nils-herrmann/clean_scirepeval huggingface dataset:
A compact printed log-periodic antenna with loaded stub

A compact printed log-periodic dipole antenna (LPDA) with distributed inductive load has been presented in this paper. By adding a stub on the top of each element, the dimension of the LPAD can be reduced by 60%. The antenna has obtained an impedance bandwidth of 10GHz (8GHz-18GHz). According to the simulation results, the designed structure achieves stable radiation patterns throughout the operating frequency band.
Adaptive UKF-SLAM based on magnetic gradient inversion method for underwater navigation

Consider the two characteristics: (1) Simultaneous localization and mapping (SLAM) is a popular algorithm for autonomous underwater vehicle, but visual SLAM is significantly influenced by weak illumination. (2)Geomagnetism-aided navigation and gravity-aided navigation are equally important methods in the field of vehicle navigation, but both are affected heavily by time-varying noises and terrain fluctuations. However, magnetic gradient vector can avoid the influence of time-varying noises, and is less affected by terrain fluctuations. To this end, we propose an adaptive SLAM-based magnetic gradient aided navigation with the following advantages: (1) Adaptive SLAM is an efficient way to deal with uncertainty of the measurement model. (2) Magnetic gradient inversion equation is a good alternative to be used as measurement equation in visual SLAM-denied environment. Experimental results show that our proposed method is an effective solution, combining magnetic gradient information with SLAM.
Posterior distribution analysis for Bayesian inference in neural networks

This study explores the posterior predictive distributions obtained with various Bayesian inference methods for neural networks. The quality of the distributions is assessed both visually and quantitatively using Kullback–Leibler (KL) divergence, Kolmogorov–Smirnov (KS) distance and precision-recall scores. We perform the analysis using a synthetic dataset that allows for a more detailed examination of the methods, and validate the findings on larger datasets. We find that among the recently proposed techniques, the simpler ones – Stochastic Gradient Langevin Dynamics (SGLD) and MC Dropout – are able to consistently provide good approximations to the “true” posterior, at the same time not requiring extensive tuning of parameters.
Making TCP/IP Viable for Wireless Sensor Networks

The TCP/IP protocol suite, which has proven itself highly successful in wired networks, is often claimed to be unsuited for wireless micro-sensor networks. In this work, we question this conventional wisdom and present a number of mechanisms that are intended to enable the use of TCP/IP for wireless sensor networks: spatial IP address assignment, shared context header compression, application overlay routing, and distributed TCP caching (DTC). Sensor networks based on TCP/IP have the advantage of being able to directly communicate with an infrastructure consisting either of a wired IP network or of IP-based wireless technology such as GPRS. We have implemented parts of our mechanisms both in a simulator environment and on actual sensor nodes. Our preliminary results are promising.
A study on LIWC categories for opinion mining in Spanish reviews

With the exponential growth of social media i.e. blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language, is positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC. LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we have conducted a comparative assessment of the performance of various classification techniques: J48, SMO and BayesNet, using precision, recall and F-measure metrics. All in all, findings have revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4% and 87.2% in each domain.
Relative Physical Knowledge of Actions and Objects

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.
Comparative genomic analysis identifies structural features of CRISPR-Cas systems in Riemerella anatipestifer

