nils-herrmann/clean_scirepeval huggingface dataset:
Accelerometer-Based Device Fingerprinting for Multi-factor Mobile Authentication

Due to the numerous data breaches, often resulting in the disclosure of a substantial amount of user passwords, the classic authentication scheme where just a password is required to log in, has become inadequate. As a result, many popular web services now employ risk-based authentication systems where various bits of information are requested in order to determine the authenticity of the authentication request. In this risk assessment process, values consisting of geo-location, IP address and browser-fingerprint information, are typically used to detect anomalies in comparison with the user’s regular behavior. In this paper, we focus on risk-based authentication mechanisms in the setting of mobile devices, which are known to fall short of providing reliable device-related information that can be used in the risk analysis process. More specifically, we present a web-based and low-effort system that leverages accelerometer data generated by a mobile device for the purpose of device re-identification. Furthermore, we evaluate the performance of these techniques and assess the viability of embedding such a system as part of existing risk-based authentication processes.
Algorithm for Computer Control of a Digital Plotter

The algorithm may be programmed without multiplication or division instructions and is eficient with respect to speed of execution and memory utilization. This paper describes an algorithm for computer control of a type of digital plotter that is now in common use with digital computers .' The plotter under consideration is capable of executing, in response to an appropriate pulse, any one of the eight linear movements shown in Figure 1. Thus, the plotter can move linearly from a point on a mesh to any adjacent point on the mesh. A typical mesh size is 1/100th of an inch. The data to be plotted are expressed in an (x , y) rectangular coordinate system which has been scaled with respect to the mesh; i.e., the data points lie on mesh points and consequently have integral coordinates. It is assumed that the data include a sufficient number of appropriately selected points to produce a satisfactory representation of the curve by connecting the points with line segments, as illustrated in Figure 2. In Figure 3, the line segment connecting
A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC

Split-merge moves are a standard component of MCMC algorithms for tasks such as multitarget tracking and fitting mixture models with unknown numbers of components. Achieving rapid mixing for split-merge MCMC has been notoriously difficult, and state-of-the-art methods do not scale well. We explore the reasons for this and propose a new split-merge kernel consisting of two sub-kernels: one combines a “smart” split move that proposes plausible splits of heterogeneous clusters with a “dumb” merge move that proposes merging random pairs of clusters; the other combines a dumb split move with a smart merge move. We show that the resulting smart-dumb/dumb-smart (SDDS) algorithm outperforms previous methods. Experiments with entity-mention models and Dirichlet process mixture models demonstrate much faster convergence and better scaling to large data sets.
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.
Smartphone use can be addictive? A case report

The use of mobile phones has become an integral part of everyday life. Young people in particular can be observed using their smartphones constantly, and they not only make or receive calls but also use different applications or just tap touch screens for several minutes at a time. The opportunities provided by smartphones are attractive, and the cumulative time of using smartphones per day is very high for many people, so the question arises whether we can really speak of a mobile phone addiction? In this study, our aim is to describe and analyze a possible case of smartphone addiction.  We present the case of Anette, an 18-year-old girl, who is characterized by excessive smartphone use. We compare Anette's symptoms to Griffiths's conception of technological addictions, Goodman's criteria of behavioral addictions, and the DSM-5 criteria of gambling disorder.  Anette fulfills almost all the criteria of Griffiths, Goodman, and the DSM-5, and she spends about 8 hr in a day using her smartphone.  Anette's excessive mobile phone usage includes different types of addictive behaviors: making selfies and editing them for hours, watching movies, surfing on the Internet, and, above all, visiting social sites. The cumulative time of these activities results in a very high level of smartphone use. The device in her case is a tool that provides these activities for her whole day. Most of Anette's activities with a mobile phone are connected to community sites, so her main problem may be a community site addiction.
Design of a High-Efficiency 40-kV, 150-A, 3-kHz Solid-State Pulsed Power Modulator

