Reviewerly/exHarmony huggingface dataset:
How can channel information strategy promote sales by combining ICT and blockchain? Evidence from the agricultural sector

The broad development of Internet has enabled the information and communications technology (ICT) and the blockchain technology (BT) to transform the traditional operational practices and customer behavior. In the context of the agricultural sector, it has prompted agricultural retailers to explore various online resources and communication channels. However, product information and physical experience in the brick-and-mortar stores influence the Internet pricing strategy and customers’ online purchasing behaviors. Considering this cross-channel effect and sustainable digital agricultural (e-agriculture) development, this study constructed a theoretical analysis model to investigate the impact of the ICT- and BT-driven agricultural cross-channel information strategy on the retailer pricing strategy and customer behavior. The results indicate that, although ICT and BT tools provide agricultural retailers and customers with more information, a cross-channel information strategy does not continuously increase retailers’ sales performance. Retailers tend to leverage this strategy when the devaluation coefficient (customers’ quality perception for a product) is low. This channel information strategy is influenced by the offline channel cost and the devaluation coefficient in online trading. These findings on the devaluation effect can guide retailers in their channel information and pricing strategies and marketing-related decisions.
Efficient Video Compression via Content-Adaptive Super-Resolution

Video compression is a critical component of Internet video delivery. Recent work has shown that deep learning techniques can rival or outperform human-designed algorithms, but these methods are significantly less compute and power-efficient than existing codecs. This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantly boosts video quality. Our method, SRVC, encodes video into two bitstreams: (i) a content stream, produced by compressing downsampled low-resolution video with the existing codec, (ii) a model stream, which encodes periodic updates to a lightweight super-resolution neural network customized for short segments of the video. SRVC decodes the video by passing the decompressed low-resolution video frames through the (time-varying) super-resolution model to reconstruct high-resolution video frames. Our results show that to achieve the same PSNR, SRVC requires 20% of the bits-per-pixel of H.265 in slow mode, and 3% of the bits-per-pixel of DVC, a recent deep learning-based video compression scheme. SRVC runs at 90 frames per second on an NVIDIA V100 GPU.
Providing music service in Ambient Intelligence: experiments with gym users

• Definition of a novel AmI system for gym environments, named Gym Intelligence. • The system provides adequate music according to the users’ physical effort. • The system is based on a novel multi-agent computational intelligence model. • The system checks for users’ physical effort changes and plays the adequate music. • We have provided an exhaustive evaluation of Gym Intelligence. Ambient Intelligence (AmI) is an interdisciplinary research area of ICT which has evolved since the 90s, taking great advantage from the advent of the Internet of Things (IoT). AmI creates, by using Artificial Intelligence (AI), an intelligent ecosystem in which computers, sensors, lighting, music, personal devices, and distributed services, work together to improve the user experience through the support of natural and intuitive user interfaces. Nowadays, AmI is used in various contexts, e.g., for building smart homes and smart cities, providing healthcare, and creating an adequate atmosphere in retail and public environments. In this paper, we propose a novel AmI system for gym environments, named Gym Intelligence, able to provide adequate music atmosphere, according to the users’ physical effort during the training. The music is taken from Spotify and is classified according to some music features , as provided by Spotify itself. The system is based on a multi-agent computational intelligence model built on two main components: ( i ) machine learning methods that forecast appropriate values for the Spotify music features, and ( ii ) a multi-objective dynamic genetic algorithm that selects a specific Spotify music track, according to such values. Gym Intelligence is built by sensing the ambient with a minimal, low-cost, and non-intrusive set of sensors, and it has been designed considering the outcome of a preliminary analysis in real gyms, involving real users. We have considered well-known regression methods and we have validated them using a collected data ( i ) about the users’ physical effort, through the sensors, and ( ii ) about the users’ music preferences, through an Android app that the users have used during the training. Among the regression methods considered, the one that provided the best results is the Random Forest, which predicted Spotify music features with a mean absolute error of 0.02 and a root mean squared error of 0.05. We have implemented Gym Intelligence and deployed it in five real gyms. We have evaluated it conducting several experiments. The experiments show how, with the help of Gym Intelligence, the users’ satisfaction about the provided background music, rose from 3.05 to 4.91 (on a scale from 1 to 5, where 5 is the maximum score).
Cloud-based complex of computer transdisciplinary models in the context of holistic educational approach

