Reviewerly/exHarmony huggingface dataset:
Counting Devices: Revisiting Existing Approaches in Today’s Settings

The ability to count and fingerprint devices (optionally, from a given type) that are in a network, and potentially behind a NAT is important not only for network management (e.g., inventory and asset management) but also for business analysis (e.g., product adoption) and security (e.g., to block traffic from a malicious device behind a NAT). As such, researchers have developed a number of solutions to address these questions. However, most existing solutions rely on incidental characteristics of end devices' behaviors. Software updates to end devices or middleboxes (e.g., firewall, NAT) could render existing solutions ineffective. As such, how effective are those solutions in today's settings, e.g., with IoT devices? We propose to answer this answer by evaluating three major approaches that rely on (1) the IP id field, (2) a device's clock skew, and (3) a combination of a device's boot time and the frequency of its TCP timestamp clock, on the network traffic of seventy IoT devices. We show that existing approaches are ineffective with recent IoT devices, and as such the problem of counting devices behind a NAT remains an open problem. Finally, we explore and discuss future potential directions.
Sensitivity Analysis of Policy Options for Urban Mental Health Using System Dynamics and Fuzzy Cognitive Maps

Urban mental health challenges call for new ways of designing policies to address the ongoing mental health issues in cities. Policymaking for mental health in cities is extremely difficult due to the complex nature of mental health, the structure of cities, and their multiple subsystems. This paper presents a general system dynamic model of factors affecting mental health and a method to test the sensitivity of the model to policy options using an approach combining system dynamics and fuzzy cognitive maps. The method is developed and tested to evaluate policies built around feedback loops. The approach succeeded in identifying the factors that substantially improve the mental health of the city population for specific contexts. It also suggests the coordination needed between different subsystems to reach these objectives.
One-Class Classification to Continuously Authenticate Users Based on Keystroke Timing Dynamics

Most current authentication mechanisms rely on static initial verification of the user; however, such authentication mechanisms do not verify user identities on already unlocked systems. Spy Hunter, a continuous authentication mechanism, constantly examines user's keystroke timing dynamics to assess the user's identity. Also, Spy Hunter preserves the privacy of the user by only utilizing the key press timings without storing information regarding which keys were pressed. Specifically, Spy Hunter implements two one-class classifiers, only utilizing genuine user samples for training the model, and mitigates adversarial inclusion of impostor data in the model. The data in this preliminary study consists of timing information from 20 users running Spy Hunter in the background while they used their systems. One-class classification was performed independently for all the users. Experimental results show that in less than 80 characters, Spy Hunter performed with as low as 2.05% False Acceptance Rate (FAR) and 2% False Rejection Rate (FRR).
Enhanced Pattern Matching Algorithms for Searching Arabic Text Based on Multithreading Technology

Many exact pattern matching algorithms have been developed and optimized for better efficiency and accuracy. In this regard, English is a well-served language compared to Arabic, which suffers some difficulties when searching its text. A few to mention include: Arabic writing direction is right-to-left, letters often change their shapes depending on their position within a word, Arabic language uses different encoding techniques for computing and digital storage and Arabic is a vocalized language, where diacritics might show above and beneath its alphabets. Hence, efficient search algorithms for Arabic text must be sought. This paper has two fold aims. The first one is to enhance three well-known exact pattern matching algorithms, namely: Brute-force (BF), Boyer-Moore (BM) and Knuth-Morris-Pratt (KMP) to search Arabic text. While the second aim is to speed up these search algorithms using multithreading technology. Experimental results showed that multithreading technology has positive influence on the performance of the original search algorithms when the size of the text is relatively large.
An Word2vec based on Chinese Medical Knowledge

Introducing a large amount of external prior domain knowledge will effectively improve the performance of the word embedded language model in downstream NLP tasks. Based on this assumption, we collect and collate a medical corpus data with about 36M (Million) characters and use the data of CCKS2019 as the test set to carry out multiple classifications and named entity recognition (NER) tasks with the generated word and character vectors. Compared with the results of BERT, our models obtained the ideal performance and efficiency results.
An Incremental Kernel Density Estimator for Data Stream Computation

