Reviewerly/exHarmony huggingface dataset:
Combining Labeled Datasets for Sentiment Analysis from Different Domains Based on Dataset Similarity to Predict Electors Sentiment

The use of social media data to mine opinions during elections has emerged as an alternative to traditional election polls. However, relying on social media data in electoral scenarios comes with a number of challenges, such as tackling sentences with domain specific terms, texts full of hate speech, noisy, informal vocabulary, sarcasm and irony. Also, in Twitter, for instance, loss of context may occur due to the imposed limit of characters to the posts. Furthermore, prediction tasks that use machine learning require labeled datasets and it is not trivial to reliably annotate them during the short period of campaigns. Motivated by the aforementioned issues, we investigate if it is possible to use or mix curated datasets from other domains as a starting point to opinion mining tasks during elections. To avoid introducing a knowledge from the other domains that could end up by disturbing the task, we propose to use similarity metrics that point out whether or not the dataset should be used. In our approach, we conduct a case study using the 2018 Brazilian Presidential Elections and labeled datasets for sentiment analysis from other domains. To identify the similarity between the datasets, we use the Jaccard distance and a metric based on word embeddings. Our experimental results show that taking into account the (dis) similarity between different domains, it is possible to achieve results closer to the ones that would be achieved with classifiers trained with annotated datasets of the electoral domain.
A Novel Approach for Compensation of Light Variation Effects with KELM Classification for Efficient Face Recognition

A novel technique for compensating the effect of light variations is proposed here for robust person identification using human face images. The proposed technique is adaptive and efficient for face recognition under varying illuminations generated due to light incident from different angles. Illumination variation’s effect is due to varying lighting conditions which are smoothly changing in nature. Therefore, illumination normalization is performed over some of the low-frequency discrete cosine transform (DCT) coefficients depending upon the illumination variations in the face image. A fuzzy modifier has been used to suppress the illumination variations on these low-frequency DCT coefficients. The number of low-frequency DCT coefficients is computed adaptively based upon the magnitude of these coefficients. The proposed approach utilizes KELM for the recognition of normalized face images and is tested over Extended YALE B face database. The experimental results clearly reveal that the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, the percentage error rate of 0%, 0.75%, and 1.11% on Subset 3, 4, and 5 of this database have been achieved, respectively.
A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles

Deep learning models have been particularly successful with image recognition using Convolutional Neural Networks (CNN). However, the learning of a contrast invariance and rotation equivariance response may fail even with very deep CNNs or by large data augmentations in training. We were inspired by the V1 visual features of the mammalian visual system to emulate as much as possible the early visual system and add more invariant capacities to the CNN. We present a new quaternion local phase convolutional neural network layer encoding three local phases. We present two experimental setups: An image classification task with four contrast levels, and a linear regression task that predicts the rotation angle of an image. In sum, we obtain new patterns and feature representations for deep learning, which capture illumination invariance and a linear response to rotation angles.
Comparison of Sentiment Analysis of Review Comments by Unsupervised Clustering of Features Using LSA and LDA

Text documents could be classified using words as features. As the number of words in the vocabulary is large, the dimension of the document space will be very high. In that case, the feature vector for a document is too long, and very sparse, and it makes clustering and classification algorithms fail. There are various ways to reduce this dimension. In this work, we used Latent Semantic Analysis (LSA), which is actuated by Singular Value Decomposition (SVD). After SVD, we have a compact representation of the documents, which are clustered. In a separate experiment, we did topic modeling using Latent Dirichlet Allocation (LDA). In this initial work, our premise is that comments are of two categories, positive and negative. We cluster the document, in the reduced dimensional space, into two, using K-means clustering. After dimension reduction by LSA and LDA, the ground truth for documents in two clusters was verified manually, and the results compared.In this work, we used tourists' comments as documents. Tourists visit to a place is influenced by comments from previous visitors. Our final goal is to extract factors that lead to positive comments and those leading to negative comments. That would help promoting tourist business by focusing on the factors that really matters for the customers.
An Adaptive Abnormal Behavior Detection using Online Sequential Learning

