Reviewerly/exHarmony huggingface dataset:
Two-Stage Adaptive Classification Cloud Workload Prediction Based on Neural Networks

Workload prediction is important for automatic scaling of resource management, and a high accuracy of workload prediction can reduce the cost and improve the resource utilization in the cloud. But, the task request is usually random mutation, so it is difficult to achieve more accurate prediction result for single models. Thus, to improve the prediction result, the authors proposed a novel two-stage workload prediction model based on artificial neural networks (ANNs), which is composed of one classification model and two prediction models. On the basis of the first-order gradient feature, the model can categorize the workload into two classes adaptively. Then, it can predict the workload by using the corresponding prediction neural network models according to the classification results. The experiment results demonstrate that the suggested model can achieve more accurate workload prediction compared with other models.
NICFS: A novel feature selection method applied to lexicon based sentiment analysis

The technique used for feature selection immensely affects the performance of classification in case of high dimensional datasets. The bag-of-word model is often used for sentiment classification using machine learning. The set of unique words in a t
Palynofloral and Palynofacies Evidences and its Implication on the Depositional Environment from Wardha Valley Coalfield, Maharashtra

The palynological and palynofacies studies have been undertaken on the sub-surface sediments of borehole AK-19 from Asthona area of the Wardha coalfield, India. The present investigation has revealed the existence of two distinct palynological assemblages, which have been designated as palynoassemblage – I and II respectively. The former, has shown a predominance of monosaccates viz., Parasaccites, Plicatipollenites, Potonieisporites, and Crucisaccites, whereas, the latter is characterized by the dominance of striate bisaccates such as Faunipollenites and subdominance of non-striate bisaccate Scheuringipollenites along with other striate forms such as Striatopodocarpites, Crescentipollenites, and Rhizomaspora. The other genera recorded mainly includes Parasaccites, Indotriradites, and Callumispora, which suggests that primarily herbaceous and the arborescent plants where luxuriantly growing in the forest with the prevalence of hygrophilous and mesophilous environmental conditions. The recovered palynoassemblages are governed mainly by the gymnosperms such as glossopterids, conifers, and cordaites, etc., flourished in near shore areas in riverine, lacustrine conditions as well as in large swamps and marshy habitat with the prevalence of initially cold climatic conditions (Palynoassemblage-I) which later on became temperate (Palynoassemblage-II). The present palynoassemblages have a closer affiliation with the known early Permian palynofloras of other Gondwana basins of India such as Umrer and Kamthi coalfields and Godavari, Damodar, Son-Mahanadi and Satpura basins. It can also be tentatively correlated with the early Permian palynoassemblages ofo South America, Antarctica, Australia, Africa and Salt range of Pakistan. Hence, Artinskian and Kungurian age (early Permian) has been assigned to the studied palynoassemblages respectively. The study of organic matter has revealed three distinct palynofacies (I-III) which are characterized by the dominance of palynomorphs (Palynofacies-I), phytoclasts (Palynofacies-II) and biodegraded organic matter (Palynofacies-III) respectively. Palynofacies-I-III reflects the proximal fresh water settings; oxidizing fluvio-deltaic near shore conditions and distal dysoxic waterlogged settings respectively. Palynofacies-I and II represent the oxidizing conditions while Palynofacies-III represents the reducing conditions.
Discerning Feature Supported Encoder for Image Representation

Inspired by the recent successes of deep architecture, the auto-encoder and its variants have been intensively explored on image clustering and classification tasks by learning effective feature representations. Conventional auto-encoder attempts to uncover the data's intrinsic structure, by constraining the output to be as much identical to the input as possible, which denotes that the hidden representation could faithfully reconstruct the input data. One issue that arises, however, is that such representations might not be optimized for specific tasks, e.g., image classification and clustering, since it compresses not only the discriminative information but also a lot of redundant or even noise within data. In other words, not all hidden units would benefit the specific tasks, while partial units are mainly used to represent the task-irrelevant patterns. In this paper, a general framework named discerning feature supported encoder (DFSE) is proposed, which integrates the auto-encoder and feature selection together into a unified model. Specifically, the feature selection is adapted to learned hidden-layer features to capture the task-relevant ones from the task-irrelevant ones. Meanwhile, the selected hidden units could in turn encode more discriminability only on the selected task-relevant units. To this end, our proposed algorithm can generate more effective image representation by distinguishing the task-relevant features from the task-irrelevant ones. Two scenarios of the experiments on image classification and clustering are conducted to evaluate our algorithm. The experiments on several benchmarks demonstrate that our method can achieve better performance over the state-of-the-art approaches in two scenarios.
LiteTE: Lightweight, Communication-Efficient Distributed-Memory Triangle Enumerating