This paper deals with the detailed design of a pulsed power modulator using insulated gate bipolar transistor (IGBT) switches for industrial applications. Output specifications of the proposed modulator are as follows: variable output pulse voltage 1~40 kV; pulse width 0.5~5 μs ; maximum pulse repetition rates 3 kHz, and average output power of 13 kW. The proposed pulsed power modulator consists of a high-voltage capacitor charger based on a high-efficiency resonant inverter and pulse generator part including a series of connected 24 pieces power cells. To verify the proposed design, PSpice modeling was performed. Finally, experimental results proved the reliability and robustness of the proposed solid-state pulsed power modulator.
Recovering Semantics of Tables on the Web

The Web offers a corpus of over 100 million tables , but the meaning of each table is rarely explicit from the table itself. Header rows exist in few cases and even when they do, the attribute names are typically useless. We describe a system that attempts to recover the semantics of tables by enriching the table with additional annotations. Our annotations facilitate operations such as searching for tables and finding related tables. To recover semantics of tables, we leverage a database of class labels and relationships automatically extracted from the Web. The database of classes and relationships has very wide coverage, but is also noisy. We attach a class label to a column if a sufficient number of the values in the column are identified with that label in the database of class labels, and analogously for binary relationships. We describe a formal model for reasoning about when we have seen sufficient evidence for a label, and show that it performs substantially better than a simple majority scheme. We describe a set of experiments that illustrate the utility of the recovered semantics for table search and show that it performs substantially better than previous approaches. In addition, we characterize what fraction of tables on the Web can be annotated using our approach.
Upper bounds on the minimum distance of spherical codes

We use linear programming techniques to obtain new upper bounds on the maximal squared minimum distance of spherical codes with fixed cardinality. Functions Qj(n, s) are introduced with the property that Qj(n, s) < 0 for some j > m iff the Levenshtein bound Lm(n, s) on A(n, s) = max{|W | : W is an (n, |W |, s) code} can be improved by a polynomial of degree at least m+1. General conditions on the existence of new bounds are presented. We prove that for fixed dimension n ≥ 5 there exist a constant k = k(n) such that all Levenshtein bounds Lm(n, s) for m ≥ 2k− 1 can be improved. An algorithm for obtaining new bounds is proposed and discussed.
DataSpotting: offloading cellular traffic via managed device-to-device data transfer at data spots

Mobile broadband demand keeps growing at an overwhelming pace. Though emerging wireless technologies will provide more bandwidth, the increase in demand may easily consume the extra bandwidth. To alleviate this problem, we propose using the content available on individual devices as caches. Particularly, when a user reaches areas with dense clusters of mobile devices, "data spots", the operator can instruct the user to connect with other users sharing similar interests and serve the requests locally. This paper presents feasibility study as well as prototype implementation of this idea.
Citation Analysis with Neural Attention Models

Automated citation analysis (ACA) can be important for many applications including author ranking and literature based information retrieval, extraction, summarization and question answering. In this study, we developed a new compositional attention network (CAN) model to integrate local and global attention representations with a hierarchical attention mechanism. Training on a new benchmark corpus we built, our evaluation shows that the CAN model performs consistently well on both citation classification and sentiment analysis tasks.
Logistic regression with an auxiliary data source

To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming <i>D<sup>p</sup></i> and <i>D<sup>a</sup></i> are two sets of examples drawn from two mismatched distributions, where <i>D<sup>a</sup></i> are fully labeled and <i>D<sup>p</sup></i> partially labeled, our objective is to complete the labels of <i>D<sup>p</sup>.</i> We introduce an auxiliary variable μ for each example in <i>D<sup>a</sup></i> to reflect its mismatch with <i>D<sup>p</sup>.</i> Under an appropriate constraint the μ's are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in <i>D<sup>p</sup>.</i> The proposed algorithm, called "Migratory-Logit" or M-Logit, is demonstrated successfully on simulated as well as real data sets.
Internet of Things: Survey and open issues of MQTT protocol