Riemerella anatipestifer infection is a contagious disease that has resulted in major economic losses in the duck industry worldwide. This study attempted to characterize CRISPR-Cas systems in the disease-causing agent, Riemerella anatipestifer (R. anatipestifer). The CRISPR-Cas system provides adaptive immunity against foreign genetic elements in prokaryotes and CRISPR-cas loci extensively exist in the genomes of archaea and bacteria. However, the structure characteristics of R. anatipestifer CRISPR-Cas systems remains to be elucidated due to the limited availability of genomic data. To identify the structure and components associated with CRISPR-Cas systems in R. anatipestifer, we performed comparative genomic analysis of CRISPR-Cas systems in 25 R. anatipestifer strains using high-throughput sequencing. The results showed that most of the R. anatipestifer strains (20/25) that were analyzed have two CRISPR loci (CRISPR1 and CRISPR2). CRISPR1 was shown to be flanked on one side by cas genes, while CRISPR2 was designated as an orphan. The other analyzed strains harbored only one locus, either CRISPR1 or CRISPR2. The length and content of consensus direct repeat sequences, as well as the length of spacer sequences associated with the two loci, differed from each other. Only three cas genes (cas1, cas2 and cas9) were located upstream of CRISPR1. CRISPR1 was also shown to be flanked by a 107 bp-long putative leader sequence and a 16 nt-long anti-repeat sequence. Combined with analysis of spacer organization similarity and phylogenetic tree of the R. anatipestifer strains, CRISPR arrays can be divided into different subgroups. The diversity of spacer organization was observed in the same subgroup. In general, spacer organization in CRISPR1 was more divergent than that in CRISPR2. Additionally, only 8 % of spacers (13/153) were homologous with phage or plasmid sequences. The cas operon flanking CRISPR1 was observed to be relatively conserved based on multiple sequence alignments of Cas amino acid sequences. The phylogenetic analysis associated with Cas9 showed Cas9 sequence from R. anatipestifer was closely related to that of Bacteroides fragilis and formed part of the subtype II-C subcluster. Our data revealed for the first time the structural features of R. anatipestifer CRISPR-Cas systems. The illumination of structural features of CRISPR-Cas system may assist in studying the specific mechanism associated with CRISPR-mediated adaptive immunity and other biological functions in R. anatipestifer.
Flagellated Magnetotactic Bacteria as Controlled MRI-trackable Propulsion and Steering Systems for Medical Nanorobots Operating in the Human Microvasculature

Although nanorobots may play critical roles for many applications in the human body such as targeting tumoral lesions for therapeutic purposes, miniaturization of the power source with an effective onboard controllable propulsion and steering system have prevented the implementation of such mobile robots. Here, we show that the flagellated nanomotors combined with the nanometer-sized magnetosomes of a single Magnetotactic Bacterium (MTB) can be used as an effective integrated propulsion and steering system for devices such as nanorobots designed for targeting locations only accessible through the smallest capillaries in humans while being visible for tracking and monitoring purposes using modern medical imaging modalities such as Magnetic Resonance Imaging (MRI). Through directional and magnetic field intensities, the displacement speeds, directions, and behaviors of swarms of these bacterial actuators can be controlled from an external computer.
Less Is More: Trading a Little Bandwidth for Ultra-Low Latency in the Data Center

Traditional measures of network goodness—goodput, quality of service, fairness—are expressed in terms of bandwidth. Network latency has rarely been a primary concern because delivering the highest level of bandwidth essentially entails driving up latency—at the mean and, especially, at the tail. Recently, however, there has been renewed interest in latency as a primary metric for mainstream applications. In this paper, we present the HULL (High-bandwidth Ultra-Low Latency) architecture to balance two seemingly contradictory goals: near baseline fabric latency and high bandwidth utilization. HULL leaves ‘bandwidth headroom’ using Phantom Queues that deliver congestion signals before network links are fully utilized and queues form at switches. By capping utilization at less than link capacity, we leave room for latency sensitive traffic to avoid buffering and the associated large delays. At the same time, we use DCTCP, a recently proposed congestion control algorithm, to adaptively respond to congestion and to mitigate the bandwidth penalties which arise from operating in a bufferless fashion. HULL further employs packet pacing to counter burstiness caused by Interrupt Coalescing and Large Send Offloading. Our implementation and simulation results show that by sacrificing a small amount (e.g., 10%) of bandwidth, HULL can dramatically reduce average and tail latencies in the data center.
A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes.

