nils-herrmann/clean_scirepeval huggingface dataset:
An IoT Smart Queue Management System with Real-Time Queue Tracking

This paper proposes a smart queue management system for delivering real-time service request updates to clients' smartphones in the form of audio and visual feedback. The proposed system aims at reducing the dissatisfaction with services with medium to long waiting times. To this end, the system allows carriers of digital ticket to leave the waiting areas and return in time for their turn to receive service. The proposed system also improves the waiting experience of clients choosing to stay in the waiting area by connecting them to the audio signal of the often muted television sets running entertainment programs, advertisement of services, or news. The system is a web of things including connected units for registering and verifying tickets, units for capturing and streaming audio and queue management, and participating client units in the form of smartphone applications. We implemented the proposed system and verified its functionality and report on our findings and areas of improvements.
VFloat: A Variable Precision Fixed- and Floating-Point Library for Reconfigurable Hardware

Optimal reconfigurable hardware implementations may require the use of arbitrary floating-point formats that do not necessarily conform to IEEE specified sizes. We present a variable precision floating-point library (VFloat) that supports general floating-point formats including IEEE standard formats. Most previously published floating-point formats for use with reconfigurable hardware are subsets of our format. Custom datapaths with optimal bitwidths for each operation can be built using the variable precision hardware modules in the VFloat library, enabling a higher level of parallelism. The VFloat library includes three types of hardware modules for format control, arithmetic operations, and conversions between fixed-point and floating-point formats. The format conversions allow for hybrid fixed- and floating-point operations in a single design. This gives the designer control over a large number of design possibilities including format as well as number range within the same application. In this article, we give an overview of the components in the VFloat library and demonstrate their use in an implementation of the K-means clustering algorithm applied to multispectral satellite images.
Effective and Efficient Multilabel Classification in Domains with Large Number of Labels

This paper contributes a novel algorithm for effective and computationally efficient multilabel classification in domains with large label sets L. The HOMER algorithm constructs a Hierarchy Of Multilabel classifiERs, each one dealing with a much smaller set of labels compared to L and a more balanced example distribution. This leads to improved predictive performance along with linear training and logarithmic testing complexities with respect to |L|. Label distribution from parent to children nodes is achieved via a new balanced clustering algorithm, called balanced k means.
Diamond: Automating Data Management and Storage for Wide-Area, Reactive Applications

Users of today’s popular wide-area apps (e.g., Twitter, Google Docs, and Words with Friends) must no longer save and reload when updating shared data; instead, these applications are reactive, providing the illusion of continuous synchronization across mobile devices and the cloud. Achieving this illusion poses a complex distributed data management problem for programmers. This paper presents the first reactive data management service, called Diamond, which provides persistent cloud storage, reliable synchronization between storage and mobile devices, and automated execution of application code in response to shared data updates. We demonstrate that Diamond greatly simplifies the design of reactive applications, strengthens distributed data sharing guarantees, and supports automated reactivity with low performance overhead.
Newsmap: a knowledge map for online news

Information technology has made possible the capture and accessing of a large number of data and knowledge bases, which in turn has brought about the problem of information overload. Text mining to turn textual information into knowledge has become a very active research area, but much of the research remains restricted to the English language. Due to the differences in linguistic characteristics and methods of natural language processing, many existing text analysis approaches have yet to be shown to be useful for the Chinese language. This research focuses on the automatic generation of a hierarchical knowledge map NewsMap, based on online Chinese news, particularly the finance and health sections. Whether in print or online, news still represents one important knowledge source that people produce and consume on a daily basis. The hierarchical knowledge map can be used as a tool for browsing business intelligence and medical knowledge hidden in news articles. In order to assess the quality of the map, an empirical study was conducted which shows that the categories of the hierarchical knowledge map generated by NewsMap are better than those generated by regular news readers, both in terms of recall and precision, on the sub-level categories but not on the top-level categories. NewsMap employs an improved interface combining a 1D alphabetical hierarchical list and a 2D Self-Organizing Map (SOM) island display. Another empirical study compared the two visualization displays and found that users’ performances can be improved by taking advantage of the visual cues of the 2D SOM display. D 2004 Elsevier B.V. All rights reserved.
A high gain steerable millimeter-wave antenna array for 5G smartphone applications

