Reviewerly/exHarmony huggingface dataset:
Accuracy vs Efficiency: Machine Learning Enabled Anomaly Detection on the Internet of Things

Anomaly detection is an important security mechanism for the Internet of Things (IoT). Existing works have been focused on developing accurate anomaly detection models. However, due to the resource-constrained nature of IoT networks and the requirement of real-time security operation, cost efficient (regarding computational efficiency and memory-consumption efficiency) approaches for anomaly detection are highly desirable in IoT applications. In this paper, we investigated machine learning (ML) enabled anomaly detection models for the IoT with regard to multi-objective optimization (Pareto optimization) that minimizes the detection error, execution time, and memory consumption simultaneously. Making use of well-known datasets consisting of network traffic traces captured in an IoT environment, we studied a variety of machine learning algorithms through the world-class H2O AI platform. Our experimental results show that the Gradient Boosting Machine, Random Forest, and Deep Learning models are the most accurate and fastest anomaly detection models; the Gradient Boosting Machine and Random Forest are the most accurate and memory-efficient models. These ML models form the Pareto-optimal set of anomaly detection models. Our results can be used by the industry to facilitate their selection of ML models for anomaly detection on various IoT networks based on their security requirements and system constraints.
A Learner’s Behavior Model for an E-Learning Hybrid Recommender System

The increasing development of e-learning systems, which provide the learners with a huge amount of varied learning resources, has raised the necessity to apply recommender systems with the aim of guiding them through the various courses, activities, learning materials, etc. at their disposal. Even though learner-oriented approaches allow the recommendations to fit the user’s needs as precisely as possible, detecting their changes to propose adaptive recommendations is still a major challenge. In order to develop a hybrid system whose knowledge would be augmented through the learner’s activity and results and that would base its analyses on the evolving student’s profile and behavior during the task, we propose to base its decisions on two learner models, both provided by educational and cognitive sciences: one is a student’s behavior model, and the other describes their general cognitive commitment. In this chapter, we focus on the student’s behavior model. The research is still in the preliminary stage, but first experimentations enabled us to validate our model, as well as the methods (supervised classification methods) and the data collection tool developed to apply this model to an automatic recognition problem. It also allowed us to identify the weaknesses of our experimental protocol and several improvements that could be made. First we present this model and its integration in our system, explaining which characteristics have been chosen to classify the learner’s behavior according to this model. Then we present our data collection tool and the results of our first experimentation.
Path Planning for AUVs with Energy-Optimal Velocity in Current Fields

Abstract This paper presents the energy-optimal velocity to solve the problem of energy-optimal path planning for autonomous underwater vehicles (AUVs) in current fields. In the proposed scheme, a sequence of thrust velocities and steering directions is predicted to enable vehicles to utilize/avoid currents and minimize their energy consumption. Each thrust velocity and steering direction are deduced through a vector analysis method under the expected displacement and corresponding current vector, which is the energy-optimal velocity. The proposed energy-optimal velocity combines the global optimization technique and guarantees the minimization of a vehicle’s energy consumption. Several experiments are performed on strong and turbulent current fields to validate the effectiveness of the proposed scheme.
Application of Support Vector Machines for Breast Calcification Cluster Detection and Mass Classification

Abstract Breast cancer has become “the most common cancer worldwide”, and it is an urgent problem in the medical field to improve the detection rate of breast cancer through early diagnosis and treatment. Based on the current research on medical image processing, our work selects breast images from the MIAS database for pre-processing, segmentation, texture feature extraction and classification, and conducts an in-depth investigation and experimental validation on the theoretical basis of support vector machine algorithm, kernel function selection, and key parameter optimization. The results show the effectiveness of support vector machines in detecting abnormal areas and classifying masses in breast images and provide research ideas for breast cancer diagnosis.
Research on the Construction Method of Driving Conditions with Optimal Deviation Based on Fuzzy C-Means Clustering

