Reviewerly/exHarmony huggingface dataset:
A Full Lifecycle Authentication Scheme for Large-scale Smart IoT Applications

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not applicable to recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for trending IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire life cycle of the IoT device for real-world scenarios of smart IoT systems, which contains factory manufacturing, daily usage, and system resetting. For each stage in the life cycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.
Length Control in Abstractive Summarization by Pretraining Information Selection

Previous length-controllable summarization models mostly control lengths at the decoding stage, whereas the encoding or the selection of information from the source document is not sensitive to the designed length.They also tend to generate summaries as long as those in the training data.In this paper, we propose a length-aware attention mechanism (LAAM) to adapt the encoding of the source based on the desired length.Our approach works by training LAAM on a summary length balanced dataset built from the original training data, and then fine-tuning as usual.Results show that this approach is effective in generating high-quality summaries with desired lengths and even those short lengths never seen in the original training set.
Precise Cable-Suspended Pick-and-Place with an Aerial Multi-robot System

Abstract This work introduces the G-Fly-Crane, a proof-of-concept aerial multi-robot system designed to demonstrate the advantage of using multiple aerial robots as a valuable tool for novel construction techniques, not requiring the use of heavy engines and costly infrastructures. We experimentally demonstrate its capability to perform pick-and-place and manipulation tasks in a construction scenario, with an increased payload capacity and dexterity compared to the single robot case. The system is composed of three aerial robots connected to a platform by three pairs of cables. The platform is equipped with a gripper, enabling the grasping of objects. The paper describes in detail the hardware and software architecture of our prototype and explains the implemented control methods. A shared control strategy incorporates the human operator in the control loop, thus increasing the overall system reliability when performing complex tasks. The paper also discusses the next steps required to bring this technology from indoor laboratory conditions to real-world applications.
Mapping of solar energy potential in Fiji using an artificial neural network approach

The concerned stakeholders have been pursuing renewable energy seriously due to its overwhelming benefits. Countries that receive less solar radiation are not lagging behind as they are working to optimize the available radiation let alone of countries that receive sufficient solar radiation over long durations such as Fiji. In view of the abundancy of this energy in Fiji, the country has been working intensely on tapping the full potential of this energy, thus proposed that by 2030; more than 50% of its energy will come from renewable energy. The accurate estimation of global solar radiation determines the reliability of performance evaluation of solar energy systems. Therefore, the key interest of this study is in respect of accurate mapping of solar radiation to aid reliable solar energy design especially in siting and sizing of photovoltaic power systems. In the light of this, this work modelled solar radiation on the earth of Fiji from common meteorological and geographical data in all locations in Fiji using Artificial Neural Networks (ANN). There are different configurations of ANN but in this study, Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were selected as the learning algorithms due to the data size, speed of computation and the success of these algorithms in solar radiation modelling. Similarly, a tangent sigmoid transfer function was used in the network. In total, twelve different configurations of ANN were considered and the best configuration was selected to predict the solar radiation potential in Fiji. Since ANN requires input data to train the network, meteorological data covering 36 years (1984–2019) and geographical data from NASA database were supplied to the network. All the locations considered were distributed evenly throughout Fiji and thus covered all the four regions and 14 provinces in Fiji. The geographical and meteorological data used to train the network are month, latitude, longitude, altitude, mean temperature, relative humidity, precipitation and solar radiation. The mean squared error of 0.118838 and correlation coefficient of 0.9402 were obtained between the ANN predicted and measured solar radiation for the entire dataset. These correlation coefficients and mean squared error showed that ANN model of solar radiation in Fiji is satisfactory and thus can be used as an alternative where solar radiation data are not available. Similarly, the network produced satisfactory solar radiation result for the locations where there are no solar radiation data. To ease solar radiation assessment of all places in Fiji, the iso-lines of the solar radiation were presented in the form of monthly maps. It is believed that this prediction will aid energy stakeholders in making best decision concerning solar energy potential in Fiji thus boosting optimal utilization of the scarce resource.
A Framework for Efficient Robotic Manipulation

