nils-herrmann/clean_scirepeval huggingface dataset:
Planar Monopole With a Coupling Feed and an Inductive Shorting Strip for LTE/GSM/UMTS Operation in the Mobile Phone

A planar monopole having a small size yet providing two wide bands for covering the eight-band LTE/GSM/UMTS operation in the mobile phone is presented. The small-size yet wideband operation is achieved by exciting the antenna's wide radiating plate using a coupling feed and short-circuiting it to the system ground plane of the mobile phone through a long meandered strip as an inductive shorting strip. The coupling feed leads to a wide operating band to cover the frequency range of 1710-2690 MHz for the GSM1800/1900/UMTS/LTE2300/2500 operation. The inductive shorting strip results in the generation of a wide operating band to cover the frequency range of 698-960 MHz for the LTE700/GSM850/900 operation. The planar monopole can be directly printed on the no-ground portion of the system circuit board of the mobile phone and is promising to be integrated with a practical loudspeaker. The antenna's radiating plate can also be folded into a thin structure (3 mm only) to occupy a small volume of 3 × 6 × 40 mm3 (0.72 cm3) for the eight-band LTE/GSM/UMTS operation; in this case, including the 8-mm feed gap, the antenna shows a low profile of 14 mm to the ground plane of the mobile phone. The proposed antenna, including its planar and folded structures, are suitable for slim mobile phone applications.
The Impact of Massively Multiplayer Online Role Playing Games (MMORPGs) on Psychological Wellbeing and the Role of Play Motivations and Problematic Use

The association between MMORPG play (World of Warcraft) and psychological wellbeing was explored through a cross sectional, online questionnaire design testing the relationship between average hours playing per week and psychological wellbeing. Play motivation including achievement, social interaction and immersion as well as problematic use were tested as mediating variables. Participants (N = 565) completed online measures including demographics and play time, health, motivations to play and problematic use. Analysis revealed a negative correlation between playing time and psychological wellbeing. A Multiple Mediation Model showed the relationship specifically occurred where play was motivated by Immersion and/or where play was likely to have become problematic. No evidence of a direct effect of play on psychological wellbeing was found when taking these mediating pathways into account. Clinical and research implications are discussed.
Stochastic models of load balancing and scheduling in cloud computing clusters

Cloud computing services are becoming ubiquitous, and are starting to serve as the primary source of computing power for both enterprises and personal computing applications. We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources such as CPU, memory and storage space. While there are many design issues associated with such systems, here we focus only on resource allocation problems, such as the design of algorithms for load balancing among servers, and algorithms for scheduling VM configurations. Given our model of a cloud, we first define its capacity, i.e., the maximum rates at which jobs can be processed in such a system. Then, we show that the widely-used Best-Fit scheduling algorithm is not throughput-optimal, and present alternatives which achieve any arbitrary fraction of the capacity region of the cloud. We then study the delay performance of these alternative algorithms through simulations.
LEARNING TO DETECT PHISHING URLs

Phishing attacks have been on the rise and performing certain actions such as mouse hovering, clicking, etc. on malicious URLs may cause unsuspecting Internet users to fall victims of identity theft or other scams. In this paper, we study the anatomy of phishing URLs that are created with the specific intent of impersonating a trusted third party to trick users into divulging personal data. Unlike previous work in this area, we only use a number of publicly available features on URL alone; in addition, we compare performance of different machine learning techniques and evaluate the efficacy of real-time application of our method. Applying it on real-world data sets, we demonstrate that the proposed approach is highly effective in detecting phishing URLs with an error rate of 0.3%, false positive rate of 0.2% and false negative rate of about 0.5%, thereby improving previous results on the important problem of phishing detection.  Phishing URL, phishing websites, machine learning, web mining, phishing attack, URL classification ---------------------------------------------------------------------***--------------------------------------------------------------------
A Unified Gradient Regularization Family for Adversarial Examples

Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best learning models including the state-of-the-art deep learning models. Recent attempts have been made to build robust models that take into account adversarial examples. However, these methods can either lead to performance drops or lack mathematical motivations. In this paper, we propose a unified framework to build robust machine learning models against adversarial examples. More specifically, using the unified framework, we develop a family of gradient regularization methods that effectively penalize the gradient of loss function w.r.t. inputs. Our proposed framework is appealing in that it offers a unified view to deal with adversarial examples. It incorporates another recently-proposed perturbation based approach as a special case. In addition, we present some visual effects that reveals semantic meaning in those perturbations, and thus support our regularization method and provide another explanation for generalizability of adversarial examples. By applying this technique to Maxout networks, we conduct a series of experiments and achieve encouraging results on two benchmark datasets. In particular, we attain the best accuracy on MNIST data (without data augmentation) and competitive performance on CIFAR-10 data.
Robust Visual Robot Localization Across Seasons Using Network Flows

Image-based localization is an important problem in robotics and an integral part of visual mapping and navigation systems. An approach to robustly match images to previously recorded ones must be able to cope with seasonal changes especially when it is supposed to work reliably over long periods of time. In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes. We formulate image matching as a minimum cost flow problem in a data association graph to effectively exploit sequence information. This allows us to deal with non-matching image sequences that result from temporal occlusions or from visiting new places. We present extensive experimental evaluations under substantial seasonal changes. Our approach achieves accurate matching across seasons and outperforms existing state-of-the-art methods such as FABMAP2 and SeqSLAM.
Mine Your Own Business: Market-Structure Surveillance Through Text Mining

W 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers’ thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and “listen” to what customers write about their and their competitors’ products. Our objective is to convert the user-generated content to market structures and competitive landscape insights. The difficulty in obtaining such market-structure insights from online user-generated content is that consumers’ postings are often not easy to syndicate. To address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. We demonstrate this approach using two cases—sedan cars and diabetes drugs—generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. We compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences.
Operating Systems for Internet of Things: A Comparative Study

The intense growth of Internet of Things (IoTs), its multidisciplinary nature and broadcasting communication pattern made it very challenging for research community/domain. Operating systems for IoTs plays vital role in this regard. Through this research contribution, the objective is to present an analytical study on the recent developments on operating systems specifically designed or fulfilled the needs of IoTs. Starting from study and advances in the field of IoTs with focus on existing operating systems specifically for IoTs. Finally the existing operating systems for IoTs are evaluated and compared on some set criteria and facts and findings are presented.
Sparsity and Incoherence in Compressive Sampling

We consider the problem of reconstructing a sparse signal x ∈ R from a limited number of linear measurements. Given m randomly selected samples of Ux, where U is an orthonormal matrix, we show that `1 minimization recovers x exactly when the number of measurements exceeds m ≥ Const · μ(U) · S · log n, where S is the number of nonzero components in x, and μ is the largest entry in U properly normalized: μ(U) = √ n ·maxk,j |Uk,j |. The smaller μ, the fewer samples needed. The result holds for “most” sparse signals x supported on a fixed (but arbitrary) set T . Given T , if the sign of x for each nonzero entry on T and the observed values of Ux are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples. Acknowledgments. E. C. is partially supported by National Science Foundation grants ITR ACI-0204932 and CCF515362, by the 2006 Waterman Award (NSF), and by a grant from DARPA. J. R. is supported by National Science Foundation grants CCF515362 and ITR ACI-0204932. E. C. would like to thank Joel Tropp and Houman Owhadi for fruitful conversations related to this project.
HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases

Recent improvements in both the performance and scalability of shared-nothing, transactional, in-memory NewSQL databases have reopened the research question of whether distributed metadata for hierarchical file systems can be managed using commodity databases. In this paper, we introduce HopsFS, a next generation distribution of the Hadoop Distributed File System (HDFS) that replaces HDFS’ single node in-memory metadata service, with a distributed metadata service built on a NewSQL database. By removing the metadata bottleneck, HopsFS enables an order of magnitude larger and higher throughput clusters compared to HDFS. Metadata capacity has been increased to at least 37 times HDFS’ capacity, and in experiments based on a workload trace from Spotify, we show that HopsFS supports 16 to 37 times the throughput of Apache HDFS. HopsFS also has lower latency for many concurrent clients, and no downtime during failover. Finally, as metadata is now stored in a commodity database, it can be safely extended and easily exported to external systems for online analysis and free-text search.
JADE – A FIPA-compliant agent framework