In this paper, a phased array antenna is designed at millimeter-wave frequency bands for future 5G based smartphone applications. The proposed antenna is a novel open slot-PIFA antenna made on a low cost FR4 board. The antenna array covers a frequency range of 26–32 GHz with a bandwidth of 6 GHz. The antenna exhibits a very good radiation pattern when integrated with the mobile phone chassis. The 8 — element antenna array exhibits a maximum gain around 13 dBi. The pattern can be steered by varying the phase shift at each antenna element.
A Real-Time QRS Detection Algorithm

We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
EVENT DETECTION IN TIME SERIES OF MOBILE COMMUNICATION GRAPHS

Anomaly and event detection has been studied widely for having many applications in fraud detection, network intrusion detection, detection of epidemic outbreaks, and so on. In this paper we propose an algorithm that operates on a time-varying network of agents with edges representing interactions between them and (1) spots "anomalous" points in time at which many agents "change" their behavior in a way it deviates from the norm; and (2) attributes the detected anomaly to those agents that contribute to the "change" the most. Experiments on a large mobile phone network (of 2 million anonymous customers with 50 million interactions over a period of 6 months) shows that the "change"-points detected by our algorithm coincide with the social events and the festivals in our data.
Power management in energy harvesting sensor networks

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.
Object Detection in Multi-view X-Ray Images

Motivated by aiding human operators in the detection of dangerous objects in passenger luggage, such as in airports, we develop an automatic object detection approach for multi-view X-ray image data. We make three main contributions: First, we systematically analyze the appearance variations of objects in X-ray images from inspection systems. We then address these variations by adapting standard appearance-based object detection approaches to the specifics of dual-energy X-ray data and the inspection scenario itself. To that end we reduce projection distortions, extend the feature representation, and address both in-plane and out-of-plane object rotations, which are a key challenge compared to many detection tasks in photographic images. Finally, we propose a novel multi-view (multi-camera) detection approach that combines single-view detections from multiple views and takes advantage of the mutual reinforcement of geometrically consistent hypotheses. While our multi-view approach can be used atop arbitrary single-view detectors, thus also for multi-camera detection in photographic images, we evaluate our method on detecting handguns in carry-on luggage. Our results show significant performance gains from all components.
On the anonymity of two-factor authentication schemes for wireless sensor networks: Attacks, principle and solutions

Two-factor authentication Wireless sensor networks User anonymity Smart card Non-tamper resistant a b s t r a c t Anonymity is among the important properties of two-factor authentication schemes for wireless sensor networks (WSNs) to preserve user privacy. Though impressive efforts have been devoted to designing schemes with user anonymity by only using lightweight symmetric key primitives such as hash functions and block ciphers, to the best of our knowledge none has succeeded so far. In this work, we take an initial step to shed light on the rationale underlying this prominent issue. Firstly, we scrutinize two previously-thought sound schemes, namely Fan et al.'s scheme and Xue et al.'s scheme, and demonstrate the major challenges in designing a scheme with user anonymity. Secondly, using these two foremost schemes as case studies and on the basis of the work of Halevi–Krawczyk (1999)  and Impagliazzo–Rudich (1989) , we put forward a general principle: Public-key techniques are intrinsically indispensable to construct a two-factor authentication scheme that can support user anonymity. Furthermore, we discuss the practical solutions to realize user anonymity. Remarkably, our principle can be applied to two-factor schemes for universal environments besides WSNs, such as the Inter-net, global mobility networks and mobile clouds. We believe that our work contributes to a better understanding of the inherent complexity in achieving user privacy, and will establish a groundwork for developing more secure and efficient privacy-preserving two-factor authentication schemes. With the rapid development of micro-electromechani-cal systems and wireless network technologies, wireless sensor networks (WSNs) have attracted increasing attention due to its wide range of applications from battlefield surveillance to civilian applications, e.g., environmental monitoring, real-time traffic control, industrial process control and home automation. As is well known, most large-scale WSNs [1–3] follow a tiered architecture due to its superiority in increasing the network capacity and scalability, accommodating the node mobility, reducing the management complexity and prolonging the network lifetime. Thus, in this work we mainly focus on the tiered WSNs as well. In many critical applications, external users are generally interested in accessing real-time information from sensor nodes, yet if the data queries are issued by the base station, efficiency, scalability and security may not be ensured over the long communication path between the base station and the sensor nodes .
Cyberbullying, self-esteem, empathy and loneliness

