Reviewerly/exHarmony huggingface dataset:
Preprocessing Methods and Tools in Modelling Japanese for Text Classification

As a subset of Artificial Intelligence, Natural Language Processing (NLP) is a breakthrough in surpassing language barrier. Japanese language characteristics bring its own challenge in morphological analysis due to the uniqueness of Japanese grammatical system. By the rapid development of NLP tools, many Japanese NLP tools developed with limited ability yet specialized in running certain preprocessing methods. In this paper, the compilation of various methods and newly discovered tools for preprocess Japanese text are delivered to help people decide which Japanese NLP tools needs to be utilized to run some preprocessing methods. All of the Japanese preprocessing methods and tools are collected through literature review. It is concluded that depending on one NLP tool is not recommended since combination of Japanese NLP tools is required to finish Japanese preprocessing phase.
Coherent State Control to Recover Quantum Entanglement and Coherence

How to analytically deal with the entanglement and coherence dynamics of separated Jaynes–Cummings nodes with continuous-variable fields is still an open question. We here generalize this model to a more common situation including either a small or large qubit-field detuning, and obtain two new analytical formulas. The X-state simplification, Fock-state shortcut and detuning-limit approximation work together in an amazingly accurate way, which agrees with the numerical results. The new formulas almost perfectly predict the two-qubit entanglement dynamics both in sudden death and rebirth phenomenon for detuning interactions. We find that when both the qubit-field detuning and amplitude of coherent states are large enough, the maximal entanglement and coherence peaks can be fully and periodically retrieved, and their revival periods both increase linearly with the increasing detuning.
Multi-level Trust Agreement in Cloud Environment

Cloud computing introduced a new paradigm in the field of information technology by enabling the user with computing resources over the internet at any time and everywhere. These resources are scalable in nature as per the user requirement, which has reduced the cost of resource purchase and setup management. These resources also require the high consideration in terms of privacy and trust, as user data is stored and manage over the internet by an external third party, external third party. Handling the trust of different entities over the cloud environment is a challenging task because of dynamic nature of trust and cloud computing, which cannot be defined in any written argument. In this proposed work, we present a hierarchal level trust model to encourage the cloud users towards usage of cloud environment in collaboration of artificial bee colony (ABC) algorithm. Usage of ABC help in searching the various cloud service provider for the same service and find the best among them having high calculated trust value or recommendation by other user in their feedback. This paper considers both the criteria for establishing the trust i.e., direct or recommendation trust. The experimental work also convey that the proposed work has higher accuracy in trust formation, success rate and prevention of any malicious attack.
Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms

Deep learning has been successfully applied to a wide variety of tasks. It generates reusable knowledge that allows transfer learning to significantly impact more scientific research areas. However, there is no automatic way to build a new model that guarantees an adequate performance. In this paper, we propose an automated neural network construction framework to overcome the limitations found in current approaches using transfer learning. Currently, researchers spend much time and effort to understand the characteristics of the data when designing a new network model. Therefore, the proposed method leverages the strength in evolutionary algorithms to automate the search and optimization process. Similarities between the individuals are also considered during the cycled evolutionary process to avoid sticking to a local optimal. Overall, the experimental results effectively reach optimal solutions proving that a time-consuming task could also be done by an automated process that exceeds the human ability to select the best hyperparameters.
Election Integrity and Electronic Voting Machines in 2018 Georgia, USA

Direct recording electronic (DRE) voting systems have been shown time and time again to be vulnerable to hacking and malfunctioning. Despite mounting evidence that DREs are unfit for use, some states in the U.S. continue to use them for local, state, and federal elections. Georgia uses DREs exclusively, among many practices that have made its elections unfair and insecure. We give a brief history of election security and integrity in Georgia from the early 2000s to the 2018 election. Nonparametric permutation tests give strong evidence that something caused DREs not to record a substantial number of votes in this election. The undervote rate in the Lieutenant Governor’s race was far higher for voters who used DREs than for voters who used paper ballots. Undervote rates were strongly associated with ethnicity, with higher undervote rates in precincts where the percentage of Black voters was higher. There is specific evidence of DRE malfunction, too: one of the seven DREs in the Winterville Train Depot polling place had results that appear to be “flipped” along party lines. None of these associations or anomalies can reasonably be ascribed to chance.
A Novel Scale Recognition Method for Pointer Meters Adapted to Different Types and Shapes

