Reviewerly/exHarmony huggingface dataset:
Trends and Perspectives in Cognitive Research

Here, we present a selective review of the tendencies in the development of mind-and-brain studies and their emerging perspectives. Our assessment is less subjective in the part, which concerns with major trends of cognitive science. Two of them are, firstly, the interdisciplinarity of research, and, secondly, an accent on intended (or at least claimed) practical results. In fact, the trends have been established decades ago but new quality was reached towards the beginning of the 21st century. Thus, natural sciences started to merge with the humanities and applied research was involved in advance of new and emerging technologies. This development requires assimilating basic cognitive results if they are sufficiently reliable. As perspectives, we discuss at more lengths four actual challenges: the necessity to implement gaze contacts in video-based conferences and distant communication; classical problem of sensus communis: evolutionary models of cognitive organization; possible methodological drawbacks in the recent brain-mapping studies of lexical representations while listening to orally presented narratives.
Impact of Time Domain Features &amp; Inertial Sensors on Activity Recognition using Randomized Selection

Activity performed by the user is one of the major components of context sensing. Now a day's users are carrying Smartphones or wearable devices with them always. The device is fully equipped with the latest sensors, thus smart devices are prominently used in activity detection. The detection of activity is mainly dependent on three things-1- the sensors used for data collection, 2- the various features extracted from the raw data and 3-Machine Learning model used for training and testing. Researchers are using different sensors and extracting more numbers of features for getting better accuracy. However, feature dimensions are dependent on time of execution. Thus, an optimization is required between number of features used and its execution time. It is also required to find out the impact of different sensors on its accuracy and execution time. In this paper we have tried to discover the trade-off between number of features & sensor used with its accuracy and execution time. The evaluation of proposed work has been done by using publicly available dataset on UCI machine learning repository. Random selection methodology is used for selecting features and 5 popular machine learning algorithms is used to compare the results. The evaluation result shows that gyroscope helps in increasing accuracy if it is used along with accelerometer. We also conclude that features have significant effect on accuracy and execution time, and from various ML models Random forest & K nearest neighbor classifiers are providing better accuracy in most of the cases of Activity Recognition.
Building a Knowledge Base of an Expert System for Personalized Stroke Risk Prognosis

Authors present an expert system based on a model with operators that characterize various states of chronic cerebral ischemia. Risk factors (or predictors) that can affect the health, are considered operators in the model. The system of interactions between arguments and counterarguments measured by expert assessments characterizes the changes in the state of the body. Predictors are arguments and protectors are counterarguments. The production rules of the knowledge base are formed on the basis of attribute combinatorics, where predictors and protectors are regarded as attributes. The expert system, which includes multiple combinations of predictors and protectors, will make it possible to form personalized prognostic hypotheses for patients at different periods of time with regard to the changes in risk factors. The coefficients may be re-evaluated if required when new knowledge emerges. Expanding the knowledge base is possible by creating rules that include new factors.
Sparse ℓ1 and ℓ Center Classifiers

The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the development of many other more sophisticated classification methods. In this paper, we propose two sparse variants of the nearest-centroid classifier, based respectively on ℓ1 and ℓ2 distance criteria. The proposed sparse classifiers perform simultaneous classification and feature selection, by detecting the features that are most relevant for the classification purpose. We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) and at a quasi-linear computational cost. The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection techniques, while having a significantly lower computational cost.
Adaptive Video Streaming via Deep Reinforcement Learning from User Trajectory Preferences

Client-side adaptive bitrate (ABR) algorithms based on deep reinforcement learning (RL) can continuously improve its adaptability to network conditions. However, most existing methods adopt fixed reward functions to train the ABR policy, which leads the results being not consistent with user-perceived quality of experience (QoE) in a long duration under various network conditions. In order to optimize the QoE, this paper proposes a novel ABR algorithm considering user preference based on short trajectory segments. The user-specific preference feedback, which is selected by the user from a pair of short track segments in advance, is collected and applied to define the training goal of RL. Specifically, we train a deep neural network to define the RL reward and integrate it with A3C-based ABR algorithm. The experiment results show that the accuracy of the proposed reward model outperforms most existing fixed reward functions by 13.6% in user preference prediction, and the optimized ABR algorithm improves QoE by 16.4% on average.
Hate Speech, Media, and Canadian Federal Law

