Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data

Social media is now playing an important role in influencing people's sentiments. It also helps analyze how people, particularly consumers, feel about a particular topic, product or an idea. One of the recent social media platforms that people use to express their thoughts is Twitter. Due to the fact that Turkish is an agglutinative language, its complexity makes it difficult for people to perform sentiment analysis. In this study, a sum of 13K Turkish tweets has been collected from Twitter using the Twitter API and their sentiments are being analyzed using machine learning classifiers. Random forests and support vector machines are the two kinds of classifiers that are adopted. Preprocessing methods were applied on the obtained data to remove links, numbers, punctuations and un-meaningful characters. After the preprocessing phase, unsuitable data have been removed and 10,500 out of the 13K downloaded dataset are taken as the main dataset. The datasets are classified to be either positive, negative or neutral based on their contents. The main dataset was converted to a stemmed dataset by removing stopwords, applying tokenization and also applying stemming on the dataset, respectively. A portion of 3,000 and 10,500 of the stemmed data with equal distribution from each class has been identified as the first dataset and second dataset to be used in the testing phase. Experimental results have shown that while support vector machines perform better when it comes to classifying negative and neutral stemmed data, random forests algorithm perform better in classifying positive stemmed data and thus a hybrid approach which consists of the hierarchical combination of random forest and support vector machines has also been developed and used to find the result of the data. Finally, the applied methodologies have been tested on both the first and the second dataset. It has been observed that while both support vector machines and random forest algorithms could not achieve an accuracy of up to 77% on the first and 72% on the second dataset, the developed hybrid approach achieve an accuracy of up to 86.4% and 82.8% on the first and second dataset, respectively.
How to balance the bioinformatics data: pseudo-negative sampling

Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance of the minority class of positive samples. Therefore, how to balance the bioinformatic data becomes a very challenging and difficult problem.In this study, we propose a new data sampling approach, called pseudo-negative sampling, which can be effectively applied to handle the case that: negative samples greatly dominate positive samples. Specifically, we design a supervised learning method based on a max-relevance min-redundancy criterion beyond Pearson correlation coefficient (MMPCC), which is used to choose pseudo-negative samples from the negative samples and view them as positive samples. In addition, MMPCC uses an incremental searching technique to select optimal pseudo-negative samples to reduce the computation cost. Consequently, the discovered pseudo-negative samples have strong relevance to positive samples and less redundancy to negative ones.To validate the performance of our method, we conduct experiments base on four UCI datasets and three real bioinformatics datasets. According to the experimental results, we clearly observe the performance of MMPCC is better than other sampling methods in terms of Sensitivity, Specificity, Accuracy and the Mathew's Correlation Coefficient. This reveals that the pseudo-negative samples are particularly helpful to solve the imbalance dataset problem. Moreover, the gain of Sensitivity from the minority samples with pseudo-negative samples grows with the improvement of prediction accuracy on all dataset.
Towards Generating Consumer Labels for Machine Learning Models

Machine learning (ML) based decision making is becoming commonplace. For persons affected by ML-based decisions, a certain level of transparency regarding the properties of the underlying ML model can be fundamental. In this vision paper, we propose to issue consumer labels for trained and published ML models. These labels primarily target machine learning lay persons, such as the operators of an ML system, the executors of decisions, and the decision subjects themselves. Provided that consumer labels comprehensively capture the characteristics of the trained ML model, consumers are enabled to recognize when human intelligence should supersede artificial intelligence. In the long run, we envision a service that generates these consumer labels (semi-)automatically. In this paper, we survey the requirements that an ML system should meet, and correspondingly, the properties that an ML consumer label could capture. We further discuss the feasibility of operationalizing and benchmarking these requirements in the automated generation of ML consumer labels.
Lightweight residual densely connected convolutional neural network

Extremely efficient convolutional neural network architectures are one of the most important requirements for limited-resource devices (such as embedded and mobile devices). The computing power and memory size are two important constraints of these devices. Recently, some architectures have been proposed to overcome these limitations by considering specific hardware-software equipment. In this paper, the lightweight residual densely connected blocks are proposed to guaranty the deep supervision, efficient gradient flow, and feature reuse abilities of convolutional neural network. The proposed method decreases the cost of training and inference processes without using any special hardware-software equipment by just reducing the number of parameters and computational operations while achieving a feasible accuracy. Extensive experimental results demonstrate that the proposed architecture is more efficient than the AlexNet and VGGNet in terms of model size, required parameters, and even accuracy. The proposed model has been evaluated on the ImageNet, MNIST, Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. It achieves state-of-the-art results on Fashion MNIST dataset and reasonable results on the others. The obtained results show the superiority of the proposed method to efficient models such as the SqueezNet. It is also comparable with state-of-the-art efficient models such as CondenseNet and ShuffleNet.
Exploring CNN-Based Viewport Prediction for Live Virtual Reality Streaming

