Reviewerly/exHarmony huggingface dataset:
A Local Path Planning for Perpendicular Parking in Limited Parking Spaces

This paper presents a local path planning algorithm for perpendicular parking of an autonomous vehicle. We propose an optimal path planning algorithm based on circle and line combination in the presence of predefined obstacles. We formulate collision avoidance conditions using equality and inequality constraints. We solve a generalized optimization problem with the constraints, which minimizes an arc-length of the parking path. We validate the performance of the perpendicular parking path generation algorithms for various parking spaces in MATLAB environments.
Use of virtual reality and 360° video as narrative resources in the documentary genre: Towards a new immersive social documentary?

Virtual reality and 360° video are some of the latest technological developments within the media and communications industry. These technologies, which are designed to facilitate viewer immersion, are currently being used to create fictional and non-fictional content, thus giving rise to a new audio-visual narrative. On the basis of these premises, this research article analyses how immersive narratives are applied to the social documentary genre in its social dimension. To this end, qualitative content analysis was performed on a sample of 49 immersive documentaries published on the WITHIN platform. This analysis, which was completed with quantitative data, allowed us to confirm that these technologies have enabled the development of immersive narratives, which has given birth to a new type of documentary – the ‘immersive social documentary’.
IDS based fake content detection on Social Network Using Bloom Filtering

In Today's Online Social Networks, Spam delivery is the most common issue. In the Receiver Side only Most of the modern spam-filtering Techniques are deployed. They are good at filtering spam for end users, but spam messages still keep wasting Internet bandwidth and the storage space of servers. We use the intrusion detection system to monitor the SMTP sessions in a university campus, and track the number and the uniqueness of the recipients' Online social Networks addresses in the outgoing messages from each individual internal host as the features for detecting spamming bots. Due to the huge number of Spams observed in the SMTP sessions, we store and manage them efficiently in the Bloom filters.
Connexivity and the Pragmatics of Conditionals

Abstract In this paper, I investigate whether the intuitions that make connexive logics seem plausible might lie in pragmatic phenomena, rather than the semantics of conditional statements. I conclude that pragmatics indeed underwrites these intuitions, at least for indicative statements. Whether this has any effect on logic choice (and what that effect might be), however, heavily depends on one’s semantic theory of conditionals and on how one chooses to logically treat pragmatic failures.
The Impact of Applying Virtual Reality Technology to Spatial Ability Learning in Elementary School Students

This study explored the effects of virtual reality (VR) technology applied to spatial ability learning in elementary school students. As supplement materials to the Geometry Unit in the curriculum, the learning materials were developed based on the mathematics competence indicators for fifth and sixth graders in Taiwan. Designed to enhance spatial concept and logical thinking, the materials tapped into VR technology to transform two-dimensional graphics into three-dimensional spaces, in view of effectively solving the problem of learning abstract 3D spaces. A quasi-experimental method was used to understand the influence of different learning methods on students’ learning effectiveness and interest in learning. The experiment found that (1) the use of interactive VR learning materials significantly improved student’s spatial ability; (2) using VR learning materials enhanced the learning effectiveness of students with low achievement in mathematics; and (3) the introduction of VR learning materials improved students’ motivation for learning spatial concepts. The findings showed that the digital learning materials developed in this study were beneficial to the teaching effectiveness of teachers and the learning needs of students.
Investigating Subjectivity Criterion for Multi-video Summarization

Currently, videos play an important role in people's every day lives and there is a continuous increase in their production and consumption. However, to make video information access a feasible task, smarter and more efficient strategies are needed in order to deal with this complex and huge data volume. In this context, automatic video summarization becomes a significant tool. This research area aims to produce a smaller version of original video, keeping its most meaningful information. Particularly for some relavant domains, like news and education, it is important a summary to be generated from a set of related videos - different videos about the same subject. In the automatic multi-video summarization field a common and essential step is to define content selection criteria. A promising approach, yet unexploited by related work, is to apply inclusion and exclusion criteria derived from human strategies. In this work, we investigate how exclusion criteria can help to keep multi-video summaries short and informative. We introduce an approach to detect subjectivity into video segments from news domain, allowing those segments to be not included into the summary. Our approach is evaluated by the means of objective measures of effectiveness. We also analyze the impact that removing such subjective segments has on multi-video summarization outputs by conducting a study with final users. Results indicate that the proposed approach is able of producing more compact summaries, keeping the relevant content for the users.
Multiple linked perspectives on hierarchical data