Purpose – This chapter has three general purposes: to trace Canada’s hate speech laws from their policy inception to their current state; to identify the importance that media and mass communication have played in the creation and development of Canada’s hate speech laws; and to demonstrate the critical relationship that media has had to significant legal cases on hate speech. Methodology/Approach – This chapter historically maps the policy development of and legal challenges to Canada’s hate speech laws. It takes directed notice of the relationship of media and mass communication to the development and implementation of those laws. It engages with libertarian and egalitarian arguments on free speech throughout the chapter testing these ideas through an examination of the legal cases cited. Findings – Canadian legislators and courts have long grappled with the balancing of rights with respect to the issue of “hate speech.” Advances in mass communication technology have added intricate challenges to that legal balancing. Awareness of media’s allure to hatemongers and racial extremists and of media’s protean characteristics make regulation of its hateful content a continuous legal challenge. Canada’s greatest challenge yet to the regulation of hate speech will be its adaptive response to the growing phenomenon of online hate. Originality/Value – This chapter highlights the little recognized prescient statements made by the Cohen Committee about the allure of media and the dangers of its technological advancements in Canadian free speech debates. Providing a comprehensive survey of Canada’s “hate speech” laws, it recognizes the importance that advancements in mass communication have played in the creation and development of Canada’s “hate speech” laws.
Semantic Image Synthesis with Trilateral Generative Adversarial Networks

We present a new method that can synthesize photorealistic images from semantic label maps using GANs with a trilateral generator. Due to the limitation of network capacity, it is difficult for scene image generation networks to consider resolution and image fidelity at the same time. The solution is to use multi-path structure instead of the traditional single-path structure. In this work, we propose a novel trilateral Generative Adversarial Network (trilateral GAN), which has fewer parameters than other recent methods to synthesize 512*1024 images with high fidelity. Moreover, we improve the semantic consistency loss and feature matching loss by using the features before activation, which can make the synthetic images have sharper edges and richer textures. Finally, we replace all the Batch-Normalization (BN) layers with Spectral Normalization (SN) in the network and use Conditional Normalization Blocks (CNB) to avoid difficult convergence during training and make the synthesized images more realistic. The proposed network has a better performance and gets higher mIoU scores on COCO-Stuff, ADE20K and Cityscapes than the state-of-the-art methods.
State Mapping of the Public Health Development Index in Aceh Province using K-Medoids method

Abstract Health level mapping is necessary to obtain information on the state of public health in Aceh Province. The study analyzed the clustering of districts/cities in Aceh Province using the Aceh Province Public Health Development Index (PHDI) in 2013 and 2018, which was sourced from the Aceh Health Office. The analytical method used is the K-Medoids grouping method. The results of this study indicate that 4 clusters are consisting of 3 conditions, namely the district/city cluster with a good health level, the district/city cluster with a medium health level, the district/city cluster with a low health level. Districts/cities that experienced significant changes in health levels in 2013 and 2018 are Aceh Besar, Lhokseumawe, Aceh Jaya, Pidie Jaya, Aceh Timur, Gayo Lues, and Simeulue. Meanwhile, Banda Aceh, Sabang, and Langsa city always occupy a cluster position with a high level of health.
ExplainEx: An Explainable Artificial Intelligence Framework for Interpreting Predictive Models

Artificial Intelligence (AI) systems are increasingly dependent on machine learning models which lack interpretability and algorithmic transparency, and hence may not be trusted by its users. The fear of failure in these systems is driving many governments to demand more explanation and accountability. Take, for example, the “Right of Explanation” rule proposed in the European Union in 2019, which gives citizens the right to demand an explanation from AI-based predictions. Explainable Artificial Intelligence (XAI) is an attempt to open up the “black box” and create more explainable systems which create predictive models whose results are easily understandable to humans. This paper describes an explanation model called ExplainEx which automatically generates natural language explanation for predictive models by consuming REST API provided by ExpliClas open-source web service. The classification model consists of four main decision tree algorithms including J48, Random Tree, RepTree and FURIA. The user interface was designed based on Microsoft.Net Framework programming platform. At the background is a software engine automating a seamless interaction between Expliclas API and the trained datasets, to provide natural language explanation to users. Unlike other studies, our proposed model is both a stand-alone and client-server based system capable of providing global explanations for any decision tree classifier. It supports multiple concurrent users in a client-server environment and can apply all four algorithms concurrently on a single dataset, returning both precision score and explanation. It is a ready tool for researchers who have datasets and classifiers prepared for explanation. This work bridges the gap between prediction and explanation, thereby allowing researchers to concentrate on data analysis and building state-of-the-art predictive models.
Microcontroller-Based Control Circuit for the Automatic Orientation of Solar Panels in the Direction of Sun