Internet of Things (IoT) is based on a wireless network that connects a huge number of smart objects, products, smart devices, and people. It has another name which is Web of Things (WoT). IoT uses standards and protocols that are proposed by different standardization organizations in message passing within session layer. Most of the IoT applications protocols use TCP or UDP for transport. XMPP, CoAP, DDS, MQTT, and AMQP are grouped of the widely used application protocols. Each one of these protocols have specific functions and are used in specific way to handle some issues. This paper provides an overview for one of the most popular application layer protocols that is MQTT, including its architecture, message format, MQTT scope, and Quality of Service (QoS) for the MQTT levels. MQTT works mainly as a pipe for binary data and provides a flexibility in communication patterns. It is designed to provide a publish-subscribe messaging protocol with most possible minimal bandwidth requirements. MQTT uses Transmission Control Protocol (TCP) for transport. MQTT is an open standard, giving a mechanisms to asynchronous communication, have a range of implementations, and it is working on IP.
PERFORMANCE COMPARISON OF OCR TOOLS

Optical Character Recognition (OCR) is a technique, used to convert scanned image into editable text format. Many different types of Optical Character Recognition (OCR) tools are commercially available today; it is a useful and popular method for different types of applications. OCR can predict the accurate result depends on text pre-processing and segmentation algorithms. Image quality is one of the most important factors that improve quality of recognition in performing OCR tools. Images can be processed independently (.png, .jpg, and .gif files) or in multi-page PDF documents (.pdf). The primary objective of this work is to provide the overview of various Optical Character Recognition (OCR) tools and analyses of their performance by applying the two factors of OCR tool performance i.e. accuracy and error rate.
ERP implementation factors: A comparison of managerial and end-user perspectives

Past research has identified factors that are important to the successful implementation of enterprise resource planning (ERP) systems. However, the identification of these factors has often been based on the perceptions of senior members within organizations that are implementing these systems. In this study, the perceptions of managers and end-users on selected implementation factors are compared. Understanding if differences exist in the perceptions of different groups within an organization and the nature of these differences can help implementers develop appropriate intervention mechanisms such as training and communication that can lead to successful ERP implementation.
Malicious URL filtering — A big data application

Malicious URLs have become a channel for Internet criminal activities such as drive-by-download, spamming and phishing. Applications for the detection of malicious URLs are accurate but slow (because they need to download the content or query some Internet host information). In this paper we present a novel lightweight filter based only on the URL string itself to use before existing processing methods. We run experiments on a large dataset and demonstrate a 75% reduction in workload size while retaining at least 90% of malicious URLs. Existing methods do not scale well with the hundreds of millions of URLs encountered every day as the problem is a heavily-imbalanced, large-scale binary classification problem. Our proposed method is able to handle nearly two million URLs in less than five minutes. We generate two filtering models by using lexical features and descriptive features, and then combine the filtering results. The on-line learning algorithms are applied here not only for dealing with large-scale data sets but also for fitting the very short lifetime characteristics of malicious URLs. Our filter can significantly reduce the volume of URL queries on which further analysis needs to be performed, saving both computing time and bandwidth used for content retrieval.
Supplementary Material : Zero-Shot Task Generalization with MultiTask Deep Reinforcement Learning

Inter/Extrapolation. In this experiment, a task is defined by three parameters: action, object, and number. The agent should repeat the same subtask for a given number of times. The agent is trained on all configurations of actions and target objects. However, only a subset of numbers is used during training. In order to interpolate and extrapolate, we define analogies based on simple arithmetic such as:
Ten principles of good business process management

The purpose of this paper is to foster a common understanding of business process management (BPM) by proposing a set of ten principles that characterize BPM as a research domain and guide its successful use in organizational practice.  The identification and discussion of the principles reflects our viewpoint, which was informed by extant literature and focus groups, including 20 BPM experts from academia and practice.  We identify ten principles which represent a set of capabilities essential for mastering contemporary and future challenges in BPM. Their antonyms signify potential roadblocks and bad practices in BPM. We also identify a set of open research questions that can guide future BPM research. Research limitation/implication – Our findings suggest several areas of research regarding each of the identified principles of good BPM. Also, the principles themselves should be systematically and empirically examined in future studies.  Our findings allow practitioners to comprehensively scope their BPM initiatives and provide a general guidance for BPM implementation. Moreover, the principles may also serve to tackle contemporary issues in other management areas.  This is the first paper that distills principles of BPM in the sense of both good and bad practice recommendations. The value of the principles lies in providing normative advice to practitioners as well as in identifying open research areas for academia, thereby extending the reach and richness of BPM beyond its traditional frontiers.
Efficient Label Propagation

