Reviewerly/exHarmony huggingface dataset:
Provenance discrimination of fine sediments by mid‐infrared spectroscopy: Calibration and application to fluvial palaeo‐environmental reconstruction

Abstract Determining sediment provenance allows a better understanding of fluvial palaeo‐dynamics, and identifying involved watersheds, at broad spatio‐temporal scales. Conventional approaches for source identification are usually based on the physical, mineralogical, geochemical, magnetic or isotopic properties of sediments. Rapid, non‐destructive and, in well‐established contexts, highly accurate, mid‐infrared spectroscopy is an alternative method for investigating sediment sources. The present research objectives are: (i) to use the mid‐infrared spectroscopy method to discriminate the provenance of fine sediments, by applying discriminant analysis on a large set of reference samples from three different watersheds in the Upper Rhine area (associated with the Rhine, Ill and Vosges tributaries); (ii) to clarify whether the provenance spectra signatures are influenced by riverine depositional contexts (bars versus banks) and, to some extent, by grain size and/or high organic matter content; and (iii) to apply the mid‐infrared spectroscopy – discriminant analysis method to a study of fluvial palaeo‐dynamics and determine the provenance of palaeo‐channel infillings. Three main sedimentary sources, divided into eight sub‐categories, have been characterized by 196 modern reference samples from 78 collecting sites. Discriminant analysis displayed a strong separating power by classifying correctly the origin of samples without any inter‐group overlap, independently from the geomorphological context (bar or bank) and associated slight changes in organic matter contents or grain size. Mid‐infrared spectroscopy – discriminant analysis investigations of the palaeo‐channel infill, complemented by radiocarbon dates and mineralogical data, allowed reconstructing general trends for the local morpho‐sedimentary dynamics over the last ca 12 millennia.
A Study of Sentiment Analysis Task and It's Challenges

Reviews act as a valuable source of information for decision making. Online e-commerce sites has provided their users to make their opinion about products and services. Huge amount of such opinions are publicly available in the form of reviews. Manufacturers, retailer as well as customers have great interest in customer reviews. A customer has interest in such reviews to determine which product or a particular brand to buy. Manufacturers can analyze the improvement area in their product from such opinionated reviews. Due to large number of reviews available on internet for analysis, it is not cost worthy to read these manually. To optimize this time consuming task there is a need of an automated system which provides summarized result of user sentiments. Sentiment analysis (SA) is the field of study that analyzes people's sentiments or opinion from reviews or opinionated text. In sentiment analysis process machine learning is used to analyze sentiments, emotions and produce a summarized result for decision making. Sentiment analysis can be viewed as a natural language processing task, the task is to develop a system that understands the people's language. Sentiment analysis is a difficult task due to ambiguous nature of human languages( like English). In this paper we present a comprehensive study of sentiment analysis tasks and the challenges for sentiment analysis system. As the aspect based sentiment analysis is an active research area in SA, we will cover this level of sentiment analysis in detail.
Optimizing Speculative Execution in Spark Heterogeneous Environments

The execution time of a stage is extended by a few slow running tasks in Spark computing environments. To tackle this so-called straggler problem, Spark adopts speculative execution mechanism under which the scheduler speculatively launch additional backup for the straggler with the hope to complete early. However, due to the characteristics of tasks and the complexity of runtime environments, the Spark original speculative execution strategy and its improved versions cannot deal with this problem effectively. In this paper, we propose a novel strategy called ETWR to improve the efficiency of speculative execution in Spark. We consider the heterogeneous environment when we around to tackle the three key points of speculative execution: straggler identification, backup node selection and effectiveness guarantee. Based on the task type classification, first, we divide the task into sub-phases and use both the process speed and progress rate within a phase to find the straggler promptly. Second, we use the Locally Weighted Regression model to estimate the execution time of the task, which will be used to calculate the task's remaining time and backup time. Third, we present iMCP model to guarantee the effectiveness of speculative tasks, which can additionally keep load balancing for nodes. Finally, the factors of fast node and better location are considered when choosing proper backup nodes. Extensive experiments show that ETWR can reduce the job execution time by 23.8 percent, and improve the cluster throughput by 33.2 percent compared with Spark-2.2.0.
Putting a price on social movements: A case study of #metoo on instagram