Solar panel consists of number of solar cells which converts solar energy into electrical energy. If they are continuously aligned in the direction of sun such that maximum amount of sun rays are received, it is possible to generate maximum electrical energy. Hence, to achieve maximum electrical energy, orienting the solar panels in the direction of sun is critical. In this paper, we propose a method to track the direction of sun and move the solar panels in the direction tracked. The control circuit consists of STM32F103C8T6 microcontroller, light-dependent resistors (LDRs) and DC gear motors. Sunlight is detected using four LDRs which are mounted on the four edges of solar panel. The microcontroller is programmed in such a way that output of LDR which receives maximum amount of sun light will direct the DC gear motors which in turn revolve the solar panels in a direction such that they receive maximum sunlight. Using the proposed method, we demonstrated the feasibility of tracking the direction of sun and adjusting the orientation of solar panels accordingly.
CAGEN - Context-Action Generation for Testing Self-learning Functions

This paper presents the general concept and the prototypical implementation for generating data for testing self-learning functions. The concept offers the possibility to create a data set consisting of several different data providers. For this purpose, a mediation pattern adapted to the data generation was developed. The concept was applied to test a self-learning comfort function by classifying the context data into three subgroups: real-, sensor-, and user data. This separation allows a more realistic simulation using the data to test a self-learning comfort function and detect possible malfunctions. The CAGEN concept was implemented as a prototype by simulating GPS and temperature data.
A Novel Way of Detecting and Correcting Transmitted Errors using Fuzzy Logic

Abstract Transmission and receival of messages always play an important role on various channels. Besides the transmission it is also necessary to be sure that only the exactly transmitted is transferred and the original message is not trampled due to the presence of some disturbance sources in the channel. Inspite of precautions if there is an error in transmission it is mandatory to recover the original message with the aid of several decoding measures. One among the decoding measures is the method of implementing the notion of fuzzy logic, which has its wide applications in several fields. Thus in this paper with the help of the concept of relative weights, the codes are studied in the sense of fuzzy logic to decode the transmitted message, detect errors in transmission and to correct them. Hence the technique of detecting and correcting errors presented in this paper provides a better outcome in comparison with several existing such methods. The prime aim of studying these concepts in fuzzy logic, is it’s precision and accuracy in terms of membership values which avoids the disparities that arises in other methods.
Data Wiping Tool: ByteEditor Technique

This Wiping Tool is an anti-forensic tool that is built to wipe data permanently from laptop's storage. This tool is capable to ensure the data from being recovered with any recovery tools. The objective of building this wiping tool is to maintain the confidentiality and integrity of the data from unauthorized access. People tend to delete the file in normal way, however, the file face the risk of being recovered. Hence, the integrity and confidentiality of the deleted file cannot be protected. Through wiping tools, the files are overwritten with random strings to make the files no longer readable. Thus, the integrity and the confidentiality of the file can be protected. Regarding wiping tools, nowadays, lots of wiping tools face issue such as data breach because the wiping tools are unable to delete the data permanently from the devices. This situation might affect their main function and a threat to their users. Hence, a new wiping tool is developed to overcome the problem. A new wiping tool named Data Wiping tool is applying two wiping techniques. The first technique is Randomized Data while the next one is enhancing wiping technique, known as ByteEditor. ByteEditor is a combination of two different techniques, byte editing and byte deletion. With the implementation of Object-Oriented methodology, this wiping tool is built. This methodology consists of analyzing, designing, implementation and testing. The tool is analyzed and compared with other wiping tools before the designing of the tool start. Once the designing is done, implementation phase take place. The code of the tool is created using Visual Studio 2010 with C# language and being tested their functionality to ensure the developed tool meet the objectives of the project. This tool is believed able to contribute to the development of wiping tools and able to solve problems related to other wiping tools.
Encoding-based memory for recurrent neural networks

Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study memorization from the point of view of the design and training of recurrent neural networks. We study how to maximize the short-term memory of recurrent units, an objective difficult to achieve using backpropagation. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. Experimental results on synthetic and real-world datasets show that the chosen encoding mechanism is superior to static encodings such as orthogonal models and the delay line. The method also outperforms RNN and LSTM units trained using stochastic gradient descent. Experiments on symbolic music modeling show that the training algorithm specialized for the memorization component improves the final performance compared to stochastic gradient descent.
AN OPTIMIZED ADABOOST ALGORITHM BASED ON K-MEANS CLUSTERING