Nowadays plenty of pointer meters are used in the field of chemical industry and electrical power system. To avoid reading their indication manually, many algorithms based on computer vision have been proposed to read pointer meters automatically. These methods, however, are limited to meters whose scales are uniform, and their accuracy is vulnerable to the error in the recognition of a meter's center. In this paper, a novel automatic reading algorithm of pointer meters based on scale seeking is proposed to overcome the weaknesses of the existing methods. Differing from the popular angle-based methods, we obtain the indication of the meter by comparing the distances between the peak of pointer and its nearest scales. The position and values of all scales can be automatically acquired and inferred by using our scale seeking and value inference algorithms, which is independent of any prior information in a database. Experiments prove that the algorithm can be applied to both meters with uniform or non-uniform scales effectively.
DWT-MFCC Method for Speaker Recognition System with Noise

The Mel-frequency Cepstral Coefficients (MFCC) method is the most popular method and has good performance as a feature extraction to date. But based on the simulation results, it is known that the speaker recognition system that uses MFCC as a feature extraction has low accuracy when applied to voice containing noise. So in this study, we propose the DWT-MFCC method to overcome this problem. The simulation results show that the DWT-MFCC method has higher accuracy compare with conventinal MFCC method when applied as feature extraction in the speaker recognition system with SNR from 15 to 40 dB.
A Simple and Robust End-to-End Encryption Architecture for Anonymous and Secure Whistleblowing

Much has been mentioned about the importance of whistleblowing. While all organizations are recommended to have a whistleblowing mechanism, there are very few completely software-based platforms that assist in performing this task securely. The primary concerns are to ensure that the whistleblower remains anonymous and the disclosures are securely delivered to the competent authority, usually media organizations. In this paper, we have analyzed the security of the state of the art software-based whistleblowing platforms and related research, identified security issues and proposed a new architecture that satisfactorily ensures the requirements for anonymous and secure whistleblowing. We have verified the strength of our solution against the existing platforms and related research with the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. Our approach is practical, backed by cryptographic security and, because of its modularity, can be easily included in the current infrastructure of many whistleblowing platforms. The results show that our architecture is simple, robust and implements a complete end-to-end encryption strategy thus enabling secure and anonymous whistleblowing.
Decision Support System for Dairy Cattle Management Using Computational Intelligence Technique

The good yield of dairy cattle relies upon the quality of the environment and genetic characteristics. This research created a 2-level data classification model for cattle. The data set contains environment of the animal house, factors related to cattle health, nutrition of food and body integrity scores based on the physiological characteristics of ruminants of Thailand. This article utilized artificial intelligence technology by presenting the application of artificial neural networks to classifying the health of cattle so that farmers can take care for daily cattle properly, since healthy cattle can produce good milk. The results of the predictive health of the model are 3 classes: normal, surveillance and risk. The results showed that the models presented in this research were higher than normal neural network structures. The classification results are verified by experts, indicating that the system can accurately analyze data as much as expert 100%. The developed model applies to web applications to facilitate the utilization of farmers and can be additionally created with future livestock use.
The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.
Random Forest Modeling for Survival Analysis of Cancer Recurrences

