nils-herrmann/clean_scirepeval huggingface dataset:
Performance Analysis of Big Data Gathering in Wireless Sensor Network Using an EM Based Clustering Scheme

Big-data is a popular term in the field of information and communication technology. Wireless Sensor Networks (WSN) is one of the eminent contributors of big data. WSN contains numerous sensor nodes that cooperatively monitor an environment. Each network consists of sensor node, memory, and communication device. Data generated by single sensor node is small but data generated by distributed sensor network is significantly large and it is termed as big-data. The critical issue in WSN is energy consumption and data gathering. This paper mainly focus on Expectation Maximization (EM) based clustering scheme implementation and the performance analysis of WSN using single mobile sink to eight mobile sink. From the analysis we also derived a relationship between the number of mobile sinks required for a particular network with a given number of sensor nodes. Experimental results show that the number of mobile sinks is also an important parameter to efficiently gather information in WSN.
Natural terrain classification using three-dimensional ladar data for ground robot mobility

In recent years, much progress has been made in outdoor autonomo us navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we f ocus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: ”scatter” to represent porous volumes such as grass and tree canopy, ”linear” to capture thin objects like wires or tree branches, and finally ”surface” to capture solid objects like ground surface, rocks or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonom ous ground vehicle for real-time data processing. Finally, we present results produced from different sta tionary laser sensors and from field tests using an unmanned ground vehicle.
Textual resource acquisition and engineering

and engineering J. Chu-Carroll J. Fan N. Schlaefer W. Zadrozny A key requirement for high-performing question-answering (QA) systems is access to high-quality reference corpora from which answers to questions can be hypothesized and evaluated. However, the topic of source acquisition and engineering has received very little attention so far. This is because most existing systems were developed under organized evaluation efforts that included reference corpora as part of the task specification. The task of answering Jeopardy!i questions, on the other hand, does not come with such a well-circumscribed set of relevant resources. Therefore, it became part of the IBM Watsoni effort to develop a set of well-defined procedures to acquire high-quality resources that can effectively support a high-performing QA system. To this end, we developed three procedures, i.e., source acquisition, source transformation, and source expansion. Source acquisition is an iterative development process of acquiring new collections to cover salient topics deemed to be gaps in existing resources based on principled error analysis. Source transformation refers to the process in which information is extracted from existing sources, either as a whole or in part, and is represented in a form that the system can most easily use. Finally, source expansion attempts to increase the coverage in the content of each known topic by adding new information as well as lexical and syntactic variations of existing information extracted from external large collections. In this paper, we discuss the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources. We demonstrate the effectiveness of each technique through its impact on candidate recall and on end-to-end QA performance.
Correcting a metacognitive error: feedback increases retention of low-confidence correct responses.

Previous studies investigating posttest feedback have generally conceptualized feedback as a method for correcting erroneous responses, giving virtually no consideration to how feedback might promote learning of correct responses. Here, the authors show that when correct responses are made with low confidence, feedback serves to correct this initial metacognitive error, enhancing retention of low-confidence correct responses. In 2 experiments, subjects took an initial multiple-choice test on general knowledge facts and made a confidence judgment after each response. Feedback was provided for half of the questions, and retention was assessed by a final cued-recall test. Taking the initial test improved retention relative to not testing, and feedback further enhanced performance. Consistent with prior research, feedback improved retention by allowing subjects to correct initially erroneous responses. Of more importance, feedback also doubled the retention of correct low-confidence responses, relative to providing no feedback. The function of feedback is to correct both memory errors and metacognitive errors.
Hopscotch Hashing

We present a new class of resizable sequential and concurrent hash map algorithms directed at both uni-processor and multicore machines. The new hopscotch algorithms are based on a novel hopscotch multi-phased probing and displacement technique that has the flavors of chaining, cuckoo hashing, and linear probing, all put together, yet avoids the limitations and overheads of these former approaches. The resulting algorithms provide tables with very low synchronization overheads and high cache hit ratios. In a series of benchmarks on a state-of-the-art 64-way Niagara II multicore machine, a concurrent version of hopscotch proves to be highly scalable, delivering in some cases 2 or even 3 times the throughput of today’s most efficient concurrent hash algorithm, Lea’s ConcurrentHashMap from java.concurr.util. Moreover, in tests on both Intel and Sun uni-processor machines, a sequential version of hopscotch consistently outperforms the most effective sequential hash table algorithms including cuckoo hashing and bounded linear probing. The most interesting feature of the new class of hopscotch algorithms is that they continue to deliver good performance when the hash table is more than 90% full, increasing their advantage over other algorithms as the table density grows.
Eratosthenes sieve based key-frame extraction technique for event summarization in videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.
Microblog Entity Linking with Social Temporal Context