This paper describes an interactive web-based tool for visualizing hierarchical data including the recently developed concept of space-reclaiming icicle plots and several more traditional hierarchy visualizations. The tool provides ways to upload, share, explore, and compare hierarchical data using a multitude of different linked hierarchy visualizations. The current version supports up to 8 hierarchy visualizations, with the space-reclaiming icicle plots among them. Several of the visualizations can be shown in linked views while typical hierarchy parameters and visual variables can be changed on user demand. The interactive tool makes use of OpenGL and Angular, an industry standard JavaScript platform, and runs in a web browser. We illustrate the usefulness of the visualization tool by applying it to the NCBI taxonomy that consists of more than 300,000 hierarchically organized species while filtering for the tetrapoda subhierarchy. Finally, we explain implementation details and discuss limitations and scalability issues of the linked visualization techniques.
Multi-robot Cooperation for Assembly: Automated Planning and Optimization

In the field of industry 4.0 and smart factories of the future, dynamic teams of robots will play an important role in the manufacturing of custom-tailored products with small lot sizes. Especially the planning for such multi-functional robot cells forms a key challenge, finding appropriate tasks for cooperating robots to perform efficient production. In this paper, we present two approaches for automated planning of assembly programs for multiple robots that separate between domain and automation level. While one uses a decomposition approach with plan-space planning and explicit decomposition rules, the other considers heuristic searching in state space. Both approaches are evaluated with different planning problems in the blocks world domain (i.e., LEGO® DUPLO®). The evaluation shows that both approaches outperform the results achieved with classical planning based on state-space search such as \(A^*\). Furthermore, a concept for post-processing programs to optimize execution time by dependency-based interleaving of robot tasks is introduced that fully integrates into both planning approaches.
A Systematic Review of Personalized Collaborative Systems

Personalization, aiming at supporting users individually, according to their individual needs and prerequisites, has been discussed in a number of domains including learning, search, or information retrieval. In the field of human–computer interaction, personalization also bears high potential as users might exhibit varying and strongly individual preferences and abilities related to interaction. For instance, there is a good amount of work on personalized or adaptive user interfaces (also under the notion of intelligent user interfaces). Personalized human–computer interaction, however, does not only subsume approaches to support the individual user, it also bears high potential if applied to collaborative settings, for example, through supporting the individuals in a group as well as the group itself (considering all of its special dynamics). In collaborative settings (remote or co-located), there generally is a number of additional challenges related to human-to-human collaboration in a group, such as group communication, awareness or territoriality, device or software tool selection, or selection of collaborators. Personalized Collaborative Systems thus attempt to tackle many of these challenges. For instance, there are collaborative systems that recommend tools, content, or team constellations. Such systems have been suggested in different domains and different collaborative settings and contexts. In most cases, these systems explicitly focus on a certain aspect of personalized collaboration support (such as team composition). This article provides a broader, concise overview of existing approaches to Personalized Collaborative Systems based on a systematic literature review considering the ACM Digital Library.
A novel pipelined neural FIR architecture for nonlinear adaptive filter

This paper presents a novel adaptive pipelined neural finite impulse response (PNFIR) filter for nonlinear signal processing. Unlike traditional pipelined recurrent neural network (PRNN), each module of the PNFIR filter is a simple architecture that includes a standard FIR filter followed by a nonlinear activation function. The complete design of proposed filter includes two subsections: The nonlinear part consists of neural FIR (NFIR) modules which is interconnected in a chained form and simultaneously executed in a parallel fashion; the linear subsection is a tapped-delay-line (TDL) linear combiner. Based on convex combination architecture, the adaptive algorithm derived from the gradient descent approach is utilized to update weights of the nonlinear and linear parts. Moreover, the analysis of stability conditions and computational complexity is also presented. Numerous simulation experimental results on nonlinear dynamic systems identification, speech signal and chaotic time series prediction show that the proposed PNFIR filter has simpler architecture, faster convergence rate, and lower computation complexity than the PRNN and joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV).
The wonderful wizard of LoC: paying attention to the man behind the curtain of lines-of-code metrics

Lines-of-code metrics (loc) are commonly reported in Programming Languages (PL), Software Engineering (SE), and Systems papers. This convention has several different, often contradictory, goals, including demonstrating the `hardness' of a problem, and demonstrating the `easiness' of a problem. In many cases, the reporting of loc metrics is done not with a clearly communicated intention, but instead in an automatic, checkbox-ticking, manner.
Trolling as a Digital Literary Practice in the Russian Language Internet

