nils-herrmann/clean_scirepeval huggingface dataset:
Modeling and Summarizing News Events Using Semantic Triples

ive News Summarization Approaches 5 Phrase-selection based (PSB) • Tentatively pairs subject and verb phrases from different sentences • Checks for compatibility Pattern-graph Fusion (PGF) • Looks for similar tokens in different sentences • Fuses the tokens, thus forming a graph PSB Example “Hurricane” “Nate” “slammed” “Louisiana” “It” “killed” “2” “people” compatible
Optimized Edge Detection Algorithm for Face Recognition

Face recognition is one of the most challenging tasks in the field of image processing. This paper presents an optimized edge detection algorithm for the task of face recognition. In this method a gradient based filter using a wide convolution kernel is applied on the image to extract the edges. Later a thinning algorithm optimized for the wide convolution kernel is applied on the extracted edges. The advantages of this method over other gradient based methods is its ability to find the missing edges more successfully and boost the significant edges of the overall facial contour.
Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks’ locations. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. Revealing an individual’s location data to other entities may prevent people from contributing to SC applications, thus rendering location privacy a critical obstacle to the growth of SC applications. This chapter first identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks’ locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users’ location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchangebased and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work. Hien To University of Southern California, Los Angeles, CA 90089 e-mail: hto@usc.edu Cyrus Shahabi University of Southern California, Los Angeles, CA 90089 e-mail:
Issues and challenges of data security in a cloud computing environment

Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.
Identification of Cognates and Recurrent Sound Correspondences in Word Lists

Identification of cognates and recurrent sound corresponde nces is a component of two principal tasks of historical linguistics: demonstrat ing the relatedness of languages, and reconstructing the histories of language families. We prop ose methods for detecting and quantifying three characteristics of cognates: recurrent soun d correspondences, phonetic similarity, and semantic affinity. The ultimate goal is to identify cogna tes and correspondences directly from lists of words representing pairs of languages that are known to be related. The proposed solutions are language independent, and are evaluated agai nst uthentic linguistic data. The results of evaluation experiments involving the Indo-Euro pean, Algonquian, and Totonac language families indicate that our methods are more accurate t han comparable programs, and achieve high precision and recall on various test sets. The r esults also suggest that combining various types of evidence substantially increases cognate identification accuracy. RÉSUMÉ. L’identification de mots apparentés et des correspondances de sons récurrents intervient dans deux des principales tâches de la linguistique hi storique: démontrer des filiations linguistiques et reconstruire l’histoire des familles de l angues. Nous proposons des méthodes de détection et de quantification de trois caractéristiques d mots apparentés: les correspondances de sons récurrents, la ressemblance phonétique et l’ affinité sémantique. Le but ultime est d’identifier les mots apparentés et les correspondances dir ctement à partir de listes de mots représentant des paires des langues dont la filiation est con nue. Les solutions proposées sont indépendantes des langues traitées et sont évaluées sur des données linguistiques réelles. Les résultats d’expériences impliquant des langues indo-euro péennes, algonquines et des langues de la famille du totonaque indiquent que nos méthodes sont pl us précises que des programmes comparables et d’atteignent une haute précision et un haut t aux de rappel sur des ensembles de test. Les résultats suggèrent également que la combinaison de divers types d’indices augmente grandement la justesse de l’identification des mots apparen tés.
Make new friends or keep the old: Gender and personality differences in social networking use

0747-5632/$ see front matter  Department o Alabama, P.O. Box 870348, Tuscaloosa, AL 35487-034 758 2003; fax: +1 205 348 8648. E-mail address: nlmuscanell@crimson.ua.edu (N.L. The present study examined the influence of gender and personality on individuals’ use of online social networking websites such as Facebook and MySpace. Participants were 238 undergraduate students who reported being members of Facebook, MySpace, or both. Based on prior research examining online behavior, we expected that gender and scores on the Big Five personality scale would moderate online social networking behavior. The results supported our predictions. Specifically, men reported using social networking sites for forming new relationships while women reported using them more for relationship maintenance. Furthermore, women low in agreeableness reported using instant messaging features of social networking sites more often than women high in agreeableness, whereas men low in openness reported playing more games on social networking sites compared to men high in openness. Overall, these results indicate the importance of examining individual differences in online behavior.
From Selective to Adaptive Security in Functional Encryption

