Reviewerly/exHarmony huggingface dataset:
Affine Recurrence Based Key Scheduling Algorithm for the Advanced Encryption Standard

Encryption algorithms such as Advanced Encryption Standard (AES) are known as symmetric encryption algorithms which use the same key for both encryption and decryption. These algorithms have a huge variety of applications such as for securing data and transferring files. They also have a key expansion algorithm which is used for expanding the given key. In AES the key expansion algorithm expands the given 128 bit key into 176 bytes which will then be used in the encryption process which spans for 10 rounds. This paper aims at making this process even more secure. This is implemented by including different steps to increase security in this key expansion process so that it becomes computationally infeasible to get the original key even if the adversary gets a hold of the different parts of the key. Current works suggest that there is a need for increased security in the key scheduling algorithm of the AES. It has also been tested to have inferior strict avalanche criteria in comparison with other contenders of the AES such as Serpent and Twofish. Usage of the concept of affine recurrence ensures this in the proposed model. In affine recurrence, no two outputs of the operation will have a relation between them. Another concept used is the AES Substitution Box (S-box), and this is done to ensure higher levels of confusion in the key scheduling process.
Collaborative Regularization for Bidirectional Domain Mapping

Learning both domain mapping and domain knowledge is crucial for different sequence-to-sequence (seq2seq) tasks. Traditionally, seq2seq model only characterized domain mapping while the knowledge in source and target domains was ignored. To strengthen seq2seq representation, this study presents a unified transformer for bidirectional domain mapping where collaborative regularization is imposed. This regularization enforces the bidirectional mapping constraint and avoids the model from overfitting for better generalization. Importantly, the unified learning objective is optimized for collaborative learning among different modules in two domains with two learning directions. Experiments on machine translation demonstrate the merit of unified transformer by comparing with the existing methods under different tasks and settings.
Adaptive boundary sine cosine optimizer with population reduction for robustness analysis of finite time horizon systems

This paper proposes an adaptive boundary sine cosine optimizer with population reduction. It is designed specifically to calculate an upper bound on the worst-case gain of a known finite horizon Linear Time Varying (LTV) system and a perturbation. The input/output behavior of the perturbation is described by a time domain Integral Quadratic Constraint (IQC). The analysis condition is formulated as a parametric Riccati differential equation which depends on the IQC representation. A nonlinear optimization problem is posed that minimizes the upper bound on the worst-case gain over the IQC parameterization. For industrial size applications like a space launcher, the number of decision variables can grow arbitrarily large depending on the number of considered perturbations as well as the type and representation of the IQC. This is aggravated by nonlinear constraints on the decision variables imposed by the Riccati differential equation making it challenging to solve. Several established Meta-Heuristics (MHs) along with the proposed algorithm are applied to an industry size worst case analysis of a space launcher during its atmospheric ascend. Their respective performances are evaluated to emphasize the advantages of the developed optimizer. This work builds the foundation of applying MHs to IQC based robustness analysis of finite horizon LTV systems.
Activity Tracking Using Ear-Level Accelerometers

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research. Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location. Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities. Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging. Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.
Can You Help Me?

Autonomous (cargo-)bikes offer many use cases, especially in urban areas. One challenge they have to face is their dependence on human assistance. This study examines the influence of light color, flashing rhythm, voices, a person's kindness and technological experience on helping behavior towards an autonomous cargo bike. An experiment with 233 participants was conducted. We found that technological experience and kindness had a positive influence on helping behavior, while light color, flashing rhythm and voice type did not have any influence. Our results imply that helping behavior towards an autonomous bicycle varies individually. However, an electronically generated voice should be used to make the vehicle appear autonomous, emphasize the absence of a human being and give clear instructions to promote helping behavior.
Multi-Modal Multi-Instance Multi-Label Learning with Graph Convolutional Network

When applying machine learning to tackle realworld problems, it is common to see that objects come with multiple labels rather than a single label. In addition, complex objects can be composed of multiple modalities, e.g. a post on social media may contain both texts and images. Previous approaches typically treat every modality as a whole, while it is not the case in real world, as every post may contain multiple images and texts with quite diverse semantic meanings. Therefore, Multi-modal Multi-instance Multi-label (M3) learning was proposed. Previous attempt at M3 learning argues that exploiting label correlations is crucial. In this paper, we find that we can handle M3 problems using graph convolutional network. Specifically, a graph is built over all labels and each label is initially represented by its word embedding. The main goal for GCN is to map those label embed dings into inter-correlated label classifiers. Moreover, multi-instance aggregation is based on attention mechanism, making it more interpretable because it naturally learns to discover which pattern triggers the labels. Empirical studies are conducted on both benchmark datasets and industrial datasets, validating the effectiveness of our method, and it is demonstrated in ablation studies that the components in our methods are essential.
Differentially Private Linear Regression Analysis via Truncating Technique

