Reviewerly/exHarmony huggingface dataset:
Student sentiment classification model based on GRU neural network and TF-IDF algorithm

Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification.
ACDER: Augmented Curiosity-Driven Experience Replay

Exploration in environments with sparse feed-back remains a challenging research problem in reinforcement learning (RL). When the RL agent explores the environment randomly, it results in low exploration efficiency, especially in robotic manipulation tasks with high dimensional continuous state and action space. In this paper, we propose a novel method, called Augmented Curiosity-Driven Experience Replay (ACDER), which leverages (i) a new goal-oriented curiosity-driven exploration to encourage the agent to pursue novel and task-relevant states more purposefully and (ii) the dynamic initial states selection as an automatic exploratory curriculum to further improve the sample-efficiency. Our approach complements Hindsight Experience Replay (HER) by introducing a new way to pursue valuable states. Experiments conducted on four challenging robotic manipulation tasks with binary rewards, including Reach, Push, Pick&Place and Multi-step Push. The empirical results show that our proposed method significantly outperforms existing methods in the first three basic tasks and also achieves satisfactory performance in multi-step robotic task learning.
Applying DNN Adaptation to Reduce the Session Dependency of Ultrasound Tongue Imaging-based Silent Speech Interfaces

Silent Speech Interfaces (SSI) perform articulatory-to-acoustic mapping to convert articulatory movement into synthesized speech.Its main goal is to aid the speech handicapped, or to be used as a part of a communication system operating in silencerequired environments or in those with high background noise.Although many previous studies addressed the speaker-dependency of SSI models, session-dependency is also an important issue due to the possible misalignment of the recording equipment.In particular, there are currently no solutions available, in the case of tongue ultrasound recordings.In this study, we investigate the degree of session-dependency of standard feed-forward DNNbased models for ultrasound-based SSI systems.Besides examining the amount of training data required for speech synthesis parameter estimation, we also show that DNN adaptation can be useful for handling session dependency.Our results indicate that by using adaptation, less training data and training time are needed to achieve the same speech quality over training a new DNN from scratch.Our experiments also suggest that the sub-optimal cross-session behavior is caused by the misalignment of the recording equipment, as adapting just the lower, feature extractor layers of the neural network proved to be sufficient, in achieving a comparative level of performance.
Difficulty-level modeling of ontology-based factual questions

Semantics-based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty-level of these system-generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approach for predicting the difficulty-level of factual questions utilizes only few naive features and, its accuracy (F-measure) is found to be close to only 50% while considering our benchmark set of 185 questions. In this paper, we propose a new methodology for this problem by identifying new features and by incorporating an educational theory, related to difficulty-level of a question, called Item Response Theory (IRT). In the IRT, knowledge proficiency of end users (learners) are considered for assigning difficulty-levels, because of the assumptions that a given question is perceived differently by learners of various proficiency levels. We have done a detailed study on the features/factors of a question statement which could possibly determine its difficulty-level for three learner categories (experts, intermediates, and beginners). We formulate ontology-based metrics for the same. We then train three logistic regression models to predict the difficulty-level corresponding to the three learner categories. The output of these models is interpreted using the IRT to find a question’s overall difficulty-level. The accuracy of the three models based on cross-validation is found to be in satisfactory range (67–84%). The proposed model (containing three classifiers) outperforms the existing model by more than 20% in precision, recall and F1-score measures.
Efficient Privacy-Preserving Electronic Voting Scheme Based on Blockchain

In the Internet era, electronic voting has replaced traditional paper voting forms with advantages of low cost, high work efficiency, and low errors. A blockchain as a trustworthy and secure decentralized and distributed network provides new ideas for electronic voting schemes. The paper proposes an electronic voting scheme based on a distributed SM2 encryption scheme with differential privacy mechanism, designing and implementing smart contracts based on the proposed secure voting scheme. The performance of the proposed voting scheme is tested on the Ethereum's private chain, and its test contents include the computing cost and network consumption of key methods. The test results show that the blockchain-based voting scheme designed in this paper has good performance, and it also proves the feasibility and correctness of the voting scheme.
Dynamic Channel and Layer Gating in Convolutional Neural Networks