This article explores trolling as a form of literary activity. It presents a number of specific types of trolling on the Russian-language Internet in connection with digital literature and the literary practices of various groups of Internet users. Techniques for writing provocative “troll-texts” were created and developed within subcultural groups in the 1990s. Later, from being a subversive practice known only to a few insiders, it became a mass technique described in meta-texts that identified rules for trolling and shared facts about its culture with all interested users. In the 2010s, the now popularised techniques of trolling came to be seen as effective strategies to deploy in online debates. At the same time, trolling began to be exploited as a weapon in online political campaigns. This study also shows that trolling texts are often structured around the fight for voice, and it is this conflict that dictates the formal properties of the discourse of trolling.
Sarcasm Detection of Media Text Using Deep Neural Networks

Ajnadkar, OmkarSarcasm detection in media text is a binary classification task where text can be either written straightly or sarcastically (with irony) where the intended meaning is the opposite of what is seemingly expressed. Performing sarcasm detection can be very useful in improving the performance of sentimental analysis where existing models fail to identify sarcasm at all. We examine the use of deep neural networks in this paper to detect the sarcasm in social media text(specifically Twitter data) as well as news headlines and compare the results. Results show that deep neural networks with the inclusion of word embeddings, bidirectional LSTM's and convolutional networks achieve better accuracy of around 88 percent for sarcasm detection.
Dynamic Target Tracking & Collision Avoidance Behaviour of Person Following Robot Using Model Predictive Control *

Executing a robust person-following behaviour is a crucial task for service robots. This requires real-time tracking of the target person and maintaining it in its field of view (FOV) as long as possible while also keeping a safe distance without collision. Such a robot has to autonomously traverse long corridors, bend around corners, curve around an obstacle and join the person back, or even wait them out. Thus, in an unknown urban setting, with other agents and service robots, dynamic target tracking and obstacle avoidance becomes a challenging task for autonomous non-holonomic robots. Model Predictive Control (MPC) has the ability to incorporate future predictions. vehicle kinematics, and non-linearity of constraints while still being reasonably fast for slow and medium walking speeds of a human being. Thus, by solving a single non-linear MPC we can track the unknown dynamic trajectory of a person in real-time, and avoid undesirable proximity.In this paper, a constrained MPC framework is designed that meets the above conditions gracefully. The person-following robot (PFR) is able to track a moving target in a simulated environment. With an adequate upper margin of 20Hz, the MPC sends velocity commands for a collision-free trajectory among an increasing number of obstacles in its perceived periphery. In an incremental fashion, it is also able to adapt to varying walking behaviour, that is, types of trajectories and walking speed.
Optimization of Phishing Website Classification Based on Synthetic Minority Oversampling Technique and Feature Selection

This paper presents a new approach for optimizing phishing website classification based on Synthetic Minority Oversampling Technique (SMOTE) together with feature selection. Classification is a kind of supervised machine learning technique that learns based on the features to identify the class. However, not all features are relevant to identify phishing websites and the class imbalance problem leads to suboptimal performances. Therefore, we propose SMOTE for handling the class imbalance problem by generating new synthetic instances for the minority class. Filter-based feature selection using Information Gain and Correlation are proposed for reducing irrelevant features. The classification performances are evaluated using K-Nearest Neighbor (KNN) classifier. The results demonstrate that SMOTE effectively increases the classification performances in terms of accuracy, precision, recall, and F-measure with more time-efficient. The performance of SMOTE combined with feature selection is validated and benchmarked with different techniques both on full features and reduced features. The results demonstrate that our proposed technique presents the highest accuracy, i.e. 97.47% on full features and 94.87% on reduced features. Hence, our proposed technique is promising in optimizing phishing website classification.
Important Scene Detection Based on Anomaly Detection using Long Short-Term Memory for Baseball Highlight Generation

This paper presents an important scene detection method based on anomaly detection using a Long Short-Term Memory (LSTM) for baseball highlight generation. In order to deal with multi-view time series features calculated from tweets and videos, we adopt an anomaly detection method using LSTM. LSTM which can maintain a long-term memory is effective for training such features. Introduction of LSTM into important scene detection of baseball videos is the biggest contribution of this paper. Experimental results show high detection performance by our method.
Penalty-Enhanced Utility-Based Multi-Criteria Recommendations

Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based multi-criteria recommendation approach has been proposed as an effective and promising solution. However, the issue of over-/under-expectations was ignored in the approach, which may bring risks to the recommendation model. In this paper, we propose a penalty-enhanced model to alleviate this issue. Our experimental results based on multiple real-world data sets can demonstrate the effectiveness of the proposed solutions. In addition, the outcomes of the proposed solution can also help explain the characteristics of the applications by observing the treatment on the issue of over-/under-expectations.
A Comparative Study of Join Algorithms in Spark

In the era of information explosion, the amount of data generated is increasing day by day, reached the threshold of petabytes or even zettabytes. In order to extract useful information from a variety of huge data sources, we need effectively computational operations performed in parallel and distributed manner on a cluster of computers. These operations involve a lot of complex and expensive processing operations. One of the typical and frequently used operations in queries is a join operation to combine more than one dataset into one. Currently, although there are some studies on join operations in Spark, there has not been any study showing an adequate and systematic comparison of join algorithms in the Spark environment. Therefore, this study is dedicated to the join operation aspects in Spark. It describes important strategies of implementing the join operation in detail, and exposes the advantages and disadvantages of each one. In addition, the work provides a more thorough comparison of the joins by using a mathematical cost model and experimental verification.
BAN-ABSA: An Aspect-Based Sentiment Analysis Dataset for Bengali and Its Baseline Evaluation

Due to the breathtaking growth of social media or newspaper user comments, online product reviews comments, sentiment analysis (SA) has captured substantial interest from the researchers. With the fast increase of domain, SA work aims not only to predict the sentiment of a sentence or document but also to give the necessary detail on different aspects of the sentence or document (i.e. aspect-based sentiment analysis). A considerable number of datasets for SA and aspect-based sentiment analysis (ABSA) have been made available for English and other well-known European languages. In this paper, we present a manually annotated Bengali dataset of high quality, BAN-ABSA, which is annotated with aspect and its associated sentiment by three native Bengali speakers. The dataset consists of 2619 positive, 4721 negative and 1669 neutral data samples from 9009 unique comments gathered from some famous Bengali news portals. In addition, we conducted a baseline evaluation with a focus on deep learning model, achieved an accuracy of 78.75% for aspect term extraction and accuracy of 71.08% for sentiment classification. Experiments on the BAN-ABSA dataset show that the CNN model is better in terms of accuracy though Bi-LSTM significantly outperforms CNN model in terms of average F1-score.
Demonstrating Thermal Flux: Using Mixed Reality to Extend Human Sight by Thermal Vision

Human vision underlies several constraints in its spatial, temporal and spectral resolution. One limitation is the visual sensation of temperatures, which can be a useful addition for human sight in education or work scenarios. This demo presents how mixed reality applications can virtually superimpose objects’ thermal properties with the physical world into a coherent environment. We present a prototype that utilizes either a smartphone or a head-mounted display to mediate thermal flux. We show the feasibility of our prototype in the context of a physics lab experiment that fosters the understanding of heat conduction for different materials. We envision thermal vision as an addition to the human sight that can be accomplished with already available technologies. Finally, we discuss how our prototype can be designed as a context-aware in-situ interface that toggles between regular and thermal vision.
A General-Purpose Number Theoretic Transform Algorithm for Compact RLWE Cryptoprocessors

Ring learning with errors (RLWE) is a commonly-used primitive in the construction of lattice-based cryptography, which is widely adopted in the design of post-quantum cryptographic algorithms and homomorphic encryption schemes. The cryptosystems based on RLWE are usually defined on a polynomial quotient ring with addition and multiplication operations. The polynomial multiplication is expensive in both time and space, hence optimizations are needed in order to achieve practical RLWE implementations. Number theoretic transform (NTT) is a technique widely used to speed up polynomial multiplications. In this paper we propose a general-purpose NTT algorithm that can support different RLWE parameters. Unlike the NTT optimizations which need significant amount of space to store the NTT parameters for each different RLWE setting, this algorithm trades a small amount of time for the elimination of most pre-computed NTT parameters. This is particularly helpful for implementations which have tight area or storage budget. For the Fan-Vercauteren fully-homomorphic encryption scheme with 192-bit classical security, our implementation of all the NTT components requires 612 bytes of read-only data on a STM32 microcontroller, while a conventional implementation requires 96 KB.