Abstract To construct a driving condition that conforms to the actual situation, this paper firstly removes, interpolates, and smooths the original data, and obtains several kinematic segments according to the division standard of kinematic segments. Then, extract the feature parameters and analyze the principal components of the kinematic segments obtained by division, and retain the principal components that can describe most of the features. Finally, the FCM clustering method is used to analyze the retained principal components, and the congestion, relative congestion, and relative unblocked and unblocked conditions are obtained. Finally, the comprehensive driving conditions are synthesized by the deviation optimization method, and their rationality and scientificity are verified by simulation analysis.
Evaluating and Improving Optical Character Recognition (OCR) Efficiency in Recognizing Mandarin Phrases with Phonetic Symbols

The development of tools and materials for those people who are visually impaired is a crucial topic in the research area of assistive technologies. In mandarin learning, most printing materials for K-4 children have phonetic symbols to assist students in learning pronunciation. One challenge in the research area is recognizing the mandarin phrases in those printing materials and transforming them into audiobooks or Braille. To our knowledge, this is the first study examining the Optical Character Recognition (OCR) performance toward phonetic symbols in mandarin. In this study, we conduct experiments on recognizing images with mandarin phrases with phonetic symbols by the side using the OCR system. We propose candidate methods to improve recognition efficiency in the future based on preliminary results.
Unveiling the Potential of Vision Transformer Architecture for Person Re-identification

Person re-identification (Re-ID) is a process to re-identify a person if he has been already seen by a camera network. Since start the convolutional neural networks (CNNs) are dominantly being used to solve the person Re-ID problem. The default limitation of CNNs i.e., local receptive field, prohibits the network to learn the distinctive global dependencies at initial layers. This study proposes a self-attention based deep architecture that learns global dependencies at each network layer to address CNN's limitation. Additionally, the introduction of a novel contextual learning module called Attention Drop Block (ADB) supports learning of less attentive areas of an image as well. The proposed model is evaluated on two public Re-ID benchmarks Market1501 and DukeMTMC-ReID, and outperformed all CNN baseline Re-ID models. The implementation and trained models are made publicly available at https://git.io/JYRE3.
Design and Implementation of a Lightweight Cryptographic Module, for Wireless 5G Communications and Beyond

With the advent of 5G and 6G networks and the anticipated expansion of the Internet of Things (IoT), novel applications are developed to address the need for low latency, capacity, higher data rate, and QoS for an unprecedentedly large number of devices. Demand for lightweight, fast, and efficient cryptographic algorithms is emerging, as an increasing number of systems that are used daily are becoming time-critical and often constrained in resources. One such algorithm that has been proposed is stream cipher Espresso, developed to simultaneously improve both hardware size and performance. At the same time, NIST states that any proposed lightweight cryptographic algorithm must fulfill the standards outlined in the Hardware API for Lightweight Cryptography specification, in order to ensure fair benchmarking. In this paper, a Lightweight Cryptographic Module compliant with these requirements is suggested. The crypto core employs an optimized implementation of the Espresso algorithm, both in comparison to other stream ciphers and to other Espresso implementations in the literature. The system is built on the Spartan-7 series xc7s100fgga676-2 Field Programmable Gate Array (FPGA) and works at a maximum frequency of 687 MHz.
Accelerating Transformer Networks through Recomposing Softmax Layers

The transformer model has become a crucial deep learning model as it provides accuracy superior to that by conventional models in various domains, such as image processing, genomics, and natural language processing. Recently, the importance of the softmax layer has increased as a growing number of transformer models process longer sequences to improve their accuracy rates. However, it is difficult to accelerate a softmax layer with methods introduced in previous studies due to the differences in the data access patterns of softmax layers and other adjacent layers. We address this challenge by accelerating the softmax layer through a recomposition strategy. By decomposing the softmax layer into multiple sub-layers, we change its data access pattern. Then, we fuse the decomposed softmax sub-layers with the subsequent and preceding operations. Softmax recomposition achieves up to 1.25 $\times$, 1.12 $\times$, 1.57 $\times$, and 1.65 $\times$ speedups in inferring BERT, GPT-Neo, BigBird, and Longformer on a modern GPU by significantly reducing the off-chip memory traffic.
Electronic Health Record Optimization for Artificial Intelligence

Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data elements that are the foundation of laboratory CDS. The direct use of artificial intelligence algorithms in CDS programs will be limited unless key elements of the EHR are structured. The identification, curation, maintenance, and preprocessing steps necessary to implement robust laboratory-based algorithms must account for the heterogeneity of data present in a typical EHR.
AirBERT: A fine-tuned language representation model for airlines tweet sentiment analysis