Cyberbullying is a unique phenomenon, distinguished from traditional bullying by the speed at which information is distributed, permanence of material and availability of victims. There is however a paucity of research in this area, and few studies have examined the factors contributing to cyberbullying behaviour. The present study investigated the influence of self-esteem, empathy and loneliness on cyberbullying victimisation and perpetration. British adolescents (N = 90) aged 16–18 years were recruited from Further Education colleges. Participants completed the Revised Cyber Bullying Inventory (RCBI, Topcu & Erdur-Baker, 2010), the UCLA Loneliness Scale (Russell, Peplau, & Ferguson, 1978), Toronto Empathy Questionnaire (TEQ, Spreng, McKinnon, Mar, & Levine, 2009) and Rosenberg Self-Esteem Scale (Rosenberg, 1965) online. Standard multiple regressions revealed that together, loneliness, empathy and self-esteem predicted levels of cyberbullying victimisation and perpetration. Self-esteem was a significant individual predictor of cyberbullying victimisation and perpetration, such that those with low self-esteem were most likely to report experience of cyberbullying. Empathy was a significant individual predictor of cyberbullying perpetration, such that as empathy decreases, likelihood of cyberbullying perpetration increases. These findings indicate that self-esteem and empathy oriented interventions may successfully address cyberbullying behaviour.
Improving relevance judgment of web search results with image excerpts

Current web search engines return result pages containing mostly text summary even though the matched web pages may contain informative pictures. A text excerpt (i.e. snippet) is generated by selecting keywords around the matched query terms for each returned page to provide context for user's relevance judgment. However, in many scenarios, we found that the pictures in web pages, if selected properly, could be added into search result pages and provide richer contextual description because a picture is worth a thousand words. Such new summary is named as image excerpts. By well designed user study, we demonstrate image excerpts can help users make much quicker relevance judgment of search results for a wide range of query types. To implement this idea, we propose a practicable approach to automatically generate image excerpts in the result pages by considering the dominance of each picture in each web page and the relevance of the picture to the query. We also outline an efficient way to incorporate image excerpts in web search engines. Web search engines can adopt our approach by slightly modifying their index and inserting a few low cost operations in their workflow. Our experiments on a large web dataset indicate the performance of the proposed approach is very promising.
Change Detection in 3D Point Clouds Acquired by a Mobile Mapping System

Thanks to the development of Mobile mapping systems (MMS), street object recognition, classification, modelling and related studies have become hot topics recently. There has been increasing interest in detecting changes between mobile laser scanning (MLS) point clouds in complex urban areas. A method based on the consistency between the occupancies of space computed from different datasets is proposed. First occupancy of scan rays (empty, occupied, unknown) are defined while considering the accuracy of measurement and registration. Then the occupancy of scan rays are fused using the Weighted Dempster–Shafer theory (WDST). Finally, the consistency between different datasets is obtained by comparing the occupancy at points from one dataset with the fused occupancy of neighbouring rays from the other dataset. Change detection results are compared with a conventional point to triangle (PTT) distance method. Changes at point level are detected fully automatically. The proposed approach allows to detect changes at large scales in urban scenes with fine detail and more importantly, distinguish real changes from occlusions.
A Survey of Automated Techniques for Formal Software Verification

The quality and the correctness of software are often the greatest concern in electronic systems. Formal verification tools can provide a guarantee that a design is free of specific flaws. This paper surveys algorithms that perform automatic static analysis of software to detect programming errors or prove their absence. The three techniques considered are static analysis with abstract domains, model checking, and bounded model checking. A short tutorial on these techniques is provided, highlighting their differences when applied to practical problems. This paper also surveys tools implementing these techniques and describes their merits and shortcomings.
Document Classification for Newspaper Articles