Probability density function ( p.d.f. ) estimation plays a very important role in the field of data mining. Kernel density estimator (KDE) is the mostly used technology to estimate the unknown p.d.f. for the given dataset. The existing KDEs are usually inefficient when handling the p.d.f. estimation problem for stream data because a bran-new KDE has to be retrained based on the combination of current data and newly coming data. This process increases the training time and wastes the computation resource. This article proposes an incremental kernel density estimator (I-KDE) which deals with the p.d.f. estimation problem in the way of data stream computation. The I-KDE updates the current KDE dynamically and gradually with the newly coming data rather than retraining the bran-new KDE with the combination of current data and newly coming data. The theoretical analysis proves the convergence of the I-KDE only if the estimated p.d.f. of newly coming data is convergent to its true p.d.f . In order to guarantee the convergence of the I-KDE, a new multivariate fixed-point iteration algorithm based on the unbiased cross validation (UCV) method is developed to determine the optimal bandwidth of the KDE. The experimental results on 10 univariate and 4 multivariate probability distributions demonstrate the feasibility and effectiveness of the I-KDE.
Secured Reversible matrix embedding based on dual image using Integer wavelet and Arnold Transform

Steganography is the art and science of hiding information. In Steganography host image is destroyed and cannot be attained to the original state. In a sensitive area such as military and medical recovery of the original cover medium is required without any loss. A special case of data hiding that retrieves the cover media and secret information without a loss of a pixel is known as reversible data hiding. In this paper reversible data hiding technique is proposed to improve the security and enhance the capacity; a hybrid model of integer wavelet transform and Arnold transform are used. The cover image is decomposed into the integer wavelet sub-band; each sub-band is further scrambled using Arnold transform before embedding scrambled secret information. In this work dual image embedding mechanism is used such that it makes matrix embedding reversible without any bookkeeping data. The experimental result shows that the proposed method provides high embedding capacity.
Chart-Type Classification Using Convolutional Neural Network for Scholarly Figures

Text-to-speech conversion by smart speakers is expected to assist visually handicapped people who are near total blindness to read documents. This research supposes a situation where such a text-to-speech conversion is applied to scholarly documents. Usually, a page in scholarly documents consists of multiple regions, i.e. ordinary text, mathematical expressions, tables, and figures. In this paper, we propose a method which classifys chart-type of scholarly figures using a convolutional neural network. The method classifies an input figure image into line charts or others. We evaluated the accuracy of the method using scholarly figures dataset collected from actual academic papers. The classification accuracy of the proposed method achieved 97%. We also compared the performance of the proposed method with that of hand-crafted features and support vector machine. The results suggest that the proposed CNN classification outperforms the conventional approach.
Mobile Robot Path Planning Using a Flower Pollination Algorithm-Based Approach

Considering the growing role of mobile robots in our everyday life and in various other applications, robust and safe navigation for these mobile robots is of utmost importance. Though the computational power of the computers has increased, there is still a need for the robust algorithms which are that can yield the most optimal, safe and least energy-demanding path. Hence, in this chapter, first, the classification of different approaches used for robot path planning has been briefly discussed, followed by the discussion of several soft computing-based approaches and the challenges associated with them. Then, a flower pollination algorithm-based approach has been discussed for mobile robot path planning. Several criteria like distance covered by the robot, two-layered safety and the number of turns required during the path traversal are used to evaluate the path for its optimality. The result of the algorithm is given using different examples.
Dimensionality Reduction for Sentiment Classification using Machine Learning Classifiers

Sentiment analysis intends to identify the opinion either positive or negative given by clients or users from review documents. Sentiment analysis utilizing machine learning strategies faces the issue of high dimensionality of the feature vector. Consequently, a feature reduction strategy is required to dispose of the unessential and noisy elements from the feature vector. Feature reduction techniques selects the prominent features for reducing size of the feature set. The features which are nearly distributed presented by different class in the feature vector, make complexity for the classifier to draw a clear decision boundary. In this work, we proposed two different approaches (i.e., Term Presence Count (TPC) and Term Presence Ratio (TPR)) to remove those redundant features in positively and negatively tagged documents with nearly equal distribution. We applied four machine learning-based classification techniques including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) for sentiment classification using movie review dataset. Finally, the classifiers are evaluated in terms of accuracy, precision, recall, and Average F-measure. Experimental results manifest that the feature dimension reduced to approximately 83% by our proposed method while improving the classification performance.
An Application of Artificial Intelligence for Detecting Emotions in Neuromarketing