Airlines operate in a competitive marketplace and must upgrade their services to meet customer safety and comfort. Post-pandemic, the government and airlines resumed flights with many restrictions, the impact which is unexplored. An increasing number of customers use social media to leave reviews and in this age of Machine Learning (ML), if a model is available to automatically polarize flyer sentiments, it can help airlines upscale. In this work, a custom dataset is scraped from Twitter by including online reviews of five Indian airlines. Multiclass sentiment analysis using three classifiers, support vector machine, K-nearest neighbor and random forest with word2vec and TF-IDF word embeddings is implemented. AirBERT, a fine-tuned deep learning attention model based on bidirectional encoder representation from transformers is proposed. From results, it is observed that on ML, Random Forest with TF-IDF performs the best but the graphical processing unit and domain corpora trained AirBERT outperforms all the other models with an accuracy of 91%. Indigo airlines and Jet Airways received the maximum percentage of positive and negative reviews respectively. In performance comparison with three existing models on the USA airlines tweets dataset, the proposed model outperforms others trained on general domain corpora and matches state-of-the-art TweetBERTv2 model accuracy. The model can be deployed by airlines and other service industries to implement a customer relationship management (CRM) system.
Research on Grey Wolf Optimization Algorithm Based on Adaptive Adjustment Strategy

Abstract A grey wolf optimization algorithm based on an adaptive adjustment strategy (Improvements-Grey Wolf Optimization, IGWO) is proposed in this paper to address the issues with the Grey Wolf Optimization (GWO) algorithm, which has a slow convergence speed in the later stages and where the local search and global search cannot be taken into effective balance. First, a chaotic logistic map is used to initialize the population. Next, an inverse trigonometric function-based mathematical model is developed to achieve convergence purposes. Finally, a new location update method is used to update the next-generation location to improve search traversability. Additionally, the CEC2013-Benchmark standard test function was used to evaluate the benefits of performance compared to the standard GWO, PSO, and DEGWO (DE-Grey wolf optimization) algorithms. The improved GWO algorithm has greatly increased the convergence speed, search capability, and stability. It performs better than the other three algorithms as well.
Mongolian-Chinese Machine Translation Based on Graph Neural Network

Abstract Most of the current Mongolian-Chinese neural machine translation relies on a sequence-to-sequence-based encoder-decoder, which does not effectively utilize the syntactic information of sentences as well as the hierarchical information of the language. To solve this problem, a graph-to-sequence-based encoder-decoder is constructed by introducing graph neural networks, making better use of syntactic information and semantic knowledge of sentences. We will build a Mongolian dependency tree library and design a densely connected graph convolutional neural network (D-GCN) based on GCN combined with a densely connected network (DenseNet). Experiments are conducted to compare with the sequence-to-sequence based Mongolian-Chinese neural machine translation model and the variant model of graph neural network, and the results verify the advantages of the D-GCN-based Mongolian-Chinese neural machine translation model in translation performance.
Research on Collision Detection Algorithm for Human-SRL Collaborative Motion Planning

Abstract The existing collision detection algorithms are commonly based on triangle patch models, which leads to complex detection logic. Aiming at this problem, this paper proposes a new collision detection algorithm. It has simple detection logic and fast detection speed, and it is mainly used for human-SRL cooperative motion planning. The algorithm is based on a human-SRL skeleton model and a multi-sphere envelope method and includes four sub-algorithms, namely, a multi-sphere envelope sub-algorithm, an SRL collision detection sub-algorithm, a collision detection sub-algorithm between SRL and human, and a collision detection sub-algorithm between SRL and environment objects. This paper first introduces the pseudo-code of the collision detection algorithm. Secondly, the implementation principles of its four sub-algorithms are expounded. Finally, the correctness of the collision detection algorithm is verified by simulation.
Response to the letter to the Editor regarding “Jaw tracking integration to the virtual patient: A 4D dynamic approach”

