Reviewerly/exHarmony huggingface dataset:
A Multi-Objective Grammatical Evolution Framework to Generate Convolutional Neural Network Architectures

Deep Convolutional Neural Networks (CNNs) have reached the attention in the last decade due to their successful application to many computer vision domains. Several handcrafted architectures have been proposed in the literature, with increasing depth and millions of parameters. However, the optimal architecture size and parameters setup are dataset-dependent and challenging to find. For addressing this problem, this work proposes a Multi-Objective Grammatical Evolution framework to automatically generate suitable CNN architectures (layers and parameters) for a given classification problem. For this, a Context-free Grammar is developed, representing the search space of possible CNN architectures. The proposed method seeks to find suitable network architectures considering two objectives: accuracy and F1-score. We evaluated our method on CIFAR-10, and the results obtained show that our method generates simpler CNN architectures and overcomes the results achieved by larger (more complex) state-of-the-art CNN approaches and other grammars.
Best-Fit Dual Signatures for PQ Transition

Post Quantum Cryptography (sometimes referred to as a quantum-proof, quantum-safe or quantum-resistant) refers to cryptographic algorithms (usually public-key algorithms) that are thought to be secure against an attack by a quantum computer. PQC is all about preparing for the era of quantum computing by updating existing mathematical-based algorithms and standards. The cryptographic community is working on post-quantum cryptography in order to provide alternatives using hard mathematical problems that cannot be broken by quantum computers. A hybrid scheme is recommended to ease the transition into the post-quantum era. A hybrid scheme is a combination of a traditional and a post-quantum scheme. The resulting scheme is at least as secure as one of the schemes used. In hybrid key establishment, two independent key exchanges are performed one with a traditional scheme like ECDHE and one with a post-quantum scheme like NewHope [1]. In this paper, we propose possible best-fit hybrid digital signatures.
ATAK Integration through ROS for Autonomous Air-ground Team

This work introduces a methodology to interact with a heterogeneous team of autonomous robots performing a search and rescue mission. The autonomous robot team consists of a quadrotor and an unmanned ground vehicle. The robot team's human interface is the Android Team Awareness Kit (ATAK), which both military and emergency management first responders have found useful. This work's contribution is creating a platform-agnostic robot operating system (ROS) node used by the various autonomy stacks of each platform. This node is called atak_bridge. In order to limit the need to modify the ATAK client applications, atak_bridge leverages currently available standard messages within the ATAK framework. This eliminated the need for any specific ATAK plugin. A robot using atak_bridge shares its location and the locations of any objects of interest it detects to a user's ATAK device. Using the same ATAK device, the user can send waypoints to the robot, which the atak_bridge interprets into appropriate autonomy stack representations. We demonstrate the feasibility of integrating ATAK, using atak_bridge, into an air-ground team of autonomous robots for a search and rescue mission. The first responder sends the aerial platform to search an area and relays to the ATAK user any possible victims' locations. Upon receiving these locations, a first responder can dispatch an autonomous ground vehicle to confirm a victim and deliver food, water, and a first aid kit.
Automatic Modulation Classification Based on the Improved AlexNet

In the military and civilian domains, the modulation classification in the communication system is an extremely important technology that needs to be constantly updated and improved. In this paper, we present an automatic modulation classification (AMC) model to do modulation classification in 5 typical types of signal modulation BPSK, QPSK, 8PSK, 16QAM, and 64QAM. The proposed algorithm uses an improved AlexNet with deep residual learning, regularization, global pooling, and PReLU activation function to extract features from constellation diagrams for better recognition performance. Compared with the original AlexNet, support vector machine (SVM), and the traditional maximum likelihood-based cumulant technique, experiment results indicate that the proposed AMC model with the improved AlexNet has achieved very good recognition results, with its robustness, generalization, and high efficiency. We also explore the identifiability and reliability of signal transmission under different SNR conditions for different modulation types.
Game Theory Based Privacy Protection for Context-Aware Services with Long-Term Time Series Data

