nils-herrmann/clean_scirepeval huggingface dataset:
Clunealgia: CT-guided therapeutic posterior femoral cutaneous nerve block.

Clunealgia is caused by neuropathy of inferior cluneal branches of the posterior femoral cutaneous nerve resulting in pain in the inferior gluteal region. Image-guided anesthetic nerve injections are a viable and safe therapeutic option in sensory peripheral neuropathies that provides significant pain relief when conservative therapy fails and surgery is not desired or contemplated. The authors describe two cases of clunealgia, where computed-tomography-guided technique for nerve blocks of the posterior femoral cutaneous nerve and its branches was used as a cheaper, more convenient, and faster alternative with similar face validity as the previously described magnetic-resonance-guided injection.
Automatic nonverbal behavior indicators of depression and PTSD: the effect of gender

Recently there has been arising interest in automatically recognizing nonverbal behaviors that are linked with psychological conditions. Work in this direction has shown great potential for cases such as depression and post-traumatic stress disorder (PTSD), however most of the times gender differences have not been explored. In this paper, we show that gender plays an important role in the automatic assessment of psychological conditions such as depression and PTSD. We identify a directly interpretable and intuitive set of predictive indicators, selected from three general categories of nonverbal behaviors: affect, expression variability and motor variability. For the analysis, we employ a semi-structured virtual human interview dataset which includes 53 video recorded interactions. Our experiments on automatic classification of psychological conditions show that a gender-dependent approach significantly improves the performance over a gender agnostic one.
Boosted decision trees as an alternative to artificial neural networks for particle identification

The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.  29.85.+c; 02.70.Uu; 07.05.Mh; 14.60.Pq
Antidepressant activity of zinc and magnesium in view of the current hypotheses of antidepressant action.

The clinical efficacy of current antidepressant therapies is unsatisfactory; antidepressants induce a variety of unwanted effects, and, moreover, their therapeutic mechanism is not clearly understood. Thus, a search for better and safer agents is continuously in progress. Recently, studies have demonstrated that zinc and magnesium possess antidepressant properties. Zinc and magnesium exhibit antidepressant-like activity in a variety of tests and models in laboratory animals. They are active in forced swim and tail suspension tests in mice and rats, and, furthermore, they enhance the activity of conventional antidepressants (e.g., imipramine and citalopram). Zinc demonstrates activity in the olfactory bulbectomy, chronic mild and chronic unpredictable stress models in rats, while magnesium is active in stress-induced depression-like behavior in mice. Clinical studies demonstrate that the efficacy of pharmacotherapy is enhanced by supplementation with zinc and magnesium. The antidepressant mechanisms of zinc and magnesium are discussed in the context of glutamate, brain-derived neurotrophic factor (BDNF) and glycogen synthase kinase-3 (GSK-3) hypotheses. All the available data indicate the importance of zinc and magnesium homeostasis in the psychopathology and therapy of affective disorders.
HIGH PRESSURE OIL-INJECTED SCREW GAS COMPRESSORS ( API 619 DESIGN ) FOR HEAVY DUTY PROCESS GAS APPLICATIONS

Compressor Engineering Group with Kobe Steel Ltd., in Hyogo, Japan. He is in charge of both oil-free and oil-flooded screw compressor engineering for process gas and industrial refrigeration. Previously in his 22 year career, Mr. Ohama was an engineer for oilflooded gas screw compressors and managed a screw compressor engineering group. He, along with his staff, developed the highpressure screw compressor “EH series” in 1997, which was the first in the world applied to 60 barg as a series. Mr. Ohama also participated in the Task Force for the preparation of API 619 Third Edition. Mr. Ohama graduated with a B.S. degree (Mechanical Engineering, 1979) and an M.S. degree () from the Saga University, Japan.
Constraint-based approach for automatic hinting of digital typefaces

The rasterization process of characters from digital outline fonts to bitmaps on displays must include additional information in the form of hints beside the shape of characters in order to produce high quality bitmaps. Hints describe constraints on sizes and shapes inside characters and across the font that should be preserved during rasterization. We describe a novel, fast and fully automatic method for adding those hints to characters. The method is based on identifying hinting situations inside characters. It includes gathering global font information and linking it to characters, defining a set of constraints, sorting them, and converting them to hints in any known hinting technology (PostScript, TrueType or other). Our scheme is general enough to be applied on any language and on complex scripts such as Chinese Japanese and Korean. Although still inferior to expert manual hinting, our method produces high quality bitmaps which approach this goal. The method can also be used as a solid base for further hinting refinements done manually.
Information Science Abstracts: Tracking the literature of information science. Part 1: Definition and map