In many real-world scenarios, the ability to automatically classify documents into a fixed set of categories is highly desirable. Common scenarios include classifying a large amount of unclassified archival documents such as newspaper articles, legal records and academic papers. For example, newspaper articles can be classified as ’features’, ’sports’ or ’news’. Other scenarios involve classifying of documents as they are created. Examples include classifying movie review articles into ’positive’ or ’negative’ reviews or classifying only blog entries using a fixed set of labels. Natural language processing o↵ers powerful techniques for automatically classifying documents. These techniques are predicated on the hypothesis that documents in di↵erent categories distinguish themselves by features of the natural language contained in each document. Salient features for document classification may include word structure, word frequency, and natural language structure in each document. Our project looks specifically at the task of automatically classifying newspaper articles from the MIT newspaper The Tech. The Tech has archives of a large number of articles which require classification into specific sections (News, Opinion, Sports, etc). Our project is aimed at investigating and implementing techniques which can be used to perform automatic article classification for this purpose. At our disposal is a large archive of already classified documents so we are able to make use of supervised classification techniques. We randomly split this archive of classified documents into training and testing groups for our classification systems (hereafter referred to simply as classifiers). This project experiments with di↵erent natural language feature sets as well as di↵erent statistical techniques using these feature sets and compares the performance in each case. Specifically, our project involves experimenting with feature sets for Naive Bayes Classification, Maximum Entropy Classification, and examining sentence structure di↵erences in di↵erent categories using probabilistic grammar parsers. The paper proceeds as follows: Section 2 discusses related work in the areas of document classification and give an overivew of each classification technique. Section 3 details our approach and implementation. Section 4 shows the results of testing our classifiers. In Section 5, we discuss possible future extensions and suggestions for improvement. Finally, in Section 6, we discuss retrospective thoughts on our approach and high-level conclusions about our results.
The Planar Ultrawideband Modular Antenna (PUMA) Array

A fully planar ultrawideband phased array with wide scan and low cross-polarization performance is introduced. The array is based on Munk's implementation of the current sheet concept, but it employs a novel feeding scheme for the tightly coupled horizontal dipoles that enables simple PCB fabrication. This feeding eliminates the need for “cable organizers” and external baluns, and when combined with dual-offset dual-polarized lattice arrangements the array can be implemented in a modular, tile-based fashion. Simple physical explanations and circuit models are derived to explain the array's operation and guide the design process. The theory and insights are subsequently used to design an exemplary dual-polarized infinite array with 5:1 bandwidth and VSWR <; 2.1 at broadside, and cross-polarization ≈ -15 dB out to θ = 45° in the D- plane.
On the Continuity of High Efficiency Modes in Linear RF Power Amplifiers

A novel formulation for the voltage waveforms in high efficiency linear power amplifiers is described. This formulation demonstrates that a constant optimum efficiency and output power can be obtained over a continuum of solutions by utilizing appropriate harmonic reactive impedance terminations. A specific example is confirmed experimentally. This new formulation has some important implications for the possibility of realizing broadband >10% high efficiency linear RF power amplifiers.
Spoken Language Understanding for a Nutrition Dialogue System

Food logging is recommended by dieticians for prevention and treatment of obesity, but currently available mobile applications for diet tracking are often too difficult and time-consuming for patients to use regularly. For this reason, we propose a novel approach to food journaling that uses speech and language understanding technology in order to enable efficient self-assessment of energy and nutrient consumption. This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user's spoken meal description. We first summarize the data collection and annotation of food descriptions performed via Amazon Mechanical Turk AMT, for both a written corpus and spoken data from an in-domain speech recognizer. We show that the addition of word vector features improves conditional random field CRF performance for semantic tagging of food concepts, achieving an average F1 test score of 92.4 on written data; we also demonstrate that a convolutional neural network CNN with no hand-crafted features outperforms the best CRF on spoken data, achieving an F1 test score of 91.3. We illustrate two methods for associating foods with properties: segmenting meal descriptions with a CRF, and a complementary method that directly predicts associations with a feed-forward neural network. Finally, we conduct an end-to-end system evaluation through an AMT user study with worker ratings of 83% semantic tagging accuracy.