Nowadays microblogging sites, such as Twitter and Chinese Sina Weibo, have established themselves as an invaluable information source, which provides a huge collection of manually-generated tweets with broad range of topics from daily life to breaking news. Entity linking is indispensable for understanding and maintaining such information, which in turn facilitates many real-world applications such as tweet clustering and classification, personalized microblog search, and so forth. However, tweets are short, informal and error-prone, rendering traditional approaches for entity linking in documents largely inapplicable. Recent work addresses this problem by utilising information from other tweets and linking entities in a batch manner. Nevertheless, the high computational complexity makes this approach infeasible for real-time applications given the high arrival rate of tweets. In this paper, we propose an efficient solution to link entities in tweets by analyzing their social and temporal context. Our proposed framework takes into consideration three features, namely entity popularity, entity recency, and user interest information embedded in social interactions to assist the entity linking task. Effective indexing structures along with incremental algorithms have also been developed to reduce the computation and maintenance costs of our approach. Experimental results based on real tweet datasets verify the effectiveness and efficiency of our proposals.
Efficient Spatio-Temporal Data Association Using Multidimensional Assignment in Multi-Camera Multi-Target Tracking

This paper proposes a novel multi-target tracking method which solves a data association problem using images from multi-cameras. In multicameras, two combinatorial problems should be solved at the same time: spatial data association between cameras and temporal data association between frames. The spatio-temporal data association problem is a well known NP-hard problem even in a small number of cameras or frames (more than 3). Current existing methods [1, 2] simplify the spatio-temporal data association problem through assumption of simple motion model (shortest path) and 3D location estimation. However, the complexity grows exponentially with the number of cameras. In this work, the spatio-temporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable algorithm, we solve the MDA problem iteratively by solving a sequence of bipartite matching problems using random splitting and merging operations. Hence, the proposed algorithm can be considered as a guided random search to find the global optimum through repeated random local searches (bipartite matchings). In addition, we design a new cost function considering 3D reconstruction accuracy, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and false positive. Our approach reconstructs 3D trajectories that represent people’s movement as 3D cylinders whose locations are estimated considering all adjacent frames . The observations during 1, ...,T frames and at cameras 1, ...K form a KT-partite (hyper) graph G = (V,E) = (I11 ∪ ...∪ IKT ,E), where vertices V are partitioned into K×T different independent sets I11, ...,IKT and each hyperedge in E contains at least one vertex in each partite set. Trajectory hypotheses set T can be defined as a set of all hyperedges E. We represent each trajectory hypothesis Tn ∈ T as a matrix whose entry in the k-th row and t-th column corresponds to an observation index at the t-th frame of the k-the camera. The problem of finding a set of disjoint trajectory hypotheses with a minimum sum of costs can be formulated as the MDA problem which is equivalent to the problem of minimizing the sum of costs of hyperedges containing one element per partite set in the hypergraph G. With binary decision variables xTn ∈ {0,1} deciding whether the trajectory Tn is in the association hypothesis H, and cost function c : T→R, the objective function and disjointness constraints are given by
VNF-FG design and VNF placement for 5G mobile networks

Network function virtualization (NFV) is envisioned as one of the critical technologies in 5th-Generation (5G) mobile networks. This paper investigates the virtual network function forwarding graph (VNF-FG) design and virtual network function (VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms (multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms (random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04%, 87.76% and 88.42% of the average total bandwidth consumption, respectively, and achieves only 13.81%, 25.04% and 25.41% of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and Greedy-NSGA-II together can also reduce the total bandwidth consumption remarkably.
Document-level sentiment classification: An empirical comparison between SVM and ANN

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.07.059 ⇑ Corresponding author. E-mail addresses: rodrigomorae@gmail.com (R. M (J.F. Valiati), wgaviao@gmail.com (W.P. Gavião Neto). Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, supervised methods consist of two stages: (i) extraction/selection of informative features and (ii) classification of reviews by using learning models like Support Vector Machines (SVM) and Naı̈ve Bayes (NB). SVM have been extensively and successfully used as a sentiment learning approach while Artificial Neural Networks (ANN) have rarely been considered in comparative studies in the sentiment analysis literature. This paper presents an empirical comparison between SVM and ANN regarding document-level sentiment analysis. We discuss requirements, resulting models and contexts in which both approaches achieve better levels of classification accuracy. We adopt a standard evaluation context with popular supervised methods for feature selection and weighting in a traditional bag-of-words model. Except for some unbalanced data contexts, our experiments indicated that ANN produce superior or at least comparable results to SVM’s. Specially on the benchmark dataset of Movies reviews, ANN outperformed SVM by a statistically significant difference, even on the context of unbalanced data. Our results have also confirmed some potential limitations of both models, which have been rarely discussed in the sentiment classification literature, like the computational cost of SVM at the running time and ANN at the training time.
The role of the media in body image concerns among women: a meta-analysis of experimental and correlational studies.