Abstract Classification plays an important role on data mining techniques. AdaBoost is a classic upgrade algorithm on data classification. The Optimization algorithm on AdaBoost emerged is endless. In this paper we make some improvement on the base of a nonlinear AdaBoost algorithm based on statistics for K-nearest neighbors. In the basic algorithm, the prediction accuracies were improved more or less while it took plenty of time to calculate the Euclidean distance between the instance and all the samples. So we raise an improved method to shorten the computing time with K-means Clustering algorithm, and the classification accuracies will be as accurate as the original. Experiment results show that improvement is an effective method while the number of samples is large. And the bigger the number of the samples is, the more time can be saving in the stage of classification for the instance.
PUBG Winner Ranking Prediction using R Interface ‘h2o’ Scalable Machine Learning Platform

Gaming is an important aspect of the new generation and with this aspect comes improvement, which can be concerning graphics, sounds, gameplay, interaction, or ranking. The ranking is a healthy competition where different players can know standing around the globe, which makes them more competitive and help them stand above all. PUBG is multiplayer gaming that is supported on various platforms with a huge number of players online every day. This huge number can lead to a huge ranking, to overcome this difficulty there is a package in R programming called H2O which helps the developer to work with large numbers of datasets and also apply all different types of algorithms ranging from Machine Learning as well as Deep Learning and various unsupervised Learning. The algorithms used in this research are Linear Regression, Random Forest in the category of Machine Learning & Deep Neural Network in the category of Deep Learning. The metric evaluation of all these algorithms was carried out by using MAE (Mean Absolute Error) for the ranking of different players.
Automatic Seeded Region Growing with Level Set Technique Used for Segmentation of Pancreas

Image division process is dynamically performed vital character in image investigation. Image division process is called segmentation which applied in many applications like crop disease detection, monitoring of traffic, metal surface crack detection, medical image analysis, and Aerial Application. Top-down approach and bottom-up approaches are running for automatic segmentation of pancreas. Scale-Invariant Feature transforms, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) approaches are compiled for analysis. NMKFCM and SIFT are execute to identify feature of image. In SIFT, ample statistics of native feature of image be located. In bottom-up, Levelset is tested for image segmentation with automatic seeded region growing. Seeded region growing is extracting region from image truly and efficiently. In NMKFC, accuracy is improved but organ not extracted separately. SIFT, more time period to exacted image without single organ segment. LevelSet works on active contour which identifies boundaries of selected organ with neighbor pixel value. Seeded region growing method can discover seeds automatically via Affinity Propagation (AP) method, so it is avoiding user interaction. The speed function is planned to governor drive of the curve; and in diverse application problems, the key is to decide the suitable stopping criteria for the evolution. LevelSet algorithm is providing single organ segmentation with more accuracy than Top-Down approaches Lelel Set method is required minimum time period to calculated image.
TRANSLATION OF FALSE FRIENDS AMONG B2 LEVEL ENGLISH LANGUAGE LEARNERS

This paper investigates how successful B2 level English language learners (ELLs), high school students are in translating a group of most common false friends (FFs) from English to BCS and vice versa and examines whether they are more successful in translating absolute or partial FFs. In line with the classification by Otwinowska-Kasztelanic (2015), false friends are considered to be a class of cognates. They are further sub-classified into absolute FFs, which have the same or similar form and dissimilar meaning in two languages (e.g. eventually, meaning finally in English and eventualno, meaning possibly in BCS) and partial false friends with the same or similar form and one same and another dissimilar meaning. (e.g. argument in English, meaning reason, the same as argument in BSC and disagreement, the meaning for which a different word is used in BCS, rasprava). Due to their deceptive nature, FFs have been researched within different theoretical frameworks - theoretical, contrastive, applied linguistics, semantics, pragmatics and translation studies. By means of a survey and descriptive and inferential statistics, this paper confirms the hypothesis that there is a statistically significant difference between correct translation of FFs from English to BCS and their correct translation from BCS to English. Furthermore, the second hypothesis was also confirmed, namely that the B2 ELLs are more successful in translating partial than in translating absolute FFs. The research results suggest that in teaching FFs as items of deceptive vocabulary both explicit and implicit methods need to be applied