Convolutional neural networks (CNN) are getting more and more complex, needing enormous computing resources and energy. In this paper, we propose methods for conditional computation in the context of image classification that allows a CNN to dynamically use its channels and layers conditioned on the input. To this end, we combine light-weight gating modules that can make binary decisions without causing much computational overhead. We argue, that combining the recently proposed channel gating mechanism with layer gating can significantly reduce the computational cost of large CNNs. Using discrete optimization algorithms, the gating modules are made aware of the context in which they are used and decide whether a particular channel and/or a particular layer will be executed. This results in neural networks that adapt their own topology conditioned on the input image. Experiments using the CIFAR10 and MNIST datasets show how competitive results in image classification with respect to accuracy can be achieved while saving up to 50% computational resources.
Marginal Association Probabilities for Multiple Extended Objects without Enumeration of Measurement Partitions

In the case of high-resolution or near field sensors, an object normally gives rise to multiple measurements per scan. One of the key tasks in tracking such objects is to differentiate the origins of the measurements. In this work, a new data association approach for extended object tracking, which is inspired by Joint Integrated Probabilistic Data Association (JIPDA), is proposed. The key idea is to calculate marginal association probabilities for individual measurements (instead of considering measurement partitions). Our problem formulation allows us to obtain the marginal association probabilities without collective exhaustion of association hypotheses and partitions. The proposed data association method is illustrated first using a simulation with Gaussian distributed measurements. Combined with an extended object measurement model, the data association quality is further assessed in a simulation and an experiment by tracking pedestrians using Lidar data from the KITTI dataset.
Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure

In these days, the humanoid robots are expected to help people in healthcare, house and hotels, industry, military and the other security environments by performing specific tasks or to replace with people in dangerous scenarios. For this purpose, the humanoid robots should be able to recognize objects and then to do the desired tasks. In this study, it is aimed for Robotis-Op3 humanoid robot to recognize the different shaped objects with deep learning methods. First of all, new models with small structure of Convolutional Neural Networks (CNNs) were proposed. Then, the popular deep neural networks models such as VGG16 and Residual Network (ResNet) that is good at object recognition were used for comparing at recognizing the objects. The results were compared in terms of training time, performance, and model complexity. Simulation results show that new models with small layer structure produced higher performance than complex models.
Evolutionary Optimization of Ensemble Learning to Determine Sentiment Polarity in an Unbalanced Multiclass Corpus

Sentiment polarity classification in social media is a very important task, as it enables gathering trends on particular subjects given a set of opinions. Currently, a great advance has been made by using deep learning techniques, such as word embeddings, recurrent neural networks, and encoders, such as BERT. Unfortunately, these techniques require large amounts of data, which, in some cases, is not available. In order to model this situation, challenges, such as the Spanish TASS organized by the Spanish Society for Natural Language Processing (SEPLN), have been proposed, which pose particular difficulties: First, an unwieldy balance in the training and the test set, being this latter more than eight times the size of the training set. Another difficulty is the marked unbalance in the distribution of classes, which is also different between both sets. Finally, there are four different labels, which create the need to adapt current classifications methods for multiclass handling. Traditional machine learning methods, such as Naïve Bayes, Logistic Regression, and Support Vector Machines, achieve modest performance in these conditions, but used as an ensemble it is possible to attain competitive execution. Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose. The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques.
Teacher educator experiences of iPad integration in pre-service teacher education: successes and challenges