We thank you for the opportunity to respond to the comments on our article "Jaw tracking integration to the virtual patient: A 4D dynamic approach".1 The technique comes from a trend to digitalize human behavior and the improvement of medical resources including minimally invasive navigation or tracking surgeries.2,3 Most of the available published research has been in the field of robotics, geolocalization of unmanned vehicles, or augmented reality, commonly using structure from motion (SfM) and simultaneous localization and mapping (SLAM) technologies.
Rethinking Activity Awareness: The Design, Evaluation and Implication of Integrating Activity Awareness into Mobile Messaging

Nowadays, different types of context information are integrated into mobile messaging to increase expressiveness and awareness, including mobile device setting, location, activity, and heart rate. Due to low recognition accuracy, sometimes users cannot accurately infer others’ status through activity awareness. Recently, activity recognition technology has advanced. However, the user behaviors of activity awareness with improved technology have not been studied. In this study, we design ActAware, a mobile instant messaging application that integrates activity awareness based on improved activity recognition technology, i.e., improved recognition accuracy and the addition of activity transition notification. We conduct a field study to explore user behaviors and found that activity awareness allows users to speculate on the reasons for chat interruption, plan communication, speculate on whether the chat partner is departing/arriving, and deepen the understanding of living patterns. Compared with disclosing other types of context information, users have fewer privacy concerns about disclosing activity information in ActAware. Based on these findings, we provide design recommendations for mobile messaging to better support activity awareness.
Differential privacy: Review of improving utility through cryptography‐based technologies

Summary Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state‐of‐the‐art and to benefit future researches, we are motivated to provide this review.
BARP: Improving Mister P Using Bayesian Additive Regression Trees — CORRIGENDUM

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Character expression for spoken dialogue systems with semi-supervised learning using Variational Auto-Encoder

Character of spoken dialogue systems is important not only for giving a positive impression of the system but also for gaining rapport from users. We have proposed a character expression model for spoken dialogue systems. The model expresses three character traits (extroversion, emotional instability, and politeness) of spoken dialogue systems by controlling spoken dialogue behaviors: utterance amount, backchannel, filler, and switching pause length. One major problem in training this model is that it is costly and time-consuming to collect many pair data of character traits and behaviors. To address this problem, semi-supervised learning is proposed based on a variational auto-encoder that exploits both the limited amount of labeled pair data and unlabeled corpus data. It was confirmed that the proposed model can express given characters more accurately than a baseline model with only supervised learning. We also implemented the character expression model in a spoken dialogue system for an autonomous android robot, and then conducted a subjective experiment with 75 university students to confirm the effectiveness of the character expression for specific dialogue scenarios. The results showed that expressing a character in accordance with the dialogue task by the proposed model improves the user’s impression of the appropriateness in formal dialogue such as job interview.
A Probability-Based Artificial Potential Field for Autonomous Vehicles in Avoiding Uncertain Obstacles

This paper deals with uncertain information of obstacles resulting in from practical obstacle-avoiding techniques for autonomous vehicles. In fact, this problem is ignored in many researches by assumptions that measurements are perfect or the vehicle can fully observe the state of obstacles. A probability model is proposed to evaluate the possibility of collision quantitatively based on the current position of the vehicle and the probability distribution of obstacles’ position. This probability model is then applied to design a new repulsive function. Hence, the resulting artificial potential field can avoid uncertain obstacles by maneuvering the vehicle in the direction of decreasing collision risk. Numerical simulations are carried out to verify the proposed collision avoidance model, and the results show that the proposed method can help autonomous vehicles to efficiently pass obstacles safely with uncertain information.
Demand-Side Obstacles to Publishing Economics Research: A View from the South

We discuss obstacles that researchers located in developing countries face to publish in economics journals. We group obstacles into two categories. ‘Supply-side’ obstacles include lack of funding, networking and certain elements of research capacity. ‘Demand-side’ obstacles arise from the practices of many journals’ editors. It is these less well-recognized obstacles that are the focus of this commentary. This set of practices combines to make it less likely that southern authors will get their work published. Significantly, the practices include a preference for manuscripts with highly reliable results (which usually require randomized controlled trials) over creative papers that confront questions that can only be imperfectly answered with the best methodologies available. This ranking discourages southern researchers from participating in journals and is a constraint for cumulative scientific progress.