Reviewerly/exHarmony huggingface dataset:
BullStop: A Mobile App for Cyberbullying Prevention

Social media has become the new playground for bullies.Young people are now regularly exposed to a wide range of abuse online.In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected.BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms.It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user.Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.
Multi‐style Chinese art painting generation of flowers

With the proposal and development of Generative Adversarial Networks, the great achievements in the field of image generation are made. Meanwhile, many works related to the generation of painting art have also been derived. However, due to the difficulty of data collection and the fundamental challenge from freehand expressions, the generation of traditional Chinese painting is still far from being perfect. This paper specialises in Chinese art painting generation of flowers, which is important and classic, by deep learning method. First, an unpaired flowers paintings data set containing three classic Chinese painting style: line drawing, meticulous, and ink is constructed. Then, based on the collected dataset, a Flower-Generative Adversarial Network framework to generate multi-style Chinese art painting of flowers is proposed. The Flower-Generative Adversarial Network, consisting of attention-guided generators and discriminators, transfers the style among line drawing, meticulous, and ink by an adversarial training way. Moreover, in order to solve the problem of artefact and blur in image generation by existing methods, a new loss function called Multi-Scale Structural Similarity to force the structure preservation is introduced. Extensive experiments show that the proposed Flower-Generative Adversarial Network framework can produce better and multi-style Chinese art painting of flowers than existing methods.
Deciding accuracy of differential privacy schemes

Differential privacy is a mathematical framework for developing statistical computations with provable guarantees of privacy and accuracy. In contrast to the privacy component of differential privacy, which has a clear mathematical and intuitive meaning, the accuracy component of differential privacy does not have a generally accepted definition; accuracy claims of differential privacy algorithms vary from algorithm to algorithm and are not instantiations of a general definition. We identify program discontinuity as a common theme in existing \emph{ad hoc} definitions and introduce an alternative notion of accuracy parametrized by, what we call, {\distance} -- the {\distance} of an input $x$ w.r.t., a deterministic computation $f$ and a distance $d$, is the minimal distance $d(x,y)$ over all $y$ such that $f(y)\neq f(x)$. We show that our notion of accuracy subsumes the definition used in theoretical computer science, and captures known accuracy claims for differential privacy algorithms. In fact, our general notion of accuracy helps us prove better claims in some cases. Next, we study the decidability of accuracy. We first show that accuracy is in general undecidable. Then, we define a non-trivial class of probabilistic computations for which accuracy is decidable (unconditionally, or assuming Schanuel's conjecture). We implement our decision procedure and experimentally evaluate the effectiveness of our approach for generating proofs or counterexamples of accuracy for common algorithms from the literature.
Investigation on the musical features of carnatic ragas using neutrosophic logic

Abstract Carnatic music is rich in its own style but more complex in the way the notes are arranged and rendered. Each Carnatic raga possess definite rules to be followed to frame its musical notes. But the significance of musical notes arrangement of the Carnatic music is unknown. This paper provides an attempt to find the significant relationship between the combinations of musical notes with the musical parameters. The objective of this work is to find the influence of various musical parameters on the Carnatic raga notes using Neutrosophic Cognitive Maps (NCMs). An analysis is made and the influences of musical parameters are cross checked with those of the Neutrosophic Cognitive Maps (NCMs). The values of the musical features of each raga are obtained using MIR MATLAB Toolbox.
Thermal Feedback System From Robot Hand for Telepresence

In this study, we propose a thermal feedback system using a flexible thermal display glove (master part) and a robot hand (slave part) for telepresence. A piezoelectric sensor in the glove detects the posture of the user, which is required to operate the robot hand, while the robot hand monitors the temperature change of a grasped object and transfers the temperature feedback through thermoelectric devices mounted on the glove. Specifically, we utilize a controller to manage the thermal feedback system. Further, a series of experiments are conducted to evaluate the performance of the thermal feedback control system under heating and cooling conditions, and the responses at various desired temperatures are examined. The maximum temperature error and standard deviation under the steady-state conditions of the control system are studied. The power consumption during the response time and holding time is calculated and analyzed. Furthermore, the operation of the integrated thermal feedback system is demonstrated, and the performance of the controlled system is evaluated.
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision

Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment.The performance of LiDAR and visionbased perception systems deteriorates significantly if certain environmental factors are present e.g.rain, fog, darkness.Perception systems based on Frequency-Modulated Continuous Wave scanning radar maintain full performance regardless of environmental conditions and with a longer range than alternative sensors.Learning to segment a radar scan based on driveability in a fully supervised manner is not feasible as labelling each radar scan on a bin-by-bin basis is both difficult and time-consuming to do by hand.We therefore weakly supervise the training of the radar-based classifier through an audio-based classifier that is able to predict the terrain type underneath the robot.By combining odometry, GPS and the terrain labels from the audio classifier, we are able to construct a terrain labelled trajectory of the robot in the environment which is then used to label the radar scans.Using a curriculum learning procedure, we then train a radar segmentation network to generalise beyond the initial labelling and to detect all permissible driving routes in the environment.
Effects of Antenna Orientation in Fall Detection Systems Based on WiFi Signals