Live virtual reality streaming (a.k.a., 360-degree video streaming) is gaining popularity recently with its rapid growth in the consumer market. However, the huge bandwidth required by delivering the 360-degree frames becomes the bottleneck, keeping this application from a wider range of deployment. Research efforts have been carried out to solve the bandwidth problem by predicting the user's viewport of interest and selectively streaming a part of the whole frame. However, currently most of the viewport prediction approaches cannot address the unique challenges in the live streaming scenario, where there is no historical user or video traces to build the prediction model. In this paper, we explore the opportunity of leveraging convolutional neural network (CNN) to predict the user's viewport in live streaming by modifying the workflow of the CNN application and the training/testing process. The evaluation results reveal that the CNN-based method could achieve a high prediction accuracy with low bandwidth usage and low timing overhead.
Computing the Affective-Aesthetic Potential of Literary Texts

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (&gt;150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 &gt; 0.5, p &lt; 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p &lt; 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results.
Psycho-Emotional Well-Being of High School Students in Relation to Their Readiness for Professional Self-Determination

The article focuses on psycho-emotional well-being of high school students in the context of their professional self-determination.The sample consisted of 456 students of 9—11 classes of general education schools (207 males, 249 females) aged 14—18 years.The methods included techniques for measuring personality, cognitive-evaluative and emotional aspects of well-being as well as a set of diagnostic methods and indicators of professional self-determination.It is shown that structure of the students’ well-being is characterized by low levels of competency and self-acceptance, low activity and cheerfulness, increased anxiety and tension.The study identified a high percentage of students with reduced scores of psycho-emotional well-being.The revealed difficulties in professional self-determination were mostly related to externally motivated choices of profession, insufficient development of cognitive interests and professional type of personality, as well as to professional identity crisis.The study shows that different aspects of psycho-emotional well-being affect the parameters of professional self-determination.
Problems of interregional integration, cognitive modeling

The paper deals with the problems of interregional socio-economic integration. To study them, it was proposed to use models and methods of cognitive modeling of complex systems. Imitation cognitive modeling of complex systems can be used to describe, explain, generate new knowledge about the system of inter-regional economic integration and the processes occurring in it. This is necessary to designing regional development strategies, to meet the challenges of improving efficiency interregional socio-economic integration in the interests of raising the quality of life of the regional population. A cognitive map of the system of interregional socio-economic interaction between the two regions has been developed. The study of the structure, stability and other properties of the cognitive map, showed that it does not contradict the real system. A simulation of scenarios for the development of situations under various influences on the system was making. Comparing scenarios gives the opportunity to choose the best one that can be used as a basis for developing a regional development strategy.
A Novel 3D Seam Extraction Method Based on Multi-Functional Sensor for V-Type Weld Seam

To meet the current flexible modern manufacturing mode, based on the idea from coarse to fine, a novel three-dimensional(3D) seam extraction algorithm is proposed in this paper which includes the global rough extraction and the local fine positioning. Firstly, a multi-functional vision system based on Digital Light Processing(DLP) projector is designed to adapt different 3D measurement tasks. Secondly, a global sensor based on DLP projector is constructed which is used to acquire the whole weld seam. Meanwhile, a new feature extraction algorithm based on 3D information of work pieces is proposed. Thirdly, a line structured light sensor based on DLP projector is to serve as local sensor. Based on the results of rough extraction, the close-range laser scanning is to improve the measurement precision. Finally, the 3D path model and pose estimation of weld seam are done which could be applied into the 3D path teaching of welding robots. The experiments show that the proposed algorithm could well finish the high-precision 3D seam path planning through the “Global-Local” method.
Transcript Management Using Blockchain Enabled Smart Contracts

Blockchain has demonstrated immense potential leading to act as one of the key catalysts in the 4th Industrial revolution. Smart Contract is the programmable transactions, which enables and enforces complex transactions on Blockchain. Blockchain-enabled Smart Contracts are driving force for ubiquitous applications of Blockchain across the fields of Health, Education, Banking and many more. In this paper, we discuss one such use case of Blockchain in the field of Education, called it Transcript Management. Whenever students move from one institute to another, they are expected to submit an official transcript from the previous institute. Currently deployed conventional method of operation is highly inefficient, lacks transparency and leaves data susceptible to forgery or tampering. This paper presents a new method involving Blockchain-enabled smart contracts for sharing and managing transcripts across different institutions. We have proposed algorithms for different modules, namely transcript issuance, verification, acceptance, and the analysis and implementation results of these modules assure both security and efficiency required for this application.
The AI Driving Olympics at NeurIPS 2018

Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains largely unproven. To help bridge this gap, we present the "AI Driving Olympics" (AI-DO), a competition with the objective of evaluating the state of the art in machine learning and artificial intelligence for mobile robotics. Based on the simple and well-specified autonomous driving and navigation environment called "Duckietown," the AI-DO includes a series of tasks of increasing complexity—from simple lane-following to fleet management. For each task, we provide tools for competitors to use in the form of simulators, logs, code templates, baseline implementations and low-cost access to robotic hardware. We evaluate submissions in simulation online, on standardized hardware environments, and finally at the competition event. The first AI-DO, AI-DO 1, occurred at the Neural Information Processing Systems (NeurIPS) conference in December 2018. In this paper we will describe the AI-DO 1 including the motivation and design objections, the challenges, the provided infrastructure, an overview of the approaches of the top submissions, and a frank assessment of what worked well as well as what needs improvement. The results of AI-DO 1 highlight the need for better benchmarks, which are lacking in robotics, as well as improved mechanisms to bridge the gap between simulation and reality.
Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.
Spam Detection Using Clustering-Based SVM

Spam detection task is of much more importance than earlier due to the increase in the use of messaging and mailing services. Efficient classification in such a variety of messages is a comparatively onerous task. There are a variety of machine learning algorithms used for spam detection, one of which is Support Vector Machine, also known as SVM. SVM is widely used to classify text-based documents. Though SVM is a widely used technique in document classification, its performance in the spam classification is not the best due to the uneven density of the training data. In order to improve the efficiency of SVM, I introduce a clustering-based SVM method. The training data is pre-processed using clustering algorithms and then the SVM classifier is implemented on the processed dataset. This method would increase the performance by overcoming the problem of uneven distribution of training data. The experimental results show that the performance is improved compared to that of SVM.
Practical Application of Machine Learning based Online Intrusion Detection to Internet of Things Networks

Internet of Things (IoT) devices participate in an open and distributed perception layer, with vulnerability to cyber attacks becoming a key concern for data privacy and service availability. The perception layer provides a unique challenge for intrusion detection where resources are constrained and networks are distributed. An additional challenge is that IoT networks are a continuous non-stationary data stream that, due to their variable nature, are likely to experience concept drift. This research aimed to review the practical applications of online machine learning methods for IoT network intrusion detection, to answer the question if a resource efficient architecture can be provided? An online learning architecture is introduced, with related IDS approaches reviewed and evaluated. Online learning provides a potential memory and time efficient architecture that can adapt to concept drift and perform anomaly detection, providing solutions for the resource constrained and distributed IoT perception layer. Future research should focus on addressing class imbalance in the data streams to ensure that minority attack classes are not missed.
Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.
cencro - Speedup of Video Quality Calculation using Center Cropping

Today's video streaming providers, e.g. Youtube, Netflix or Amazon Prime, are able to deliver high resolution and high-quality content to end users. To optimize video quality and to reduce transmission bandwidth, new encoders and smarter encoding schemes are required. Encoding optimization forms an important part of this effort in reducing bandwidth and results in saving considerable amount of bitrate. For such optimization, accurate and computationally fast video quality models are required, e.g. Netflix's VMAF. However, VMAF is a full-reference (FR) metric, and the calculation of such metrics tend to be slower in comparison to other metrics, due to the amount of data that needs to be processed, especially for high resolutions of 4k and beyond. We introduce an approach to speed up video quality metric calculations in general. We use VMAF as an example with a video database up to 4K resolution videos, to show that our approach works well. Our main idea is that we reduce each frame of the reference and distorted video based on a center crop of the frame, assuming that most important visual information are presented in the middle of most typical videos. In total we analyze 18 different crop settings and compare our results with uncropped VMAF values and subjective scores. We show that this approach - named cencro - is able to save up to 95% computation time, with just an overall error of 4% considering a 360p center crop. Furthermore, we checked other full-reference metrics, and show that cencro performs similar good. As a last evaluation, we apply our approach to full-hd gaming videos, also in this scenario cencro can be successfully applied. The idea behind cencro is not restricted to full-reference models and can also be applied to other type of video quality models or datasets, or even for higher resolution videos such as 8K.
Image reflection removal using end‐to‐end convolutional neural network

Single image reflection removal is an ill-posed problem. To solve this problem, this study develops a network structure based on a deep encoder–decoder RRnet. Unlike most deep learning strategies applied in this context, the authors find that redundant information increases the difficulty of predicting images on the network; thus, the proposed method uses mixed reflection image cascaded edges as input to the network. The proposed network structure is divided into two parts: the first part is a deep convolutional encoder–decoder network. Its function uses the mixed reflection image and the target edge as input to predict the target layer. The second part is an identical encoder–decoder network structure. Its function uses the mixed reflection image and the reflection edge as input to predict the image reflection layer. In addition, the authors use joint loss to optimise the network model. To train the neural network, they also create an image dataset for reflection removal, which includes a true mixed reflection image and a synthetic mixed reflection image. They use four evaluation indicators to evaluate the proposed method and the other six methods. The experimental results indicate that the proposed method is superior to previous methods.