In the real world, normal and abnormal behavior patterns vary depending on a given environment, which means that the abnormal behavior detection model should be customized. To address this issue, in this paper, we employ OS-ELM (Online Sequential Extreme Learning Machine) and Autoencoder for adaptive abnormal behavior detection. First, state-transition probability tables of a target during an initial learning period are learned as normal behaviors. Then, Autoencoder-based anomaly detection is performed for the state-transition probability tables of subsequent time frames. The abnormal behavior detection model is updated by using OS-ELM algorithm every time a new probability table or behavior comes. The number of abnormal behavior detection instances is dynamically tuned to reflect the recent normal patterns or modes. Also, the table is compressed to reduce the computation cost. Evaluation results using a driving dataset of cars show that the proposed abnormal behavior detection accurately identifies normal and aggressive driving patterns with the optimal number of the abnormal behavior detection instances.
Evolution of Games Towards The Discovery of Noble Uncertainty

This paper presents two directions of game evolution using the multiplayer incomplete information game - Mahjong and two-person complete information game - Chess. The directions of these games show the transitions from higher uncertainty to lower uncertainty and vice versa, whereas the game refinement measure (GR) had been employed for the assessment. It indicates that people have been searching for a harmonic balance between deterministic and stochastic aspects when playing games, which in this study, called the noble uncertainty. Currently, this study conjectured that noble uncertainty can be found at GR ∈ [0.07, 0.08] where evolutionary changes of both Mahjong and Chess have converged to it.
On the Quality of Identifiers in Test Code

Meaningful, expressive identifiers in source code can enhance the readability and reduce comprehension efforts. Over the past years, researchers have devoted considerable effort to understanding and improving the naming quality of identifiers in source code. However, little attention has been given to test code, an important resource during program comprehension activities. To better grasp identifier quality in test code, we conducted a survey involving manually written and automatically generated test cases from ten open source software projects. The survey results indicate that test cases contain low quality identifiers, including the manually written ones, and that the quality of identifiers is lower in test code than in production code. We also investigated the use of three state-of-the-art rename refactoring recommenders for improving test code identifiers. The analysis highlights their limitations when applied to test code and supports mapping out a research agenda for future work in the area.
Real-Time UAV Tracking Based on PSR Stability

In this paper, a real-time unmanned aerial vehicle (UAV) tracking method is proposed. This approach builds a target representation from histograms of oriented gradient (HOG) and ColorNames features. Correlation filters have been utilized in tracking recently because of their high efficiency. To better fuse the tracking results from different features, peak-to-sidelobe ratio (PSR) is employed to evaluate robustness of our trackers. A stability measure is proposed, based on the PSR values computed over a short period of time which is also used to predict object position. Additionally, we show that the proposed PSR stability enables our tracking method to be robust to various appearance variations. The method is carried out on five UAV tracking datasets and achieves appealing results comparable to state-of-the-art trackers but at a lower computational complexity.
Robust mission planning for Autonomous Marine Vehicle fleets

Mission planning for Autonomous Marine Vehicles (AMVs) is non-trivial because significant uncertainty is present when profiling the operating environment, especially for underwater missions. Mission complexity is compounded for each vehicle added to the mission. In practice, fleet operations are formulated as separate temporal problems by the operator and solved using a temporal planner. This paper proposes a planning method that uses energy as the base planning resource instead of time. Unlike temporal planners, energy planners account for physical loads endured by the vehicles. The extent of uncertainty in the vehicle loads is clarified by using the vehicle dynamics model and Monte Carlo simulation on the model parameters. The planning method is a multistage procedure to decompose operator specified task, obstacle, and vehicle data into an energy formulation of the Team Orienteering Problem (TOP) which is then solved using Discrete Strengthened PSO (DStPSO). The DStPSO algorithm has been modified to include a selective swarm size decay method that allows for larger initial swarm sizes to promote early exploration and preserves a percentage of the best performing particles on each iteration to save computational resources. The planner produces near-optimal routes containing feasible trajectories for individual vehicles that maximise tasks completed according to individual vehicle energy constraints. A case-study mission for long-term, large-scale, underwater inspection of a wind turbine array was converted into input data to evaluate the planner. Energy planning presents the opportunity for vehicles to actively monitor the feasibility of their individual plan against their current energy consumption, allowing for advanced reasoning and fault handling to occur in situ without operator assistance.
LiDAR Performance Review in Arctic Conditions

