Reviewerly/exHarmony huggingface dataset:
A Small Sample Image Recognition Method Based on ResNet and Transfer Learning

With the vigorous development of artificial intelligence big data era and the advent of 5G era, the amount of network information shows a blowout-like growth. As a result, the accurate query of information is faced with unprecedented challenges. The image as a material reproduction of visual perception, has a large number of retrieval requests all the time, but the traditional image target recognition annotation is mainly based on pixel-level supervised learning. In the face of massive high-quality image recognition, it is very difficult for users to query the target content accurately and quickly. Therefore, this paper studies the animal classification model based on Convolutional Neural Network (CNN), using transfer learning to pre-train the characteristics of the network and combined with the hybrid classification model of CNN. In the experiment, CATS/DOGS were used as the data set, and PyTorch was used to train the network model. Experimental research shows that the accuracy of 96.43% is achieved by using CNN+ transfer learning algorithm, which is significantly higher than that of traditional methods. For small-scale data sets, it effectively solves the non-transferability of manual feature extraction, and improves the accuracy and robustness.
Quantitative Augmented Reality-Assisted Free-Hand Orthognathic Surgery Using Electromagnetic Tracking and Skin-Attached Dynamic Reference

The purpose of this study was to develop a quantitative AR-assisted free-hand orthognathic surgery method using electromagnetic (EM) tracking and skin-attached dynamic reference. The authors proposed a novel, simplified, and convenient workflow for augmented reality (AR)-assisted orthognathic surgery based on optical marker-less tracking, a comfortable display, and a non-invasive, skin-attached dynamic reference frame. The 2 registrations between the physical (EM tracking) and CT image spaces and between the physical and AR camera spaces, essential processes in AR-assisted surgery, were pre-operatively performed using the registration body complex and 3D depth camera. The intraoperative model of the maxillary bone segment (MBS) was superimposed on the real patient image with the simulated goal model on a flat-panel display, and the MBS was freely handled for repositioning with respect to the skin-attached dynamic reference tool (SRT) with quantitative visualization of landmarks of interest using only EM tracking. To evaluate the accuracy of AR-assisted Le Fort I surgery, the MBS of the phantom was simulated and repositioned by 6 translational and three rotational movements. The mean absolute deviations (MADs) between the simulation and post-operative positions of MBS landmarks by the SRT were 0.20, 0.34, 0.29, and 0.55 mm in x- (left lateral, right lateral), y- (setback, advance), and z- (impaction, elongation) directions, and RMS, respectively, while those by the BRT were 0.23, 0.37, 0.30, and 0.60 mm. There were no significant differences between the translation and rotation surgeries or among surgeries in the x-, y-, and z-axes for the SRT. The MADs in the x-, y-, and z-axes exhibited no significant differences between the SRT and BRT. The developed method showed high accuracy and reliability in free-hand orthognathic surgery using EM tracking and skin-attached dynamic reference.
Hand Gesture Signal Classification using Machine Learning

This research work focuses on identifying a specific hand gesture from the given EMG signal, acquired by sensor-based band. Surface EMG and machine learning techniques are used for the identification and classification purpose. The raw EMG signal captured using the sensor is initially passed through suitable preprocessing steps to avoid the noise artifacts. Followed by this, 8 different time-domain features are collected from these raw EMG signals, using which a feature matrix is created. SVM and KNN are the machine learning classifiers used here. The entire system is implemented in MATLAB 2019a. Using these methods, a promising accuracy of 93% is obtained through KNN and an accuracy of 83% using SVM.
Defense of Word-Level Adversarial Attacks via Random Substitution Encoding

The adversarial attacks against deep neural networks on computer vision tasks have spawned many new technologies that help protect models from avoiding false predictions. Recently, word-level adversarial attacks on deep models of Natural Language Processing (NLP) tasks have also demonstrated strong power, e.g., fooling a sentiment classification neural network to make wrong decisions. Unfortunately, few previous literatures have discussed the defense of such word-level synonym substitution based attacks since they are hard to be perceived and detected. In this paper, we shed light on this problem and propose a novel defense framework called Random Substitution Encoding (RSE), which introduces a random substitution encoder into the training process of original neural networks. Extensive experiments on text classification tasks demonstrate the effectiveness of our framework on defense of word-level adversarial attacks, under various base and attack models.
Developing Self-Management Application of Fall Prevention Among Older Adults: A Content and Usability Evaluation

