Reviewerly/exHarmony huggingface dataset:
Use of learning classifier systems in microscopic toll plaza simulation models

This study presents the application of XCS learning algorithm in simulating drivers' lane selection behaviour in microscopic simulation models of toll plazas. XCS is a special case of learning classifier systems, a machine learning technique that ties reinforcement learning (RL) and genetic algorithm. The proposed formulation translates an agent's lane selection decision into a learning problem, assuming that its ultimate objective is to reduce delay and crash risk. The agent is simulated without any notion of the outcome of its decisions. Through multiple learning episodes and the outcome of its actions, it learns the best policy to implement in a given network setting. A hypothetical toll plaza simulation network developed in Paramics simulation software is used to conduct experimental analyses. The results demonstrate that the agent's toll lane selection decision yields superior results in terms of delay and crash risk compared with those of minimum queue lane selection, minimum risk lane selection, random lane selection and multinomial probit model-based lane selection behaviours. Although both the delay and crash risk objectives are not used simultaneously during learning, this study is intended as a proof of concept to demonstrate the feasibility of implementing RL algorithms in microscopic traffic simulation models.
A Method for Localizing the Eye Pupil for Point-of-Gaze Estimation

The estimation of the point of gaze in a scene presented on a digital screen has many applications, such as fatigue detection and attention tracking. Some popular applications of eye tracking through gaze estimation are depicted in Fig. 1. When estimating the point of gaze, indentifying the visual focus of a person within a scene is required. This is known as the eye fix or point of fixation. Finding the point of gaze involves tracking different features of human eyes. Various methods are available for eye tracking, some of which use special contact lenses, whereas others focus on electrical potential measurements. Optical tracking is a nonintrusive technique that uses a sequence of image frames of eyes that have been recorded using video-capturing devices. This technique is popularly known as video oculography.
A visual modeling approach for the Semantic Web Rule Language

Although the Semantic Web Rule Language (SWRL) is not a W3C standard, it is widely used for semantic web-based projects as well as for innovative rule-based applications. Thereby, it is used to infer new knowledge from a given fact base. Today, SWRL rules are developed and managed by technical expe rts in text-based editors using software applications such as the Stanford Protégé toolkit. In this paper we present a visual approach which allows users to analyse and modify SWRL rules. By building upon a visual modeling language, the approach includes validation mechanisms and layouting algorithms for visually representing new as well as existing rules. The approach further provides import and export interfaces to common SWRL exchange formats. In this way, its compatibility with widely-used reasoners and semantic web platforms is guaranteed. For ensuring its feasibility, the approach has been prototypically realized using the SeMFIS platform and evaluated using the sample rules as provided in the SWRL specification.
Defining Generic Tasks to Guide UAVs in a MUM-T Aerial Combat Environment

In the fighter jet domain, it is very difficult to delegate tasks to unmanned systems because such a MUM-T scenario contains tasks from different dimensions, e.g. from AI, SEAD, OCA, etc. At the same time, there is a demand for a uniform and transparent operating concept, e.g. using a tactical map. Therefore, a way is looked for to describe the many different tasks with a uniform framework. In the presented approach a task command is split into an environmental object (physical, logical) to which the instruction refers. A military action to be performed on this object and a time parameterization (slack-time). The object specification corresponds to the classification as it is usual for vectorized GIS data (point, line, area). The military action results from the different types of use (AI, SEAD, etc.) and the domains of action (Intelligence, Effect, etc.).
Chemometric analysis of Mesoamerican obsidian sources