The subject of this paper is the application of artificial intelligence for detecting emotions in neuromarketing. The goal is to enable the identification of user emotions through a webcam, using convolutional neural networks. The first part of the paper describes the neural networks, the basic types, and their differences. The greatest attention has been given to the description and application of convolutional neural networks. A Convolutional Neural Network, also known as CNN, is specialized in processing data that has a grid-like topology, such as an image. User emotion recognition is enabled using the face-api.js library. It implements the following models: SSD Mobilenet V1, Tiny Face Detector and MTCNN. Tiny Face Detector, used in the application, is a model for real-time face detection with small size, speed, and moderate resource consumption. The model is compatible with the web and mobile platforms. In the second part of the paper, an application was developed, which uses the face-api.js library to detect emotions. It has been developed as a tool to support neuromarketing research. It allows the marketer to create research to analyze advertising material. Its basic functionality is to display advertising content and collect data while watching. Data is stored and graphically displayed to the marketer. This section describes in detail how the detection process works. In the third part of the paper, evaluation was made. Evaluation of the developed solution was performed by experiment. The results show that the emotions of the user can be recognized by the developed system, with a satisfactory level of precision. The advertising content has previously entered parameters, which represent the desired results. By comparing these parameters and the obtained results, the marketer decides whether the advertisement is successful.
Quantum-resistant Lattice-based Authentication for V2X Communication in C-ITS

Although there has been a lot of research on a vehicle to everything (V2X) for a cooperative-intelligent transport system (C-ITS), there has been little research on quantum-resistant V2X schemes. Since the speed of improvement in quantum computing is groundbreaking, we need to develop a quantum-resistant scheme in advance. In this paper, we propose a quantum-resistant lattice-based scheme for C-ITS, making safe even in the quantum era. Our proposed scheme uses a simplified NTRU Prime and BLISS that are considered to support both security and practicality. In addition, the scheme is a pseudonym-based authentication scheme as other V2X authentication schemes because pseudonym-based authentication provides both security and anonymity. Lastly, our proposed scheme is robust against several known attacks such as impersonation, replay, and quantum attacks.
AudiDoS: Real-Time Denial-of-Service Adversarial Attacks on Deep Audio Models

Deep learning has enabled personal and IoT devices to rethink microphones as a multi-purpose sensor for understanding conversation and the surrounding environment. This resulted in a proliferation of Voice Controllable Systems (VCS) around us. The increasing popularity of such systems is also prone to attracting miscreants, who often want to take advantage of the VCS without the knowledge of the user. Consequently, understanding the robustness of VCS, especially under adversarial attacks, has become an important research topic. Although there exists some previous work on audio adversarial attacks, their scopes are limited to embedding the attacks onto pre-recorded music clips, which when played through speakers cause VCS to misbehave. As an attack-audio needs to be played, the occurrence of this type of attacks can be suspected by a human listener. In this paper, we focus on audio-based Denial-of-Service (DoS) attack, which is unexplored in the literature. Contrary to previous work, we show that adversarial audio attacks in real-time and overthe-air are possible, while a user interacts with VCS. We show that the attacks are effective regardless of the user's command and interaction timings. In this paper, we present a first-of-itskind imperceptible and always-on universal audio perturbation technique that enables such DoS attack to be successful. We thoroughly evaluate the performance of the attacking scheme across (i) two learning tasks, (ii) two model architectures and (iii) three datasets. We demonstrate that the attack can introduce as high as 78% error rate in audio recognition tasks.
The dendritic neuron model is a universal approximator

According to the biological composition of neurons, the DNM was invented based on the dendrites of neurons, which can break through the functions that traditional simple neuron model (McCulloch and Pitts, 1943) cannot achieve. While by using traditional single-layer model, the approximation to any continuous function cannot be achieved by a single neuron. In this paper, an accurate mathematical method is used to demonstrate that the DNM can approximate any continuous function which could be achieved with only one neuron.
An RNS Implementation of the Elliptic Curve Cryptography for IoT Security

Public key cryptography plays a vital role in many information and communication systems for secure data transaction, authentication, identification, digital signature, and key management purpose. Elliptic curve cryptography (ECC) is a widely used public key cryptographic algorithm. In this paper, we propose a hardware-software codesign implementation of the ECC cipher. The algorithm is modelled in C language. Compute-intensive components are identified for their efficient hardware implementations. In the implementation, residue number system (RNS) with projective coordinates are utilized for performing the required arithmetic operations. To manage the hardware-software codeign in an integrated fashion Xilinx platform studio tool and Virtex-5 xc5vfx70t device based platform is utilized. An application of the implementation is demonstrated for encryption of text and its respective decryption over prime fields. The design is useful for providing an adequate level of security for IoTs.
Cache Side-Channel Attack on Mail User Agent

