nils-herrmann/clean_scirepeval huggingface dataset:
Advancing wireless link signatures for location distinction

Location distinction is the ability to determine when a device has changed its position. We explore the opportunity to use sophisticated PHY-layer measurements in wireless networking systems for location distinction. We first compare two existing location distinction methods - one based on channel gains of multi-tonal probes, and another on channel impulse response. Next, we combine the benefits of these two methods to develop a new link measurement that we call the complex temporal signature. We use a 2.4 GHz link measurement data set, obtained from CRAWDAD , to evaluate the three location distinction methods. We find that the complex temporal signature method performs significantly better compared to the existing methods. We also perform new measurements to understand and model the temporal behavior of link signatures over time. We integrate our model in our location distinction mechanism and significantly reduce the probability of false alarms due to temporal variations of link signatures.
Differentiable Learning of Logical Rules for Knowledge Base Completion

Learned models composed of probabilistic logical rules are useful for many tasks, such as knowledge base completion. Unfortunately this learning problem is difficult, since determining the structure of the theory normally requires solving a discrete optimization problem. In this paper, we propose an alternative approach: a completely differentiable model for learning sets of first-order rules. The approach is inspired by a recently-developed differentiable logic, i.e. a subset of first-order logic for which inference tasks can be compiled into sequences of differentiable operations. Here we describe a neural controller system which learns how to sequentially compose the these primitive differentiable operations to solve reasoning tasks, and in particular, to perform knowledge base completion. The long-term goal of this work is to develop integrated, end-to-end systems that can learn to perform high-level logical reasoning as well as lower-level perceptual tasks.
SMART IRRIGATION SYSTEM THROUGH WIRELESS SENSOR NETWORKS

Irrigation is one of the most powerful sources in India but it is hard for an individual person to monitor continuously and regularly. This is due to laziness of mankind. In order to make this irrigation easier our system comprises some changes in the usual irrigation system. The newly developed project controls water supply automatically in water crisis areas through moisture sensor. This paper covers the application of Sensor based Irrigation system through wireless sensor networks, which uses a renewable energy as a source. In this system Wireless Sensor Networks Plays a major role in Environment monitoring system and provides unmanned irrigation. WSN consists of moisture sensors, Energy harvesting systems, embedded controllers and uses Super capacitors as storage device.
OLAP, Relational, and Multidimensional Database Systems

Many people ask about the difference between implementing On-Line Analytical Processing (OLAP) with a Relational Database Management System (ROLAP) versus a Mutidimensional Database (MDD). In this paper, we will show that an MDD provides significant advantages over a ROLAP such as several orders of magnitude faster data retrieval, several orders of magnitude faster calculation, much less disk space, and le,~ programming effort.
An implementation study of the AODV routing protocol

The Ad hoc On-Demand Distance Vector (AODV) routing protocol is designed for use in ad hoc mobile networks. Because of t he difficulty of testing an ad hoc routing protocol in a real-world environme nt, a simulation was first created so that the protocol design could be tested i n a variety of scenarios. Once simulation of the protocol was nearly complete , the simulation was used as the basis for an implementation in the Linux opera ting system. In the course of converting the simulation into an implement ation, certain modifications were needed in AODV and the Linux kernel due to b th simplifications made in the simulation of AODV and to incompatib ilities of the Linux kernel and the IP-layer to routing in a mobile environment. This paper details many of the changes that were necessary during th e development of the implementation.
On Schema Matching with Opaque Column Names and Data Values

Most previous solutions to the schema matching problem rely in some fashion upon identifying "similar" column names in the schemas to be matched, or by recognizing common domains in the data stored in the schemas. While each of these approaches is valuable in many cases, they are not infallible, and there exist instances of the schema matching problem for which they do not even apply. Such problem instances typically arise when the column names in the schemas and the data in the columns are "opaque" or very difficult to interpret. In this paper we propose a two-step technique that works even in the presence of opaque column names and data values. In the first step, we measure the pair-wise attribute correlations in the tables to be matched and construct a dependency graph using mutual information as a measure of the dependency between attributes. In the second stage, we find matching node pairs in the dependency graphs by running a graph matching algorithm. We validate our approach with an experimental study, the results of which suggest that such an approach can be a useful addition to a set of (semi) automatic schema matching techniques.
eAGROBOT — A robot for early crop disease detection using image processing

