Reviewerly/exHarmony huggingface dataset:
Bengali VADER: A Sentiment Analysis Approach Using Modified VADER

Sentiment analysis is an essential field of natural language processing (NLP) that classifies the opinion expressed in a text according to its polarity (e.g., positive, negative or neutral). Bengali NLP research is lagging behind English NLP, where there are very few works on Bengali sentiment analysis. In this paper, we approach this issue by modifying a popular English tool VADER to support Bengali sentiment polarity identification. We have compiled a Bengali polarity lexicon from the English polarity lexicon of VADER. Furthermore, we have modified the functionalities of English VADER, so that it can directly classify Bengali text sentiments without the requirement of Bengali to English translation using tools such as Google Translator, MyMemory Translator, etc. Our experiments demonstrate that the modified Bengali VADER significantly improves the sentiment analysis result of Bengali text over the current model.
A novel robust kernel for classifying high-dimensional data using Support Vector Machines

This paper presents a new semantic kernel for classification of high-dimensional data in the framework of Support Vector Machines (SVM). SVMs have gained widespread application due to their relatively higher accuracy. The efficacy of SVMs, however, depends upon the separation of the data itself as well as the kernel function. Text data, for instance, is difficult to classify due to synonymy and polysemy in its contents, having multi-topical instances that can result in mislabeling, and being highly sparse in the bag-of-words representation. While the soft margin parameter and kernel tricks are used in SVM to deal with outliers and non-linearly separable data, using data statistics and correlation has not been fully explored in the literature. This paper explore the use co-similarity (i.e., soft co-clustering) to find latent relationships between documents motivated by the success of co-clustering and subspace clustering methods. It has been shown that the use of weighted higher-order paths between instances in the data can be a good measure of similarity values which can then be used for both classification and to correct mislabeled (or outlier) data in the training set. The proposed kernel is generic in nature and suitable for sparse, dyadic data where direct co-occurrences are not necessary common as in the case of textual data, link-analysis in social media networks, co-authorship, etc. It also studies the impact of noise in the training data and provides a technique to re-label such instances. It is also observed that re-labelling of selected training data reduces the adverse effect of outliers or label noise and can greatly improve the classification of the test data. To the best of our knowledge, we are the first to introduce a supervised co-similarity based kernel function and also provide mathematical formulation to show that it is a valid Mercer's kernel. Our experiments show that the proposed framework outperforms current and state-of-the-art methods in terms of classification accuracy and is more resilient to label noise.
Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Summary As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large‐scale distributed databases is an NP‐hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC‐GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global–local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top‐K query plans is improved for a given distributed query. Moreover, this method decreases the overhead. However, it needs a longer execution time.
Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters

One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers. The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes.
Autonomous Control of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are being increasingly used in different applications in both military and civilian domains [...]
Unifying Heterogeneous Classifiers With Distillation

In this paper, we study the problem of unifying knowledge from a set of classifiers with different architectures and target classes into a single classifier, given only a generic set of unlabelled data. We call this problem Unifying Heterogeneous Classifiers (UHC). This problem is motivated by scenarios where data is collected from multiple sources, but the sources cannot share their data, e.g., due to privacy concerns, and only privately trained models can be shared. In addition, each source may not be able to gather data to train all classes due to data availability at each source, and may not be able to train the same classification model due to different computational resources. To tackle this problem, we propose a generalisation of knowledge distillation to merge HCs. We derive a probabilistic relation between the outputs of HCs and the probability over all classes. Based on this relation, we propose two classes of methods based on cross-entropy minimisation and matrix factorisation, which allow us to estimate soft labels over all classes from unlabelled samples and use them in lieu of ground truth labels to train a unified classifier. Our extensive experiments on ImageNet, LSUN, and Places365 datasets show that our approaches significantly outperform a naive extension of distillation and can achieve almost the same accuracy as classifiers that are trained in a centralised, supervised manner.
Fast AES Implementation: A High-Throughput Bitsliced Approach