ABSTRACT Social media has enhanced the dissemination of information relating to specific social movements, allowing activists to quickly mobilize and more easily communicate. However, the practice of “hashtag activism” has unforeseen consequences; it gives users with commercial interests a great deal of control over when and how hashtags are utilized. To investigate the intersection between commodification, social movements, and social media, we examined the use of #metoo and three other #metoo‐related hashtags on the popular social networking platform Instagram as a case study. The aim of the #metoo social movement, which gained traction in 2017–2018, was to raise awareness on the issue of sexual violence. We collected a sample of 200 randomly selected posts to analyze their content and relevance to the movement. Our findings not only indicate that 1 in 5 posts within our sample had a commercial aspect, but also revealed a variety of commodifying strategies employed by the users.
Clothescounter: A framework for star-oriented clothes mining from videos

This paper presents a novel framework, ClothesCounter, which aims to automatically identify clothes worn by certain stars in videos. At first, several deep convolutional neural networks (CNN) models were utilized to preprocess video data in order to detect clothing images from original video frames, including human body detection, human posture selection, human pose estimation, face verification, and clothing detection. We then propose a method for extracting features of clothing images based on triplet loss that can map clothing images into a compact feature space. In the learned feature space, we present a two-stage clustering algorithm that does not require the number of clusters. Our framework was examined in a large-scale video dataset. Experimental results demonstrate the feasibility and effectiveness of our proposed method.
Estimation of ergodicity limits of bag-of-words modeling for guaranteed stochastic convergence

This paper suggests an efficient dual ergodicity limits-based bag-of-words (DEL-BoW) modeling technique. The suggested DEL-BoW technique estimates two limits of ergodicity of a discrete random variable (drv) that is formed from the BoW classification performance of multiple runs. The first limit of ergodicity is estimated with a relatively larger ball of convergence to keep the drv shorter. Hence both robustness against random initialization and estimation of the optimal model-order are realized with a reduced number of iterations. Once the optimal model-order is estimated, the radius of ball of convergence is reduced and a second limit of ergodicity is estimated. Reducing the ball of convergence enlarges the size of the considered performance drv that enhances the classification performance. Experiments conducted on Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets resulted in classification accuracy of 86.91%, 72.57%, 90.57%, and 90.86%, respectively. Comparison with state-of-the-art techniques shows the excellent performance of the DEL-BoW modeling process.
Attention-driven Interaction Systems for Augmented Reality

Augmented reality (AR) glasses enable the embedding of visual content in a real-world surroundings. In this PhD project, I will implement user interfaces which adapt to the cognitive state of the user, for example by avoiding distractions or re-directing the user's attention towards missed information. For this purpose, sensory data from the user is captured (Brain activity via EEG of fNIRS, eye tracking, physiological measurements) and modeled with machine learning techniques. The focus of the cognitive state estimation is centered around attention related aspects. The main task is to build models for an estimation of a person's attentional state from the combination and classification of multimodal data streams and context information, as well as their evaluation. Furthermore, the goal is to develop prototypical user interfaces for AR glasses and to test their usability in different scenarios.
Reputation System for IoT Data Monetization Using Blockchain

Internet of Things (IoT) is growing exponentially and bringing revolution in today’s modern society. IoT based smart devices are source of convenience to human life and producing huge amount of data on daily basis. This data is useful for consumers like industries, marketplaces, and researchers to extract valuable and functional data from raw data generated by these devices. This data is used by industries and developers to provide more efficient devices and services to users. Owner of the IoT device can generate revenue by selling IoT device data to interested consumers. However, on the other hand consumers do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional systems for data trading have many limitations, such as they are centralized, lack reputation system, security and involve third party. Therefore in this paper, we have leveraged the IoT with blockchain technology to provide a trustful trading through automatic review system for monetizing IoT data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. All transactions are secure and payments are automated without any human intervention. Data quality is ensured to consumer through reviews and ratings about the data. Additionally, Ethereum blockchain system requires gas for every transaction. We have used 2 parameters: gas consumption, string input length and in terms of time and cost, and examined our model.
Modelling Compliance Threats and Security Analysis of Cross Border Health Data Exchange

Digital health data is created, stored and processed in healthcare IT infrastructures. These infrastructures are the target of large-scale cyber-attacks and are found to be vulnerable, primarily for two main reasons: the heterogeneity of infrastructure and the numerous stakeholders (medical staff, managers, patients, regulators etc.). Furthermore, the stakeholders have different attitudes, skills, awareness and data handling practices that offer many opportunities for malicious activities. Healthcare in general is characterised by a multitude of regulations and adherence to them is essential to the functioning of the system. Compliance management is usually described in terms of risks and involves activities such as risk identification, assessment and treatment. Our paper conceptualises the notion of a “compliance threat” and discusses the security of cross-border health data exchange. The paper presents the architecture of the System Security Modeller and illustrates the security risk assessment of the “break glass” scenario which requires health data communication in an emergency situation.
An Improved D-CNN Based on YOLOv3 for Pedestrian Detection

