Reviewerly/exHarmony huggingface dataset:
Unstructured Medical Text Classification using Linguistic Analysis: A Supervised Deep Learning Approach

A vast amount of unstructured text that contains valuable information is available over the web. This text is changing and proliferating, making it hard for people to process, read, and remember. Data mining and information extraction algorithms are used to develop new automation techniques to process the unstructured text. Among this publicly available text, there are a considerable amount of online medical articles, which provides valuable information about diseases, symptoms, operations, treatments, drugs, etc. Automatic unstructured text classification offers practical information management that does not depend on the subjective criteria of classification. It also provides useful information by obtaining and correlating relevant data present in documents. It also classifies, identifies and presents all sources of knowledge and reduces the time for retrieving information by simplifying access to content. Therefore, medical information needs to be classified into their respected categories (such as Diabetes, Cancer, Depression, Pediatrics, etc.). In this paper, we propose to use a deep learning approach for unstructured medical text classification at the document level. In our classification model we used two types of features: (i) content-based features (stylistic and complexity), and (ii) health domain-specific features. Moreover, rather than dealing with binary classification, this work handles multiclasses medical articles classification. This classification is done based on linguistic features that are extracted from the text, it also incorporates medical domain-specific terms/keywords as part of the classification feature set. These domain-specific features are extracted by applying topic modeling technique to spot the most probable terms for each medical class. Our experiments shows a reasonable classification accuracy for such a large number of classes.
A Survey on Data Stream Mining Towards the Internet of Things Application

In the era of the Internet of Things, a widespread of applications depend on time with the various number of different data generated and collected from different devices available. These devices depend on the type of application. These fast stream data are real-time and large in dimension for the purpose of making decision as well as predicting future occurrence and analytics. Datastream analytics of internet technology for both businesses and everyday life is very valuable in terms of developing good quality of life. In this study, first of all, we focus on the concept of Internet of Things and its relationship with its architecture, large and flowing data. In addition, the approach of Internet of Things applied knowledge discovery process and deep learning frameworks are presented in this paper. Finally, the Internet of Things and its features are introduced in this work as well as the commonly used tools.
Duplicate Question Detection based on Neural Networks and Multi-head Attention

It is well known that using only one neural network can not get a satisfied accuracy for the problem of Duplicate Question Detection. In order to break through this dilemma, different neural networks are ensembled serially to strive for better accuracy. However, many problems, such as vanishing gradient or exploding gradient, will be encountered if the depth of neural network is blindly increased. Worse, the serial integration may be poor in computational performance since it is less parallelizable and needs more time to train. To solve these problems, we use ensemble learning with treating different neural networks as individual learners, calculating in parallel, and proposing a new voting mechanism to get better detection accuracy. In addition to the classical models based on recurrent or convolutional neural network, Multi-Head Attention is also integrated to reduce the correlation and the performance gap between different models. The experimental results in Quora question pairs dataset show that the accuracy of our method can reach 89.3%.
Market Intelligence Analysis on Age Estimation and Gender Classification on Events with deep learning hyperparameters optimization and SDN Controllers

Nowadays, understanding demographic information on social influencer events is important for target customer analysis. Hence monitoring the crowded event requires an intelligent sophisticated technology with human gender classification and classify age group. This paper is using hyper-parameter optimization and SDN controller on age gender classification on events cameras to monitor and classify to cover the whole event. Many cameras will need and lead to weak performance in classification. This system targets for getting better result in multi-IP-cameras managing port under SDN controller. Classification for image processing is used by fast R-CNN with hyper-parameter optimization model data training and achieving result from SDNs based web cameras. With constraints of hardware, this system will rely on group of Sony IP-Cameras with SDN controller environment. And OpenCV2 libraries is used to train hyper-parameter optimization model, SDN controller run on Mininet, Openflow protocol and IMDB Image Datasets and Asia Image Dataset are used to achieve better satisfactory performance.
Development of Approximate Compressor Based Hybrid Dadda Multiplier for Image De-noising Applications