The authors examined a new assessment of behavioral regulation and contributions to achievement and teacher-rated classroom functioning in a sample (N = 343) of kindergarteners from 2 geographical sites in the United States. Behavioral regulation was measured with the Head-Toes-Knees-Shoulders (HTKS) task, a structured observation requiring children to perform the opposite of a dominant response to 4 different oral commands. Results revealed considerable variability in HTKS scores. Evidence for construct validity was found in positive correlations with parent ratings of attentional focusing and inhibitory control and teacher ratings of classroom behavioral regulation. Hierarchical linear modeling indicated that higher levels of behavioral regulation in the fall predicted stronger levels of achievement in the spring and better teacher-rated classroom self-regulation (all ps < .01) but not interpersonal skills. Evidence for domain specificity emerged, in which gains in behavioral regulation predicted gains in mathematics but not in language and literacy over the kindergarten year (p < .01) after site, child gender, and other background variables were controlled. Discussion focuses on the importance of behavioral regulation for successful adjustment to the demands of kindergarten.
Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness

This paper proposes a new Multi-Dimensional SAX-GA approach to pattern discovery using genetic algorithms (GA). The approach is capable of discovering patterns in multi-dimensional financial time series. First, the several dimensions of data are converted to a Symbolic Aggregate approXimation (SAX) representation, which is, then, feed to a GA optimization kernel. The GA searches for profitable patterns occurring simultaneously in the multi-dimensional time series. Based on the patterns found, the GA produces more robust investment strategies, since the simultaneity of patterns on different dimensions of the data, reinforces the strength of the trading decisions implemented. The proposed approach was tested using stocks from S&P500 index, and is compared to previous reference works of SAX-GA and to the Buy & Hold (B&H) classic investment strategy.
Models for retrieval with probabilistic indexing

in this article three retrieval models for probabilistic indexing are described along with evaluation results for each. First is the binary independence indexing @II) model, which is a generalized version of the Maron and Kuhns indexing model. In this model, the indexing weight of a descriptor in a document is an estimate of the probability of relevance of this document with respect to queries using this descriptor. Second is the retrieval-with-probabilistic-indexing (RPI) model, which is suited to different kinds of probabilistic indexing. For that we assume that each indexing scheme has its own concept of “correctness” to which the probabilities relate. In addition to the probabilistic indexing weights, the RPI model provides the possibility of reIevance weighting of search terms. A third mode1 that is similar was proposed by Croft some years ago as an extension of the binary independence retrieval model but it can be shown that this model is not based on the probabilistic ranking principle. The probabilistic indexing weights required for any of these models can be provided by an application of the Darmstadt indexing approach (DIA) for indexing with descriptors from a controlled vocabuIary. The experimental results show signi~cant improvements over retrieval with binary indexing. Finally, suggestions are made regarding how the DIA can be applied to probabilistic indexing with free text terms.
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect

Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.
Leaf Disease Detection of Cotton Plant Using Image Processing Techniques

Identification of the symptoms of plant diseases by means of image processing techniques is of prime concern in the area of research. There is a need for a plant disease diagnosis system that may support farmers during their daily struggle. The proposed work is a development of an efficient diagnosis system that focuses on plant disease identification by processing acquired digital images of leaves of the plant. These images are made to undergo a set of pre-processing methods for image enhancement. The enhanced image is segmented using thresholding based segmentation approaches to extract the region of interest i.e., diseased portion. Later, a satisfying set of visual texture features from the region of interest are extracted for detecting diseases accurately. Finally, treatment measures are provided to control the disease. The advisory helps farming community in effective decision making to protect their crop from diseases.
The Future of Memory: Remembering, Imagining, and the Brain

During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here, we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and nontemporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning.
Parametric study of microstrip patch antenna on LCP substrate for 70 GHz applications