In this work, a high-throughput bitsliced AES implementation is proposed, which builds upon a new data representation scheme that exploits the parallelization capability of modern multi/many-core platforms. This representation scheme is employed as a building block to redesign all of the AES stages to tailor them for multi/many-core AES implementation. With the proposed bitsliced approach, each parallelization unit processes an unprecedented number of thirty-two 128-bit input data. Hence, a high order of prallelization is achieved by the proposed implementation technique. Based on the characteristics of this new implementation model, the ShiftRows stage can be implicitly handled through input rearrangement and is simplified to the point where its computing process can be neglected. In this implementation, costly Byte-wise operations are performed through register shift and swapping. In addition, the need for look-up table based I/O operations, which are used by the Substitute Bytes stage is eliminated through using S-box logic circuit. The S-box logic circuit is optimized to simultaneously process 32 chunks of 128-bit input data. We develop high-throughput CTR and ECB AES encryption/decryption on 6 CUDA-enabled GPUs, which achieve 1.47 and 1.38 Tbps of encryption throughput on Tesla V100 GPU, respectively.
Falcon

We contribute user-centered prefetching and indexing methods that provide low-latency interactions across linked visualizations, enabling cold-start exploration of billion-record datasets. We implement our methods in Falcon, a web-based system that makes principled trade-offs between latency and resolution to optimize brushing and view switching times. To optimize latency-sensitive brushing actions, Falcon reindexes data upon changes to the active view a user is brushing in. To limit view switching times, Falcon initially loads reduced interactive resolutions, then progressively improves them. Benchmarks show that Falcon sustains real-time interactivity of 50fps for pixel-level brushing and linking across multiple visualizations with no costly precomputation. We show constant brushing performance regardless of data size on datasets ranging from millions of records in the browser to billions when connected to a backing database system.
A hybrid approach for identifying the structure of a Bayesian network model

Bayesian network (BN) has been widely used in modeling expert knowledge and reasoning in many application domains, due to its power of representing probabilistic knowledge over a set of interacting variables under significant uncertain. However, the actual construction of a Bayesian network model for a domain remains to be a challenging task since it involves learning the causal structure and the conditional probability table of BN variables from experts, and/or observational data. In this paper, we explore the unique challenge of applying Bayesian network to model how public safety officials represent and reason about threats of evolving mass protest. Anticipating threats of mass protests is crucial for choosing proper intervention strategies, but it is also inherently difficult because a large number of interacting factors contribute to the dynamics of protests with high uncertainty and contingency. In constructing the BN model for this complex domain, we found that traditional methods of discovering BN structure either from data or experts are inadequate, due to the scarcity of data and the highly stochastic nature of mass protest events. Instead, we proposed a hybrid approach (called “ISM-K2”) which enhances the BN structure learning methods (K2 algorithm) by expert knowledge elicited using the ISM (interpretive structural model) method. We show that the BN model constructed using ISM-K2 approach is superior than three other base-line models (the logistic regression model, the BN constructed only by expert knowledge from ISM, and the BN constructed only by data learning). Finally, we show the potential of using our BN threat assessment model for supporting practical policing decisions.
DMove

We present DMove, directional motion-based interaction for Augmented Reality (AR) Head-Mounted Displays (HMDs) that is both hands- and device-free. It uses directional walk-ing as a way to interact with virtual objects. To use DMove, a user needs to perform directional motions such as mov-ing one foot forward or backward. In this research, we first investigate the recognition accuracy of the motion direc-tions of our method and the social acceptance of this type of interactions together with users' comfort rating for each direction. We then optimize its design and conduct a sec-ond study to compare DMove in task performance and user preferences (workload, motion sickness, user experience), with two approaches-Hand interaction (Meta 2-like) and Head+Hand interaction (HoloLens-like) for menu selection tasks. Based on the results of these two studies, we provide a set of guidelines for DMove and further demonstrate two applications that utilize directional motions.
A Survey of Full-Body Motion Reconstruction in Immersive Virtual Reality Applications