The global increase in human life expectancy has created a need for healthcare and remote monitoring technology suitable for the elderly. One of the biggest health treats to older adults are falls occurring at home during daily activities. This reality has prompted the design of device-free fall detection systems that employ WiFi signals as sensing probes. Several works have addressed the design and development of these detection platforms. However, these works have not considered the impact of the antenna orientation on the system's sensing capabilities. To close the gap, we present a systematic analysis of the effects of antenna orientation on the performance of a WiFi-based fall detection systems. The analysis was conducted on the grounds of an experimental platform that transmits WiFi-like waveforms from which the Doppler signature of a fall event can be computed. Our work shows how the orientation of the antenna affects spectrograms of the received probe signal. The results also show that signals transmitted with a horizontal positioning improve the performance in the classification stage.
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q&A is the most effective way to elicit user requirements. Obviously, complex Q&A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.
Privacy‐preserving evaluation for support vector clustering

The authors proposed a privacy-preserving evaluation algorithm for support vector clustering with a fully homomorphic encryption. The proposed method assigns clustering labels to encrypted test data with an encrypted support function. This method inherits the advantageous properties of support vector clustering, which is naturally inductive to cluster new test data from complex distributions. The authors efficiently implemented the proposed method with elaborate packing of the plaintexts and avoiding non-polynomial operations that are not friendly to homomorphic encryption. These experimental results showed that the proposed model is effective in terms of clustering performance and has robustness against the error that occurs from homomorphic evaluation and approximate operations.
Lightweight Public/Private Auditing Scheme for Resource-Constrained End Devices in Cloud Storage

Data integrity protection, an important feature in cloud storage services, can be achieved using auditing schemes. However, existing public and private auditing schemes are somewhat inefficient in practice. For example, in private auditing schemes, the trusted third-party is generally not able to settle disputes between users and the cloud storage server. In most existing public auditing schemes, it takes a user tens of seconds to generate data tags for every MB of file outsourced, which is clearly impractical particularly on resource-constrained end devices. Since the user is likely to have information than the auditor, we divide the verification phase into private verification and public verification phases. Then, we propose a public/private auditing model and a security model for public/private auditing. In our public/private auditing model, the user uses private verification phase to audit outsourced data promptly in most cases, and the auditor uses public verification phase to audit outsourced data only when dispute occurs or the user is not available to audit. Then, we propose a public/private auditing scheme, and present its security proof in the random oracle model under the discrete logarithm assumption. Experimental findings demonstrate that our scheme only need tens of microseconds to generate data tags for every MB file outsourced. In other words, the efficiency of our scheme is almost as high as existing high-performing private auditing schemes, and our scheme is more effective in comparison to existing efficient public auditing schemes.
Normalizing Flow Policies for Multi-agent Systems

Stochastic policy gradient methods using neural representations have had considerable success in single-agent domains with continuous action spaces. These methods typically use networks that output the parameters of a diagonal Gaussian distribution from which the resulting action is sampled. In multi-agent contexts, however, better policies may require complex multimodal action distributions. Based on recent progress in density modeling, we propose an alternative for policy representation in the form of conditional normalizing flows. This approach allows for greater flexibility in action distribution representation beyond mixture models. We demonstrate their advantage over standard methods on a set of tasks including human behavior modeling and reinforcement learning in multi-agent settings.
A study on encoding-based oracle bone script recognition

Oracle bone script recognition (OBSR) has been a fundamental problem in research on oracle bone scripts for decades. Despite being intensively studied, existing OBSR methods are still subject to limitations regarding recognition accuracy, speed and robustness. Furthermore, the dependency of these methods on expert knowledge hinders the adoption of OBSR systems by the general public and also discourages social outreach of research outputs. Addressing these issues, this study proposes an encoding-based OBSR system that applies image pre-processing techniques to encode oracle images into small matrices and recognize oracle characters in the encoding space. We tested our methods on a collection of oracle bones from the Yin Ruins in XiaoTun village, and achieved a high accuracy rate of 99% within a time range of milliseconds.
An Efficient Hardware Architecture for Finding Frequent Items in Data Streams

This paper presents a novel method for finding frequent items in data streams using a homogeneous counter-based hardware array. The presented array is a close hardware adaptation of the well-known Space-Saving algorithm. The array can identify frequent items with frequencies larger than a specified threshold with a single pass through the input dataset. When implemented on an Intel Arria 10 FPGA, 40% of the FPGA resources are sufficient for 1000+ hardware counters. This is enough to guarantee the identification of all frequent items with frequency thresholds as low as 0.1% of the size of any dataset, regardless of the input distribution. Compared to other hardware implementations, the presented architecture is the fastest on average, and the first to retain the guarantee property of the Space-Saving algorithm on the reported frequent items. In addition, test results on synthetic and real datasets showed that the presented method can achieve more than 11x throughput speedup compared to software implementations of the algorithm and up to 50% better precision compared to other hardware implementations.
The Measurement of the Software Ecosystem’s Productivity with GitHub