This paper proposes an approximate circuit design for 4:2 compressor which shows a significant amount of improvement in performance metrics (area, power consumption, delay, transistor count and error metrics) concerning the conventional 4:2 compressor. Subsequently, this paper also presents the hybrid architecture for the Dadda multiplier design, which enhances the efficiency of the system. This hybrid Dadda multiplier architecture is utilized in a modified median filter for image de-noising application. With the help of this technique, a significant amount of improvement is observed in the peak signal to noise ratio (PSNR) of the system. For circuit and system-level simulation is done in Cadence Virtuoso using gpdk 45nm library and MATLAB, respectively.
Optimized Threshold Implementations: Minimizing the Latency of Secure Cryptographic Accelerators

Threshold implementations have emerged as one of the most popular masking countermeasures for hardware implementations of cryptographic primitives. In this work, we first provide a generic construction for $$d+1$$ TI sharing which achieves the minimal number of output shares for any n-input Boolean function of degree $$t=n-1$$ and for any d. Secondly, we demonstrate the applicability of our results on a first-order and second-order $$d+1$$ low-latency PRINCE implementation.
Extending Telepresence Technology as a Middle Stage between Humans to AI Robots Transition in the Workplace of the Future

This paper introduces the concept of extending the telepresence technology (TP) in the way of physical interactions. Related work in the field was based on four years operation of National telepresence infrastructure to support research, development and technology transfer in Slovakia (NTI), where the need of extended collaborative features was identified. In our research we described a new way of human remote-control interfaces working with telepresence technology based on VR and hand or body sensors. At the other hand we proposed the telepresence endpoint’s extensions design inspired by robotics elements such as robotic arms or humanoid robotic shells with face projection. The extended telepresence communication protocols were introduced. Multiple use case scenarios of extended telepresence technology were described in the way which will lead to the new approached in research in the field. Paper should be considered as a summary introduction to the problematics of extending the telepresence technology and each proposed concept will be described in more details in separate following manuscripts.
A Control System of Electric Wheelchair

Abstract This paper designs a control system for electric wheelchairs for electric wheelchairs, including: main controller, sensor module, power management module and auxiliary module, sensor module includes position sensor; power management module includes power supply and power self-starting device, The starting device acts as a power switch and is in a normally open state to disconnect the power supply. When the self-starting device is under pressure, the switch is closed and the power is turned on; after the system is powered on, the position sensor takes the seat position information of the electric wheelchair user in real time. Sending to the main controller, the main controller determines whether the user is in a safe position according to the received position information. If in the safe position, the main controller controls the power control module to supply power to the driving device of the wheelchair; if it is determined that the user is not in a safe position Then, the main controller controls the power control module to not supply power to the driving device of the wheelchair, and the main controller controls the voice module in the auxiliary module to issue a voice alarm signal that is not in a safe position. Through the system designed in this paper, the user’s safety and convenience are improved.
About Asymmetric Execution of the Asymmetric ElGamal Cipher

The paper continues the consideration of the concept of asymmetrically executable cryptosystems (ciphers), introduced by A. A. Varfolomeev at the SibCon 2016 and RusCrypto 2018 conferences, as applied to the ElGamal asymmetric cipher. These cryptosystems (ciphers) significantly increase the difficulty for an attacker to recover plain text with various regulatory restrictions on the size of cryptographic keys. A method for transforming a classical asymmetric cipher into an asymmetrically executable is proposed.
A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network

The vision-based surveillance systems are widely used in analysis traffic information due to their ease of installation and accuracy of their results. In this paper, an image processing system in real-time has been proposed to detect and classify the vehicles at intersections. This information can be used to estimate traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The detection operation is performed by the background subtraction technique, the approximated median filter is used to extract and update the background, then the vehicles will be tracked in the detection area. After that the vehicle classification will be implemented by using the convolutional neural network (CNN). The system is applied to videos obtained by stationary cameras. The experiments demonstrate that this system is able to robustly detect and classify the vehicles.
Smart Travel Itinerary Planning Application using Held-Karp Algorithm and Balanced Clustering Approach