The recurrence of breast cancer is a prevailing problem that decreases the quality of patients' lives, creates high burdens on the healthcare system, and impacts the wellbeing of society. Advanced sensing provides an unprecedented opportunity to increase information visibility and characterize patterns of event occurrences. However, few, if any, of previous works have investigated survival analysis of breast cancer recurrences based on large amount of data readily available in the health system. There is a dire need to leverage data to decipher important factors that play a role in the recurrence of breast cancer. This paper presents an ensemble method of random survival forest for time-to-event analysis of breast cancer recurrences in the surveillance, epidemiology, and end results (SEER) data from year 1973 to 2015. Our model characterizes the survival function among patients with and without recurrences of breast cancer. Ensemble models are constructed via sampling and bootstrapping into the big data. Experimental results show that the age when cancer recurrence happens and time-between-recurrences approximately follow the Gaussian and exponential distributions with the means of $61.35 \pm 14.03$ and 2.61 years, respectively. In addition, the results show age, surgery status, stage of tumors, and histological grade are significant factors that influence the probability of breast cancer recurrences. The proposed survival analysis approach shows strong potentials to help healthcare practitioners in prognosis, treatment, and decision-making of breast cancer recurrences.
Cybersecurity as a social phenomenon

Humans are social creatures. Our behaviour is influenced by our perceptions of those around us, often to a much greater degree than we realize. However, we tend to make mistakes in our understanding of those around us and the situations that we encounter. We do so because our cognitive resources have limits, meaning that we have developed systems of coming to quick conclusions based on limited information. These processes are known as heuristics. This is not a flaw; rather it is an adaptive strategy that allows us to navigate and survive in our social worlds. Nevertheless, these tendencies may lead people to engage in cybersecurity in risky ways, either as the instigators of attacks, the targets of attacks, or the cybersecurity professionals who seek to prevent and mitigate attacks. Examples of this include group dynamics in which individuals overestimate the abilities of their own group whilst underestimating the abilities of competing groups, or failing to recognize the threat of cybersecurity risks that are difficult to visualize. In ways like those used with marketing and advertising campaigns, social engineers aim to exploit these quirks of social influence and human decision making. A greater understanding of these processes will enable us to develop more informed prevention and mitigation strategies in order to address the increasing challenges we face within cybersecurity.
Genetic Algorithm Based Deep Learning Model Selection for Visual Data Classification

Significant progress has been made by researchers in image classification mainly due to the accessibility of large-scale public visual datasets and powerful Convolutional Neural Network(CNN) models. Pre-trained CNN models can be utilized for learning comprehensive features from smaller training datasets, which support the transfer of knowledge from one source domain to different target domains. Currently, there are numerous frameworks to handle image classifications using transfer learning including preparing the preliminary features from the early layers of pre-trained CNN models, utilizing the mid-/high-level features, and fine-tuning the pre-trained CNN models to work for different targeting domains. In this work, we proposed to build a genetic algorithm-based deep learning model selection framework to address various detection challenges. This framework automates the process of identifying the most relevant and useful features generated by pre-trained models for different tasks. Each model differs in numerous ways depending on the number of layers.
Towards the support of user preferences in search-based product line architecture design

Software Product Lines (SPLs) is a reuse approach in which a family of products is generalized in a common architecture that can be adapted to different clients. The Product Line Architecture (PLA) is one of the most important artifacts of a SPL. PLA design requires great human effort as it involves several factors that are usually in conflict. To ease this task, PLA design can be formulated as an optimization problem with many factors, i.e, as a multi-objective optimization problem. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. This approach supported by OPLA-Tool has already been used in several works demonstrating its applicability. However, MOA4PLA does not take into account aspects that are subjective, such as the preferences of a particular Decision Maker (DM). To do so, this paper presents a proposal to incorporate the user preferences in the optimization process performed by MOA4PLA, through an interactive process in which the DM subjectively evaluates the solutions in processing time. Thus, the solutions generated can be better suited to the DM's needs or preferences. In order to allow the user interaction, modifications were made in MOA4PLA and implemented in the OPLA-Tool. Aiming at an initial validation of the proposal, an exploratory study was carried out, composed of two experiments: a qualitative and a quantitative. These experiments were realized with the participation of a software architect. Empirical results pointed out that the proposed interactive process enables the generation of PLAs that are in accordance with the architect's preferences. Another significant contribution are the lessons learned on how to improve the interactive process.
Projection-based Augmented Reality Interface for Robot Grasping Tasks