Software productivity has always been one of the most critical metrics for measuring software development.However, with the open-source community (e.g., GitHub), new software development models are emerging.The traditional productivity metrics do not provide a comprehensive measure of the new software development models.Therefore, it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community's production activities.Based on the natural ecosystem, this paper proposes concepts related to the productivity of open source software ecosystems, analyses influencing factors of open source software ecosystem productivity, and constructs a measurement model using these factors.Model validation experiments show that the model is compatible with a large portion of open source software ecosystems in GitHub.This study can provide references for participants of the open-source software ecosystem to choose proper types of ecosystems.The study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.
Machine learning activity detection using ML.Net

Our living environment is becoming more and more aware of our presence and starts to react and interact with us. The smart house has long moved from concept to reality., as our homes are now digitalized with all sorts of smart devices. Since we now have more data available the ever., an important part is to be able to analyze the data and provide the user with useful information. Neural networks are a good way of providing systems capable of handling large volumes of data that are able to learn based on users' data. Our proposed neural network system consists of a .NET software application that processes data from the CASAS activity detection system. The software application uses the ML.NET NET machine learning framework for predicting the users' activity. The ML.NET framework was chosen since its open-source and cross-platform.
Autonomous and Remote Controlled Humanoid Robot for Fitness Training

The world population currently counts more of 617 million people over 65 years old. COVID-19 has exposed this population group to new restrictions, leading to new difficulties in care and assistance by family members. New technologies can reduce the degree of isolation of these people, helping them in the execution of healthy activities such as performing periodic sports routines. NAO robots find in this a possible application; being able to alternate voice commands and execution of movements, they can guide elderly people in performing gymnastic exercises. Additional encouragement could come through demonstrations of the exercises and verbal interactions using the voice of loved ones (for example, grandchildren). These are transmitted in real time to the NAO which streams the video of older people exercising, bringing the two parties involved closer together. This proposal, realized with the robot NAO V6, allows to have a help at home ready to motivate, teach the exercises and train the elderly living alone at home.
A voice-based annotation system for collaborative computer-aided design

Abstract In this paper, we describe a voice-based interaction mechanism to annotate 3D models directly from a computer-aided design (CAD) modeling environment. The audio signal is captured and automatically transcribed to a textual 3D note, which is attached to the geometry and made available to other product information and business processes across the enterprise via a product data management system. Our approach provides a more natural and intuitive method to capture design and engineering knowledge that is particularly effective when large amounts of information need to be communicated. We discuss the rationale of the software architecture and the value of this modality for capturing knowledge in a collaborative engineering context. Finally, we examine the results of an experiment to validate our proposal. Our results show that 3D annotations are an effective mechanism to communicate design knowledge, which suggests the need for further developments in the areas of multimodal interaction methods and interfaces for CAD and collaborative tools.
Context, Challenges, and Prospects for Agricultural Extension in Nigeria

Agricultural extension programs have been implemented in Nigeria by governmental and nongovernmental agencies from the colonial era to the present day as a means toward bolstering economic development, rural livelihoods, food security, and trade relations. Nevertheless, funding and staffing levels in agricultural extension remain low compared to Nigeria’s farming population. With a brief review of past initiatives, current challenges, and potential opportunities, this article gives recommendations in three focus areas for maximizing the effectiveness of Nigerian agricultural extension: (1) prioritize human education over input provisioning in the definition of agricultural extension’s primary purpose; (2) aim for household food security, not solely business expansion, to ensure the inclusion of the most vulnerable farmers; and (3) foster multidirectional communication among academic researchers, extension agents, and farmers. Overall, this article argues that taking a farmer-centric educational approach to agricultural extension, rather than a farm-centric business approach, will have the most profound and sustained impact on Nigerian agricultural development. Keywords: Nigeria; agricultural extension; rural development; fertilizer subsidies; farmer education; household food security; agribusiness; research-farmer linkage
Diversified Semantic Attention Model for Fine-Grained Entity Typing

Fine-grained entity typing, which aims to assign specific types to entity mentions in text, is attracting increasing attention in the field of natural language processing (NLP). However, it is quite a challenging problem due to the highly ambiguous nature of many entity mentions. Most existing entity typing methods based on attention mechanism generally extract the salient features separately from the entity mention and the contextual words. However, these approaches suffer from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two main limitations</i> : (1) They ignore the rich information contained by entity mentions when applying the attention mechanisms. (2) They do not consider the diversity of attention processes which can be beneficial in finding the discriminative features. To address these issues, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diversified semantic attention model (DSAM)</i> for fine-grained entity typing, and the main novelties are: (1) It explicitly pursues the diversity of attention and is able to maximally gather discriminative information. (2) It integrates two level attentions—the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mention-level attention</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-level attention—</i> to jointly capture the rich information from mentions and contexts to enhance their mutual promotions. (3) It combines the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention maps constraint</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention segments constrain</i> to exploit the subtle semantic differences for distinguishing the subtypes. Importantly, the proposed DSAM approach can be trained end-to-end without employing ad-hoc features or post-processing. Extensive experiments using three benchmark datasets demonstrated that our DSAM approach achieves competitive performance compared to the current state-of-the-art methods used for fine-grained entity typing.