Information Science Abstracts (ISA) is the oldest abstracting and indexing (A&I) publication covering the field of information science. A&I publications play a valuable “gatekeeping” role in identifying changes in a discipline by tracking its literature. This article briefly reviews the history of ISA as well as the history of attempts to define “information science” because the American Documentation Institute changed its name to ASIS in 1970. A new working definition of the term for ISA is derived from both the historical review and current technological advances. The definition departs from the previous document-centric definitions and concentrates on the Internet-dominated industry of today. Information science is a discipline drawing on important concepts from a number of closely related disciplines that become a cohesive whole focusing on information. The relationships between these interrelated disciplines are portrayed on a “map” of the field, in which the basic subjects are shown as a central “core” with related areas surrounding it.
Smart cities : A conjuncture of four forces

Despite the ongoing discussion of the recent years, there is no agreed definition of a ‘smart city’, while strategic planning in this field is still largely unexplored. Inspired by this, the purpose of this paper was to identify the forces shaping the smart city conception and, by doing so, to begin replacing the currently abstract image of what it means to be one. The paper commences by dividing the recent history of smart cities into two large sections – urban futures and the knowledge and innovation economy. The urban futures strand shows that technology has always played an important role in forward-looking visions about the city of the future. The knowledge and innovation economy strand shows that recent technological advancements have introduced a whole new level of knowledge management and innovation capabilities in the urban context. The paper proceeds to explicate the current technology push and demand pull for smart city solutions. On one hand, technology advances rapidly and creates a booming market of smart city products and solutions around it. On the other hand, there is demand on the side of cities that seek to address the problems of efficiency and sustainability, making the ground fertile for a smart city product economy. The research route of this paper eventually allows the identification of the underlying – and often forgotten – principles of what it means to be ‘smart’ in an urban context and yields conclusions about strategic planning for the development of smart cities today.
The Influence of Social Network Sites ( SNS ) upon Academic Performance of Malaysian Students

Social network sites (SNS) have attracted considerable attention among teens and young adults who tend to connect and share common interest. Despite this popularity, the issue of students’ adoption of social network sites is still being unexplored fully in Malaysia. Driven by this factor, this study was designed to analyze the impact of social network sites on students’ academic performance in Malaysia. Using a conceptual approach, the study gathered that more students prefer the use of Facebook and Twitter in academic related discussions in complementingconventional classroom teaching and learning process. Thus, it is imperative that lecturers and academic institutions should implement the use of these applications in promoting academic excellence. As for profit oriented organizations such as bookshops, computer and smartphoneone vendors, they can promote their products through these applications and engage students to make purchases via them having understood that many students prefer and use Facebook, Twitter and Google+. The discussion from this study however does not represent the general sampling of Malaysian university students.
Multilingual Multi-document Summarization with Enhanced hLDA Features

This paper presents the state of art research progress on multilingual multi-document summarization. Our method utilizes hLDA (hierarchical Latent Dirichlet Allocation) algorithm to model the documents firstly. A new feature is proposed from the hLDA modeling results, which can reflect semantic information to some extent. Then it combines this new feature with different other features to perform sentence scoring. According to the results of sentence score, it extracts candidate summary sentences from the documents to generate a summary. We have also attempted to verify the effectiveness and robustness of the new feature through experiments. After the comparison with other summarization methods, our method reveals better performance in some respects.
Your Remnant Tells Secret: Residual Resolution in DDoS Protection Services

The increasing prevalence of Distributed Denial of Service (DDoS) attacks on the Internet has led to the wide adoption of DDoS Protection Service (DPS), which is typically provided by Content Delivery Networks (CDNs) and is integrated with CDN's security extensions. The effectiveness of DPS mainly relies on hiding the IP address of an origin server and rerouting the traffic to the DPS provider's distributed infrastructure, where malicious traffic can be blocked. In this paper, we perform a measurement study on the usage dynamics of DPS customers and reveal a new vulnerability in DPS platforms, called residual resolution, by which a DPS provider may leak origin IP addresses when its customers terminate the service or switch to other platforms, resulting in the failure of protection from future DPS providers as adversaries are able to discover the origin IP addresses and launch the DDoS attack directly to the origin servers. We identify that two major DPS/CDN providers, Cloudflare and Incapsula, are vulnerable to such residual resolution exposure, and we then assess the magnitude of the problem in the wild. Finally, we discuss the root causes of residual resolution and the practical countermeasures to address this security vulnerability.
Life Insurance Customers segmentation using fuzzy clustering