In this yearlong qualitative study, situated within cognitive and social constructivist theory, the authors investigate the adoption of tablets (iPads) by a group of nine teacher educators for use in pre-service teacher instruction. Using data gathered from focus groups following each of three teaching terms, the authors examined how teacher educators represent their experiences in using tablet technology and the ways they integrate tablets into their teaching and mentoring practices. Participants were generally positive in their appraisal of tablets, citing promising applications for inquiry-based and collaborative learning, reflective teaching, exploratory pedagogy and authentic student assessment, among others. Several challenges to tablet use were identified, including technical infrastructure issues, a requirement for instructor support, concerns about equity in student access, cross-platform compatibility and the general pedagogical shift to iPads as a teaching platform. Recommendations and implications for practice are discussed.
Exploratory factor analysis and validity of the virtual reality symptom questionnaire and computer use survey

The widespread use of virtual reality head-mounted-displays (HMDs) calls for a re-examination of the impact of prolonged exposure to fixed visual displays at close ocular proximity. The purpose of this study is to validate the Virtual Reality Symptoms Questionnaire (VRSQ), created to understand symptoms of prolonged HMDs use, and Computer Use Survey (CUS), created to assess general physical and visual discomfort symptoms. Participants (N = 100) recorded their general discomfort symptoms using the CUS, performed an interactive task using a HMD for thirty minutes, and then answered the CUS again along with the VRSQ. VRSQ, analysed using an exploratory factor analysis, indicated a clear two-factor solution, and demonstrated very good internal consistency (α = 0.873). The CUS, also analysed using an exploratory factor analysis, indicated a four-factor solution, and demonstrated good internal consistency (α = 0.838). Practitioner Summary: A quantitative-experimental study was conducted to explore the factor structure and validate both the Virtual Reality Symptoms Questionnaire (VRSQ), and the Computer Use Survey (CUS). Findings indicate the VRSQ and CUS are precise and accurate survey instruments for evaluating discomfort after VR-HMD use and the latter for computer use. Abbreviations: VRSQ: virtual reality symptom questionnaire; CUS: computer use survey; OLED: organic light-emitting diode; MSQ: pensacola motion symptom questionnaire; SSQ: simulator sickness questionnaire; 3 D: three-dimensional computer generated space; VR: virtual reality; VR-HMD: virtual reality head-mounted-display; HMDs: head-mounted-displays; EFA: exploratory factor analysis
TransNet

Wearables are poised to transform health and wellness through automation of cost-effective, objective, and real-time health monitoring. However, machine learning models for these systems are designed based on labeled data collected, and feature representations engineered, in controlled environments. This approach has limited scalability of wearables because (i) collecting and labeling sufficiently large amounts of sensor data is a labor-intensive and expensive process; and (ii) wearables are deployed in highly dynamic environments of the end-users whose context undergoes consistent changes. We introduce TransNet , a deep learning framework that minimizes the costly process of data labeling, feature engineering, and algorithm retraining by constructing a scalable computational approach. TransNet learns general and reusable features in lower layers of the framework and quickly reconfigures the underlying models from a small number of labeled instances in a new domain, such as when the system is adopted by a new user or when a previously unseen event is to be added to event vocabulary of the system. Utilizing TransNet on four activity datasets, TransNet achieves an average accuracy of 88.1% in cross-subject learning scenarios using only one labeled instance for each activity class. This performance improves to an accuracy of 92.7% with five labeled instances.
Defending Against Multiple and Unforeseen Adversarial Videos

Adversarial robustness of deep neural networks has been actively investigated. However, most existing defense approaches are limited to a specific type of adversarial perturbations. Specifically, they often fail to offer resistance to multiple attack types simultaneously, i.e., they lack multi-perturbation robustness. Furthermore, compared to image recognition problems, the adversarial robustness of video recognition models is relatively unexplored. While several studies have proposed how to generate adversarial videos, only a handful of approaches about defense strategies have been published in the literature. In this paper, we propose one of the first defense strategies against multiple types of adversarial videos for video recognition. The proposed method, referred to as MultiBN, performs adversarial training on multiple adversarial video types using multiple independent batch normalization (BN) layers with a learning-based BN selection module. With a multiple BN structure, each BN brach is responsible for learning the distribution of a single perturbation type and thus provides more precise distribution estimations. This mechanism benefits dealing with multiple perturbation types. The BN selection module detects the attack type of an input video and sends it to the corresponding BN branch, making MultiBN fully automatic and allowing end-to-end training. Compared to present adversarial training approaches, the proposed MultiBN exhibits stronger multi-perturbation robustness against different and even unforeseen adversarial video types, ranging from Lp-bounded attacks and physically realizable attacks. This holds true on different datasets and target models. Moreover, we conduct an extensive analysis to study the properties of the multiple BN structure.
La diffusion des TIC ameliore‐t‐elle qualitativement les institutions politiques en Afrique?