This paper discusses how to study the linear regression model accurately while guaranteeing \(\epsilon \)-differential privacy. The parameters involved in linear regression are sensitive to one single record in database. As a result, a large scale of noise has to be added into the parameters to protect the records in database, which leads to inaccurate results. To improve the accuracy of published results, the existing works enforce \(\epsilon \)-differential privacy by perturbing the coefficients in the objective function(loss function) of one optimization problem, which is constructed to derive parameters of linear regression, rather than adding noise to the parameters directly. And the scale of noise generated in the above technique is proportional to the square of dimensionality. Obviously, if the dimensionality is high, the scale of noise will be very large, i.e., curse of dimensionality. To settle this issue, this paper firstly studies a truncating length in a differential private way, where the length limits the maximal influence of one record on the coefficients of objective function. And then the noisy truncating coefficients are published with the truncating length limitation. Finally, the parameters involved in linear regression can be derived based on the objective function with noisy coefficients. The experiments on real datasets validate the effectiveness of our proposals.
Prototype Generation for Multi-label Nearest Neighbours Classification

Numerous Prototype Selection and Generation algorithms for instance based classifiers and single label classification problems have been proposed in the past and are available in the literature. They build a small set of prototypes that represents as best as possible the initial training data. This set is called the condensing set and has the benefit of low computational cost while preserving accuracy. However, the proposed Prototype Selection and Generation algorithms are not applicable to multi-label problems where an instance may belong to more than one classes. The popular Binary Relevance transformation method is also inadequate to be combined with a Prototype Selection or Generation algorithm because of the multiple binary condensing sets it builds. Reduction through Homogeneous Clustering (RHC) is a simple, fast, parameter-free single label Prototype Generation algorithm that is based on k-means clustering. This paper proposes a RHC variation for multi-label training datasets. The proposed method, called Multi-label RHC (MRHC), inherits all the aforementioned desirable properties of RHC and generates multi-label prototypes. The experimental study based on nine multi-label datasets shows that MRHC achieves high reduction rates without negatively affecting accuracy.
An efficient robust automatic clustering algorithm for interval data

In recent years, clustering analysis for interval data has attracted the attention of many researchers. Nevertheless, an algorithm that can automatically determine the number of clusters, and can effectively detect the outlier intervals at the same time has not been studied so far. Therefore, in this paper, we propose a robust automatic clustering algorithm that only can automatically determine the number of clusters but also can assign the outlier intervals into separated clusters. The proposed algorithm is then applied in detecting the abnormal images consisting of the new image categories, and the images contaminated with noise.
The CyberSecurity Audit Model (CSAM)

This chapter presents the outcome of two empirical research studies that assess the implementation and validation of the cybersecurity audit model (CSAM), designed as a multiple-case study in two different Canadian higher education institution. CSAM can be applied for undertaking cybersecurity audits in any organization or nation state in order to evaluate and measure the cybersecurity assurance, maturity, and cyber readiness. The architecture of CSAM is explained in central sections. CSAM has been examined, implemented, and established under three research scenarios: (1) cybersecurity audit of all model domains, (2) cybersecurity audit of numerous domains, and (3) a single cybersecurity domain audit. The chapter concludes by showing how the implementation of the model permits one to report relevant information for future decision making in order to correct cybersecurity weaknesses or to improve cybersecurity domains and controls; thus, the model can be implemented and sufficiently tested at any organization.
Review on Classification Algorithm and Evaluation System of Machine Learning

Classification is an important part of machine learning, and considerable progress has been made in related algorithms and applications. This article introduces 5 basic single classifier models in solving classification problems. Firstly, the core idea of the model is explained. Secondly, the advantages and disadvantages of each model and application scenarios are compared. Thirdly, the evaluation method of the classifier and the performance index of the classifier are discussed. Finally, the bottleneck and development status of the basic classification model are analyzed.
Perceptual and Semantic Processing in Cognitive Robots

The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction.
Server monitoring model based on Balanced Scorecard and SNMP of an electric power company

An organization must be efficient and effective, to acomplish its strategic objectives and growth, technological infrastructure being a critical element in their performance, where servers play an important role, as they allow host and process large volumes of data. The late detection of incidents on servers can cause from financial losses to damage to the organizational image. In this work, an automated server monitoring model is proposed, based on the Balanced Scorecard (BSC) methodology, considering four of the fourteen strategic objectives which are related to the Information Technology (IT) area, identifying thirteen key processing indicators (KPIs). To reduce incident detection time, they are mapped out with the SNMP protocol OIDs in the model implementation. Finally, a comparison has been made between traditional model and proposed BSC model applied on a power distribution company on similar scenarios located at 3820 masl, obtaining a reduction of 223.61 minutes, which represents a very significant improvement. This represents a promising result in the automated control of servers incident detection improving it by 86.6%.
Convolutional Neural Network (CNN) on Realization of Facial Recognition Devices using Multi Embedded Computers

