique. Hybridization of these two techniques is proposed for path planning and control, over multiple mobile robots in static and dynamic environments. The effectiveness of the resulting technique, known as ‘Fuzzy-WOA’, is tested through MATLAB simulation coupled with real-time experiments. Upon testing, a good agreement is observed between these platforms. Furthermore, the proposed technique is found to be more efficient when compared to other existing techniques, with a significant improvement of about 20.63% in terms of path lengths.
Color image encryption system using combination of robust chaos and chaotic order fractional Hartley transformation

The paper proposes a robust color image encryption scheme based on a real fractional Hartley transform with chaotic transform orders. The multilayer scheme comprises of a PWLCM (piecewise linear chaotic map) based circular blending of color components, PWNCA (piecewise nonlinear chaotic map) based nonlinear processing in spatial domain followed by a multiple chaotic order fractional Hartley transform to obtain encrypted image. The input parameters for chaotic mapping used in each encryption stage serve as secret keys. A reality preserving transform enables to eliminate the complexity issues of transform. The proposed scheme not only overcomes the limitation of linearity in transform orders of multiparameter scheme but also the vulnerability of transform to information leakage. Besides this, the use of multilayer security has led to larger key space. Results and analysis substantiate the fact that proposed scheme is robust, highly sensitive and shows considerably better performance than other similar-state-of-art schemes.
Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

Abstract With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.
Neural Data-to-Text Generation with LM-based Text Augmentation

For many new application domains for datato-text generation, the main obstacle in training neural models consists of a lack of training data.While usually large numbers of instances are available on the data side, often only very few text samples are available.To address this problem, we here propose a novel fewshot approach for this setting.Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.As the text augmentation can introduce noise to the training data, we use cycle consistency as an objective, in order to make sure that a given data sample can be correctly reconstructed after having been formulated as text (and that text samples can be reconstructed from data).On both the E2E and WebNLG benchmarks, we show that this weakly supervised training paradigm is able to outperform fully supervised seq2seq models with less than 10% annotations.By utilizing all annotated data, our model can boost the performance of a standard seq2seq model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.* Work done prior to joining Amazon.The Blue Spice is a restaurant that serves English cuisine.
Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

Current research on smart contracts focuses on technical, conceptual, and legal aspects but neglects organizational requirements and sustainability impacts. We consider this a significant research gap and explore the relationship between smart contracts and sustainability in supply chains. First, we define the concept of smart contracts in terms of supply chain management. Then, we conduct a content analysis of the literature to explore the overlapping research fields of smart contracts and sustainability in supply chains. Next, we develop a semi-structured assessment framework to model the potential environmental and social impacts induced by smart contracts on supply chains. We propose a conceptual framework for supply chain maturity by mapping the relationships between organizational development, sustainability, and technology. We identify smart contracts as a foundational technology that enables efficient and transparent governance and collaborative self-coordination of human and non-human actors. Thus, we argue that smart contracts can contribute to the economic and social development of networked value chains and Society 5.0. To stimulate interdisciplinary research on smart contracts, we conclude the article by formulating research propositions and trade-offs for smart contracts in the context of technology development, business process and supply chain management, and sustainability.
The Effect of Plants on the Energy Output of Green Roof Photovoltaic Systems in Tropical Climates

The rapid rise in the number of fossil fuel uses over the last few decades has increased carbon dioxide (CO2) emissions. The purpose of implementing renewable energy solutions, such as solar, hydro, wind, biomass, and other renewable energy sources, is to mitigate global climate change worldwide. Solar energy has received more attention over the last few decades as an alternative source of energy, and it can play an essential role in the future of the energy industry. This is especially true of energy solutions that reduce land use, such as off-grid and on-grid solar rooftop technologies. This study aims to evaluate the energy conversion efficiency of photovoltaic (PV) systems in tropical environments. It also explores the effect of growing plants beneath PV panels. Two identical grid-connected PV systems—each containing five solar panels—were installed. The overall power production of each PV system was about 1.4 kWp. All the collected data were processed and analysed in the same way and by the same method. The PV systems were installed in two different environments—one with the possibility of growing the plants beneath the PV panels (PViGR module) and one with no possibility of growing the plants beneath the PV panels (PViSR module). The experiments were conducted in the Bo Yang District of Songkhla, Thailand over a 12-month period. Our findings indicate that green roof photovoltaic (GRPV) systems can produce around 2100 kWh of electricity in comparison to the 2000 kWh produced by other solar energy systems. Thereby, growing plants beneath PV panels increases electricity production efficiency by around 2%. This difference comes from the growing of plants underneath GRPV systems. Plants do not only help to trap humidity underneath GRPV systems but also help to cool the PV panels by absorbing the temperature beneath GRPV systems. Thus, in the production of electrical energy; the system was clearly showing significant differences in the mentioned results of both PV solar systems, which are evident for great energy efficiency performances in the future.
GenExp: Multi-objective pruning for deep neural network based on genetic algorithm

Unstructured deep neural network (DNN) pruning have been widely studied. However, previous schemes only focused upon compressing the model’s memory footprint, which had led to relatively low reduction ratio in computational workload. This study demonstrates that the main reason behind is the inconsistent distribution of memory footprint and workload of the DNN model among different layers. Based on this observation, we propose to map the network pruning flow as a multi-objective optimization problem and design an improved genetic algorithm, which can efficiently explore the whole pruning structure space with both pruning goals equally constrained, to find the suitable solution that strikes a judicious balance between the DNN’s model size and workload. Experiments show that the proposed scheme can achieve up to 34 % further reduction on the model’s computational workload compared to the state-of-the-art pruning scheme [11] , [33] for ResNet50 on the ILSVRC-2012 dataset. We have also deployed the pruned ResNet50 models on a dedicated DNN accelerator, and the measured data have shown a considerable 6 × reduction in inference time compared to FPGA accelerator implementing dense CNN model quantized in INT8 format, and a 2.27 × improvement in power efficiency over 2080Ti GPU-based implementations, respectively.