Management of crops from early stage to mature harvest stage involves identification and monitoring of plant diseases, nutrient deficiency, controlled irrigation and controlled use of fertilizers and pesticides. Although the number of remote sensing solutions is increasing, the availability and ground visibility during critical growth stages of crops continue to be major concerns. eAGROBOT (a prototype) is a ground based agricultural robot that overcomes challenges existing in large and complex satellite based solutions and helpdesk form of solutions available as m-Services. It provides a small, portable and reliable platform to automatically survey farmland, detect diseases as well as spray the pesticide. In future, the farmer can obtain a consolidated view of the farm along with decision support statistics for planning purposes. The development of eAGROBOT, real time testing results obtained from cotton and groundnut plantations and future focus has been detailed in this paper.
#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media

We present a study of 10-month Twitter discussions on the controversial topic of Edward Snowden. We demonstrate how behavioral differences of opinion groups can distort the presence of opinions on a social media platform. By studying the differences between a numerical minority (anti-Snowden) and a majority (pro-Snowden) group, we found that the minority group engaged in a "shared audiencing" practice with more persistent production of original tweets, focusing increasingly on inter-personal interactions with like-minded others. The majority group engaged in a "gatewatching" practice by disseminating information from the group, and over time shifted further from making original comments to retweeting others'. The findings show consistency with previous social science research on how social environment shapes majority and minority group behaviors. We also highlight that they can be further distorted by the collective use of social media design features such as the "retweet" button, by introducing the concept of "amplification'" to measure how a design feature biases the voice of an opinion group. Our work presents a warning to not oversimplify analysis of social media data for inferring social opinions.
The evolution of the language faculty: Clarifications and implications

In this response to Pinker and Jackendoff's critique, we extend our previous framework for discussion of language evolution, clarifying certain distinctions and elaborating on a number of points. In the first half of the paper, we reiterate that profitable research into the biology and evolution of language requires fractionation of "language" into component mechanisms and interfaces, a non-trivial endeavor whose results are unlikely to map onto traditional disciplinary boundaries. Our terminological distinction between FLN and FLB is intended to help clarify misunderstandings and aid interdisciplinary rapprochement. By blurring this distinction, Pinker and Jackendoff mischaracterize our hypothesis 3 which concerns only FLN, not "language" as a whole. Many of their arguments and examples are thus irrelevant to this hypothesis. Their critique of the minimalist program is for the most part equally irrelevant, because very few of the arguments in our original paper were tied to this program; in an online appendix we detail the deep inaccuracies in their characterization of this program. Concerning evolution, we believe that Pinker and Jackendoff's emphasis on the past adaptive history of the language faculty is misplaced. Such questions are unlikely to be resolved empirically due to a lack of relevant data, and invite speculation rather than research. Preoccupation with the issue has retarded progress in the field by diverting research away from empirical questions, many of which can be addressed with comparative data. Moreover, offering an adaptive hypothesis as an alternative to our hypothesis concerning mechanisms is a logical error, as questions of function are independent of those concerning mechanism. The second half of our paper consists of a detailed response to the specific data discussed by Pinker and Jackendoff. Although many of their examples are irrelevant to our original paper and arguments, we find several areas of substantive disagreement that could be resolved by future empirical research. We conclude that progress in understanding the evolution of language will require much more empirical research, grounded in modern comparative biology, more interdisciplinary collaboration, and much less of the adaptive storytelling and phylogenetic speculation that has traditionally characterized the field.
Strategic purchasing , supply management , and firm performance