Due to recent advances in virtual reality (VR) technology, the development of immersive VR applications that track body motions and visualize a full-body avatar is attracting increasing research interest. This paper reviews related research to gather and to critically analyze recent improvements regarding the potential of full-body motion reconstruction in VR applications. We conducted a systematic literature search, matching VR and full-body tracking related keywords on IEEE Xplore, PubMed, ACM, and Scopus. Fifty-three publications were included and assigned in three groups: studies using markerless and marker-based motion tracking systems as well as systems using inertial measurement units. All analyzed research publications track the motions of the user wearing a head-mounted display and visualize a full-body avatar. The analysis confirmed that a full-body avatar can enhance the sense of embodiment and can improve the immersion within the VR. The results indicated that the Kinect device is still the most frequently used sensor (27 out of 53). Furthermore, there is a trend to track the movements of multiple users simultaneously. Many studies that enable multiplayer mode in VR use marker-based systems (7 out of 17) because they are much more robust and can accurately track full-body movements of multiple users in real-time.
The Dissimilarity-Consensus Approach to Agreement Analysis in Gesture Elicitation Studies

We introduce the dissimilarity-consensus method, a new approach to computing objective measures of consensus between users' gesture preferences to support data analysis in end-user gesture elicitation studies. Our method models and quantifies the relationship between users' consensus over gesture articulation and numerical measures of gesture dissimilarity, e.g., Dynamic Time Warping or Hausdorff distances, by employing growth curves and logistic functions. We exemplify our method on 1,312 whole-body gestures elicited from 30 children, ages 3 to 6 years, and we report the first empirical results in the literature on the consensus between whole-body gestures produced by children this young. We provide C# and R software implementations of our method and make our gesture dataset publicly available.
ARPen

Modeling in Augmented Reality (AR) lets users create and manipulate virtual objects in mid-air that are aligned to their real environment. We present ARPen, a bimanual input technique for AR modeling that combines a standard smartphone with a 3D-printed pen. Users sketch with the pen in mid-air, while holding their smartphone in the other hand to see the virtual pen traces in the live camera image. ARPen combines the pen's higher 3D input precision with the rich interactive capabilities of the smartphone touchscreen. We studied subjective preferences for this bimanual input technique, such as how people hold the smartphone while drawing, and analyzed the performance of different bimanual techniques for selecting and moving virtual objects. Users preferred a bimanual technique casting a ray through the pen tip for both selection and translation. We provide initial design guidelines for this new class of bimanual AR modeling systems.
"Beautiful Seams"

This paper tracks a debate that occurred, first, within the field of Ubiquitous Computing but quickly spread to CHI and beyond, in which design scholars argued that seamlessness had long been an implicit and privileged design virtue, often at the expense of seamfulness. Seamless design emphasizes clarity, simplicity, ease of use, and consistency to facilitate technological interaction. Seamful design emphasizes configurability, user appropriation, and revelation of complexity, ambiguity or inconsistency. Here we review these literatures together and argue that, rather than rival approaches, seamful and seamless design are complements, each emphasizing different aspects of downstream user agency. Ultimately, we situate this debate within the larger, perennial discussion about the strategic revelation and concealment of human and technological operations, and therein the role of design.
Multi-Target State Estimation Using Interactive Kalman Filter for Multi-Vehicle Tracking

In this paper, an interactive Kalman filter (IKF) is proposed to demonstrate the interaction between targets as to how the behavior of a desired target is affected by the behavior of its neighbors. The IKF utilizes two types of interactions available in multi-agent systems, namely, cooperative and competitive. The IKF is similar to the distributed Kalman filter (DKF) in terms of architecture, method of representation of equations, and use of neighborhood weight matrix while IKF appears to be a general form of DKF. In this method, a network of IKF nodes is constructed such that each node is associated with every target. There are edges between nodes for which the corresponding targets have effect on each other. Time-varying weights are used to control the interaction information exchanged among IKF nodes. The method of calculating interaction weights in the weight matrix plays a key role on the estimation results. The calculation of optimal IKF gain and evaluations on MOTP, MOTA, and MSE metrics illustrate the effectiveness of the proposed filter in vehicle tracking.
Evolutionary inversion of class distribution in overlapping areas for multi-class imbalanced learning