Distributed-memory triangle enumerating has attracted considerable interests due to its potential capability to process huge graphs quickly. However, existing algorithms suffer from low speed due to high communication cost and load imbalance. To solve the problems, we propose LiteTE, a lightweight, communication-efficient triangle enumerating scheme. To reduce communication cost, LiteTE proposes several techniques, including a graph partitioning method to fully leverage the large memory of commodity servers and the high bandwidth of modern networks and a fast broadcast algorithm to effectively utilize the bidirectional bandwidth of cables and the aggregate bandwidth of clusters. To reduce load imbalance, LiteTE proposes three-level techniques, including a codesign technique of graph partitioning and partition-level load balance, a decentralized dynamic node-level load balance technique, and a chunk-based lock-free work-stealing technique, all of which are lightweight and incur no or hardly any communication cost. The experimental results show that LiteTE reduces communication cost and load imbalance considerably and achieves much better performance in metrics, such as setup time, runtime, scalability, and load balance than the state-of-the-art algorithms. On a small-scale cluster, LiteTE enumerates the 15 trillion triangles in a graph of 92 billion edges in 15 min, while other algorithms fail to complete.
Robot Ipsa Loquitur

Accidents are becoming automated. From self-driving cars to self-flying drones, robots are increasingly colliding with the world. And one of the most pressing questions raised by these technologies—indeed, one of the great regulatory challenges of the coming era—is how the law should account for crashes involving such complex automated systems. By now, many have weighed in, including the field’s luminaries. And, though responses vary, a tentative consensus has emerged on at least one front. Age-old liability regimes are essentially nonstarters. For negligence and design defect theories both require a showing of fault. And in a world where vexingly complex robots roam, how could one possibly find the needle of fault in a haystack comprised of millions of lines of computer code?This Article challenges that view. In sharp contrast to the prevailing wisdom, it argues that widespread debates over the so-called ‘vexing tort problems’ raised by modern robots have overlooked a crucial issue: inference. Fault, after all, needn’t be shown by pointing directly to a faulty line of code. Like all facts, it can be proven indirectly through circumstantial evidence. Indeed, as the ancient negligence rule of res ipsa loquitur makes plain, sometimes an accident can “speak for itself.”Using the first robot accused of negligence as a case study, this Article shows how advanced data-logging technologies in modern machines provide richly detailed records of accidents that, themselves, speak to the fault of the parties involved. In doing so, it offers the first wide ranging account of how inference-based analysis can—and, in fact, already does—elegantly resolve liability determinations for otherwise confoundingly complex accidents. After showing that the purportedly novel challenges posed by robots are neither unprecedented, unresolvable, nor even unique to emerging technologies, the Article then takes a more practical turn. Drawing from a rich vein of precedent involving automated accidents, it outlines steps that courts, practitioners, and policymakers can take to streamline fault determinations using an approach it calls robot ipsa loquitur. With trillion-dollar markets and millions of lives on the line, it argues that drastic calls by leading experts to upend conventional liability are ahistorical, contrary to tort law’s fundamental goals, and unnecessary to protect the interests of accident victims. A simpler, more productive approach would let the robot speak for itself.
Explicit Separable two dimensional Moment Invariants for object recognition

Image moment invariants has been widely used in the fields of pattern recognition and computer vision, since they are able to represent pattern features independently of geometric transformations. Currently, Separable Moments and their invariants are gaining more interest, due to their capability for combining the basic properties of different orthogonal moments. However, most of the existing separable moment invariants are derived indirectly from the geometric invariants, based on the relationship orthogonal polynomials and the geometric basis. Therefore, in this paper, we propose a direct approach to construct a set of discrete separable Tchebichef-Krawtchouk Moment Invariants which are simultaneously invariant to Rotation, Scaling and Translation transformation, based on the explicit form of the Tchebichef and Krawtchouk polynomials. Consequently, the experimental and theoretical results validate the effectiveness of the proposed method and show their superiority in image classification and pattern recognition in comparison with the existing methods.
The Impact of Student Opt-Out on Educational Predictive Models