More and more applications are promoting cus-tomized or personalized services. In order for these applications to provide meaningful output, it collects users’ personal information over time. Some personal information (e.g. education level or income level) can only be captured by users actively updating their profile to reflect these changes. We refer to these as long-term time series data, as they do not change frequently. If applications can keep up to date on a diverse and large set of personal features, they can provide higher quality service. However, this quality of service comes at the cost of the user sacrificing their privacy. There has been numerous research on protecting privacy of time series data for context aware services, but the privacy leakage of personal information updates during the whole life-cycle of the series has received only scant attention.Motivated users concerned about their privacy, we discuss in detail the privacy leakage risk, focusing on long-term time-series data from the perspective of game theory. Then, we propose a reward-privacy model, targeting the privacy-aware data-updates for the entire life-cycle in context-aware services by leveraging a three-party Stackelberg game. We theoretically prove that a Nash Equilibrium exists in the proposed model, and then use simulations to validate that a Nash Equilibrium exists for different parameters of the productivity function. By using our proposed framework, users have guidance to decide not only the timing of submitting personal updates, but also the granularity or obscurity level for their data.
High Bit Resolution, Wavelength-Insensitive Coherent Electro-Optic Digital-to-Analog Converters Based on Circuit Topology

An electro-optic digital-to-analog converter consisting of Si cascaded Y branches presented wavelength-insensitive 4-bit-converted analog intensity with an identical input voltage of &lt; 50 mV. The same configuration demonstrated up to 8-bit operation.
Criminality Facilitated by Digital ICTs: Exploration of a General Theory of Intention and Behavior by Juxtaposing Social Learning and Deterrence

This study aims to develop and tentatively substantiate an inclusive theoretical model that elucidates the multiple realities that account for the overarching nature of behavioral intention and behavior connected to Information and Communication Technologies (ICT) assisted ethical behavior among Omani undergraduates. The emergent model juxtaposes ethical intention and behavior with constructs that are compatible with socio-cognitive and self- control theories. Based on a phenomenalist interpretive framework, the study applied a constructivist approach to grounded theory (GT). Data was obtained through focus group discussion sessions with undergraduates and semi-structured interviews with academics and professionals at Sultan Qaboos University (SQU). The comparative analysis generated four themes, which the participants devised to scrutinize the phenomena of ethical intent and behavior. The interviewees reflected on the central functions of moral agency, distinguished between the different patterns of ICT-assisted deviance among undergraduates, and acknowledged the different types of motives that underlie deviance. The results of the qualitative data revealed that the unique features of ICT spur and curb anomalous behavior. Additionally, social learning, perceived deterrents, and internal controls contribute to ethical behaviors.
Anomaly Detection for Skin Lesion Images Using Replicator Neural Networks

This paper presents an investigation on the task of anomaly detection for images of skin lesions. The goal is to provide a decision support system with an extra filtering layer to inform users if a classifier should not be used for a given sample. We tested anomaly detectors based on autoencoders and three discrimination methods: feature vector distance, replicator neural networks, and support vector data description fine-tuning. Results show that neural-based detectors can perfectly discriminate between skin lesions and open world images, but class discrimination cannot easily be accomplished and requires further investigation.
Cheating Like The Neighbors: Logarithmic Complexity For Fitness Evaluation In Genetic Algorithms

While neighborhood-based algorithms can use incremental evaluation to obtain the fitness of a modified solution candidate, genetic crossover makes changes that are too big to easily allow reusing previous quality values. In this paper, we extend our previous work and evaluate the possibility of extending persistent data structures to carry residual fitness values that can be reused for later evaluation of derived solution candidates when applied to Multidimensional 0-1 Knapsack Problems. We show potential speedups especially on very large problem instances when compared to classic array-based implementations.
An Accurate Automatic Traffic Signal Detector Using CNN Model

The main objective is to train and build an efficient artificial neural network model, such that the model’s accuracy is high enough to be able to apply it in real life. If the detector is not trained well or rather we can say, if the accuracy of the detector is not high enough, the automated car/driver will end up doing a wrong recognition. This can cause many accidents. Our main aim here is to check and increase the accuracy of the designed model and also, at the same time, ensuring minimal data loss. This can be achieved by proper preprocessing of the data and preventing overfitting. The result shows and compares how the accuracy of the machine learning model increases after proper data preprocessing is performed before feeding it into the model. Also, keeping into account that model overfitting is prevented.
Causal Modelling: The Two Cultures