The paper represents the authors’ cloud-based complex of computer dynamic models and their transdisciplinary facilities. Proper theoretical background for the complex design is elaborated and the process of the computer models development is covered. The models in the complex are grouped in the sections according to the curriculum subjects (Physics, Algebra, Geometry, Biology, Geography, and Informatics). Each of the sections includes proper models along with their description and transdisciplinary didactic support. The paper also presents recommendations as for using of the complex to provide holistic learning of Mathematics, Science and Informatics at secondary school. The prospects of further research are outlined.
Advanced Lattice Sieving on GPUs, with Tensor Cores

In this work, we study GPU implementations of various state-of-the-art sieving algorithms for lattices (Becker-Gama-Joux 2015, Becker-Ducas-Gama-Laarhoven 2016, Herold-Kirshanova 2017) inside the General Sieve Kernel (G6K, Albrecht et al. 2019). In particular, we extensively exploit the recently introduced Tensor Cores – originally designed for raytracing and machine learning – and demonstrate their fitness for the cryptanalytic task at hand. We also propose a new dual-hash technique for efficient detection of 'lift-worthy' pairs to accelerate a key ingredient of G6K: finding short lifted vectors. We obtain new computational records, reaching dimension 180 for the SVP Darmstadt Challenge improving upon the previous record for dimension 155. This computation ran for 51.6 days on a server with 4 NVIDIA Turing GPUs and 1.5TB of RAM. This corresponds to a gain of about two orders of magnitude over previous records both in terms of wall-clock time and of energy efficiency.
User Embedding for Expert Finding in Community Question Answering

The number of users who have the appropriate knowledge to answer asked questions in community question answering is lower than those who ask questions. Therefore, finding expert users who can answer the questions is very crucial and useful. In this article, we propose a framework to find experts for given questions and assign them the related questions. The proposed model benefits from users’ relations in a community along with the lexical and semantic similarities between new question and existing answers. Node embedding is applied to the community graph to find similar users. Our experiments on four different Stack Exchange datasets show that adding community relations improves the performance of expert finding models.
Benefitting from the Grey Literature in Software Engineering Research

Researchers generally place the most trust in peer-reviewed, published information, such as journals and conference papers. By contrast, software engineering (SE) practitioners typically do not have the time, access, or expertise to review and benefit from such publications. As a result, practitioners are more likely to turn to other sources of information that they trust, e.g., trade magazines, online blog posts, survey results, or technical reports, collectively referred to as grey literature (GL). Furthermore, practitioners also share their ideas and experiences as GL, which can serve as a valuable data source for research. While GL itself is not a new topic in SE, using, benefitting, and synthesizing knowledge from the GL in SE is a contemporary topic in empirical SE research and we are seeing that researchers are increasingly benefitting from the knowledge available within GL. The goal of this chapter is to provide an overview of GL in SE, together with insights on how SE researchers can effectively use and benefit from the knowledge and evidence available in the vast amount of GL.
Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis

Prosody modeling is an essential component in modern text-to-speech (TTS) frameworks. By explicitly providing prosody features to the TTS model, the style of synthesized utterances can thus be controlled. However, predicting natural and reasonable prosody at inference time is challenging. In this work, we analyzed the behavior of non-autoregressive TTS models under different prosody-modeling settings and proposed a hierarchical architecture, in which the prediction of phoneme-level prosody features are conditioned on the word-level prosody features. The proposed method outperforms other competitors in terms of audio quality and prosody naturalness on objective and subjective evaluation.
ECG signals recruitment to implement a new technique for medical image encryption

Electrocardiogram (ECG) signal utilization for medical image encryption (ImgEnc) algorithm was proposed in this manuscript. It will utilize the chaotic logistic map and the generalized Arnold map (Cat Map). To generate initial conditions for Chaotic Maps (ChMp), the ECG signal and wolf algorithm are utilized. Autoblocking diffusion (AutBlk) is done only in the process of encryption. A control parameter generated from the plain-image (Plainimg) generates the main stream, which is effective and safe against selected plaintext (Plaintxt) and known Plaintxt attacks. Experimental findings show that with good efficiency, the proposed algorithm can achieve high security.
Discriminative active learning for domain adaptation

Domain Adaptation aiming to learn a transferable feature between different but related domains has been well investigated and has shown excellent empirical performances. Previous works mainly focused on matching the marginal feature distributions using the adversarial training methods while assuming the conditional relations between the source and target domain remained unchanged, i.e., ignoring the conditional shift problem. However, recent works have shown that such a conditional shift problem exists and can hinder the adaptation process. To address this issue, we have to leverage labeled data from the target domain, but collecting labeled data can be quite expensive and time-consuming. To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation. Specifically, we propose three-stage active adversarial training of neural networks: invariant feature space learning (first stage), uncertainty and diversity criteria and their trade-off for query strategy (second stage) and re-training with queried target labels (third stage). Empirical comparisons with existing domain adaptation methods using four benchmark datasets demonstrate the effectiveness of the proposed approach. Furthermore, by comparing different query strategies, we could demonstrate the benefits of our proposed method.
The Future with Advanced Analytics: A Sequential Analysis of the Disruptive Technology’s Scope