Recent developments in pedestrian detection techniques shows that former algorithms cannot satisfy accurate and speedy detection for practical applications. Adding to the list of modern algorithms in deep learning, these methods are capable to fulfill the requirement of modern applications for pedestrian detection. In this paper, a deep convolutional neural network (D-CNN) based single class You Only Look Once (YOLOv3) state-of-the-art approach is proposed to overcome the problem of pedestrian detection in the contemporary application namely advanced driving assistance system (ADAS), and video surveillance system in terms of false detection (FD) and miss rate (MR). The proposed model is trained on INRIA datasets, which are universally applicable for pedestrian detection. Furthermore, it is effectively demonstrated in different scenarios.
Graph-Based Cooperative Robot Path Planning in Agricultural Environments

This paper describes a method of using dual robot arms to cooperatively pick apples in an unstructured orchard environment. Each arm is equipped with an RGB-D (color- plus-depth) camera in an eye-in-hand configuration. The first robot arm, termed the Grasp arm, is positioned relatively close to the tree and designated with picking apples. It uses its camera to locate apples that are within view and also within reach. The second Search arm, is located nearby and is used to detect apples that are hidden from the grasp arm and to plan a clear path for it to those fruits using a method based on rapidly-exploring random trees. Fruit location and clear path information is encoded into a graph representation that is expressive enough to encode memory and lends itself to various decision-making algorithms. It embodies the idea of using clear paths for planning, as opposed to mapping obstacles, hence maintaining a lower-dimensional representation. Computer simulation and experimental results are presented based on a preliminary implementation.
BoRe

News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to user interests, which degrades the recommendation effectiveness of existing methods. In this article, we propose a probabilistic generative model, BoRe, where user interests and crowd effects are used to adapt to the instability of reader consumption behaviors, and reading sequences are utilized to adapt user interests evolving over time. Further, the extreme sparsity problem in the domain of news severely hinders accurately modeling user interests and reading sequences, which discounts BoRe’s ability to adapt to the instability. Accordingly, we leverage domain-specific features to model user interests in the situation of extreme sparsity. Meanwhile, we consider groups of users instead of individuals to capture reading sequences. Besides, we study how to reduce the computation to allow online application. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of BoRe on real-world datasets. The experimental results show the superiority of BoRe, compared with the state-of-the-art competing methods.
Investigating EV Driving as Meaningful Practice

Studies show that people find meanings such as freedom and independence in driving. However, the transition towards electric vehicles (EV's) challenges these meanings as they present different driving experiences such as shorter driving range and missing supportive infrastructures. This suggests that people find other meaning in EV driving. This paper presents a qualitative study with 11 Danish participants who reflect on their experiences of driving EV's in everyday life. As driving is embedded in many practices along with being a practice in itself, we draw on social practice theory as a framework to unfold how participants make use of technology to make EV driving a meaningful and desirable practice. We report on how participants facilitate their driving practices using interactive technology and charging infrastructure. We discuss these findings under three headings with ideas to inspire future HCI research and design for meaningful, sustainable EV driving practice.
Towards Support of Global Views on Common Concepts employing Local Views

Multi-disciplinary data exchange still poses many challenges: Heterogeneous data sources, diverging data views, and lack of communication lead to defects, late resolving of errors, and mismatches over the project lifecycle. Semantic approaches such as ontologies are a viable solution to derive common concepts between disciplines to limit negative effects in their collaboration. However, the application of these semantic approaches is still quite limited due to its inherent complexity. The purpose of this paper, is to discuss the concept of local glossaries as a step towards a Common Concept Glossary (CCG) method: A tool-supported method to enable and simplify the creation of common concepts derived from local glossaries that are built by discipline-specific workgroups. The local concepts can aid by making changes visible and enabling traceability and maintainability of common models between different disciplines.
Hand blink reflex in virtual reality: The role of vision and proprioception in modulating defensive responses