Data-efficient learning of manipulation policies from visual observations is an outstanding challenge for real-robot learning. While deep reinforcement learning (RL) algorithms have shown success learning policies from visual observations, they still require an impractical number of real-world data samples to learn effective policies. However, recent advances in unsupervised representation learning and data augmentation significantly improved the sample efficiency of training RL policies on common simulated benchmarks. Building on these advances, we present a Framework for Efficient Robotic Manipulation (FERM) that utilizes data augmentation and unsupervised learning to achieve extremely sample-efficient training of robotic manipulation policies with sparse rewards. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels, such as reaching, picking, moving, pulling a large object, flipping a switch, and opening a drawer in just 15-50 minutes of real-world training time. We include videos, code, and additional information on the project website -- https://sites.google.com/view/efficient-robotic-manipulation.
Cross-Lingual Event Detection via Optimized Adversarial Training

In this work, we focus on Cross-Lingual Event Detection where a model is trained on data from a source language but its performance is evaluated on data from a second, target, language.Most recent works in this area have harnessed the language-invariant qualities displayed by pre-trained Multi-lingual Language Models.Their performance, however, reveals there is room for improvement as the crosslingual setting entails particular challenges.We employ Adversarial Language Adaptation to train a Language Discriminator to discern between the source and target languages using unlabeled data.The discriminator is trained in an adversarial manner so that the encoder learns to produce refined, language-invariant representations that lead to improved performance.More importantly, we optimize the adversarial training process by only presenting the discriminator with the most informative samples.We base our intuition about what makes a sample informative on two disparate metrics: sample similarity and event presence.Thus, we propose leveraging Optimal Transport as a solution to naturally combine these two distinct information sources into the selection process.Extensive experiments on 8 different language pairs, using 4 languages from unrelated families, show the flexibility and effectiveness of our model that achieves state-of-the-art results.
A Sketch-based Index for Correlated Dataset Search

Dataset search is emerging as a critical capability in both research and industry: it has spurred many novel applications, ranging from the enrichment of analyses of real-world phenomena to the improvement of machine learning models. Recent research in this field has explored a new class of data-driven queries: queries consist of datasets and retrieve, from a large collection, related datasets. In this paper, we study a specific type of data-driven query that supports relational data augmentation through numerical data relationships: given an input query table, find the top-k tables that are both joinable with it and contain columns that are correlated with a column in the query. We propose a novel hashing scheme that allows the construction of a sketch-based index to support efficient correlated table search. We show that our proposed approach is effective and efficient, and achieves better trade-offs that significantly improve both the ranking accuracy and recall compared to the state-of-the-art solutions.
A new hybrid teaching platform for college English based on IoT

To enhance the practical ability of the new mixture teaching of college English and integrate the teaching resources of blended English, based on internet of things (IoT), a new blended teaching platform is proposed. By constructing a new information fusion model of college English blended teaching resources, the message of college English mixed teaching resources is scheduled, and the model of information mining and feature clustering analysis is constructed. The entropy weight analysis method is used to extract the information characteristics, and the hybrid network embedded design method is used to design and implement the software to optimise the configuration. The simulation results show that the new hybrid college English teaching resource scheduling method can provide good information fusion, and help to improve the output stability and reliability of the teaching platform.
Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining

Background. Since the COVID-19 pandemic, social media has become an important arena for the public to transmit and exchange messages, feelings, opinions, and information about the epidemic. In the era of social media, many UGC contents from self-media and various information about the epidemic on social media have strong emotional colors. These contents are not only rich in resources for text sentiment analysis but also reveal the laws and characteristics of the evolution of users’ emotional tendencies in public health emergencies. It even ties together the interaction between media content and society. Objective. As the Sina Weibo platform’s characteristics of communication are real-time, open, and “many-to-many,” the objective of this study is to collect Weibo-blog contents tagged with the outbreak of COVID-19 in a certain metropolis in China and analyze the emotional evolution situation of Weibo-blogs of the unexpected public health emergency involved. This will provide a dynamic understanding of the mechanisms underlying the evolution of emotional conditions in the context of public health emergencies. Methods. This paper uses a Python crawler, the SnowNLP sentiment analysis model, and correlation analysis to calculate the emotional tendencies of the event “Covid-19 outbreak in a Chinese metropolis” on the Sina Weibo platform. The study was carried out in terms of the evolutionary stages of the event and the factors which induce it. Results. This paper revealed characteristics of time-varying laws and dynamic propagation of users’ emotional evolution in public health emergencies. (1) This study refers to the life cycle model of COVID-19, combined with the statistics of the time series of the quantities of Weibo-blog posting, and divides the law of the quantities of Weibo-blog posting changing with the event into three stages: outbreak period, stalemate period, and resolution period. (2) Users’ emotional tendencies are changeable and unstable which are easily induced by various factors. (3) There is a significant positive correlation between the reported confirmed cases and the quantities of Weibo-blog posts. (4) Individual emotional tendencies will have a positive changing trend with the public’s average emotional tendencies after the event occurs. (5) There is no correlation between reposts, comments, and Weibo-blog emotional tendencies. Conclusion. The research found that, given staged evolution and repeated fluctuations of emotional tendencies, relevant departments should effectively use this law and set up different response plans according to different stages. In addition, what is highly coupled with users’ emotional tendencies is not only the information about the virus but more large-scale infected by different intensities of emotions.
Automatic Sarcasm Detection: Systematic Literature Review

Sarcasm is an integral part of human language and culture. Naturally, it has garnered great interest from researchers from varied fields of study, including Artificial Intelligence, especially Natural Language Processing. Automatic sarcasm detection has become an increasingly popular topic in the past decade. The research conducted in this paper presents, through a systematic literature review, the evolution of the automatic sarcasm detection task from its inception in 2010 to the present day. No such work has been conducted thus far and it is essential to establish the progress that researchers have made when tackling this task and, moving forward, what the trends are. This study finds that multi-modal approaches and transformer-based architectures have become increasingly popular in recent years. Additionally, this paper presents a critique of the work carried out so far and proposes future directions of research in the field.
Multi-sensor fusion algorithm in cooperative vehicle-infrastructure system for blind spot warning

With the rapid development of electric vehicles and artificial intelligence technology, the automatic driving industry has entered a rapid development stage. However, there is a risk of traffic accidents due to the blind spot of vision, whether autonomous vehicles or traditional vehicles. In this article, a multi-sensor fusion perception method is proposed, in which the semantic information from the camera and the range information from the LiDAR are fused at the data layer and the LiDAR point cloud containing semantic information is clustered to obtain the type and location information of the objects. Based on the sensor equipments deployed on the roadside, the sensing information processed by the fusion method is sent to the nearby vehicles in real-time through 5G and V2X technology for blind spot early warning, and its feasibility is verified by experiments and simulations. The blind spot warning scheme based on roadside multi-sensor fusion perception proposed in this article has been experimentally verified in the closed park, which can obviously reduce the traffic accidents caused by the blind spot of vision, and is of great significance to improve traffic safety.
Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics

Abstract Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings’ computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir’s output to include multiple, concurrent measures of the ring arrays’ magnetic states.
On the predicted replicability of two decades of experimental research on system justification: A Z‐curve analysis

Abstract We examine the predicted replicability of experimental research on system justification theory (SJT) by conducting a z‐curve analysis. Z‐curve is a meta‐analytic technique similar to p‐curve, but which performs better under conditions of heterogeneity. It estimates the predicted replication rate, average power, false discovery risk, and file drawer ratio (FDR) of a sample of studies. The z‐curve based on 116 papers and 232 unique samples suggests that the experimental SJT literature is likely to show low rates of replicability, as indicated by an overall average statistical power of 16%. Moderator analyses suggest that this may be driven in part by publication pressures, that the replicability of research in this area has improved since 2015, and that studies using system threat manipulations show particularly low estimated replication rates (ERR). Implications for the replicability and validity of the experimental SJT literature are discussed, and recommendations to increase the rigor of research are put forth.
SofaGym: An Open Platform for Reinforcement Learning Based on Soft Robot Simulations

