nils-herrmann/clean_scirepeval huggingface dataset:
Using Social Media for Emergency Response and Urban Sustainability : A Case Study of the 2012 Beijing Rainstorm

With the proliferation of social media, information generated and disseminated from these outlets has become an important part of our everyday lives. For example, this type of information has great potential for effectively distributing political messages, hazard alerts, or messages of other social functions. In this work, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management and urban sustainability.
Query by Example of Speaker Audio Signals Using Power Spectrum and MFCCs

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%.
A chip-scale packaging technology for 60-GHz wireless chipsets

In this paper, we present a cost-effective chip-scale packaging solution for a 60-GHz industrial-scientific-medical band receiver (Rx) and transmitter (Tx) chipset capable of gigabit-per-second wireless communications. Envisioned applications of the packaged chipset include 1-3-Gb/s directional links using amplitude shift-keying or phase shift-keying modulation and 500-Mb/s-1-Gb/s omni-directional links using orthogonal frequency-division multiplexing modulation. This paper demonstrates the first fully package-integrated 60-GHz chipset including receive and transmit antennas in a cost-effective plastic package. A direct-chip-attach (DCA) and surface mountable land-grid-array (LGA) package technology is presented. The size of the DCA package is 7times11 mm2 and the LGA package size is 6times13 mm2. Optionally, the Tx and Rx chip can be packaged together with Tx and Rx antennas in a combined 13times13 mm2 LGA transceiver package
TUNABLE AND SWITCHABLE BANDPASS FILTERS USING SLOT-LINE RESONATORS

In this paper, novel uniplanar tunable and switchable bandpass filters are designed by using the centrally-loaded slot-line resonator. From the voltage-wave distribution along the resonator, the appropriate location for the loading element is determined to be the center of the slot-line resonator, where the voltages of the fundamental signal and second harmonic are maximum and zero, respectively. As a result, the fundamental frequency can be tuned while the second harmonic remains almost unchanged. For the first time, the properties of the centrally-loaded slot-line resonator are analyzed by using the evenand odd-mode method, and their respective resonant frequencies are derived. The demonstrated tunable bandpass filter can give a 30.9% frequency tuning range with acceptable insertion loss when a varactor is used as the loading element. By replacing the loading varactors with PIN diodes, a switchable bandpass filter is realized in which the attenuation in the fundamental passband can be controlled. In experiments, the switchable bandpass filter exhibits a 2.13 dB insertion loss in the fundamental passband when the PIN diodes are off and more than 49 dB isolation across the passband when the PIN diodes are on. Received 8 October 2010,  Zhi-Hua Bao (bao.zh@ntu.edu.cn).
Understanding JavaScript event-based interactions

Web applications have become one of the fastest growing types of software systems today. Despite their popularity, understanding the behaviour of modern web applications is still a challenging endeavour for developers during development and maintenance tasks. The challenges mainly stem from the dynamic, event-driven, and asynchronous nature of the JavaScript language. We propose a generic technique for capturing low-level event-based interactions in a web application and mapping those to a higher-level behavioural model. This model is then transformed into an interactive visualization, representing episodes of triggered causal and temporal events, related JavaScript code executions, and their impact on the dynamic DOM state. Our approach, implemented in a tool called Clematis, allows developers to easily understand the complex dynamic behaviour of their application at three different semantic levels of granularity. The results of our industrial controlled experiment show that Clematis is capable of improving the task accuracy by 61%, while reducing the task completion time by 47%.
The effects of psychotomimetic drugs on primary suggestibility

Mescaline, LSD-25, psilocybin, and a combination of these three drugs were each administered in moderate doses at 7-day intervals to 24 volunteer subjects according to a latin square design. Primary suggestibility, that type closely related to hypnosis, was measured both before and 2 hrs following each administration of drugs, using the 17-item Stanford Suggestibility Scale. A Trance Indicator Score was also obtained, representing both behavioral observations and subjective reports concerning the degree of trance-like or hypnotic-like experiences which may have accompanied each suggestibility testing session. Major findings were that mescaline, LSD-25, and the combination of drugs each produced an average enhancement in primary suggestibility closely comparable to that produced by an induction of hypnosis, the latter being tested for each subject after the completion of the drug series. Psilocybin did not enhance primary suggestibility. Associated trance phenomena were also substantially increased during all of the drug trials, but significantly less so for psilocybin. Significant correlations were obtained between suggestibility and Trance Indicator Scores following each drug and after induction of hypnosis, although psilocybin produced substantially less correlation. Although these results confirm speculations previously made in the literature that LSD-25 may increase suggestibility, it must be emphasized that they cannot be generalized beyond the specific type of suggestibility measured. Second, the differences between psilocybin and the other drugs on primary suggestibility restrict generalizations regarding the action of otherwise similar psychotomimetic drugs.
An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder–decoder pair for training and recovery of images with two-dimensional ${\text{16}}\times {\text{16}}$ pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.
Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping

The recent advent of multichannel near-infrared spectroscopy (NIRS) has expanded its technical potential for human brain mapping. However, NIRS measurement has a technical drawback in that it measures cortical activities from the head surface without anatomical information of the object to be measured. This problem is also found in transcranial magnetic stimulation (TMS) that transcranially activates or inactivates the cortical surface. To overcome this drawback, we examined cranio-cerebral correlation using magnetic resonance imaging (MRI) via the guidance of the international 10-20 system for electrode placement, which had originally been developed for electroencephalography. We projected the 10-20 standard cranial positions over the cerebral cortical surface. After examining the cranio-cerebral correspondence for 17 healthy adults, we normalized the 10-20 cortical projection points of the subjects to the standard Montreal Neurological Institute (MNI) and Talairach stereotactic coordinates and obtained their probabilistic distributions. We also expressed the anatomical structures for the 10-20 cortical projection points probabilistically. Next, we examined the distance between the cortical surface and the head surface along the scalp and created a cortical surface depth map. We found that the locations of 10-20 cortical projection points in the standard MNI or Talairach space could be estimated with an average standard deviation of 8 mm. This study provided an initial step toward establishing a three-dimensional probabilistic anatomical platform that enables intra- and intermodal comparisons of NIRS and TMS brain imaging data.
A Novel Feature Hashing With Efficient Collision Resolution for Bag-of-Words Representation of Text Data

Text Mining is widely used in many areas transforming unstructured text data from all sources such as patients' record, social media network, insurance data, and news, among others into an invaluable source of information. The Bag Of Words (BoW) representation is a means of extracting features from text data for use in modeling. In text classification, a word in a document is assigned a weight according to its frequency and frequency between different documents; therefore, words together with their weights form the BoW. One way to solve the issue of voluminous data is to use the feature hashing method or hashing trick. However, collision is inevitable and might change the result of the whole process of feature generation and selection. Using the vector data structure, the lookup performance is improved while resolving collision and the memory usage is also efficient.
A silicon vector light sensor for proximity sensing applications

In this paper, we introduce the 3D design of a vector light sensor for angular proximity detection applications. The sensor structure is based on creating photo sensors on side walls of micro-sized (micron scale) mesa pyramids etched in silicon. On each facet of the mesa pyramid, p-n junctions are formed and used as photodiodes. Unlike planar pixels, this 3D pixel design allows for estimation of the direction of the light incident on the sensor. We present the operating principle, fabrication details and the experimental verification of the device's performance. The sensor's unique design, simple fabrication process, and readout integrated circuit's compatibility adds merits to the current planar designs and could be utilized in angular proximity applications such as multi-focus level imaging or environment mapping for real-time dynamic virtual and augmented visualizations.
Designing intelligent energy harvesting communication systems

From being a scientific curiosity only a few years ago, energy harvesting (EH) is well on its way to becoming a game-changing technology in the field of autonomous wireless networked systems. The promise of long-term, uninterrupted and self-sustainable operation in a diverse array of applications has captured the interest of academia and industry alike. Yet the road to the ultimate network of perpetual communicating devices is plagued with potholes: ambient energy is intermittent and scarce, energy storage capacity is limited, and devices are constrained in size and complexity. In dealing with these challenges, this article will cover recent developments in the design of intelligent energy management policies for EH wireless devices and discuss pressing research questions in this rapidly growing field.
SOCIAL ENTREPRENEURSHIP LEADERSHIP THAT FACILITATES SOCIETAL TRANSFORMATION — AN EXPLORATORY STUDY 5

This study provides a comparative analysis of seven cases of social entrepre-neurship that have been widely recognized as successful. The paper suggests factors associated with successful social entrepreneurship, particularly with social entrepreneurship that leads to significant changes in the social, political and economic contexts for poor and marginalized groups. It generates hypotheses about core innovations, leadership, organization, and scaling up in successful social entrepreneurship. The paper concludes with a discussion of the implications for the practice of social entrepreneurship, for further research, and for the continued development of support technologies and institutions that will encourage future social entrepreneurship.
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar (HSG) to represent the 3D scene structure, which characterizes a joint distribution over the functional and geometric space of indoor scenes. The proposed HSG captures three essential and often latent dimensions of the indoor scenes: i) latent human context, describing the affordance and the functionality of a room arrangement, ii) geometric constraints over the scene configurations, and iii) physical constraints that guarantee physically plausible parsing and reconstruction. We solve this joint parsing and reconstruction problem in an analysis-by-synthesis fashion, seeking to minimize the differences between the input image and the rendered images generated by our 3D representation, over the space of depth, surface normal, and object segmentation map. The optimal configuration, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable solution space, jointly optimizing object localization, 3D layout, and hidden human context. Experimental results demonstrate that the proposed algorithm improves the generalization ability and significantly outperforms prior methods on 3D layout estimation, 3D object detection, and holistic scene understanding.
Development of vehicle tracking system using GPS and GSM modem