We offer descriptive and normative standards for the principled pursuit of causal inference.These standards address critiques of both the algorithmic and the data modeling cultures identified in (Breiman, 2001), and provide a fruitful synthesis of both cultures.We contrast the resulting "cautious causal inference" with overly optimistic methods inspired by algorithmic data analysis methods prevalent in machine learning, as well as older approaches to causal modeling that employ overly restrictive parametric models.
A Comparative Performance Study on Immersive Analytics to Support Everyday Use

With the rapid growth of Big Data, Immersive Analytics became an emerging area to help analyze large scale and multidimensional data set. Immersive Analytics relies on immersive data visualization to produce analytical reasoning for data analytics. In this paper, we present two immersive analytics platforms operated on HMD and aim to support everyday use by highlighting their design approaches and differences. We conducted user case studies to further compare their analytical usability and summarized our findings in interaction modalities, physical navigations and visual representations. The research findings provide guidance to future immersive analytics design for everyday use.
An extended Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualizationz Technologies

For a given specific machine learning task, very often several machine learning algorithms and their right configurations are tested in a trial-and-error approach, until an adequate solution is found. This wastes human resources for constructing multiple models, requires a data analytics expert and is time-consuming. Meta learning addresses these problems and supports non-expert users by recommending a promising learning algorithm based on meta features computed from a given dataset. In the present paper, a new concept for enhancing the predictive performance of meta learning classification models by generating new meta examples is introduced. Our concept is realized and evaluated in a microservice-based meta learning framework. This framework makes use of a powerful Big Data software stack, container visualization, modern web technologies and a microservice architecture. In this demonstration and for evaluation purpose, time series model selection is taken as a use case for applying meta learning. It is shown that the proposed microservice-based meta learning framework introduces an excellent performance in assigning the adequate forecasting model for the chosen time series datasets. Moreover, our new concept for generating new meta examples enhances the predictive performance of the meta learner up to 16.77% and 27.07% in the case of using the original and encoded representation forms of meta features respectively. The recommendation of the most appropriate forecasting model results in a well acceptable low framework overhead demonstrating that the framework can provide an efficient approach to solve the problem of model selection in the context of Big Data.
Particle Swarm Optimization to Support Low Protein Diets for Diabetic Nephropathy Patients

Diabetic nephropathy is a major cause of morbidity and mortality in patients with type 1 and 2 diabetes Mellitus. One way to prevent the development of microalbuminuria and to delay the development of macroalbuminuria for patients with diabetic nephropathy is by taking one treatment that is adjusting low-protein diets. Dietary therapy has often been recommended by doctors in patients with diabetic nephropathy, but people have difficulty determining which foods can be consumed daily. Therefore in this work, a Food Recommendation System was built to support a Low Protein Diet to help diabetic nephropathy patients determine their daily food consumption. This research uses Particle Swarm Optimization (PSO) method to get the maximum variation of food ingredients. In this work, 130 patients were tested. Based on the test results on 120 patient data, the food ingredients obtained is almost the same with different weight of ingredients according to patient needs. While the results of testing 10 other data conducted by experts obtained an average evaluation value of 3.5 points from 5 points.
Study on Korean Language Teaching Evaluation Scheme Using Artificial Intelligence Technology

At present, Korean teaching in domestic colleges and universities can be said to be limited to the traditional teaching model of face-to-face teaching by teachers and students. This teaching mode is not only boring, it is difficult to improve students' interest in learning. Students are in a passive position of receiving knowledge, which is not conducive to the development of students' language application ability, and at the same time, it is difficult to realize information exchange and emotional exchange between teachers and students. If you want to master a language, you must first master certain vocabulary, so vocabulary teaching is the most basic content of language teaching in Korean. The advent of artificial intelligence technology has lowered the barriers for children to access quality education, and education has been democratized and liberalized, so everyone has an equal right to receive quality education. Using the AI-based education model, teachers can better grasp students' learning and correct them. Students can also communicate and evaluate each other's homework, so as to achieve the purpose of mutual learning and common progress.
A Entity Attention-based model for Entity Relation Classification for Chinese Literature Text