Today every application like e-healthcare, agriculture, etc., is connected through smart devices to reduce workforce and enhance productivity. Many applications like defences, banking an utilities, media and entertainment, transportation, banking, retail, agriculture, education, manufacturing, etc., are using smart devices in their working-structure to improved growth of a business/production. These applications are generating a lot of data, which called as “big data” and this data is increasing at a huge rate. For example, most of the data (90%) is generated in last decade only. Together this, we required modern tools to analyses this data for generating useful results. But in near future, this analytics process may shift towards automation. How these automated analytics by deep learning (by robots/machines) will change future forever. Also, with this automated/advanced analytics process we need to provide a disruptive environment which is more towards to protecting nature. This article provide detail explanation regarding “how machines can be useful in learning process through its automate learning process” and “how machine/Artificial Intelligence (AI) can be useful in detecting vulnerabilities/intrusion without much human interaction instantly” and so on. In 21st century, most of tasks will be completed by machines or artificial intelligences. This work discusses several useful terms, scenarios (with many examples in several applications), tools, open issues with opportunities towards automated analytics, i.e., with discussing that “How AI will change near future”.
Reported Individual Costs and Benefits of Sharing Open Data among Canadian Academic Faculty in Ecology and Evolution

Abstract Open data facilitate reproducibility and accelerate scientific discovery but are hindered by perceptions that researchers bear costs and gain few benefits from publicly sharing their data, with limited empirical evidence to the contrary. We surveyed 140 faculty members working in ecology and evolution across Canada's top 20 ranked universities and found that more researchers report benefits (47.9%) and neutral outcomes (43.6%) than costs (21.4%) from openly sharing data. The benefits were independent of career stage and gender, but men and early career researchers were more likely to report costs. We outline mechanisms proposed by the study participants to reduce the individual costs and increase the benefits of open data for faculty members.
Enabling Edge-Cloud Video Analytics for Robotics Applications

Emerging deep learning-based video analytics tasks demand computation-intensive neural networks and powerful computing resources on the cloud to achieve high accuracy. Due to the latency requirement and limited network bandwidth, edge-cloud systems adaptively compress the data to strike a balance between overall analytics accuracy and bandwidth consumption. However, the degraded data leads to another issue of poor tail accuracy, which means the extremely low accuracy of a few semantic classes and video frames. Autonomous robotics applications especially value the tail accuracy performance but suffer using the prior edge-cloud systems.We present Runespoor, an edge-cloud video analytics system to manage the tail accuracy and enable emerging robotics applications. We train and deploy a super-resolution model tailored for the tail accuracy of analytics tasks on the server to significantly improves the performance on hard-to-detect classes and sophisticated frames. During online operation, we use an adaptive data rate controller to further improve the tail performance by instantly adjusting the data rate policy according to the video content. Our evaluation shows that Runespoor improves class-wise tail accuracy by up to 300%, frame-wise 90%/99% tail accuracy by up to 22%/54%, and greatly improves the overall accuracy and bandwidth trade-off.
Discriminative Invariant Alignment for Unsupervised Domain Adaptation

As one of the most prevalent branches of transfer learning, domain adaptation is dedicated to generalizing the knowledge of a source domain to a target domain to perform machine learning tasks. In domain adaptation, the key strategy is to overcome the shift between different domains and learn shared features with domain invariance. However, most existing methods focus on extracting the common features of the source and target domains, and do not consider the shift problem of class center in the target domain caused by this process. Specifically, when we align the domain distributions, we often ignore the inherent feature attributes of the data, or under the guidance of false pseudo-labels, cause the target domain data to be far away from the class center after projection. This is not conducive to classification task. To address these problems, in this study, we propose a novel domain adaptation method, referred to as discriminative invariant alignment (DIA), for image representation. DIA enriches the knowledge matrix by combining the class discriminative information of the source domain and local data structure information of the target domain into a new framework. By introducing the maximum margin criterion of the source domain, the classification boundaries are expanded. To verify the performance of the proposed method, we compared DIA with several state-of-the-art methods on five benchmark databases. The experimental results show that DIA is superior to the state-of-the-art methods.
Beyond Value Perturbation: Local Differential Privacy in the Temporal Setting