This article aims to outline the key results of testing and encountered challenges of various LiDARs, radar and stereo camera in arctic weather conditions. The test session was conducted in two different urban areas in Finland in the middle of January 2019. The arctic conditions turned out to be challenging for the sensors dedicated more to areas where temperature stays relatively warm. The aim of this one-week test session was to assess performance deterioration when powdered snow, salted road, snowy ground and sun light influence reliability of the future automated driving functions. This study focuses mainly on the issues with hardware that are basic building blocks for the situation awareness software modules. Furthermore, the countermeasures such as protecting sensors and mounting positions have been proposed.The test results indicate that some sensors significantly lose performance when temperature drops to less than -10 degrees centigrade. The problem is not merely mechanical freezing of the spinning LiDAR components but properties of laser illumination may change due to temperature variation, too. Since LiDAR is an optical device, they also suffer when there is turbulent snow in front of the sensor. The turbulence looks like a noise and partially blocks the laser echoes from surrounding environment. The performance, measured by laser pulse echo count, can with some sensors drop more than 50 percent. This seriously diminishes the sensing range and furthermore, makes pattern recognition unreliable. The two other sensor types which were taken into account are stereo vision and radar. They have a role in automated driving to compensate performance degradation of LiDARs due to arctic conditions.
Filtering Bengali Political and Sports News of Social Media from Textual Information

Web is being updated rapidly day by day. As a result of the modern media development, news also published very quickly. Generally, it takes less time to appear in the newspapers than an incident happens. But searching the relevant news through internet about one’s choice is the foremost task. News classification is the promising area that fulfills what they want. As Bengali is the common language for a huge number of people; they feel comfortable to use it for publishing news in different news portal and social media. Different post and comments are also written in Bangla. So Bangla new in web are promptly increased, classification of Bangla news according to the subject is definitely needed. This paper focused on some machine learning methods such as Naive Bayes Classifier that is based on probability theory, Support Vector Machine and Neural Networks to classify news as well as post or comments written in Bangla. 1000 long written post are collected from different news portal as well as social media. A Self- created News Corpus is used for experimental analysis. For training and testing a Feature Vector is constructed which is based on TF-IDF. The result shows that the Naive Bayes performs better than other three algorithms. In order to get better accuracy stop word are removed from documents. Bengali stemming has done to reduce the size of the dictionary. Moreover a comparison has been made to show the accuracy of different algorithm.
Clustering datasets with demographics and diagnosis codes

Clustering data derived from Electronic Health Record (EHR) systems is important to discover relationships between the clinical profiles of patients and as a preprocessing step for analysis tasks, such as classification. However, the heterogeneity of these data makes the application of existing clustering methods difficult and calls for new clustering approaches. In this paper, we propose the first approach for clustering a dataset in which each record contains a patient's values in demographic attributes and their set of diagnosis codes. Our approach represents the dataset in a binary form in which the features are selected demographic values, as well as combinations (patterns) of frequent and correlated diagnosis codes. This representation enables measuring similarity between records using cosine similarity, an effective measure for binary-represented data, and finding compact, well-separated clusters through hierarchical clustering. Our experiments using two publicly available EHR datasets, comprised of over 26,000 and 52,000 records, demonstrate that our approach is able to construct clusters with correlated demographics and diagnosis codes, and that it is efficient and scalable.
A Critical Analysis of Software Testing Tools

Abstract Software testing is a crucial part of software development, it ensures that the software been developed performs all functional requirements and is free from any form of defect and errors. This ensures that the software is of good quality and standard. While testing a software, it is important to be time and cost consciousness. This reason has made most testers switch from the manual testing process to automation of software testing, to reduce time and cost. But then selecting a software testing tool for automated testing that best fit a project is important yet challenging task, the objective of this paper is to evaluate some of the most used software testing tool, identify their strength and weakness and also the field where they can be employed, either for mobile testing, web service testing or both. The method utilized in the paper involved the analysis of recommended literatures to pinpoint necessary testing tools selected based on inclusion and exclusion criteria, that were evaluated. The result of the analysis indicted that based on the selected criteria, testing tools that supports web platform testing made up 17%, while tools that supported desktop and mobile platforms was 10% and 7% respectively. also, 7% of the tools examined were found to be open source tools while 12% were licensed tools. 10% of the testing tools examined supported the test result and report generation criterion while tools that require the knowledge of programming language was least as only 3% of examined tools supported this criterion. It was observed that there is no one perfect tool for testing, but for a particular testing purpose, tradeoffs can be made to select the best tool depending on the size of the project, the budgeted cost for testing, the platform of the application and also the language that is used to develop the project.
Social Intelligence: The Way We Interact, The Way We Go