Research suggests that exposure to mass media depicting the thin-ideal body may be linked to body image disturbance in women. This meta-analysis examined experimental and correlational studies testing the links between media exposure to women's body dissatisfaction, internalization of the thin ideal, and eating behaviors and beliefs with a sample of 77 studies that yielded 141 effect sizes. The mean effect sizes were small to moderate (ds = -.28, -.39, and -.30, respectively). Effects for some outcome variables were moderated by publication year and study design. The findings support the notion that exposure to media images depicting the thin-ideal body is related to body image concerns for women.
Perceiving mass in mixed reality through pseudo-haptic rendering of Newton's third law

In mixed reality, real objects can be used to interact with virtual objects. However, unlike in the real world, real objects do not encounter any opposite reaction force when pushing against virtual objects. The lack of reaction force during manipulation prevents users from perceiving the mass of virtual objects. Although this could be addressed by equipping real objects with force-feedback devices, such a solution remains complex and impractical. In this work, we present a technique to produce an illusion of mass without any active force-feedback mechanism. This is achieved by simulating the effects of this reaction force in a purely visual way. A first study demonstrates that our technique indeed allows users to differentiate light virtual objects from heavy virtual objects. In addition, it shows that the illusion is immediately effective, with no prior training. In a second study, we measure the lowest mass difference (JND) that can be perceived with this technique. The effectiveness and ease of implementation of our solution provides an opportunity to enhance mixed reality interaction at no additional cost.
A Systems Theoretic Approach to the Security Threats in Cyber Physical Systems Applied to Stuxnet

Cyber physical systems (CPSs) are increasingly being adopted in a wide range of industries such as smart power grids. Even though the rapid proliferation of CPSs brings huge benefits to our society, it also provides potential attackers with many new opportunities to affect the physical world such as disrupting the services controlled by CPSs. Stuxnet is an example of such an attack that was designed to interrupt the Iranian nuclear program. In this paper, we show how the vulnerabilities exploited by Stuxnet could have been addressed at the design level. We utilize a system theoretic approach, based on prior research on system safety, that takes both physical and cyber components into account to analyze the threats exploited by Stuxnet. We conclude that such an approach is capable of identifying cyber threats towards CPSs at the design level and provide practical recommendations that CPS designers can utilize to design a more secure CPS.
Stochastic Gradient Boosting

Gradient boosting constructs additive regression models by sequentially tting a simple parameterized function (base learner) to current \pseudo"{residuals by least{squares at each iteration. The pseudo{residuals are the gradient of the loss functional being minimized, with respect to the model values at each training data point, evaluated at the current step. It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure. Speci cally, at each iteration a subsample of the training data is drawn at random (without replacement) from the full training data set. This randomly selected subsample is then used in place of the full sample to t the base learner and compute the model update for the current iteration. This randomized approach also increases robustness against overcapacity of the base learner. 1 Gradient Boosting In the function estimation problem one has a system consisting of a random \output" or \response" variable y and a set of random \input" or \explanatory" variables x = fx1; ; xng. Given a \training" sample fyi;xig N 1 of known (y;x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y;x){values, the expected value of some speci ed loss function (y; F (x)) is minimized F (x) = argmin F (x) Ey;x (y; F (x)): (1) Boosting approximates F (x) by an \additive" expansion of the form
A Survey of In-Band Full-Duplex Transmission: From the Perspective of PHY and MAC Layers

In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.
Implementation of children tracking system on android mobile terminals

Recently, all over the world crime against children is increasing at higher rates and it is high time to offer safety support system for the children going to schools. This paper focuses on implementing children tracking system for every child attending school. However the existing systems are not powerful enough to prevent the crime against children since these systems give information about the children group and not about each child resulting in low assurance about their child safety to parents and also does not concentrate on sensing the cry of the child and intimating the same to its parents. The proposed system includes a child module and two receiver modules for getting the information about the missed child on periodical basis. The child module includes ARM7 microcontroller (1pc 2378) Global positioning system (GPS) Global system for mobile communication (GSM) Voice playback circuit and the receiver module includes Android mobile device in parent's hand and the other as monitoring database in control room of the school. Finally implementation results for the proposed system are provided in this paper.
Databases, features and classifiers for speech emotion recognition: a review

Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person’s speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.