We propose a combination of portable X-ray fluorescence (pXRF) and chemometrics to discriminate between Mesoamerican obsidian sources and to assign archaeological artifacts of unknown origin to their respective deposits using a procedure that does not require any type of calibration or reference standards. A set of 109 samples of known origin and a total of 257 samples of unknown origin were analyzed with a portable XRF spectrometer. The resultant spectra were used as spectral signatures for the chemometric data analysis. First, we applied spectral pre-treatment techniques, such as CluPA algorithm for peak alignment and the Savitzky-Golay and Extended Multiplicative Signal Correction for data smoothing and noise removal, combined with methods for the selection of a spectral range containing the variables with the most relevant information (iPLS and). The full spectrum of the obsidian samples was divided into 20 subintervals, fitting a local regression model (PLS) to each subinterval. The performance was evaluated by the Root Mean Square Error of Cross-Validation, the Root Mean Square Error of Prediction and the correlation coefficient. The selected spectral regions were then analyzed with ROBPCA algorithm for the discrimination of outliers and the projection of the observations in the PCA space. For the classification, we propose a robust procedure (RSIMCA) which is based on a ROBPCA method for high-dimensional data. The classification rules were obtained by using the orthogonal and the score distances, from which it is possible to distinguish samples that belong to a given group. Using the proposed methodology, we were able to provide evidence about which variables were meaningful for the classification and provided information about group membership or provenance. This approach proves to be a valid technique for the quantitative analysis of XRF spectra.
Differentially Private Release of the Distribution of Clustering Coefficients across Communities

Aiming to provide more information about the behaviors between groups or patterns between clusters in social networks, we propose a two-step differentially private method to release the distribution of clustering coefficients across communities. The DPLM algorithm improves a Louvain method to partition one network using an exponential mechanism. We introduce an absolute gain of modularity to sanitize neighboring communities. Otherwise, the algorithm is difficult to converge due to the randomness introduced. The DPCC algorithm charts the noisy distribution of clustering coefficients as a histogram, which presents the results in an intuitive manner. We conduct experiments on three real-world datasets to evaluate the proposed method. The experimental results indicate that the proposed method provides valuable distribution results while guaranteeing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi mathvariant="normal">ε</mml:mi></mml:mrow></mml:math>-differential privacy. Moreover, the DPLM algorithm can obtain better modularity for the networks.
Mental Model Analysis of Biogas Energy Perceptions and Policy Reveals Potential Constraints in a Japanese Farm Community

Biogas systems are complex and involve many local stakeholders who produce and utilize energy and digestate. If the systems are managed properly, they offer environmental and socioeconomic benefits to the community. However, further expansion may be challenging when differences in values and perspectives exist among stakeholders. This study analyzed perceptions among local biogas stakeholders by using a mental model approach. A local community in a northern Japanese island was chosen as a case study, and 22 stakeholders were asked to develop individual mental models of the biogas system. We found that many stakeholders shared the cognitive benefits of biogas, while there were perception differences regarding digestate use. Arable farmers mentioned technical and non-technical constraints for accepting digestate, while dairy and non-farmers were ambivalent about these demand-side constraints. This perception difference may lead to potential obstacles for future expansion of biogas systems in the region. Therefore, biogas policy should incorporate actions for better usage of digestate. These include the mandatory planning of digestate use when designing a new biogas plant, as well as actions to improve the attractiveness of digestate for arable farmers. These findings are useful for other livestock-intensive areas where the number of biogas plants is rapidly increasing but digestate management is yet organized.
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance

This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the level of entities like objects, scenes, humans and faces in the video). We investigate several summarization models which capture notions of diversity, coverage, representation and importance, and argue the utility of these different models depending on the application. While most of the prior work on submodular summarization approaches has focused on combining several models and learning weighted mixtures, we focus on the explainability of different models and featurizations, and how they apply to different domains. We also provide implementation details on summarization systems and the different modalities involved. We hope that the study from this paper will give insights into practitioners to appropriately choose the right summarization models for the problems at hand.
Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism

Relational interlinked dependencies between concepts constitute the structure of abstract knowledge and are crucial in learning conceptual knowledge and the meaning of concepts. To explore pre-service teachers’ declarative knowledge of physics concepts, we have analyzed concept networks, which agglomerate 12 pre-service teacher students’ representations of the key elements in electricity and magnetism. We show that by using network-based methods, the interlinked connections of nodes, locally and globally, can be analyzed to reveal how different elements of the network are supported through their connections to other nodes in the network. Nodes with high global connectivity initialize contiguous concept patchworks within the network and are thus most often found to be abstract, general, and advanced concepts. Locally cohesive concepts, on the other hand, are nearly always auxiliary supporting concepts, related to specific textbook-type experiments and model-type conceptional elements. Comparisons of group-level knowledge and individual pre-service teacher students’ knowledge in the form of networks shows that while in group-level the aggregated knowledge is expert-like, at the individual level pre-service teacher students possess only a fraction of that knowledge.
Deep Models for Converting Sarcastic Utterances into their Non Sarcastic Interpretation