Facial recognition is a field that is still being researched and developed. However, artificial intelligence, particularly the artificial neural network (JST) Convolutional Neural Network, is often used in facial recognition systems. The Convolutional Neural Network method is a supervised learning method (laryngeal supervision) commonly used, so it is appropriate for facial recognition applications. In this application, image processing of input imagery before the image is processed in artificial neural networks using the Convolutional Neural Network method.The study used the Convolutional Neural Network method on facial recognition. The way to be used in the facial recognition system to be obtained, disadvantages, and advantages of the method used. (face recognition). This research can be analyzed, namely accuracy in each learning rate and get results. The accuracy factor is derived from how precisely the person's name is recognized from the face image.In facial recognition systems with the Convolutional Neural Network (CNN), it has analysis results that refer to increased accuracy. This is due to the Convolutional Neural Network structure itself, an optimization of the Backpropagation method. With the specific role of the Convolutional Neural Network method that has been specified to solve the problem of image recognition. For the result testing; for the Front Side detection, the best accuracy is 85.60% on the learning rate (α) 0.01 and Epoch 30 for object 3 (K), for the Left Side detection, the best accuracy is 92.96% on the learning rate (α) 0.01 and Epoch 20 for object 3 (K), and for the Right Side detection the best accuracy is 95.36% on the learning rate (α) 0.01 and Epoch 25 for object 3 (K).
NTR<sub>sh</sub>: A New Secure Variant of NTRUEncrypt Based on Tripternion Algebra

Abstract Since cryptography relied on mathematical theories and applications of computer science, mathematical algorithms are built with high difficulty. In this paper, we make an improvement and modification NTRU cryptosystem called NTRSH by using a new tripternion algebra and changing the mathematical structure for public and private keys, as well as for text encryption and decryption to obtain more and higher security.
Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.
A Novel Approach for the Detection of Web Service Anti-Patterns Using Word Embedding Techniques

An anti-pattern is defined as a standard but ineffective solution to solve a problem. Anti-patterns in software design make it hard for software maintenance and development by making source code very complicated for understanding. Various studies revealed that the presence of anti-patterns in web services leads to maintenance and evolution-related problems. Identification of anti-patterns at the design level helps in reducing efforts, resources, and costs. This makes the identification of anti-patterns an exciting issue for researchers. This work introduces a novel approach for detecting anti-patterns using text metrics extracted from the Web Service Description Language (WSDL) file. The framework used in this paper builds on the presumption that text metrics extracted at the web service level have been considered as a predictor for anti-patterns. This paper empirically investigates the effectiveness of three feature selection techniques and the original features, three data sampling techniques, the original data, four word embedding techniques, and nine classifier techniques in detecting web service anti-patterns. Data Sampling techniques are employed to counter the class imbalance problem suffered by the data set. The results confirm the predictive ability of text metrics obtained by different word embedding techniques in predicting anti-patterns.
The Novel Method of Pedestrian Fall Detection Based on PSO and RF Using Accelerometer Data

Pedestrian fall detection attracting a lot of research due to its importance in building a warning system to avoid negative consequences. There are many techniques for fall detection that have using different devices such as cameras, environmental sensors, wearable sensors, etc. However, the popularity of smartphones with many embedded sensors has motivated research on detecting abnormal activity such as falling based on sensor data. High accuracy of fall detection in daily life activities is always a practical challenge. This paper put forward a new feature set that pulls out from time, frequency and Hjorth domains, which calculate from the smartphone’s accelerometer data. This study also employs Particle Swarm Optimization (PSO) to optimize the parameters of the Random Forest classification, which aims to improve the accuracy of the fall detection system. Experimental results on the MobiAct 2.0 dataset have shown that the put forward system has improved detection efficiency from 96.9% to 98.5% in F-measure. In addition, the detection efficiency in falls is 17% to 26.7% higher on the F-measure than the methods proposed by the authors C. Chatzaki et al.
Machine Learning Privacy Aware Anonymization Using MapReduce Based Neural Network

Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect and preserve the personal identities of Individuals in BigData framework, which is termed as BigData Privacy Aware Machine Learning. For addressing a large volume of data, MapReduce-based neural networks parallelism is taken into consideration with classification of data volume. Human contextual information as applied through collaborative Machine Learning is proposed. The result of our experiment shows that relating human knowledge to neural network and parallelism by MapReduce framework can yield a better and measurable classification results for large scale Applications.