Analysis and design of a microstrip patch antenna on LCP substrate with optimum performance at 70 GHz is presented. Different design techniques such as analytical modeling, numerical optimization, and numerical variation of dimensions have been considered and an optimal design is presented. The design is a trade-off between different antenna parameters and finding the inset position of the feed line at higher frequencies is a challenge. It is observed that the patch length of antenna is a critical parameter which determines the resonant frequency than its width. A patch width of 1.8 times the patch length can be selected to achieve optimal performance at resonant frequency. A -10 dB bandwidth of 2 GHz has been achieved which makes it suitable for broadband communication applications. Preliminary theoretical results have been presented.
Musicmapper: interactive 2d representations of music samples for in-browser remixing and exploration

Much of the challenge and appeal in remixing music comes from manipulating samples. Typically, identifying distinct samples of a song requires expertise in music production software. Additionally, it is di cult to visualize similarities and di↵erences between all samples of a song simultaneously and use this to select samples. MusicMapper is a web application that allows nonexpert users to find and visualize distinctive samples from a song without any manual intervention, and enables remixing by having users play back clusterings of such samples. This is accomplished by splitting audio from the Soundcloud API into appropriately-sized spectrograms, and applying the tSNE algorithm to visualize these spectrograms in two dimensions. Next, we apply k-means to guide the user’s eye toward related clusters and set k = 26 to enable playback of the clusters by pressing keys on an ordinary keyboard. We present the source code and a demo video of the MusicMapper web application that can be run in most modern browsers.
A study on various data mining techniques for crop yield prediction

India is a country where agriculture and agriculture related industries are the major source of living for the people. Agriculture is a major source of economy of the country. It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop. This leads to huge financial loss for the farmers thus leading to the suicide. Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc. Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality, pH, EC, N, P, K etc. As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining. Through data mining we extract the knowledge from the huge size of data. This paper presents the study about the various data mining techniques used for predicting the crop yield. The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed. This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation.
An online security protocol for NFC payment: Formally analyzed by the scyther tool

Nowadays, NFC technology is integrated into bank cards, smartphones and sales point terminals in order to immediately execute payment transactions without any physical contact. EMV is the standard intended to secure both contact (traditional) and contactless-NFC payment operations. In fact, researchers in recent years have detected some security vulnerabilities in this protocol (EMV). Therefore, in this paper, we introduce the risks entailed by the vulnerabilities of EMV and particularly those at stake in the case of NFC payment. Hence, in order to overcome EMV weaknesses, we propose a new security protocol based on an online communication with a trusted entity. The proposal is destined to secure contactless-NFC payment transactions using NFC bank cards that are unconnected client payment devices (without Wi-Fi or 4G). A security verification tool called Scyther is used to analyze the correctness of the proposal.
Decentralized Decision Making in the Game of Tic-tac-toe

Traditionally, the game of Tic-tac-toe is a pencil and paper game played by two people who take turn to place their pieces on a 3times3 grid with the objective of being the first player to fill a horizontal, vertical, or diagonal row with their pieces. What if instead of having one person playing against another, one person plays against a team of nine players, each of whom is responsible for one cell in the 3times3 grid? In this new way of playing the game, the team has to coordinate its players, who are acting independently based on their limited information. In this paper, we present a solution that can be extended to the case where two such teams play against each other, and also to other board games. Essentially, the solution uses a decentralized decision making, which at first seems to complicate the solution. However, surprisingly, we show that in this mode, an equivalent level of decision making ability comes from simple components that reduce system complexity
Approaches for Computational Sarcasm Detection : A Survey

Sentiment Analysis deals not only with the positive and negative sentiment detection in the text but it also considers the prevalence and challenges of sarcasm in sentiment-bearing text. Automatic Sarcasm detection deals with the detection of sarcasm in text. In the recent years, work in sarcasm detection gains popularity and has wide applicability in sentiment analysis. This paper complies the various approaches that are developed to tackle the problem of sarcasm detection. In this paper, we describe Rule-based, Machine Learning and Deep Learning approaches for detecting sarcasm and also describes various datasets. We also give details of different features used by various sarcasm detection approaches from past upto the present.