Inductive learning from multi-class and imbalanced datasets is one of the main challenges for machine learning. Most machine learning algorithms have their predictive performance negatively affected by imbalanced data. Although several techniques have been proposed to deal with this difficulty, they are usually restricted to binary classification datasets. Thus, one of the research challenges in this area is how to deal with imbalanced multiclass classification datasets. This challenge become more difficult when classes containing fewer instances are located in overlapping regions of the data attribute space. In fact, several studies have indicated that the degree of class overlapping has a higher effect on predictive performance than the global class imbalance ratio. This paper proposes a novel evolutionary ensemble-based method for multi-class imbalanced learning called the evolutionary inversion of class distribution in overlapping areas for multi-class imbalanced learning (EVINCI). EVINCI uses a multiobjective evolutionary algorithm (MOEA) to evolve a set of samples taken from an imbalanced dataset. It selectively reduces the concentration of less representative instances of the majority classes in the overlapping areas while selecting samples that produce more accurate models. In experiments performed to evaluate its predictive accuracy, EVINCI was superior to state-of-the-art ensemble-based methods for imbalanced learning.
Enhanced Secured Map Reduce layer for Big Data privacy and security

The publication and dissemination of raw data are crucial elements in commercial, academic, and medical applications. With an increasing number of open platforms, such as social networks and mobile devices from which data may be collected, the volume of such data has also increased over time move toward becoming as Big Data. The traditional model of Big Data does not specify any level for capturing the sensitivity of data both structured and unstructured. It additionally needs to incorporate the notion of privacy and security where the risk of exposing personal information is probabilistically minimized. This paper introduced security and privacy layer between HDFS and MR Layer (Map Reduce) known as new proposed Secured Map Reduce (SMR) Layer and this model is known as SMR model. The core benefit of this work is to promote data sharing for knowledge mining. This model creates a privacy and security guarantee, resolve scalability issues of privacy and maintain the privacy-utility tradeoff for data miners. In this SMR model, running time and information loss have a remarkable improvement over the existing approaches and CPU and memory usage are also optimized.
Measuring the Separability of Shape, Size, and Color in Scatterplots

Scatterplots commonly use multiple visual channels to encode multivariate datasets. Such visualizations often use size, shape, and color as these dimensions are considered separable--dimensions represented by one channel do not significantly interfere with viewers' abilities to perceive data in another. However, recent work shows the size of marks significantly impacts color difference perceptions, leading to broader questions about the separability of these channels. In this paper, we present a series of crowdsourced experiments measuring how mark shape, size, and color influence data interpretation in multiclass scatterplots. Our results indicate that mark shape significantly influences color and size perception, and that separability among these channels functions asymmetrically: shape more strongly influences size and color perceptions in scatterplots than size and color influence shape. Models constructed from the resulting data can help designers anticipate viewer perceptions to build more effective visualizations.
Utility-based multi-criteria recommender systems

Recommender systems have been demonstrated as a useful tool in assisting decision makings. Multi-criteria recommender systems take advantage user preferences in multiple criteria to produce better recommendations. In this paper, we propose a utility-based multi-criteria recommendation algorithm, in which we learn the user expectations by different learning-to-rank methods. Our experimental results based on the real-world data sets demonstrate the effectiveness of the proposed models.
Improving Security and Privacy Attribute Based Data Sharing in Cloud Computing

Data sharing is a convenient and economic service supplied by cloud computing. Data contents privacy also emerges from it since the data is outsourced to some cloud servers. To protect the valuable and sensitive information, various techniques are used to enhance access control on the shared data. In these techniques, Ciphertext-policy attribute-based encryption (CP-ABE) can make it more convenient and secure. Traditional CP-ABE focuses on data confidentiality merely, while the user's personal privacy protection is an important issue at present. CP-ABE with hidden access policy ensures data confidentiality and guarantees that user's privacy is not revealed as well. However, most of the existing schemes are inefficient in communication overhead and computation cost. Moreover, most of those works take no consideration about authority verification or the problem of privacy leakage in authority verification phase. To tackle the problems mentioned above, a privacy preserving CP-ABE scheme with efficient authority verification is introduced in this paper. Additionally, the secret keys of it achieve constant size. Meanwhile, the proposed scheme achieves the selective security under the decisional n-BDHE problem and decisional linear assumption. The computational results confirm the merits of the presented scheme.