Purchasing has increasingly assumed a pivotal strategic role in supply-chain management. Yet, claims of the strategic role of purchasing have not been fully subjected to rigorous theoretical and empirical scrutiny. Extant research has remained largely anecdotal and theoretically under-developed. In this paper, we examine the links among strategic purchasing, supply management, and firm performance. We argue that strategic purchasing can engender sustainable competitive advantage by enabling firms to: (a) foster close working relationships with a limited number of suppliers; (b) promote open communication among supply-chain partners; and (c) develop long-term strategic relationship orientation to achieve mutual gains. Using structural equation modeling, we empirically test a number of hypothesized relationships based on a sample of 221 United States manufacturing firms. Our results provide robust support for the links between strategic purchasing, supply management, customer responsiveness, and financial performance of the buying firm. Implications for future research and managerial practice in supply-chain management are also offered.
Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation

Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm eywords: ow-power data compression ompressed sensing (CS) lock sparse Bayesian learning (BSBL) lectrocardiography (ECG) lectroencephalography (EEG) has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in field programmable gate array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.
Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials

Time-series classification has attracted considerable research attention due to the various domains where time-series data are observed, ranging from medicine to econometrics. Traditionally, the focus of time-series classification has been on short time-series data composed of a few patterns exhibiting variabilities, while recently there have been attempts to focus on longer series composed of multiple local patrepeating with an arbitrary irregularity. The primary contribution of this paper relies on presenting a method which can detect local patterns in repetitive time-series via fitting local polynomial functions of a specified degree. We capture the repetitiveness degrees of time-series datasets via a new measure. Furthermore, our method approximates local polynomials in linear time and ensures an overall linear running time complexity. The coefficients of the polynomial functions are converted to symbolic words via equi-area discretizations of the coefficients' distributions. The symbolic polynomial words enable the detection of similar local patterns by assigning the same word to similar polynomials. Moreover, a histogram of the frequencies of the words is constructed from each time-series' bag of words. Each row of the histogram enables a new representation for the series and symbolizes the occurrence of local patterns and their frequencies. In an experimental comparison against state-of-the-art baselines on repetitive datasets, our method demonstrates significant improvements in terms of prediction accuracy.
Improving Bag-of-Visual-Words model using visual n-grams for human action classification

The Bag-of-Visual-Words model has emerged as an effective approach to represent local video features for human actions classification. However, one of the major challenges in this model is the generation of the visual vocabulary. In the case of human action recognition, losing spatial-temporal relationships is one of the important reasons that provokes the low descriptive power of classic visual words. In this work we propose a three-level approach to construct visual n-grams for human action classification. First, in order to reduce the number of non-descriptive words generated by K-means clustering of the spatio-temporal interest points, we propose to apply a variant of the classsical Leader-Follower clustering algorithm to create an optimal vocabulary from a pre-established number of visual words. Second, with the aim of incorporating spatial and temporal constraints to the Bag-of-Visual-Words model, we exploit the spatio-temporal relationships between interest points to build a graphbased representation of the video. Frequent subgraphs are extracted for each action class and a visual vocabulary of n-grams is constructed from the labels (descriptors) of selected subgraphs. Finally, we build a histogram by using the frequency of each n-gram in the graph representing a video of human action. The proposed approach combines the representational power of graphs with the efficiency of the Bag-of-Visual-Words model. Extensive validation on five challenging human actions datasets demonstrates the effectiveness of the proposed model compared to state-of-the-art methods.
Abnormal crowd behavior detection based on social attribute-aware force model

In this paper, a novel social attribute-aware force model is presented for abnormal crowd pattern detection in video sequences. We take social characteristics of crowd behaviors into account in order to improve the effectiveness of the simulation on the interaction behaviors of the crowd. A quick unsupervised method is proposed to estimate the scene scale. Both the social disorder attribute and congestion attribute are introduced to describe the realistic social behaviors by using statistical context feature. Through the semantic attribute-aware enhancement, we obtain an improved model on the basis of social force. We validate our method in public available datasets for abnormal detection, and the experimental results show promising performance compared with other state of the art methods.
String similarity search and join: a survey