Entity relation classification is one of the basic tasks in natural language processing. The performance of the existing relational classification in Chinese literature text is not ideal. To address the issues, we propose a entity attention-based model for entity relation classification for Chinese literature text. Our proposed model extracts key information from entity by using attention mechanism, and filters out redundant information. In addition, we integrate entity type information into the model to help the model classify relation more reasonably. Experimental results show that the proposed model outperforms the state-of-the-art methods on Chinese literature text.
Student-Industry Matching for Internship Placement

In this project, a profile matching approach is proposed to assist internship placement tasks. Students often have difficulty finding and applying for internship placements, while companies spend much time sorting applications. Computer-assisted applications help job placement and onboarding tasks by mapping the potential job seekers to the corresponding companies. However, lack of focus put on internship placement, as the student profiles are limited to academic achievement without extensive work experience. A student-industry profile matching approach is proposed to evaluate students' profiles and map the academic criteria to potential companies offering internship placement. The fuzzy matching technique is used to model the correlation ratio between the student profiles and the industry profiles.
An Improved Approach for Deep Web Data Extraction

The World Wide Web is a valuable wellspring of data which contains information in a wide range of organizations. The different organizations of pages go about as a boundary for performing robotized handling. Numerous business associations require information from the World Wide Web for doing insightful undertakings like business knowledge, item insight, serious knowledge, dynamic, assessment mining, notion investigation, and so on Numerous scientists face trouble in tracking down the most fitting diary for their exploration article distribution. Manual extraction is arduous which has directed the requirement for the computerized extraction measure. In this paper, approach called ADWDE is proposed. This drew closer is essentially founded on heuristic methods. The reason for this exploration is to plan an Automated Web Data Extraction System (AWDES) which can recognize the objective of information extraction with less measure of human intercession utilizing semantic marking and furthermore to perform extraction at a satisfactory degree of precision. In AWDES, there consistently exists a compromise between the degree of human intercession and precision. The objective of this examination is to diminish the degree of human intercession and simultaneously give exact extraction results independent of the business space to which the site page has a place.
Simplifying Data Traffic Classification with Byte Importance Distillation

Network traffic classification (NTC) plays an important role in network measurement and management. Recently, Artificial Intelligence (AI) based NTC has been widely considered a good candidate because of its accuracy in processing both clear and encrypted data traffic. However, those AI-based NTC schemes usually apply full-length packets, e.g., through padding. Such lengthy inputs can lead to complex designs of NTC models, which can be challenging to network devices. To tackle this issue, we propose a byte importance distillation scheme to extract the packet payload bytes that contribute the most to traffic classification results. In the proposed scheme, a Bayesian Multilayer Perception (BMLP) is first initialized based on full-size data packets. The byte importance is then defined as the corresponding mean absolute weights in the first layer of a BMLP model with K-integration. By choosing the important bytes, input data packets can be reduced dramatically to a few bytes that contribute the most to the classification output. Two popular AI-based NTC models, i.e., MLP based and CNN based, are implemented to evaluate the proposed byte importance distillation scheme. The results demonstrate that the data packets optimized from the proposed scheme can speed up the MLP and CNN based NTC models by one to two magnitudes, depending on the implementation platforms, while maintaining high classification accuracy. In comparison, packets that are reduced to the same size through traditional dimension reduction approaches such as principal component analysis and convolutional block attention module cannot maintain high classification accuracy of the AI-based NTCs.
Unsupervised IoT Fingerprinting Method via Variational Auto-encoder and K-means

With the rapid growth of the number of IoT devices on the Internet, security problems of IoT devices are becoming more and more serious, which bring more challenges to network administrators. The first task to solve these problems for network administrators is being aware of IoT devices in the network. Previous IoT device identification methods typically use supervised machine learning methods, which require a large amount of labeled sample data. However, it is difficult to obtain a large number of labeled samples effectively. In order to address this problem, we propose an unsupervised IoT device fingerprinting method at the network level, which can effectively cluster IoT devices without labeled samples. We deeply analyze the temporal and spatial dimension characteristics of network traffic, which can adequately reflect the differences between different IoT devices. By using these features, we develop a clustering framework based on variational autoencoder and K-means algorithms. We conduct evaluation experiments on a public dataset including 24 different IoT devices. The experimental results show that our clustering algorithm can achieve accuracy of 86.7% outperforming a k-NN based state-of-art supervised approach.
Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data

The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.