Privacy concerns may lead people to opt-in or opt-out of having their educational data collected. These decisions may impact the performance of educational predictive models. To understand this, we conducted a survey to determine the propensity of students to withhold or grant access to their data for the purposes of training predictive models. We simulated the effects of opt-out on the accuracy of educational predictive models by dropping a random sample of data over a range of increments, and then contextualize our findings using the survey results. We find that grade predictive models are fairly robust and that kappa scores do not decrease unless there is signiicant opt-out, but when there is, the deteriorating performance disproportionately affects certain subpopulations.
Automatic Correction of Arabic Dyslexic Text

This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%.
A Case Study: Assessing Effectiveness of the Augmented Reality Application in Augusta Raurica

This paper aims to study and compare the augmented reality experience of two user groups at Augusta Raurica, a Roman archaeological site and an open-air museum in Switzerland. The user groups differentiated within the scope of this study digital natives and digital immigrants. The paper applies the experience economy framework for assessing the differences between the user groups. The data gathered from the surveys and analysis of selected interviews reveal that digital immigrants have better engagement with the augmented reality application of Augusta Raurica than digital natives do. The findings are supporting the debate on assisting tourism destinations to develop more engaging augmented reality content targeting both user groups.
Building geochemically based quantitative analogies from soil classification systems using different compositional datasets

Soil heterogeneity is a major contributor to the uncertainty in near-surface biogeochemical modeling. We sought to overcome this limitation by exploring the development of a new classification analogy concept for transcribing the largely qualitative criteria in the pedomorphologically based, soil taxonomic classification systems to quantitative physicochemical descriptions. We collected soil horizons classified under the Alfisols taxonomic Order in the U.S. National Resource Conservation Service (NRCS) soil classification system and quantified their properties via physical and chemical characterizations. Using multivariate statistical modeling modified for compositional data analysis (CoDA), we developed quantitative analogies by partitioning the characterization data up into three different compositions: Water-extracted (WE), Mehlich-III extracted (ME), and particle-size distribution (PSD) compositions. Afterwards, statistical tests were performed to determine the level of discrimination at different taxonomic and location-specific designations. The analogies showed different abilities to discriminate among the samples. Overall, analogies made up from the WE composition more accurately classified the samples than the other compositions, particularly at the Great Group and thermal regime designations. This work points to the potential to quantitatively discriminate taxonomically different soil types characterized by varying compositional datasets.
Not-equal

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Christopher A. Le Dantec, Editor
Context-Aware Recommender System Frameworks, Techniques, and Applications: A Survey

Tailored services or information for mobile users became very crucial; due to the enormous amount of information and services that are available for mobile users during their various activities. Recommendation system can be merged with context awareness to provide tailored services, information, moreover learning resources for users, based on their current context and profile. Context Aware Recommendation Systems (CARSs) have various usages also many applications and services depend on it, CARSs nowadays become widely used in various domains. This paper presents a survey of CARSs overview, frameworks, techniques, algorithms, and applications. Moreover, the paper discusses the issues of CARSs in general and investigates the proposed solutions.
Towards a Distraction-free Waze

Real-time traffic monitoring has had widespread success via crowd-sourced GPS data. While drivers benefit from this low-level, low-latency road information, any high-level traffic data such as road closures and accidents currently have very high latency as such systems rely solely on human reporting. Increasing the detail and decreasing the latency of this information can have significant value. In this paper we explore this idea by using a camera along with an in-vehicle computer to run computer vision algorithms that continuously observe the road conditions in high-detail. Abnormalities are automatically reported via 4G LTE to a local server on the edge, which collects and stores the data, and relays updates to other vehicles inside its zone. In this paper we develop and test such a system, which we call LiveMap. We demonstrate its accuracy on detecting hazards and characterize the system latency achieved.
GPS/INS integration based on adaptive interacting multiple model

The extended Kalman filter (EKF) had widely been used in the inertial navigation system (INS) and global positioning system (GPS) integrated navigation system. Nevertheless, the EKF contains instability caused by linearisation and numerous calculations of the Jacobian matrix. To solve the problem, the adaptive interacting multiple model (AIMM) filter algorithm is proposed in order to achieve a better navigation solution. The soft-switching feature, which is offered by interacting multiple model, allows conversion of process noise between lower and upper limits, meanwhile the measurement covariance is adjusted online by Sage adaptive filter. Considering the need to update the pseudo-range and Doppler data, the updating strategy of classification measurement is proposed. The results of the GPS/INS integrated navigation are estimated in the form of data of real ship, and experimental results indicate that the higher position accuracy can be obtained in AIMM filter.
Fractal Image Interpolation: A Tutorial and New Result