In this paper, a mobile application that could assist tourists in arranging travel itineraries has been developed. In the case of making an itinerary, often tourists have been determined the tourist destinations they want to visit, but they confused in determining the order of efficient visits. Balanced clustering is an approach to classify tourist destinations based on the proximity of their location, which gives the results of a cluster whose cardinality between members of the cluster is balanced. Furthermore, each cluster group from the results of this clustering will be seen as a case of the Traveling Salesman Problem (TSP), which we need to find the most efficient sequence of visits in each cluster that contains a list of tourist destinations. The algorithm used for the completion of the TSP is the Held-Karp algorithm. From the running time measurement test, it is obtained that the Held-Karp algorithm solves the problem of finding the TSP route faster than using the Brute Force approach. In addition, the implementation of balanced clustering using the Hungarian algorithm can make the number of tourist destinations to be visited in each day become balance.
Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

It is recommended for a novice person to engage trained personnel before starting an unfamiliar aerobic or weight routine to gain real-time expert feedbacks. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method was implemented inside an Augmented Reality (AR) desktop app that employed a single RGB camera to capture still images of the subject as he or she progressed through the routine. The background-subtracted body pose image was compared against the exemplar image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function managed an accuracy of 93.67%.
Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking

Social media and the interactive Web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. Authors used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. Authors used the prototype to analyze a dataset generated from online ads from southern California and used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. An unexpected outcome of the second dataset was the discovery of the use of emojis in an expanded ontology.
Privacy Violating Opensource Intelligence Threat Evaluation Framework: A Security Assessment Framework For Critical Infrastructure Owners

Open Source Intelligence (OSINT) is the process of using tools and information to passively learn enough information about an organization to begin the cyber attack cycle. OSINT gathering is often the first step in a malicious cyber campaign. Social Media and the Internet (e.g., search engines, open source tools, blogs) are quickly becoming invaluable sources of information for malicious cyber actors. These two very popular forms of technology are notorious for inherently accumulating and archiving personal information about people, organizations, and networks. These technologies also give information perpetual life, which can be summoned at any time by those in possession of the right knowledge (i.e., Data Science) and tools to extract this information. We posit that the extent of these privacy violations have increased to the point where OSINT identified vulnerabilities may be as useful to attackers as zero-day vulnerabilities, since organizations are likely just as unaware of their presence. We refer to the tools and techniques used to identify these types of vulnerabilities as privacy violating OSINT (PV-OSINT). In this paper, we bring to light the very serious damage that can be done to critical infrastructure (CI), by the use of PV-OSINT. We tested our methodology with a real CI, we then shared our findings with the CI owner to verify the efficacy of our approach. Then we developed a framework that could be used by CI owners to constantly assess the security of their networks using PV-OSINT.
Classification of Airline Tweet Using Naïve-Bayes Classifier for Sentiment Analysis

Wide range of customers like family persons, business man, sportsman and youth are traveling via Airline. Hence feedback of persons matters a lot if they are involved. Direct feedbacks of customer may be positive or negative but analyzing their Tweets is important for the betterment i.e. how the Tweets is? Analysis of individual tweet is so much critical if the volume is high. Most of the times Tweets are ambiguous, which depends on the nature of customers i.e. positive person will always give positive Tweets and in other side negative tweets come from negative person. So ultimately, our work is to find the sentiments of descriptive Tweets as a result via their words and their expression in quantities format whether they are happy or not. The novel factor inside this work is to examine ambiguous Tweets and neutralize them according to proposed algorithm. The complete work is based on twitter dataset, here we are using US airline and performs different level of mining and processing for getting most accurate results. Here, improved sentiment analysis model has been proposed based on naïve Bayes classifier to classify tweets based on sentiments and neutralized tweets from ambiguous to positive or negative.
Learning fine-grained control for mapless navigation

We consider the problem of learning a control policy that allows an autonomous mobile robot to navigate safely to target positions in an environment, without access to an obstacle map. The policy can operate in environments of arbitrary size and may be deployed in resource-constrained settings where storing and maintaining an accurate map are infeasible or prohibitively expensive. The learned policy, trained end-to-end using deep reinforcement learning, outputs continuous control commands to the actuators of a simulated two-wheel differential drive robot. A new reward function is proposed to encourage the robotic agent to learn local recovery and exploration behaviours, which greatly improves the ability of the agent to solve challenging navigation tasks in new environments. The performance of the learned policy is compared to an agent equipped with full knowledge of the obstacle map. Even though the learned policy may solve many navigation tasks, we conclude that some tasks still require the use of a global path planner. However, coupled with a high-level path planner to provide intermediate target beacons to the goal, the learned policy may be employed as an effective low-level component with reactive collision avoidance behaviours and local navigation skills in static or dynamic environments.
DNN-based laughter synthesis