JADE is a software framework to develop agent applications in compliance with the FIPA specifications for interoperable intelligent multi-agent systems. The goal is to simplify development while ensuring standard compliance through a comprehensive set of system services and agents. JADE can then be considered an agent middle-ware that implements an Agent Platform and a development framework. It deals with all those aspects that are not peculiar of the agent internals and that are independent of the applications, such as message transport, encoding and parsing, or agent life-cycle. This paper presents the JADE software describing its intended uses, as well as being a walkthrough of JADE internal architecture. The main architectural issues are discussed, and the major design decisions are outlined.
A Primer on 3GPP Narrowband Internet of Things

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced in 3GPP Release 13 for providing wide-area coverage for IoT. This article provides an overview of the air interface of NB-IoT. We describe how NB-IoT addresses key IoT requirements such as deployment flexibility, low device complexity, long battery lifetime, support of massive numbers of devices in a cell, and significant coverage extension beyond existing cellular technologies. We also share the various design rationales during the standardization of NB-IoT in Release 13 and point out several open areas for future evolution of NB-IoT.
A CMOS Low Voltage Charge Pump

Charge pump circuits are used for obtaining higher voltages than normal power supply voltage in flash memories, DRAMs and low voltage designs. In this paper, we present a charge pump circuit in standard CMOS technology that is suited for low voltage operation. Our proposed charge pump uses a cross- connected NMOS cell as the basic element and PMOS switches are employed to connect one stage to the next. The simulated output voltages of the proposed 4 stage charge pump for input voltage of 0.9 V, 1.2 V, 1.5 V, 1.8 V and 2.1 V are 3.9 V, 5.1 V, 6.35 V, 7.51 V and 8.4 V respectively. This proposed charge pump is suitable for low power CMOS mixed-mode designs.
IdentiDroid: Android can finally Wear its Anonymous Suit

Because privacy today is a major concern for mobile applications, network anonymizers are widely available on smartphones, such as Android. However despite the use of such anonymizers, in many cases applications are still able to identify the user and the device by different means than the IP address. The reason is that very often applications require device services and information that go beyond the capabilities of anonymous networks in protecting users’ identity and privacy. In this paper, we propose two solutions that address this problem. The first solution is based on an approach that shadows user and application data, device information, and resources that can reveal the user identity. Data shadowing is executed when the smartphone switches to the “anonymous modality”. Once the smartphone returns to work in the normal (i.e. non-anonymous) modality, application data, device information and resources are returned back to the state they had before the anonymous connection. The second solution is based on run-time modifications of Android application permissions. Permissions associated with sensitive information are dynamically revoked at run-time from applications when the smartphone is used under the anonymous modality. They are re-instated back when the smartphone returns to work in the normal modality. In addition, both solutions offer protection from applications that identify their users through traces left in the application’s data storage or through exchanging identifying data messages. We developed IdentiDroid, a customized Android operating system, to deploy these solutions and built IdentiDroid Profile Manager, a profile-based configuration tool that allows one to set different configurations for each installed Android application. With this tool, applications running within the same device are configured to be given different identifications and privileges to limit the uniqueness of device and user information. We analyzed 250 Android applications to determine what information, services, and permissions can identify users and devices. Our experiments show that when IdentiDroid is deployed and properly configured on Android devices, users’ anonymity is better guaranteed by either of the proposed solutions with no significant impact on most device applications.
Mo(bile) Money, Mo(bile) Problems: Analysis of Branchless Banking Applications

Mobile money, also known as branchless banking, leverages ubiquitous cellular networks to bring much-needed financial services to the unbanked in the developing world. These services are often deployed as smartphone apps, and although marketed as secure, these applications are often not regulated as strictly as traditional banks, leaving doubt about the truth of such claims. In this article, we evaluate these claims and perform the first in-depth measurement analysis of branchless banking applications. We first perform an automated analysis of all 46 known Android mobile money apps across the 246 known mobile money providers from 2015. We then perform a comprehensive manual teardown of the registration, login, and transaction procedures of a diverse 15% of these apps. We uncover pervasive vulnerabilities spanning botched certification validation, do-it-yourself cryptography, and other forms of information leakage that allow an attacker to impersonate legitimate users, modify transactions, and steal financial records. These findings show that the majority of these apps fail to provide the protections needed by financial services. In an expanded re-evaluation one year later, we find that these systems have only marginally improved their security. Additionally, we document our experiences working in this sector for future researchers and provide recommendations to improve the security of this critical ecosystem. Finally, through inspection of providers’ terms of service, we also discover that liability for these problems unfairly rests on the shoulders of the customer, threatening to erode trust in branchless banking and hinder efforts for global financial inclusion.
Lifted First-Order Probabilistic Inference

Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level.  2003] presented a method to perform inference directly on the first-order level, but this method is limited to special cases. In this paper we present the first exact inference algorithm that operates directly on a first-order level, and that can be applied to any first-order model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference.
Relating emotional intelligence to social competence and academic achievement in high school students.