The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, fleet management and others. Furthermore, the number of vehicles on the road globally is also expected to increase rapidly. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) modem is undertaken with the aim of enabling users to locate their vehicles with ease and in a convenient manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This paper presents the development of the vehicle tracking system's hardware prototype. Specifically, the system will utilize GPS to obtain a vehicle's coordinate and transmit it using GSM modem to the user's phone through the mobile network. The main hardware components of the system are u-blox NEO-6Q GPS receiver module, u-blox LEON-G100 GSM module and Arduino Uno microcontroller. The developed vehicle tracking system demonstrates the feasibility of near real-time tracking of vehicles and improved customizability, global operability and cost when compared to existing solutions.
Web pages classification: An effective approach based on text mining techniques

Some web pages on Internet contain important content that are useful in a long time period or even forever. On the other hand, there are some web pages that are valuable only in a short time period. It is difficult to classify these types of web pages automatically due to their contents. This is an important task for improving the performance of search engines and web page recommender engines. In this project, webpages were classified into two categories with machine learning algorithms. For this purpose, natural language processing and text mining techniques were used for text pre-processing. Then appropriate information was extracted from texts and eventually web pages were classified by using machine learning algorithms. Compared to other approaches, most of the focus in this project is on text pre-processing stage and new strategies were presented to fill the gap. The results indicate that the proposed approach had better performance than other approaches.
Extensible effects: an alternative to monad transformers

We design and implement a library that solves the long-standing problem of combining effects without imposing restrictions on their interactions (such as static ordering). Effects arise from interactions between a client and an effect handler (interpreter); interactions may vary throughout the program and dynamically adapt to execution conditions. Existing code that relies on monad transformers may be used with our library with minor changes, gaining efficiency over long monad stacks. In addition, our library has greater expressiveness, allowing for practical idioms that are inefficient, cumbersome, or outright impossible with monad transformers.
 Our alternative to a monad transformer stack is a single monad, for the coroutine-like communication of a client with its handler. Its type reflects possible requests, i.e., possible effects of a computation. To support arbitrary effects and their combinations, requests are values of an extensible union type, which allows adding and, notably, subtracting summands. Extending and, upon handling, shrinking of the union of possible requests is reflected in its type, yielding a type-and-effect system for Haskell. The library is lightweight, generalizing the extensible exception handling to other effects and accurately tracking them in types.
Female Pattern Hair Loss: a clinical and pathophysiological review*

Female Pattern Hair Loss or female androgenetic alopecia is the main cause of hair loss in adult women and has a major impact on patients' quality of life. It evolves from the progressive miniaturization of follicles that lead to a subsequent decrease of the hair density, leading to a non-scarring diffuse alopecia, with characteristic clinical, dermoscopic and histological patterns. In spite of the high frequency of the disease and the relevance of its psychological impact, its pathogenesis is not yet fully understood, being influenced by genetic, hormonal and environmental factors. In addition, response to treatment is variable. In this article, authors discuss the main clinical, epidemiological and pathophysiological aspects of female pattern hair loss.
A Fast Distributed Algorithm for Mining Association Rules

With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partition and distribution of a centralized database, it is important to inuestzgate eficient methods for distributed mining of association rules. This study discloses some interesting relationships between locally large and globally large itemsets and proposes an interesting distributed association rule mining algorithm, FDM (Fast Distributed Mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Our performance study shows that FDM has a superior performance over the direct application of a typical sequential algorithm. Further performance enhancement leads to a few variations of the algorithm.
KCoFI: Complete Control-Flow Integrity for Commodity Operating System Kernels

We present a new system, KCoFI, that is the first we know of to provide complete Control-Flow Integrity protection for commodity operating systems without using heavyweight complete memory safety. Unlike previous systems, KCoFI protects commodity operating systems from classical control-flow hijack attacks, return-to-user attacks, and code segment modification attacks. We formally verify a subset of KCoFI's design by modeling several features in small-step semantics and providing a partial proof that the semantics maintain control-flow integrity. The model and proof account for operations such as page table management, trap handlers, context switching, and signal delivery. Our evaluation shows that KCoFI prevents all the gadgets found by an open-source Return Oriented Programming (ROP) gadget-finding tool in the FreeBSD kernel from being used, it also reduces the number of indirect control-flow targets by 98.18%. Our evaluation also shows that the performance impact of KCoFI on web server bandwidth is negligible while file transfer bandwidth using OpenSSH is reduced by an average of 13%, and at worst 27%, across a wide range of file sizes. Postmark, an extremely file-system intensive benchmark, shows 2x overhead. Where comparable numbers are available, the overheads of KCoFI are far lower than heavyweight memory-safety techniques.
Key factors for adopting inner source

A number of organizations have adopted Open Source Software (OSS) development practices to support or augment their software development processes, a phenomenon frequently referred to as Inner Source. However the adoption of Inner Source is not a straightforward issue. Many organizations are struggling with the question of whether Inner Source is an appropriate approach to software development for them in the first place. This article presents a framework derived from the literature on Inner Source, which identifies nine important factors that need to be considered when implementing Inner Source. The framework can be used as a probing instrument to assess an organization on these nine factors so as to gain an understanding of whether or not Inner Source is suitable. We applied the framework in three case studies at Philips Healthcare, Neopost Technologies, and Rolls-Royce, which are all large organizations that have either adopted Inner Source or were planning to do so. Based on the results presented in this article, we outline directions for future research.