OpenAI Gym is one of the standard interfaces used to train Reinforcement Learning (RL) Algorithms. The Simulation Open Framework Architecture (SOFA) is a physics-based engine that is used for soft robotics simulation and control based on real-time models of deformation. The aim of this article is to present SofaGym, an open-source software to create OpenAI Gym interfaces, called environments, out of soft robot digital twins. The link between soft robotics and RL offers new challenges for both fields: representation of the soft robot in an RL context, complex interactions with the environment, use of specific mechanical tools to control soft robots, transfer of policies learned in simulation to the real world, etc. The article presents the large possible uses of SofaGym to tackle these challenges by using RL and planning algorithms. This publication contains neither new algorithms nor new models but proposes a new platform, open to the community, that offers non existing possibilities of coupling RL to physics-based simulation of soft robots. We present 11 environments, representing a wide variety of soft robots and applications; we highlight the challenges showcased by each environment. We propose methods of solving the task using traditional control, RL, and planning and point out research perspectives using the platform.
Investigating the acceptance of applying chat-bot (Artificial intelligence) technology among higher education students in Egypt

Chat-bot as an (AI) technology has taken great attention in recent years and especially in the education sector. before applying such new technology. it is vital to Understand the determinates that affect the behaviour intention for a student to accept or reject this technology in higher education, to understand this behaviour intension, the current research applied the unified theory of acceptance and use of technology (UTAUT) with excluding for two moderators from the original model which are experience and Voluntariness of use. Additionally, this research excluded facilitating conditions and behaviour use as it aimed to investigate only the intension behaviour of the students. This study also aimed to examine the role of demographic factors (gender and age) effect on the model research independent variables and the behaviour intension variable. Therefore, the researcher put the objectives of the study that are represented in, developing a framework for the acceptance of chat-bot technology on the behaviour intension of students in higher education in Egypt. To achieve these objectives, the researcher collects data about the required variables by making a questionnaire. This questionnaire targeted students at the Arab academy for science and technology and maritime transport (AASTMT). AASTMT was selected because it represents one of the oldest private universities in Egypt that apply artificial intelligence technology in its educational system. The final sample consisted of 385 responses. data were analyzed through data testing, descriptive analysis, correlations, regression, and structural equation modelling (SEM). Results indicated a significant impact of performance expectancy, effort expectancy and social influence on students' behaviour intention to accept the chat-bot technology in their higher education in Egypt. Moreover, the results have shown that there is no moderating role of demographic factors (gender and age) proved in the relation between performance expectancy, effort expectancy, social influence, and behaviour intention
Fast re-OBJ: real-time object re-identification in rigid scenes

Re-identifying objects in a rigid scene across varying viewpoints (object Re-ID) is a challenging task, in particular when there are similar, even identical objects coexist in the same environment. Discriminative features play no doubt an essential role in addressing this challenge, while for practical deployment, real-time performance is another desired attribute. We therefore propose a novel framework, named Fast re-OBJ, that is able to improve both Re-ID accuracy and processing speed via tight coupling between the instance segmentation module and embedding generation module. The rich object encoding in the instance segmentation backbone is directly shared to the embedding generation module for training a more discriminative representation via a triplet network. Moreover, we create datasets with the segmentation outputs using real-time object detectors to train and evaluate our object embedding module. With extensive experiments, we prove that our proposed Fast re-OBJ improves the object Re-ID accuracy by 5% and the speed is $$5\times $$ faster compared to the state-of-the-art methods. The dataset and code repository are publicly available at: https://tinyurl.com/bdsb53c4 .