This paper presents a research and development project for studying aging and technology in fall prevention. Falls are an important global health problem in an aging global population. Up to 50% of serious falls may be fatal. Falls result from the cumulative effects of cognitive, musculoskeletal and sensory decline on postural control and substantially affect the activities of daily living, leading to a lower quality of life and physical injury. A near-fall, misstep and a prior fall are established risk factors for a more serious fall. The fear of falling may reduce physical activity and further predispose to falling. However, limitations in the reporting and documentation of fall events create "silent events"-events that are neither documented nor acted upon. An "Age-Techcare" Application (App) was designed using open innovation methods with local older adult populations and health care professionals through a mixed-methodology approach. The App comprised a digital diary for the self-reporting of fall events and an exercise video to strengthen balance as a fall-prevention intervention. The older adults recorded four fall events: a near-fall, the fear of falling, a fall, or no-fall. Prompts to watch the video and the number of times the video was watched were also recorded on the App. Reports retrieved from the App were analyzed after a 10-week pilot study among older adults accessing the App on their smartphones (n = 28) and through their smartTV (n = 23). All participants used the App to self-report fall events. Near-falls were the most frequently reported fall event among both smartphone and smartTV groups. The scale of silent falls (including a fear of falling and near falls) is greater than anticipated (according to prevailing literature) and significant, especially among the older cohort of participants who had previously experienced falls and are living alone. The exercise video was regularly accessed within a self-report-fall-prevention feedback loop. Watching a preventive exercise video clip as a preventive intervention is positively associated with self-reporting of all events. We have shown that the utility and effectiveness of an App in the self-management of fall events to raise self-awareness, document risk and prompt preventive action. As we address the health needs of an aging global population, Apps such as this will need to be further developed and interface with health and social care services. The facility for older adults to negotiate ideas and practices of risk and safety-the hallmark of the aging-in-place and healthy aging discourse-is important to them in their acceptance of dynamic and diverse technology.
Flexible Integration of Blockchain with Business Process Automation: A Federated Architecture

Blockchain technology enables various business transactions to be performed in an immutable and transparent manner. Within the business process management community, blockchain technology has been positioned as a way to better support the execution of inter-organisational business processes, where the entities involved may not completely trust each other. However, the architectures proposed thus far in the literature for blockchain-enabled business process management can be described as "heavy-weight", since they promote the blockchain platform as the monolithic focal point of all business logic and process operations. We propose an alternative: a federated and flexible architecture that leverages the capabilities of blockchain, but without overloading the functionalities of the blockchain platform with those already extant in Business Process Management Systems (BPMSs). We illustrate its benefits, and demonstrate its feasibility, through the implementation of a prototype.
Optimization and Comparative Analysis of YOLOV3 Target Detection Method Based on Lightweight Network Structure

As an open source target detection network, YOLOV3 has clear superiority in terms of accuracy and speed. However, the hardware configuration requirements are relatively high in actual applications, and the detection effect and real-time performance of Tiny-Yolov3 for embedded platforms are difficult to achieve expectations. In order to solve these problems, an improved YOLOV3 model optimization method based on a lightweight network structure is proposed, using lightweight network structure as the backbone network to replace the original convolution structure and residual module in YOLOV3. The size of the model network is reduced. The problem of high computational complexity and slow inference speed appearing on the embedded end is solved based on this targeted optimization. In case of slight loss of detection performance, this method can significantly reduce the model size and improving the detection speed compared with traditional methods on multiple public data sets.
Man-in-the-Middle Attacks Against Machine Learning Classifiers Via Malicious Generative Models

Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adversarial examples. In the past few years, many efforts have been spent on exploring query-optimisation attacks to find adversarial examples of either black-box or white-box DNN models, as well as the defending countermeasures against those attacks. In this article, we explore vulnerabilities of DNN models under the umbrella of Man-in-the-Middle (MitM) attacks, which have not been investigated before. From the perspective of an MitM adversary, the aforementioned adversarial example attacks are not viable anymore. First, such attacks must acquire the outputs from the models multiple times before actually launching attacks, which is difficult for the MitM adversary in practice. Second, such attacks are one-off and cannot be directly generalised onto new data examples, which decreases the rate of return for the attacker. In contrast, using generative models to craft adversarial examples on the fly can mitigate the drawbacks. However, the adversarial capability of the generative models, such as Variational Auto-Encoder (VAE), has not been extensively studied. Therefore, given a classifier, we investigate using a VAE decoder to either transform benign inputs to their adversarial counterparts or decode outputs from benign VAE encoders to be adversarial examples. The proposed method can endue more capability to MitM attackers. Based on our evaluation, the proposed attack can achieve above 95 percent success rates on both MNIST and CIFAR10 datasets, which is better or comparable with state-of-the-art query-optimisation attacks. In the meantime, the attack is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> times faster than the query-optimisation attacks.
Fuzzy extraction and PUF based three party authentication protocol using USB as mass storage device

Due to the rapid advancement in on-demand communication and information technologies, the peripheral connection of computers through Universal Serial Bus (USB) is considered as a remarkable interface standard that is extensively used in Mass Storage Devices (MSDs) of consumers. The MSDs of current USB consumers are easy to carry as they facilitate with relatively advance transmission speed, however, use of the USB MSDs is restricted in many sensitive commercial environments due to extreme security concerns. Therefore, to resist against illegal access, the storage devices of consumers are protected using numerous security measures. However, still these devices are vulnerable to various security attacks. In this regard, many security protocols for USB have been presented previously, but they all are not appropriate for fully satisfying all the security needs. Therefore, in order to protect stored confidential information of consumers, this article introduces an identity based three-party authentication and key exchange protocol using PUF that would be helpful to protect consumer’s confidential information in storage devices. Moreover, on the basis of the physically uncloneable function (PUF), this article further presents an efficient protocol for the USB MSDs using fuzzy extractor. Our proposed protocol has rigid security analysis that indicates the resistance of our protocol against many known attacks. All the cryptographic operations of the proposed protocol are implemented on the system having good specification using python programming language. Furthermore, performance analysis of our protocol with related protocols of USB MSDs endorses the usefulness of our protocol by maintaining least possible communication and computation overheads.
Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization

In “Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization,” Lei and Shanbhag consider a class of convex stochastic Nash games, possibly corrupted by parametric misspecification and characterized by a possibly nonconvex potential function. The authors present an asynchronous inexact proximal best-response (BR) scheme in which, at any step, a randomly selected player computes an inexact BR step (via stochastic approximation) and other players keep their strategies invariant. Misspecification is addressed by a simultaneous learning process reliant on an increasing batch size of sampled gradients. Almost-sure convergence guarantees are provided to the set of Nash equilibria, and such claims can be extended to delay-afflicted regimes, generalized potential games (with coupled strategy sets), and weighted potential games. In fact, equilibria of this potential game are stationary points of the potential function and asynchronous inexact BR schemes are, in essence, randomized block-coordinate schemes for a subclass of stochastic nonconvex optimization problems.
A Comparative Study between CNN, LSTM, and CLDNN Models in The Context of Radio Modulation Classification

Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) are the main classes of artificial neural networks used for recognition and prediction problems. Recently, it has been applied in the field of communications to identify the modulation types of the signals according to their features. In this paper, we use the RadioML2016.10b dataset generated in a real system using GNU radio to classify radio modulation by two types of neural networks, namely CNN and LSTM. The two networks automatically learn from in-phase and quadrature (I&Q) time domain data without manual expert features requirement. New architecture of Convolutional Long- Short Term Deep Neural Network (CLDNN) has been proposed that integrates selected architectures of CNNs, LSTM and deep neural networks (DNN) models. Different CLDNN architectures have been tested with different number of memory cells in the LSTM layers. In the proposed model setting, the modifications included three convolutional CNN layers, followed by one LSTM layer with 50 computing units and two fully connected DNN layers, which perform better result and higher accuracy compared to other settings. A great improve in performance has been achieved on the test data set with signal to noise ratio (SNR) varying from –18 dB to 20 dB. CLDNN model provided a 2-3% relative improvement in accuracy over the results of CNN and LSTM individual models.
Design and Modeling of Unconventional Quadrotors