Label propagation is a popular graph-based semisupervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost of O(n+cn), where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the same labeling results as those yielded by optimal labeling scores. The key to our approach is to iteratively compute lower and upper bounds of labeling scores to prune unnecessary score computations. This idea significantly reduces the computational cost to O(cnt) where t is the average number of iterations for each label and t ≪ n in practice. Experiments demonstrate the significant superiority of our algorithm over existing label propagation methods.
Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces

A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation
A Wall Climbing Robot for Oil Tank Inspection

Thousands of storage tanks in oil refineries have to be inspected manually to prevent leakage and/or any other potential catastrophe. A wall climbing robot with permanent magnet adhesion mechanism equipped with nondestructive sensor has been designed. The robot can be operated autonomously or manually. In autonomous mode the robot uses an ingenious coverage algorithm based on distance transform function to navigate itself over the tank surface in a back and forth motion to scan the external wall for the possible faults using sensors without any human intervention. In manual mode the robot can be navigated wirelessly from the ground station to any location of interest. Preliminary experiment has been carried out to test the prototype.
Open data kit sensors: a sensor integration framework for android at the application-level

Smartphones can now connect to a variety of external sensors over wired and wireless channels. However, ensuring proper device interaction can be burdensome, especially when a single application needs to integrate with a number of sensors using different communication channels and data formats. This paper presents a framework to simplify the interface between a variety of external sensors and consumer Android devices. The framework simplifies both application and driver development with abstractions that separate responsibilities between the user application, sensor framework, and device driver. These abstractions facilitate a componentized framework that allows developers to focus on writing minimal pieces of sensor-specific code enabling an ecosystem of reusable sensor drivers. The paper explores three alternative architectures for application-level drivers to understand trade-offs in performance, device portability, simplicity, and deployment ease. We explore these tradeoffs in the context of four sensing applications designed to support our work in the developing world. They highlight a range of sensor usage models for our application-level driver framework that vary data types, configuration methods, communication channels, and sampling rates to demonstrate the framework's effectiveness.
Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies

The effectiveness of the treatment of breast cancer depends on its timely detection. An early step in the diagnosis is the cytological examination of breast material obtained directly from the tumor. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies to characterize these biopsies as either benign or malignant. Instead of relying on the accurate segmentation of cell nuclei, the nuclei are estimated by circles using the circular Hough transform. The resulting circles are then filtered to keep only high-quality estimations for further analysis by a support vector machine which classifies detected circles as correct or incorrect on the basis of texture features and the percentage of nuclei pixels according to a nuclei mask obtained using Otsu's thresholding method. A set of 25 features of the nuclei is used in the classification of the biopsies by four different classifiers. The complete diagnostic procedure was tested on 737 microscopic images of fine needle biopsies obtained from patients and achieved 98.51% effectiveness. The results presented in this paper demonstrate that a computerized medical diagnosis system based on our method would be effective, providing valuable, accurate diagnostic information.
A 77–81-GHz 16-Element Phased-Array Receiver With $\pm {\hbox{50}}^{\circ}$  Beam Scanning for Advanced Automotive Radars

A 16-element phased-array receiver has been developed for advanced W-band automotive radars. The phased-array receiver is based on a single SiGe chip with RF beamforming capabilities, which is packaged using low-cost bond-wire techniques and attached to a 16-element linear microstrip array. The antenna results in a directivity of 29.3 dB and a gain of 28.0 dB at 77-81 GHz, and can be scanned to ±50° in the azimuth plane in ~ 1° steps. The packaging details are presented together with the steps taken to ensure a wideband impedance match and low coupling between the phased-array channels. Gain measurements done at 79 GHz agree well with simulations. The 16-element phased array receiver was used with a 2-element frequency-modulated continuous-wave transmitter at 76.5-77 GHz and high-resolution millimeter-wave images were obtained. The work shows that complex millimeter-wave phased arrays can be packaged using traditional bond-wire techniques, and can be a powerful solution for advanced automotive radars.