In a functional encryption (FE) scheme, the owner of the secret key can generate restricted decryption keys that allow users to learn specific functions of the encrypted messages and nothing else. In many known constructions of FE schemes, security is guaranteed only for messages that are fixed ahead of time (i.e., before the adversary even interacts with the system). This so-called selective security is too restrictive for many realistic applications. Achieving adaptive security (also called full security), where security is guaranteed even for messages that are adaptively chosen at any point in time, seems significantly more challenging. The handful of known adaptively-secure schemes are based on specifically tailored techniques that rely on strong assumptions (such as obfuscation or multilinear maps assumptions). We show that any sufficiently-expressive selectively-secure FE scheme can be transformed into an adaptively-secure one without introducing any additional assumptions. We present a black-box transformation, for both public-key and private-key schemes, making novel use of hybrid encryption, a classical technique that was originally introduced for improving the efficiency of encryption schemes. We adapt the hybrid encryption approach to the setting of functional encryption via a technique for embedding a “hidden execution thread” in the decryption keys of the underlying scheme, which will only be activated within the proof of security of the resulting scheme. As an additional application of this technique, we show how to construct functional encryption schemes for arbitrary circuits starting from ones for shallow circuits (NC1 or even TC0).
Maximum energy harvesting from medium voltage electric-field energy using power line insulators

One essential requirement for a modern Smart Grid is continuous monitoring of the grid variables. This task is efficiently achieved using self-powered electronic devices deployed throughout the grid network. Within the vicinity of the grid power lines, the high voltages of these lines can provide a source of energy for such loads using capacitive coupling. This is known as Electric-Field Energy Harvesting (EFEH). However, the reported strategies that use this principle have not explored how to maximise their power transfer, which is essential for practical systems since the available energy from EFEH is invariably quite small. In this paper, a novel approach for optimum energy harvesting is proposed using the parasitic capacitance of medium-voltage power line insulators. A follow-on power electronic conversion system is then used to maintain the load conditions that maximise the extracted energy. The system performance has been analysed using theoretical studies and computer simulations to identify the operating conditions that maximise the extracted energy. Results obtained so far suggest that up to 100 mW can be harvested from 22 kV grid feeders, using only the capacitive coupling that exists between the line insulator harvester and the feeder conductor.
On-Chip Compensation of Ring VCO Oscillation Frequency Changes Due to Supply Noise and Process Variation

A novel circuit technique that stabilizes the oscillation frequency of a ring-type voltage-controlled oscillator (RVCO) is demonstrated. The technique uses on-chip bias-current and voltage-swing controllers, which compensate RVCO oscillation frequency changes caused by supply noise and process variation. A prototype phase-locked loop (PLL) having the RVCO with the compensation circuit is fabricated with 0.13-μm CMOS technology. At the operating frequency of 4 GHz, the measured PLL rms jitter improves from 20.11 to 5.78 ps with 4-MHz RVCO supply noise. Simulation results show that the oscillation frequency difference between FF and SS corner is reduced from 63% to 6% of the NN corner oscillation frequency.
Electromagnetic coupling of twisted pair cables

Increasing deployment of digital subscriber line (DSL) technology operating in the long wave, medium wave and sometimes short wave frequency ranges, has raised the question of how such systems interact with rival radio frequency sources and equipment. Two of the major standards bodies in the field, ITU and ETSI, imposed by the standardization of such promising technologies like ADSL, SHDSL and VDSL, have recently begun to elaborate performance tests for radio frequency interference (RFI) scenarios for inclusion into their standards documents. Existing out-door loops of the copper telephone network can have large dimensions, are often unshielded and sometimes not even twisted, thus, they can act like antennas for both RFI egress and ingress. The RFI egress issue requires basic knowledge of the relationship between a given current distribution ( ) on a twisted pair communication cable and the electromagnetic field , generated by that current distribution at a given distance . This problem is reciprocal to the RFI ingress issue, which requires knowledge of the relationship between a disturbing high-frequency electric field in the surroundings of a twisted pair cable, and the terminal voltage of that cable across a load impedance. This contribution provides the theoretical background to the field theoretical problem and supplies simple yet accurate approximation formulae for the prediction of RFI egress and RFI ingress.
Can infants map meaning to newly segmented words? Statistical segmentation and word learning.

The present experiments investigated how the process of statistically segmenting words from fluent speech is linked to the process of mapping meanings to words. Seventeen-month-old infants first participated in a statistical word segmentation task, which was immediately followed by an object-label-learning task. Infants presented with labels that were words in the fluent speech used in the segmentation task were able to learn the object labels. However, infants presented with labels consisting of novel syllable sequences (nonwords; Experiment 1) or familiar sequences with low internal probabilities (part-words; Experiment 2) did not learn the labels. Thus, prior segmentation opportunities, but not mere frequency of exposure, facilitated infants' learning of object labels. This work provides the first demonstration that exposure to word forms in a statistical word segmentation task facilitates subsequent word learning.
"In vivo" spam filtering: a challenge problem for KDD

Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue in vivo spam filtering as an accessible domain for investigating them.
Black-Box Calibration for ADCs With Hard Nonlinear Errors Using a Novel INL-Based Additive Code: A Pipeline ADC Case Study