In this paper, we propose a new design of unconventional quadrotors with simple mechanisms. They are able to change the orientation and the length of their arms independently thanks to their adaptive morphologies, these quadrotors can be used for specific missions and in more congested spaces. Thus, the usefulness of certain configurations that our quadrotors can make will be shown. In addition, a detailed and generic model is proposed for three possible cases: 1) the extension of the arms, 2) the rotation of the arms, 3) the hybridization between the extension and rotation of the arms. We stress that the modeling of these unconventional quadrotors is more challenging compared to the standard quadrotors, because of the geometrical asymmetry of the quadrotor structures and the constant change of the inertia and the center of gravity during the flight. In this work, a preliminary prototype of our quadrotor with rotating arms is presented with the electronic architecture.
Language Identification for Multilingual Machine Translation

Machine translation is the process of translating a text in one natural language into another natural language using computer system. Translating a document containing a single source language contents is easy but when the information in the source document is given in multilingual format then there is a need to identify the languages that are involved in such multilingual document. Language identification is the task in natural language processing that automatically identifies the natural language in which the content in given document are written in. Language identification is the fundamental and crucial step in many NLP applications. In this paper, n-gram based and machine learning based language identifiers are trained and used to identify three Indian languages such as Hindi, Marathi and Sanskrit present in a document given for machine translation. It is observed that, support vector machine based language identifier is more accurate than any other technique and it achieves 89% accuracy that is 18% more than traditional n-gram based approach. The inclusion of language identification component in machine translation improved the quality of translation.
Data-Driven Diffusion Recommendation in Online Social Networks for the Internet of People

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability.
Augmented Reality to Increase Interaction and Participation: A Case Study of Undergraduate Students in Mathematics Class

This work focuses on the Augmented Reality trying to improve students' interaction and participation in the educational dialogue, preventing the drop out, tested with university mathematics courses. Students often have difficulties on some topics related to the transition between different representations, within the same representations and language, for example when dealing with the study of two-variable functions, but also about the exact differential forms and the identification of the domain to integrate a function of several variables. These difficulties lead to a decrease in interaction and participation, and sometimes to dropping out of the course. Augmented Reality has been used to overcome some of these difficulties, also with the use of some technological tools (3D glasses, computers, tablets) and innovative methodologies. In order to evaluate the impact of this approach on students' interaction and participation, an experimentation with an e-learning platform based on Augmented Reality was carried out evaluating some affective and interaction parameters, computed through a Fuzzy Cognitive Map.
A deep learning approach for facility patient attendance prediction based on medical booking data

Abstract Nowadays, data-driven methodologies based on the clinical history of patients represent a promising research field in which personalized and intelligent healthcare systems can be opportunely designed and developed. In this perspective, Machine Learning (ML) algorithms can be efficiently adopted to deploy smart services to enhance the overall quality of healthcare systems. In this work, starting from an in-depth analysis of a data set composed of millions of medical booking records collected from the public healthcare organization in the region of Campania, Italy, we have developed a predictive model to extract useful knowledge on patients, medical staff, and related healthcare structures. In more detail, the main contribution is to suggest a Deep Learning (DL) methodology able to predict the access of a patient in one or more medical facilities of a fixed set in the immediate future, the subsequent 2 months. A structured Temporal Convolutional Neural Network (TCNN) is designed to extract temporal patterns from the administrative medical history of a patient. The experiment shows the goodness of the designed methodology. Finally, this work represents a novel application of a TCNN model to a multi-label classification problem not linked to text categorization or image recognition.
Evolving complex yet interpretable representations: application to Alzheimer’s diagnosis and prognosis

With increasing accuracy and availability of more data, the potential of using machine learning (ML) methods in medical and clinical applications has gained considerable interest. However, the main hurdle in translational use of ML methods is the lack of explainability, especially when non-linear methods are used. Explainable (i.e. human-interpretable) methods can provide insights into disease mechanisms but can equally importantly promote clinician-patient trust, in turn helping wider social acceptance of ML methods. Here, we empirically test a method to engineer complex, yet interpretable, representations of base features via evolution of context-free grammar (CFG). We show that together with a simple ML algorithm evolved features provide higher accuracy on several benchmark datasets and then apply it to a real word problem of diagnosing Alzheimer's disease (AD) based on magnetic resonance imaging (MRI) data. We further demonstrate high performance on a hold-out dataset for the prognosis of AD.