This study investigated the discriminant, criterion and incremental validity of an ability measure of Emotional Intelligence (EI). High school students (N = 77) took the Mayer-Salovey-Caruso Emotional Intelligence Test - Spanish Version (MSCEIT V. 2.0, 2002), a measure of Big Five personality traits (BFQ; Caprara, Barbanelli, & Borgogni , 1993), an General Intelligence test (IGF-r 5; Yuste, 2002), and a social competence inventory (AECS; Moraleda, González, & García-Gallo, 1998). Students' academic grades also were obtained from official school records at the end of the school year. As predicted, the MSCEIT was discriminable from well-established measures of personality and intelligence. The test was also moderately related to social competence and predicted students' final grades. Most of the findings remained significant after personality and academic intelligence were statistically controlled. The potential utility of EI in the context of academic institutions is discussed.
Towards a Semantic Driven Framework for Smart Grid Applications: Model-Driven Development Using CIM, IEC 61850 and IEC 61499

Power and energy systems are on the verge of a profound change where Smart Grid solutions will enhance their efficiency and flexibility. Advanced ICT and control systems are key elements of the Smart Grid to enable efficient integration of a high amount of renewable energy resources which in turn are seen as key elements of the future energy system. The corresponding distribution grids have to become more flexible and adaptable as the current ones in order to cope with the upcoming high share of energy from distributed renewable sources. The complexity of Smart Grids requires to consider and imply many components when a new application is designed. However, a holistic ICT-based approach for modelling, designing and validating Smart Grid developments is missing today. The goal of this paper therefore is to discuss an advanced design approach and the corresponding information model, covering system, application, control and communication aspects of Smart Grids.
A Survey of Energy Efficient Network Protocols for Wireless Networks

Wireless networking has witnessed an explosion of interest from consumers in recent years for its applications in mobile and personal communications. As wireless networks become an integral component of the modern communication infrastructure, energy efficiency will be an important design consideration due to the limited battery life of mobile terminals. Power conservation techniques are commonly used in the hardware design of such systems. Since the network interface is a significant consumer of power, considerable research has been devoted to low-power design of the entire network protocol stack of wireless networks in an effort to enhance energy efficiency. This paper presents a comprehensive summary of recent work addressing energy efficient and low-power design within all layers of the wireless network protocol stack.
Deep modeling of gene expression regulation in an Erythropoiesis model

The fate of differentiation of G1E cells is determined, among other things, by a handful of transcription factors (TFs) binding the neighborhood of appropriate gene targets. The problem of understanding the dynamics of gene expression regulation is a feature learning problem on high dimensional space determined by the sizes of gene neighborhoods, but that can be projected on a much lower dimensional manifold whose space depends on the number of TFs and the number of ways they interact. To learn this manifold, we train a deep convolutional network on the activity of TF binding on 20Kb gene neighborhoods labeled by binarized levels of target gene expression. After supervised training of the model we achieve 77% accuracy as estimated by 10-fold CV. We discuss methods for the representation of the model knowledge back into the input space. We use this representation to highlight important patterns and genome locations with biological importance.
Trip analyzer through smartphone apps

Broad usage of Smartphones and mobile apps enables both individual trip summary and regional travel demand analysis. In this paper, we describe a trip analysis system, as part of smarter transit service. This trip analysis system consists of mobile apps and a centralized analyzer. It identifies the travel mode and purpose of the trips sensed by mobile devices, provides trip summaries and insights to mobile subscribers, and generates meaningful patterns to support traffic operation planning and transit system design. It is developed and deployed to the Smartphones of the volunteers in Dubuque, IA, to serve both the volunteers and the transit agencies. Preliminary evaluation has demonstrated the applicability of the design.