Time series has numerous application scenarios. However, since many time series data are personal data, releasing them directly could cause privacy infringement. All existing techniques to publish privacy-preserving time series perturb the values while retaining the original temporal order. However, in many value-critical scenarios such as health and financial time series, the values must not be perturbed whereas the temporal order can be perturbed to protect privacy. As such, we propose "local differential privacy in the temporal setting" (TLDP) as the privacy notion for time series data. After quantifying the utility of a temporal perturbation mechanism in terms of the costs of a missing, repeated, empty, or delayed value, we propose three mechanisms for TLDP. Through both analytical and empirical studies, we show the last one, Threshold mechanism, is the most effective under most privacy budget settings, whereas the other two baseline mechanisms fill a niche by supporting very small or large privacy budgets.
Observed impacts of utility-scale photovoltaic plant on local air temperature and energy partitioning in the barren areas

Rapid progress of solar photovoltaic (PV) technology has caused growing interest in understanding interactions between large scale PV plants and near-surface atmosphere. However few attempts have been made to quantify the impact of PV modules on surface radiative forcing and energy partitioning process. Here, this issue is explored experimentally and analytically in the two adjacent sites located at the PV plant and the natural barren field respectively in Wujiaqu in Xinjiang of China. The results showed that the physical effect of PV panels is not symmetrical in the whole day. During the daytime, compared with the reference site, net shortwave radiative forcing increases 8%, a warming effect on the integrated underlying surface (0.1 K) and a cooling effect (∼-2.6 K) on the ground surface were found in the PV plant. 9.2% of the net radiation (NR) was converted into electric energy (PE), sensible heat flux (H) increased by 30.6% hence resulted in the convection heating effects of 0.64 K and 0.32 K on the near-surface air temperature at the height of 2 m and 10 m respectively, while latent heat flux (LE) and ground surface heat flux (GS) decreased by 49% and 3% respectively related to the reference site. At night, PV panels produce a cooling effect of −0.2 K and −2.3 K on the ground and integrated underlying surface respectively, and less GS is released in the PV plant which contribute to the cooling effects of −0.24 K and −0.08 K on the air temperature at the height of 2 m and 10 m respectively related to the reference site. In the whole day, in the PV plant, H increased by 27.6%, LE and GS decreased by 47.4% and 6.7% respectively, air temperature increased by 0.16 K and 0.1 K at 2 m and 10 m respectively.
Improving Distantly-Supervised Relation Extraction Through BERT-Based Label and Instance Embeddings

Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi-instance learning and by providing additional information but manage to recognize mainly the top frequent relations, neglecting those in the long-tail. We propose REDSandT (Relation Extraction with Distant Supervision and Transformers), a novel distantly-supervised transformer-based RE method that manages to capture a wider set of relations through highly informative instance and label embeddings for RE by exploiting BERT’s pre-trained model, and the relationship between labels and entities, respectively. We guide REDSandT to focus solely on relational tokens by fine-tuning BERT on a structured input, including the sub-tree connecting an entity pair and the entities’ types. Using the extracted informative vectors, we shape label embeddings, which we also use as an attention mechanism over instances to further reduce noise. Finally, we represent sentences by concatenating relation and instance embeddings. Experiments in the two benchmark datasets for distantly-supervised RE, NYT-10 and GDS, show that REDSandT captures a broader set of relations with higher confidence, achieving a state-of-the-art AUC (0.424) in NYT-10 and an excellent AUC (0.862) in GDS.
Application of Motion Sensor Based on Neural Network in Basketball Technology and Physical Fitness Evaluation System

Basketball is a sport that requires high athletes’ skills and physical fitness and is deeply loved by the people in our country. This paper studies the application of neural network-based motion sensors in basketball technology and physical fitness evaluation system. The ideal effect of the system is to scientifically analyze relevant data through intelligent algorithms and provide more accurate diagnosis suggestions. Recognizing human movements requires collecting various data of the human body through motion sensors. The data acquisition components of this system are based on considerations of portability and power consumption and are equipped with equipment with strong computing power to realize the functions of data preprocessing, training, and recognition of the recognition model. The system only needs to send the data in the data collector to the computing device; it can effectively realize the action recognition and judge whether the athlete’s technical action and physical fitness level meet the standard. From the experimental data, the pass rate of the subjects in the 1000-meter run was 83.3%, and the excellent rate was 10%; the pass rate in the 1-mile run was 90%, and the excellent rate was 6.7%; and the pass rate in the 20-meter round trip was only at 56.67%; it can be seen that there is still room for improvement in the reaction speed and agility of most subjects. According to intelligent data analysis, athletes can better understand where they have shortcomings and improve their physical fitness and basketball skills through targeted training.