Abstract This paper empirically analyses the impact of the diffusion of information and communication technology (ICT) on the quality of institutions simultaneously captured by democracy and corruption in a few African countries. To do so, we specify a panel Tobit model in which democracy is measured by the Institutional Quality Index proposed by the ICRG, while corruption is approximated by the Corruption Control Index. Our model is estimated on a sample of 40 African countries over the period 1995–2018. Overall, the main results show that ICT diffusion significantly improves the quality of democracy and reduces the propensity to corruption in the sample countries. We suggest strengthening the rule of law to increase the quality of democracy, a source of positive externalities on corruption.
An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference

In the era of big data, users pay more attention to data privacy issues in many application fields, such as healthcare, finance, and so on. However, in the current application scenarios of machine learning as a service, service providers require users' private inputs to complete neural network inference tasks. Previous works have shown that some cryptographic tools can be used to achieve the secure neural network inference, but the performance gap is still existed to make those techniques practical. In this paper, we focus on the efficiency problem of privacy-preserving neural network inference and propose novel 3-party secure protocols to implement amounts of nonlinear activation functions such as ReLU and Sigmod, etc. Experiments on five popular neural network models demonstrate that our protocols achieve about $$1.2\times $$ – $$11.8\times $$ and $$1.08\times $$ – $$4.8\times $$ performance improvement than the state-of-the-art 3-party protocols (SecureNN [28]) in terms of computation and communication overhead. Furthermore, we are the first to implement the privacy-preserving inference of graph convolutional networks.
Smart teaching evaluation model using weighted naive bayes algorithm

There is a certain subjectivity in the teaching evaluation process, which leads to a low accuracy of the intelligent scoring system. In order to promote the intelligent development of teaching evaluation, based on machine learning, this study briefly introduces the background and current status of teaching evaluation, and describes in detail the relevant algorithm principles of data analysis and modeling using data mining technology and machine learning methods. Moreover, this study describes the establishment process of the traditional classroom teaching evaluation system and uses the classification algorithm in machine learning in the construction of evaluation models to further improve the scientificity and feasibility of teaching evaluation. In addition, in this study, empirical algorithm is used as the basic algorithm to evaluate teaching quality, and the topic word distribution obtained by joint model training is used as the original knowledge. Finally, this research analyzes the performance of this research system through a control experiment. The research results show that the scores of the research model are close to the standard manual scores and can provide a theoretical reference for subsequent related research.
REACT: A Model-Based Runtime Environment for Adapting Communication Systems

Trends such as the Internet of Things or edge computing lead to a growing number of networked devices. Hence, it is becoming increasingly important to manage communication systems at runtime. Adding self-adaptive capabilities is one approach to reduce administrative effort and cope with changing execution contexts. Existing frameworks for building self-adaptive software can help to reduce development effort in general. Yet, they are neither tailored towards the use in communication systems nor easily usable without profound knowledge in self-adaptive systems development. In this paper, we propose REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. It addresses the heterogeneity and distribution aspects of networks and reduces development effort. REACT empowers developers of communication systems to add adaptive behavior without having experience in self-adaptive systems development. We show the effectiveness and efficiency of our prototype in an experimental evaluation based on two distinct use cases from the communication systems domain: cloud resource management and software-defined networking. The first use case includes a comparison with Rainbow, which represents a state-of-the-art model-based framework for building self-adaptive systems. The second use case applies REACT in a sophisticated, real-world communication system scenario.