This paper reviews the implementation of fractal based image interpolation, the associated visual artifacts of the interpolated images, and various techniques, including novel contributions, that alleviate these awkward visual artifacts to achieve visually pleasant interpolated image. The fractal interpolation methods considered in this paper are based on the plain Iterative Function System (IFS) in spatial domain without additional transformation, where we believe that the benefits of additional transformation can be added onto the presented study without complication. Simulation results are presented to demonstrate the discussed techniques, together with the pros and cons of each techniques. Finally, a novel spatial domain interleave layer has been proposed to add to the IFS image system for improving the performance of the system from image zooming to interpolation with the preservation of the pixel intensity from the original low resolution image.
StoryPrint

In this paper, we propose StoryPrint, an interactive visualization of creative storytelling that facilitates individual and comparative structural analyses. This visualization method is intended for script-based media, which has suitable metadata. The pre-visualization process involves parsing the script into different metadata categories and analyzing the sentiment on a character and scene basis. For each scene, the setting, character presence, character prominence, and character emotion of a film are represented as a StoryPrint. The visualization is presented as a radial diagram of concentric rings wrapped around a circular time axis. A user then has the ability to toggle a difference overlay to assist in the cross-comparison of two different scene inputs.
GRAW+: A two‐view graph propagation method with word coupling for readability assessment

Existing methods for readability assessment usually construct inductive classification models to assess the readability of singular text documents based on extracted features, which have been demonstrated to be effective. However, they rarely make use of the interrelationship among documents on readability, which can help increase the accuracy of readability assessment. In this article, we adopt a graph‐based classification method to model and utilize the relationship among documents using the coupled bag‐of‐words model. We propose a word coupling method to build the coupled bag‐of‐words model by estimating the correlation between words on reading difficulty. In addition, we propose a two‐view graph propagation method to make use of both the coupled bag‐of‐words model and the linguistic features. Our method employs a graph merging operation to combine graphs built according to different views, and improves the label propagation by incorporating the ordinal relation among reading levels. Experiments were conducted on both English and Chinese data sets, and the results demonstrate both effectiveness and potential of the method.
A circular-linear dependence measure under Johnson–Wehrly distributions and its application in Bayesian networks

Circular data jointly observed with linear data are common in various disciplines. Since circular data require different techniques than linear data, it is often misleading to use usual dependence measures for joint data of circular and linear observations. Moreover, although a mutual information measure between circular variables exists, the measure has drawbacks in that it is defined only for a bivariate extension of the wrapped Cauchy distribution and has to be approximated using numerical methods. In this paper, we introduce two measures of dependence, namely, (i) circular-linear mutual information as a measure of dependence between circular and linear variables and (ii) circular-circular mutual information as a measure of dependence between two circular variables. It is shown that the expression for the proposed circular-linear mutual information can be greatly simplified for a subfamily of Johnson–Wehrly distributions. We apply these two dependence measures to learn a circular-linear tree-structured Bayesian network that combines circular and linear variables. To illustrate and evaluate our proposal, we perform experiments with simulated data. We also use a real meteorological data set from different European stations to create a circular-linear tree-structured Bayesian network model.
Learn-select-track: An approach to multi-object tracking

Object tracking algorithms rely on user input to learn the object of interest. In multi-object tracking, this can be a challenge when the user has to provide a lot of locations to track. This paper presents a new approach that reduces the need for user input in multi-tracking. The approach uses density based clustering to analyse the colours in one frame and find the best separation of colours. The colours selected from the detection are learned and used in subsequent frames to track the colours through the video. With this training approach, the user interaction is limited to selecting the colours rather than selecting the multiple location to be tracked. The training algorithm also provides online training even when training on thousands of features.
A stable non-interleaving early operational semantics for the pi-calculus

We give the first non-interleaving early operational semantics for the pi-calculus which generalises the standard interleaving semantics and unfolds to the stable model of prime event structures. Our starting point is the non-interleaving semantics given for CCS by Mukund and Nielsen, where the so-called structural (prefixing or subject) causality and events are defined from a notion of locations derived from the syntactic structure of the process terms. We conservatively extend this semantics with a notion of extruder histories, from which we infer the so-called link (name or object) causality and events introduced by the dynamic communication topology of the pi-calculus. We prove that the semantics generalises both the standard interleaving early semantics for the pi-calculus and the non-interleaving semantics for CCS. In particular, it gives rise to a labelled asynchronous transition system unfolding to prime event structures.