Sarcasm is a form of speech in which the the implied sentiment is the opposite of literal meaning. In this paper, we present the task of sarcasm interpretation, defined as converting a sarcastic utterance into its non-sarcastic (literal) interpretation. We present three approaches for the task: (a) a rule-based approach that considers sarcasm as a form of dropped negation and associate negation words with verbs present in the sarcastic utterance, (b) statistical machine translation-based (SMT) approach that address the sarcasm interpretation task as monolingual machine translation and (c) three deep learning-based (DL) architectures, Encoder-Decoder Network, Attention Network and Pointer Generator Network. We also discuss the scope of future work to further enhance the proposed models for sarcasm interpretation.
Security in Hybrid Vehicular Communication Based on ITS G5, LTE-V, and Mobile Edge Computing

The introduction of a Cooperative Intelligent Transportation System (C-ITS) is an enabler for increased safety on roads and future autonomous vehicle deployments. Further, traffic efficiency is intended to be increased by having smoother and more efficient traffic flow. New applications are planned in the area of driver convenience, public transportation and commercial carriage of goods. In this context, the term vehicle-to-everything (V2X) communication is used to describe real-time communication in transportation.
Automatic detection of influencers in social networks: Authority versus domain signals

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain‐specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network‐based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.
Subspace Clustering of High Dimensional Data Using Differential Evolution

Subspace clustering approaches cluster high dimensional data in different subspaces. It means grouping the data with different relevant subsets of dimensions. This technique has become very effective as a distance measure becomes ineffective in a high dimensional space. This chapter presents a novel evolutionary approach to a bottom up subspace clustering SUBSPACE_DE which is scalable to high dimensional data. SUBSPACE_DE uses a self-adaptive DBSCAN algorithm to perform clustering in data instances of each attribute and maximal subspaces. Self-adaptive DBSCAN clustering algorithms accept input from differential evolution algorithms. The proposed SUBSPACE_DE algorithm is tested on 14 datasets, both real and synthetic. It is compared with 11 existing subspace clustering algorithms. Evaluation metrics such as F1_Measure and accuracy are used. Performance analysis of the proposed algorithms is considerably better on a success rate ratio ranking in both accuracy and F1_Measure. SUBSPACE_DE also has potential scalability on high dimensional datasets.
Exploiting Knowledge Graph in Neural Machine Translation

Neural machine translation (NMT) can achieve promising translation quality on resource-rich languages due to end-to-end learning. However, the widely-used NMT system only focuses on modeling the inner mapping from source to target without resorting to external knowledge. In this paper, we take English-Chinese translation as a case study to exploit the use of knowledge graph (KG) in NMT. The main idea is utilizing the entity relations in knowledge graph as constraints to enhance the connections between the source words and their translations. Specifically, we design two kinds of constraints. One is monolingual constraint that employs the entity relations in KG to augment the semantic representation of the source words. The other is bilingual constraint which enforces the entity relations between the source words to be shared by their translations. In this way, external knowledge can participate in the translation process and help to model semantic relationships between source and target words. Experimental results demonstrate that our method outperforms the state-of-the-art system.
Cloud-Based Predictive Intelligence and Its Security Model

Predictive computing is a relatively new area of research. Predictive computing helps people to predict the future or unknown events. It combines various statistical approaches like predictive analytics, predictive modeling, data mining, big data, and machine learning. Predictive computing uses current and historical facts to predict future events. It looks for relationships and patterns between data variables. The outcomes of data variables can be predicted if we know the values of explanatory variables. Cloud computing is another new technology that provides everything-as-a-service (XaaS) and is used widely in various businesses. All storage and computing devices use cloud platform due to its elasticity, scalability, and dynamicity. Cloud-based predictive computing is a technology that uses data available on the cloud. Presently, the data from the social sites (e.g., Facebook, Gmail, LinkedIn, election data, etc.) are stored on cloud, and the volume of this data is enormous which needs innovative predictive computing design and architecture. This chapter represents the cloud-based predictive intelligence and its security model. Architecture for predictive intelligence is proposed and compared with the existing models. An attack prediction algorithm is also proposed and compared for the accuracy in the predictive intelligence.
GARDENIA