This paper presents an augmented reality (AR) interface for robot programming of pick-and-place tasks as well as assembly operations. The aim of the AR interface is to increase the intuitiveness and ease of robot programming. Marker tracking is used to automate sensor registration for easier workspace setup. Object recognition is employed to transform robot programming from an absolute coordinate system to an object-based system. This transformation provides flexibility to robot programming as the AR interface can be applied to workspaces that are configured differently to accomplish the same task. A spatially immersive display method is adopted to provide a projection-based direct overlay virtual workspace, so as to give users a better frame of reference in relation to the real workspace.
Classification of Breast Microscopic Imaging using Hybrid CLAHE-CNN Deep Architecture

Breast cancer is the most prevalent form of cancer and can occur in both men and women, although it is most common among women. The problem with manual pathology examination of breast cancer is that it is time-consuming as it requires scanning through images of tissue under various distinct magnification levels to obtain accurate diagnoses. The advancement in computer assisted diagnosis system help to improve the early diagnosis process using various machine learning algorithms. This paper proposes the hybrid architecture of CLAHE and deep convolutional network for the classification of the breast microscopic imaging. The proposed architecture is experimented with BreakHis dataset that comprises of 7909 images in total having 2480 images of benign class and rest is of malignant class. Proposed method is experimented with two level of test i.e., with and without pre-processing of images with CNN architecture. Initially, proposed method uses the traditional CNN architecture to classify the images into benign and malignant. Later, the experiment is performed with hybrid architecture of Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by the watershed algorithm, as a pre-processing for image enhancement followed by CNN architecture for image classification. The experimental result suggest that the proposed hybrid architecture outperforms traditional methods in terms of accuracy gained around 3%.
Capturing Signals of Enthusiasm and Support Towards Social Issues from Twitter

Social media enables organizations to learn what users say about their products online, and to engage with their potential audiences. Social media has also been allowing individual users and the public to signal their enthusiasm, support, or lack thereof for a broad range of topics. In this paper, we analyze the robustness of a prior framework for tagging tweets across the dimensions of enthusiasm (labels: enthusiastic, passive) and support (labels: supportive, non-supportive). We investigate the quality of annotations in a collection of tweets about three topics, namely, cyberbullying, LGBT rights, and Chronic Traumatic Encephalopathy (CTE) in the National Football League. We train models that achieve >70% and 80% F1 score for classifying tweets for enthusiasm and support, respectively. We assess how text-based signals of enthusiasm and support vary depending on the different annotators. Finally, we propose and demonstrate a network analysis-based approach for combining the annotated tweets with account and hashtag mention networks. This step helps to identify top accounts and hashtags related to the considered categories (enthusiasm and support). Our work offers an alternative or supplemental classification schema and prediction model to standard sentiment analysis and stance detection.
Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

Abstract To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called interleaved commonsense reasoning and probabilistic planning (i corpp ), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of i corpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate i corpp ’s reasoning and planning components. In particular, we extend probabilistic action language pBC + to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system pbcplus2pomdp , which compiles a pBC + action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC + action description. Our experiments show that it retains the advantages of i corpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.
Deep CNN for 3D Face Recognition

Three dimensional face analysis is being widely investigated since it appears as a robust solution to overcome the limits of two dimensional technologies. 3D methods allow to relate the recognition process on features not depending on lightning, head poses, make up and occlusions. This paper proposes a new approach to the problem consisting of a novel image representation, where specific facial descriptors replace the RGB traditional channels and a convolutional neural network performs the classification. We chose to use MobileNetV2, a relatively new network, as it has a low amount of parameters to train. The method has been evaluated on the Bosphorus database, and even though it is still a preliminary study, the results obtained with our method are extremely encouraging; the recognition rate achieved is 97.560% and it is comparable to the state of the art. This result, reached despite the fact that the Bosphorus database has a great number of subjects (105) but a low number of scans (4666), shows the effectiveness of this representation combined with convolutional neural networks.