MUA (Mail User Agent) programs support email encryption functionality for providing confidentiality of the email contents. They encrypt email contents using email encryption standards such as OpenPGP or S/MIME, mostly implemented by GnuPG, or GPG in practice. In order to understand security implication of the structures and analyze any possible vulnerabilities of MUA programs, in this paper, we investigated practical MUAs supporting e-mail encryption. As a result, we found severe vulnerabilities in a list of MUAs that allow cache side-channel attacks in virtualized desktop environments. Our analysis reveals that the root cause originates from the lack of verification and control over third-party cryptographic libraries they adopt. In order to substantiate the importance of the vulnerability we found, we delivered FLUSH+RELOAD attack on those MUA programs and demonstrated that the attack restores 92% of the RSA private keys when recipients read a single encrypted email.
Entanglement restoration using environment-assisted measurement and quantum measurement reversal

Based on the quantum technique of environment-assisted measurement and quantum measurement reversal, we systematically investigate the restoration of quantum entanglement for distributing a two-qubit pure state via three typical kinds of noisy quantum channels-amplitude damping, phase damping and depolarizing quantum channels. Special attention is paid to the unsymmetrical two-qubit arbitrarily entangled states transmission. The analytical or numerical simulations results show that for arbitrarily one-qubit entangled state or two-qubit states transmission, the output distant entanglement could be drastically restored to the initial value in all these three cases.
Multi-attention deep recurrent neural network for nursing action evaluation using wearable sensor

A nursing action evaluation system that can assess the performance of students practicing patient handling related nursing skills becomes an urgent need for solving the nursing educator shortage problem. Such an evaluation system should be designed with less hand-crafted procedures for its scalability. Additionally, realizing high accuracy of nursing action recognition, especially fine-grained action recognition remains a problem. This reflects in the recognition of the correct and incorrect methods when students perform a nursing action, and low accuracy of that would mislead the nursing students. We propose a multi-attention deep recurrent neural network (MA-DRNN) model for nursing action recognition by directly processing the raw acceleration and rotational speed signals from wearable sensors. Data samples of target nursing actions in a nursing skill called patient transfer were collected to train and compare the models. The experiment results show that the proposed model can reach approximately 96% recognition accuracy for four target fine-grained nursing action classes helped by the attention mechanism on time and layer domains, which outperforms the state-of-the-art models of wearable sensor-based HAR.
Fog-based Data Fusion for Heterogeneous IoT Sensor Networks: A Real Implementation

The Internet of Things (IoT) is an environment that can be divided in three large layers: the sensor/actuator level where a wide variety of objects with different computing, sensors and communication capabilities resides, the communication layer with wireless technologies such as ZigBee, Bluetooth and emerging 6LoWPAN (e.g LoRa), and the intelligence layer, where computing analytics/decisions occur. IoT can be used for monitoring, inferring problems, decision making at a business level or actuating at the edge via IoT nodes. As the IoT sensor network grows, an enormous amount of data from multiple sources flows to the intelligence layer. In order to make decisions based on analytics over these data, the measurements need to be precise and accurate. Data fusion is an effective way to improve data quality, however, IoT environments are still evolving and the best way and location where data fusion should happen is an open problem. This paper presents one potential strategy for IoT sensor data fusion by implementing multi-sensor data fusion as microservices using a container platform built into an opensource IoT middleware based in a fog computing infrastructure which is can scale automatically as the influx of data from the IoT nodes grows. A number of data fusion tests were performed for different amounts of IoT nodes and sensor readings over ZigBee and LoRa using a specific data fusion algorithm. The results show that, the strategy can be effectively used in IoT heterogeneous environments.
Twitter Opinion Analysis About Topic 5G Technology

Nowadays, Twitter has become a rich source for sentiment analysis and opinion mining data since every day thousand of users freely expresses their opinions in this social network. In this research, we analyze and classify the sentiment of shared publications that have the hashtag "#5G" as positive, negative or neutral. We use Google Cloud AutoML Natural Language Sentiment Analysis and we obtained a classification model with accuracy and recall of 80.89%, likewise applying Latent Dirichlet Allocation for the detection of topics. The result shows that is possible to identify main factors about public opinion in the acceptance or rejection of technology 5G, this information can be useful for technology companies.