This article presents the Graph Algorithm Repository for Designing Next-generation Accelerators (GARDENIA), a benchmark suite for studying irregular graph algorithms on massively parallel accelerators. Applications with limited control and data irregularity are the main focus of existing generic benchmarks for accelerators, while available graph processing benchmarks do not apply state-of-the-art algorithms and/or optimization techniques. GARDENIA includes emerging graph processing workloads from graph analytics, sparse linear algebra, and machine-learning domains, which mimic massively multithreaded commercial programs running on modern large-scale datacenters. Our characterization shows that GARDENIA exhibits irregular microarchitectural behavior, which is quite different from structured workloads and straightforward-implemented graph benchmarks.
Convolutional Neural Network-Based Classification of Histopathological Images Affected by Data Imbalance

In this paper we experimentally evaluated the impact of data imbalance on the convolutional neural networks performance in the histopathological image recognition task. We conducted our analysis on the Breast Cancer Histopathological Database. We considered four phenomena associated with data imbalance: how does it affect classification performance, what strategies of preventing imbalance are suitable for histopathological data, how presence of imbalance affects the value of new observations, and whether sampling training data from a balanced distribution during data acquisition is beneficial if test data will remain imbalanced. The most important findings of our experimental analysis are the following: while high imbalance significantly affects the performance, for some of the metrics small imbalance. Sampling training data from a balanced distribution had a decremental effect, and we achieved a better performance applying a dedicated strategy of dealing with imbalance. Finally, not all of the traditional strategies of dealing with imbalance translate well to the histopathological image recognition setting.
Efficient and high‐throughput application‐specific integrated circuit implementations of HIGHT and PRESENT block ciphers

In this study, low-cost and high-throughput hardware implementations of the HIGHT (HIGh security and lightweigHT) and PRESENT lightweight block ciphers are presented. One of the most complex blocks in the HIGHT algorithm is addition modulo . In the proposed structure for implementation of this modular adder, the authors used the structure of Ladner-Fischer, Han-Carlson, Kogge-Stone and Sklansky adders, which are parallel prefix adders with low critical path delay and suitable hardware resources. In the PRESENT block cipher, for two key lengths 80-bit and 128-bit, the S-box is implemented based on an area-optimised combinational logic circuit. In the proposed S-box structure, the number of logic gates and critical path delay is reduced by using Karnaugh mapping and further factorisation. Also, to reduce the latency and increase throughput, the loop unrolling technique is applied in the structures. Implementation results of the proposed architectures in 180 nm complementary metal–oxide–semiconductor technology for different unroll factors are achieved. The results show improvements in terms of execution time, throughput and throughput/area compared to other related works.
Visualizing Spatiotemporal Models with Virtual Reality: from Fully Immersive Environments to Applications in Stereoscopic View

Summary Recent advances in computing hardware and software present an unprecedented opportunity for statisticians who work with data indexed in space and time to visualize, explore and assess the structure of the data and to improve resulting statistical models. We present results of a 3-year collaboration with a team of visualization experts on the use of stereoscopic view and virtual reality (VR) to visualize spatiotemporal data with animations on non-trivial manifolds. We first present our experience with fully immersive VR with motion tracking devices that enable users to explore global three-dimensional time–temperature fields on a spherical shell interactively. We then introduce a suite of applications with VR mode, freely available for smartphones, to port a visualization experience to any interested people. We also discuss recent work with head-mounted devices such as a VR headset with motion tracking sensors.
Toward high-performance, memory-efficient, and fast reinforcement learning—Lessons from decision neuroscience

Insights from decision neuroscience raise hope for intelligent brain-inspired solutions to robot learning in real dynamic environments.
Resampling detection of recompressed images via dual-stream convolutional neural network

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical network streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.