This paper presents a digital nonlinearity calibration technique for ADCs with strong input–output discontinuities between adjacent codes, such as pipeline, algorithmic, and SAR ADCs with redundancy. In this kind of converter, the ADC transfer function often involves multivalued regions, where conventional integral-nonlinearity (INL)-based calibration methods tend to miscalibrate, negatively affecting the ADC’s performance. As a solution to this problem, this paper proposes a novel INL-based calibration which incorporates information from the ADC’s internal signals to provide a robust estimation of static nonlinear errors for multivalued ADCs. The method is fully generalizable and can be applied to any existing design as long as there is access to internal digital signals. In pipeline or subranging ADCs, this implies access to partial subcodes before digital correction; for algorithmic or SAR ADCs, conversion bit/bits per cycle are used. As a proof-of-concept demonstrator, the experimental results for a 1.2 V 23 mW 130 nm-CMOS pipeline ADC with a SINAD of 58.4 dBc (in nominal conditions without calibration) is considered. In a stressed situation with 0.95 V of supply, the ADC has SINAD values of 47.8 dBc and 56.1 dBc, respectively, before and after calibration (total power consumption, including the calibration logic, being 15.4 mW).
Improving Security Screening: A Comparison of Multistatic Radar Configurations for Human Body Imaging

Active millimeter-wave radar can be used for imaging concealed objects and detecting contraband at security checkpoints. Current state-of-the-art systems are based on monostatic or quasimonostatic configurations that can misrepresent areas of the target when the specular reflection is oriented away from the incident direction. This article presents a fully multistatic millimeter-wave imaging architecture for human-body screening. Transmitters placed off the receiving aperture panel allow for maximizing the surface of the human body that can be reconstructed. A reduced number of transmitters allows for fast imaging, which minimizes the target motion effects. Multiple three-dimensional (3-D) simulation-based examples are presented to validate the proposed system.
Is 3D technique superior to 2D in Down syndrome screening? Evaluation of six second and third trimester fetal profile markers.

The objective of this article is to investigate whether in the clinical setting of second trimester ultrasound (US) investigations, 3D multiplanar correction prior to the measurement of Down syndrome (DS) facial markers (nasal bone length, prenasal thickness, fetal profile line, maxilla-nasion-mandible angle, prenasal thickness to nasal bone length ratio, and prefrontal space ratio) is superior to subjective judgment of a correct midsagittal plane by 2D technique.



Measurements were performed on 2D images and 3D volumes (corrected to the midsagittal plane), acquired during the same scanning session.



All six markers were measured in 105 datasets (75 of euploid fetuses and 30 of DS fetuses). The maxilla-nasion-mandible angle measured on 2D images was significantly larger than on 3D volumes (p < 0.01). In all other markers, there was no significant difference between measurements performed on 2D images or 3D volumes. No statistical difference was found for any marker between measurements performed on images acquired by either 2D or 3D US in their ability to discriminate between normal and DS fetuses.



Nasal bone length, prenasal thickness, fetal profile line, prenasal thickness to nasal bone length ratio, and prefrontal space ratio can be confidently used as DS markers in second trimester US examinations performed by 2D US.
Reverse Curriculum Generation for Reinforcement Learning

Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These tasks present considerable difficulties for reinforcement learning approaches, since the natural reward function for such goal-oriented tasks is sparse and prohibitive amounts of exploration are required to reach the goal and receive a learning signal. Past approaches tackle these problems by manually designing a task-specific reward shaping function to help guide the learning. Instead, we propose a method to learn these tasks without requiring any prior task knowledge other than obtaining a single state in which the task is achieved. The robot is trained in “reverse”, gradually learning to reach the goal from a set of starting positions increasingly far from the goal. Our method automatically generates a curriculum of starting positions that adapts to the agent’s performance, leading to efficient training on such tasks. We demonstrate our approach on difficult simulated fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.
Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface

There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.
A Broadband Substrate Integrated Waveguide (SIW) Planar Balun

A broadband planar balun is presented in this work that makes use of a substrate integrated waveguide (SIW) technique using a printed circuit board process. The proposed balun structure is able to operate at millimeter wave frequencies and it does not require any tight line coupling sections as frequently used in monolithic microwave integrated circuit balun design. In addition, this balun can be integrated with other planar topologies including nonplanar circuits made of the same substrate for achieving high efficiency. This balun structure consists of a 3 dB SIW power divider and microstrip lines that are placed on both sides of the substrate at balanced ports to obtain an 180deg phase shift. The concept is validated by simulations and measurements. Our measured results suggest that a 10 dB return loss at unbalanced port can easily be achieved across a 42% bandwidth from 19 to 29 GHz. Measured amplitude and phase imbalance between two balanced ports are within 1 dB and plusmn5deg, respectively.