String similarity search and join are two important operations in data cleaning and integration, which extend traditional exact search and exact join operations in databases by tolerating the errors and inconsistencies in the data. They have many real-world applications, such as spell checking, duplicate detection, entity resolution, and webpage clustering. Although these two problems have been extensively studied in the recent decade, there is no thorough survey. In this paper, we present a comprehensive survey on string similarity search and join. We first give the problem definitions and introduce widely-used similarity functions to quantify the similarity. We then present an extensive set of algorithms for string similarity search and join. We also discuss their variants, including approximate entity extraction, type-ahead search, and approximate substring matching. Finally, we provide some open datasets and summarize some research challenges and open problems.
Challenges with Automation in Digital Forensic Investigations

The use of automation in digital forensic investigations is not only a technological issue, but also has political and social implications. This work discusses some challenges with the implementation and acceptance of automation in digital forensic investigation, and possible implications for current digital forensic investigators. Current attitudes towards the use of automation in digital forensic investigations are examined, as well as the issue of digital investigators’ knowledge acquisition and retention. The argument is made for a well-planned, careful use of automation going forward that allows for a more efficient and effective use of automation in digital forensic investigations while at the same time attempting to improve the overall quality of expert investigators. Targeting and carefully controlling automated solutions for beginning-investigators may improve the speed and quality of investigations while at the same time letting expert digital investigators spend more time utilizing expert-level knowledge required in manual phases of investigations. By considering how automated solutions are being implemented into digital investigations, investigation unit managers can increase the efficiency of their unit while at the same time maximizing their return on investment for expert-level digital investigator training.
Investigating Capsule Networks with Dynamic Routing for Text Classification

In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain “background” information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve competitive results over the compared baseline methods on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over the competitors. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling1.
Unsupervised feature learning for 3D scene labeling

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.
Kinect v2 for mobile robot navigation: Evaluation and modeling

With the introduction of the Microsoft Kinect for Windows v2 (Kinect v2), an exciting new sensor is available to robotics and computer vision researchers. Similar to the original Kinect, the sensor is capable of acquiring accurate depth images at high rates. This is useful for robot navigation as dense and robust maps of the environment can be created. Opposed to the original Kinect working with the structured light technology, the Kinect v2 is based on the time-of-flight measurement principle and might also be used outdoors in sunlight. In this paper, we evaluate the application of the Kinect v2 depth sensor for mobile robot navigation. The results of calibrating the intrinsic camera parameters are presented and the minimal range of the depth sensor is examined. We analyze the data quality of the measurements for indoors and outdoors in overcast and direct sunlight situations. To this end, we introduce empirically derived noise models for the Kinect v2 sensor in both axial and lateral directions. The noise models take the measurement distance, the angle of the observed surface, and the sunlight incidence angle into account. These models can be used in post-processing to filter the Kinect v2 depth images for a variety of applications.
Object-Centric Representation Learning from Unlabeled Videos

Supervised (pre-)training currently yields state-of-the-art performance for representation learning for visual recognition, yet it comes at the cost of (1) intensive manual annotations and (2) an inherent restriction in the scope of data relevant for learning. In this work, we explore unsupervised feature learning from unlabeled video. We introduce a novel object-centric approach to temporal coherence that encourages similar representations to be learned for object-like regions segmented from nearby frames. Our framework relies on a Siamese-triplet network to train a deep convolutional neural network (CNN) representation. Compared to existing temporal coherence methods, our idea has the advantage of lightweight preprocessing of the unlabeled video (no tracking required) while still being able to extract object-level regions from which to learn invariances. Furthermore, as we show in results on several standard datasets, our method typically achieves substantial accuracy gains over competing unsupervised methods for image classification and retrieval tasks.
Simulation of spiking neural networks -- architectures and implementations

The fast simulation of large networks of spiking neurons is a major task for the examination of biology-inspired vision systems. Networks of this type label features by synchronization of spikes and there is strong demand to simulate these e,ects in real world environments. As the calculations for one model neuron are complex, the digital simulation of large networks is not e>cient using existing simulation systems. Consequently, it is necessary to develop special simulation techniques. This article introduces a wide range of concepts for the di,erent parts of digital simulator systems for large vision networks and presents accelerators based on these foundations.