Welcome to the last issue of the IEEE Transactions on Computational Social Systems (TCSS) of this year, with a special focus on “blockchainbased secure and trusted computing for IoT.” Here, we have 18 regular articles and a brief discussion on social intelligence. I would like to take this opportunity to thank and congratulate everyone, especially our editorial board for a great job well done. Looking forward to working with you all in 2020!
Deep Video Precoding

Several groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG H.264/AVC, H.265/HEVC, VVC, Google VP9 and AOMedia AV1, AV2, as well as existing container and transport formats, without imposing any changes at the client side. Such compatibility is a crucial aspect when it comes to practical deployment, especially when considering the fact that the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems. In our current design, the core precoding component comprises a cascaded structure of downscaling neural networks that operates during video encoding, prior to transmission. This is coupled with a precoding mode selection algorithm for each independently-decodable stream segment, which adjusts the downscaling factor according to scene characteristics, the utilized encoder, and the desired bitrate and encoding configuration. Our framework is compatible with all current and future codec and transport standards, as our deep precoding network structure is trained in conjunction with linear upscaling filters (e.g., the bilinear filter), which are supported by all web video players. Extensive evaluation on FHD (1080p) and UHD (2160p) content and with widely-used H.264/AVC, H.265/HEVC and VP9 encoders, as well as a preliminary evaluation with the current test model of VVC (v.6.2rc1), shows that coupling such standards with the proposed deep video precoding allows for 8% to 52% rate reduction under encoding configurations and bitrates suitable for video-on-demand adaptive streaming systems. The use of precoding can also lead to encoding complexity reduction, which is essential for cost-effective cloud deployment of complex encoders like H.265/HEVC, VP9 and VVC, especially when considering the prominence of high-resolution adaptive video streaming.
A Value-sensitive Design Perspective of Cryptocurrencies: A Research Agenda

Cryptocurrencies and their underlying blockchain technology have begun to transform numerous industries. Although we have seen an uptrend in the types of created cryptocurrencies, it has not yet translated into mainstream adoption., In this paper, we use value-sensitive design principles to identify values among current and potential cryptocurrency adopters. Using Bitcoin as the context for this qualitative research study, we use grounded theory analytical techniques to discover manifested values among users and non-users. We develop a cryptocurrency value-sensitive design framework to summarize our results. As our main contribution, we offer a research agenda based on the cryptocurrency stakeholders’ underlying value system. This agenda can help information systems scholars apply this value-sensitive design perspective to their own cryptocurrency research.
Person re-identification combining deep features and attribute detection

Attributes-Based Re-Identification is a way of identifying individuals when presented with multiple pictures taken under varying conditions. The method typically builds a classifier to detect the presence of certain appearance characteristics in an image, and creates feature descriptors based on the output of the classifier. We improve attribute detection through spatial segregation of a person's limbs using a skeleton prediction method. After a skeleton has been predicted, it is used to crop the image into three parts - top, middle and bottom. We then pass these images to an attribute prediction network to generate robust feature descriptors. We evaluate the performance of our method on the VIPeR, PRID2011 and i-LIDS data sets, comparing our results against the state-of-the-art to demonstrate competitive overall matching performance.
Spectroscopic techniques applied to discriminate soils for forensic purposes

Soils are a complex mixture with a variety of mineralogical, chemical, biological and physical properties, which can be explored within forensic case work. This study aimed to apply energy-dispersive X-ray fluorescence (EDXRF) and Fourier transform infrared (FTIR) spectroscopic techniques to discriminate soil samples collected in southern Brazil in a forensic context. Four replicates of soil were collected at four sites: two sites from the same parent material (claystone) and two other sites from limestone and granite/gneiss respectively. The physical and chemical (organic and mineral composition) and spectroscopic techniques (EDXRF and FTIR) produced 16 quantitative variables from only 2 g of sample. The main results from a forensic context were the separation of the soils collected from close neighbourhoods developed on the same parent material and the separation of soils collected in the A and B horizons of the same soil profile. The highest degree of similarity in the clustering of samples collected at the same site was 98.6% (B horizon in claystone domain). In addition to the parent material, the effect of organic matter on the chemical and mineralogical characteristics of the A horizon was important in the grouping dynamics of samples. This work demonstrated the potential of spectroscopic techniques in a forensic context.