One of the important issues in service organizations is to identify the customers, understanding their difference and ranking them. Recently, the customer value as a quantitative parameter has been used for segmenting customers. A practical solution for analytical development is using analytical techniques such as dynamic clustering algorithms and programs to explore the dynamics in consumer preferences. The aim of this research is to understand the current customer behavior and suggest a suitable policy for new customers in order to attain the highest benefits and customer satisfaction. To identify such market in life insurance customers, We have used the FKM.pf.niose fuzzy clustering technique for classifying the customers based on their demographic and behavioral data of 1071 people in the period April to October 2014. Results show the optimal number of clusters is 3. These three clusters can be named as: investment, security of life and a combination of both. Some suggestions are presented to improve the performance of the insurance company.
On Rendering Synthetic Images for Training an Object Detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.
Development of Platform Independent Hand Gesture Controlled Wearable Mouse

Human-Computer Interaction (HCI) is an ever growing and a commercially developing technology. Smart devices and wearable electronics are becoming very popular, but as of now, there is no available alternative to practically replace the conventional mouse. This paper presents a design for a fully operational hand-gesture controlled wearable mouse. It is easy to wear and remove that makes it ideal for daily use. It is a platform-independent design using Human Interface Device (HID) technology. It is a simple plug and play type device without the need for a special driver and other software support. From motion detection point of view, what are the differences between accelerometer and gyroscope are also clearly explained here with help of graphs and results. The suitable sensor has been used to detect hand gestures and accordingly change the position of the mouse pointer.
Modelling 3D City Using High Resolution Stereo Camera Imagery

This paper presents concepts and methods to develop digital 3D city model from photography. The aim of current research is to create an effective approach of modelling city with huge amount of complex architectures. Taking advantage of advanced photogrammetry and High Resolution Stereo Camera (HRSC) technology, high quality ground details can be mapped to GIS datasets and the accurate 3D information can be explored. We describe a technique to triangulate (mesh) polygonal surface for creating 3D visual objects. The technique provides high quality and accuracy for the creation of geographic objects. Also, we introduce the method to improve visualization performance by means of rectification and optimization. Finally, we discuss the data management of 3D city model.
Virtual Reality Intervention for Older Women with Breast Cancer

This study examined the effects of a virtual reality distraction intervention on chemotherapy-related symptom distress levels in 16 women aged 50 and older. A cross-over design was used to answer the following research questions: (1) Is virtual reality an effective distraction intervention for reducing chemotherapy-related symptom distress levels in older women with breast cancer? (2) Does virtual reality have a lasting effect? Chemotherapy treatments are intensive and difficult to endure. One way to cope with chemotherapy-related symptom distress is through the use of distraction. For this study, a head-mounted display (Sony PC Glasstron PLM - S700) was used to display encompassing images and block competing stimuli during chemotherapy infusions. The Symptom Distress Scale (SDS), Revised Piper Fatigue Scale (PFS), and the State Anxiety Inventory (SAI) were used to measure symptom distress. For two matched chemotherapy treatments, one pre-test and two post-test measures were employed. Participants were randomly assigned to receive the VR distraction intervention during one chemotherapy treatment and received no distraction intervention (control condition) during an alternate chemotherapy treatment. Analysis using paired t-tests demonstrated a significant decrease in the SAI (p = 0.10) scores immediately following chemotherapy treatments when participants used VR. No significant changes were found in SDS or PFS values. There was a consistent trend toward improved symptoms on all measures 48 h following completion of chemotherapy. Evaluation of the intervention indicated that women thought the head mounted device was easy to use, they experienced no cybersickness, and 100% would use VR again.
Applications of DEC-MDPs in multi-robot systems

Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, team of patrolling robots, rescue-robot colonies, mine-clearance robots, etc. In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decentralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.
10T SRAM Using Half- $V_{\text {DD}}$ Precharge and Row-Wise Dynamically Powered Read Port for Low Switching Power and Ultralow RBL Leakage