Abstract Our research focused on the role of vision and proprioception in modulating a defensive reflex (hand blink reflex, HBR) whose magnitude is enhanced when the threatened hand is inside the peripersonal space of the face. We capitalized on virtual reality, which allows dissociating vision and proprioception by presenting a virtual limb in congruent/incongruent positions with respect to the participants' limb. In experiment 1, participants placed their own stimulated hand in far/near positions with respect to their face (postural manipulation task), while observing a virtual empty scenario. Vision was not informative, but the HBR was significantly enhanced in near compared with far position, suggesting that proprioception is sufficient for the HBR modulation to occur. In experiment 2, participants did not perform the postural manipulation but they (passively) observed the avatar's virtual limb performing it. Proprioceptive signals were not informative, but the HBR was significantly enhanced when the observed virtual limb was near to the face, suggesting that visual information plays a role in modulating the HBR. In experiment 3, both participants and avatar performed the postural manipulation, either congruently (both of them far/near) or incongruently (one of them far, the other near). The HBR modulation was present only in congruent conditions. In incongruent conditions, the conflict between vision and proprioception confounded the system, abolishing the difference between far and near positions. Taken together, these findings promote the view that observing a virtual limb modulates the HBR, providing also new evidence on the role of vision and proprioception in modulating this subcortical reflex.
Hydrogeochemistry as a Tool for Platinum Group Element (PGE) Exploration — A Case Study from Sittampundi Anorthosite Complex, Southern India

The use of surface and ground water for exploration of platinum group elements (PGE) has been studied in the Sittampundi anorthosite complex, Tamil Nadu, south India, where PGE mineralization is known to occur in basic and ultrabasic rocks. Groundwater interacting with mineralized rocks creates a geochemical signature which can be linked to the occurrence and distribution of PGE, especially Pd because of its leachability and relatively high mobility in aqueous environment. Forty surface and ground water samples collected from the area were analyzed for PGE and several other major, minor and trace elements using high resolution inductively coupled plasma mass spectrometer (HR-ICP-MS) to investigate the distribution patterns of individual PGE and other elements for the purpose of evaluating the utility of hydrogeochemistry as a tool for PGE exploration studies. Ground and surface water chemistry reflected the chemical composition of host lithology, particularly the basic-ultrabasic rocks of the region, with the dispersion of PGE and high anomalous concentrations of Pd, Ru and Rh along with several other metals such as Zn, V, Cr and Ni indicating PGE mineralization. The results of this study suggest that groundwater chemistry has significant potential as a PGE exploration tool with advantages such as rapid and low-cost sampling, high contrast anomalies and a uniform sampling medium.
FarsBase: The Persian knowledge graph

Over the last decade, extensive research has been done on automatic construction of knowledge graphs from Web resources, resulting in a number of large-scale knowledge graphs such as YAGO, DBpedia, BabelNet, and Wikidata. Despite that some of these knowledge graphs are multilingual, they contain fe w or no linked data in Persian, and do not support tools for extracting knowledge from Persian information sources. FarsBase (available at http://farsbase.net/about) is the first Persian multi-source knowledge graph, which is specifically designed for semantic search engines to support Persian knowledge. FarsBase uses a diverse set of hybrid and flexible techniques to extract and integrate knowledge from various sources, such as Wikipedia, Web tables and unstructured texts. It also supports entity linking, which allows integration with other knowledge graphs. To maintain high accuracy for triples, we adopt a low-cost mechanism for verifying candidate knowledge by human experts, where the candidates for human verification are prioritized using different heuristics. FarsBase is being used as the semantic-search system of a Persian search engine and efficiently answers hundreds of semantic queries per second.
Tetrapartite entanglement measures of GHZ state with nonuniform acceleration

Using the single-mode approximation, we first calculate entanglement measures such as negativity and von Neumann entropy for a tetrapartite GHZ entangled system in nonuniform acceleration frame. We then analyse the whole entanglement measures, the algebraic residual measure π4 and geometric average measure Π4. We find that the difference between π4 and Π4 is very slight or disappears with the increasing accelerated observers. The entanglement properties are compared among the different cases from two accelerated observers and others remaining stationary to all four accelerated observers, which are accelerated in nonuniform acceleration. The results presented here show that there still exists entanglement for the complete system even when acceleration r tends to infinity. The degree of entanglement is always equal to zero for the 1-1 tangle case. We also verify the existence of the Unruh effect even in nonuniform acceleration frame. It is also found that the von Neumann entropy increases with the increasing accelerated observers. It is interesting to see that S2 and S3 with two and three involved noninertial qubits first increase and then decrease with the acceleration parameter r, but S2 is equal to constant 1 for zero involved noninertial qubit. The special cases in the limit ra,b,c,d → π/4 are also studied.