Just like speech is part of our daily life, non verbal sounds are the same. Until nowadays speech has been taken into consideration by the most of researchers, whose based their research work on analyzing, recognizing, synthesizing and even translating it. Some of them decided to attack the non verbal sounds such as laugh, which is a means used by human to express his feelings and his state. So, during this paper, we will focus on analyzing and synthesizing it. For this purpose, deep neural network (DNN)and recurrent neural network (RNN) will be used as a benchmark of the synthesis procedure. This procedure consists of two main stages. The first stage is the analysis and preprocessing of the database and the second is the synthesis stage. We will perform an objective and subjective evaluation to ensure the performance of the DNN-based laughter synthesis.
End-to-end Tibetan Ando dialect speech recognition based on hybrid CTC/attention architecture

End-to-end automatic speech recognition reduces the difficulty of building a speech recognition system through single network architecture. The tokenization, pronunciation dictionary and phonetic context-dependency trees required in the traditional deep learning-based speech recognition are omitted in this system to simplify the complex modeling process. This paper proposes a method to realize Tibetan Ando dialect speech recognition with end-to-end speech recognition model based on hybrid connectionist temporal classification (CTC)/attention. A bidirectional long short-term memory network (BLSTM) is used for the encoder network through 80 mel-scale filter-bank coefficients alone with pitch features form total 83-dimensionals acoustic features to train the network. We compared proposed method with the methods only based on CTC architecture and the structure only based on attention architecture by adjusting CTC weight of the system. The result shows that the hybrid model can obtain optimal weight to achieves the highest recognition rate of 64.5% when the CTC weight is 0.2.
Chinese-Khmer Parallel fragments Extraction from Comparable Corpus Based on Dirichlet Process

Aiming at the problems existing in the Chinese-Khmer parallel corpus, such as single field, small scale, and poor timeliness, a method of Chinese-Khmer parallel fragment extraction from comparable corpus based on Dirichlet process is proposed. The method firstly obtains the topic distribution of bilingual comparable corpus through bilingual topic model, then uses Poisson distribution to randomly divide the bilingual texts, sets up a threshold to initially filter parallel fragment of comparable corpus, and then obtains the matching probability between parallel fragments by Dirichlet process. Obtaining the final parallel fragments by Gibbs sampling. From the comparison experiments, the method of parallel fragment extraction from bilingual comparable corpus based on Dirichlet process can obtain higher quality parallel fragments without providing any parallel data, which is more suitable for languages where bilingual resources are low.
Age-sensitive well-being support

Many age-specific mental health problems can be related to maladaptive cognitive strategies for engaging with and processing the external environment in relation to self-perception and ego identity. These are essentially attentional balance problems, reflected in maladjustments of the subjective sense of presence in the external world. We present a theoretical rationale for well-being design that takes account of developmental changes, including cognitive aging, sleep, and the sense of presence. For example, young children may suffer from attentional problems, where events in the immediate environment are overly distracting. We see this as an overactive presence mechanism, or hyperpresence. As people become older, more waking time is spent in reminiscence and daydreaming. Eventually, the ability to give timely attention to the environment, as needed to carry out intentions, may become lost in hyperabsence. We specify several practical strategies for designing interactive technologies to help alleviate cognitive and behavioral problems, by restoring a more balanced relation between individual and environment.
Hardware Overclocking to Improve the Efficiency of Ethereum Cryptocurrency Mining

Mining task is remarkable by the high amount of resources, such as electrical power, which is needed for its execution. In order to reduce the amount of resources consumed, while maintaining high mining performance, the term of mining efficiency was introduced. This term considering both mining performance and consumed electrical power.In this paper we consider mining hardware overclocking in order to maximize mining efficiency. Overclocking methods are considered, such as increasing memory clock rate, decreasing core clock rate, decreasing hardware voltage. A complex technique is created that allows to apply the considered approaches in order to achieve maximum mining efficiency for a hardware system being overclocked.