We present, in this paper, a new 10T static random access memory cell having single ended decoupled read-bitline (RBL) with a 4T read port for low power operation and leakage reduction. The RBL is precharged at half the cell’s supply voltage, and is allowed to charge and discharge according to the stored data bit. An inverter, driven by the complementary data node (QB), connects the RBL to the virtual power rails through a transmission gate during the read operation. RBL increases toward the <inline-formula> <tex-math notation="LaTeX">$V_{\text {DD}}$ </tex-math></inline-formula> level for a read-1, and discharges toward the ground level for a read-0. Virtual power rails have the same value of the RBL precharging level during the write and the hold mode, and are connected to true supply levels only during the read operation. Dynamic control of virtual rails substantially reduces the RBL leakage. The proposed 10T cell in a commercial 65 nm technology is <inline-formula> <tex-math notation="LaTeX">$2.47\times $ </tex-math></inline-formula> the size of 6T with <inline-formula> <tex-math notation="LaTeX">$\beta = 2$ </tex-math></inline-formula>, provides <inline-formula> <tex-math notation="LaTeX">$2.3\times $ </tex-math></inline-formula> read static noise margin, and reduces the read power dissipation by 50% than that of 6T. The value of RBL leakage is reduced by more than 3 orders of magnitude and <inline-formula> <tex-math notation="LaTeX">$({I_{\mathrm{\scriptscriptstyle ON}}}/{I_{\mathrm{\scriptscriptstyle OFF}}})$ </tex-math></inline-formula> is greatly improved compared with the 6T BL leakage. The overall leakage characteristics of 6T and 10T are similar, and competitive performance is achieved.
Geo-spatial aerial video processing for scene understanding and object tracking

This paper presents an approach to extracting and using semantic layers from low altitude aerial videos for scene understanding and object tracking. The input video is captured by low flying aerial platforms and typically consists of strong parallax from non-ground-plane structures. A key aspect of our approach is the use of geo-registration of video frames to reference image databases (such as those available from Terraserver and Google satellite imagery) to establish a geo-spatial coordinate system for pixels in the video. Geo-registration enables Euclidean 3D reconstruction with absolute scale unlike traditional monocular structure from motion where continuous scale estimation over long periods of time is an issue. Geo-registration also enables correlation of video data to other stored information sources such as GIS (geo-spatial information system) databases. In addition to the geo-registration and 3D reconstruction aspects, the key contributions of this paper include: (1) exploiting appearance and 3D shape constraints derived from geo-registered videos for labeling of structures such as buildings, foliage, and roads for scene understanding, and (2) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels derived from geo-registered videos. Experimental results on extended time aerial video data demonstrates the qualitative and quantitative aspects of our work.
Disconnected Recurrent Neural Networks for Text Categorization

www.PosterPresentations.com RNN can model the entire sequence and capture long-term dependencies, but it does not do well in extracting key patterns. In contrast, convolutional neural network (CNN) is good at Extracting local and position-invariant features. We present a novel model named disconnected recurrent neural network (DRNN), which incorporates position-invariance into RNN by constraining the distance of information flow in RNN. DRNN achieves the best performance on several benchmark datasets for text categorization.
Accurate and Fast Asymmetric Locality-Sensitive Hashing Scheme for Maximum Inner Product Search

The problem of Approximate Maximum Inner Product (AMIP) search has received increasing attention due to its wide applications. Interestingly, based on asymmetric transformation, the problem can be reduced to the Approximate Nearest Neighbor (ANN) search, and hence leverage Locality-Sensitive Hashing (LSH) to find solution. However, existing asymmetric transformations such as L2-ALSH and XBOX, suffer from large distortion error in reducing AMIP search to ANN search, such that the results of AMIP search can be arbitrarily bad. In this paper, we propose a novel Asymmetric LSH scheme based on Homocentric Hypersphere partition (H2-ALSH) for high-dimensional AMIP search. On the one hand, we propose a novel Query Normalized First (QNF) transformation to significantly reduce the distortion error. On the other hand, by adopting the homocentric hypersphere partition strategy, we can not only improve the search efficiency with early stop pruning, but also get higher search accuracy by further reducing the distortion error with limited data range. Our theoretical studies show that H2-ALSH enjoys a guarantee on search accuracy. Experimental results over four real datasets demonstrate that H2-ALSH significantly outperforms the state-of-the-art schemes.