nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of worldwide earthquake mortality using multivariate demographic and seismic data.

In this paper, mortality in the immediate aftermath of an earthquake is studied on a worldwide scale using multivariate analysis. A statistical method is presented that analyzes reported earthquake fatalities as a function of a heterogeneous set of parameters selected on the basis of their presumed influence on earthquake mortality. The ensemble was compiled from demographic, seismic, and reported fatality data culled from available records of past earthquakes organized in a geographic information system. The authors consider the statistical relation between earthquake mortality and the available data ensemble, analyze the validity of the results in view of the parametric uncertainties, and propose a multivariate mortality analysis prediction method. The analysis reveals that, although the highest mortality rates are expected in poorly developed rural areas, high fatality counts can result from a wide range of mortality ratios that depend on the effective population size.
An architecture for secure software defined radio

Software defined radio (SDR) is a rapidly evolving technology which implements some functional modules of a radio system in software executing on a programmable processor. SDR provides a flexible mechanism to reconfigure the radio, enabling networked devices to easily adapt to user preferences and the operating environment. However, the very mechanisms that provide the ability to reconfigure the radio through software also give rise to serious security concerns such as unauthorized modification of the software, leading to radio malfunction and interference with other users' communications. Both the SDR device and the network need to be protected from such malicious radio reconfiguration.
 In this paper, we propose a new architecture to protect SDR devices from malicious reconfiguration. The proposed architecture is based on robust separation of the radio operation environment and user application environment through the use of virtualization. A secure radio middleware layer is used to intercept all attempts to reconfigure the radio, and a security policy monitor checks the target configuration against security policies that represent the interests of various parties. Therefore, secure reconfiguration can be ensured in the radio operation environment even if the operating system in the user application environment is compromised. We have prototyped the proposed secure SDR architecture using VMware and the GNU Radio toolkit, and demonstrate that the overheads incurred by the architecture are small and tolerable. Therefore, we believe that the proposed solution could be applied to address SDR security concerns in a wide range of both general-purpose and embedded computing systems.
A Single-Chip Solar Energy Harvesting IC Using Integrated Photodiodes for Biomedical Implant Applications

In this paper, an ultra-compact single-chip solar energy harvesting IC using on-chip solar cell for biomedical implant applications is presented. By employing an on-chip charge pump with parallel connected photodiodes, a 3.5 <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math></inline-formula> efficiency improvement can be achieved when compared with the conventional stacked photodiode approach to boost the harvested voltage while preserving a single-chip solution. A photodiode-assisted dual startup circuit (PDSC) is also proposed to improve the area efficiency and increase the startup speed by 77%. By employing an auxiliary charge pump (AQP) using zero threshold voltage (ZVT) devices in parallel with the main charge pump, a low startup voltage of 0.25 V is obtained while minimizing the reversion loss. A <inline-formula> <tex-math notation="LaTeX">$4\, {\mathbf{V}}_{\mathbf{in}}$</tex-math></inline-formula> gate drive voltage is utilized to reduce the conduction loss. Systematic charge pump and solar cell area optimization is also introduced to improve the energy harvesting efficiency. The proposed system is implemented in a standard 0.18- <inline-formula> <tex-math notation="LaTeX">$\mu\text{m}$</tex-math></inline-formula> CMOS technology and occupies an active area of 1.54 <inline-formula> <tex-math notation="LaTeX">$\text{mm}^{2}$</tex-math></inline-formula>. Measurement results show that the on-chip charge pump can achieve a maximum efficiency of 67%. With an incident power of 1.22 <inline-formula> <tex-math notation="LaTeX">$\text{mW/cm}^{2}$</tex-math></inline-formula> from a halogen light source, the proposed energy harvesting IC can deliver an output power of 1.65 <inline-formula> <tex-math notation="LaTeX">$\mu\text{W}$</tex-math></inline-formula> at 64% charge pump efficiency. The chip prototype is also verified using <italic>in-vitro</italic> experiment.
An Introduction to the Construction and Verification of Alphard Programs (Abstract)

The programming language Alphard is designed to provide support for both the methodologies of “well-structured” programming and the techniques of formal program verification. Language constructs allow a programmer to isolate an abstraction, specifying its behavior publicly while localizing knowledge about its implementation. The verification of such an abstraction consists of showing that its implementation behaves in accordance with its public specifications; the abstraction can then be used with confidence in constructing other programs, and the verification of that use employs only the public specifications.
 This paper introduces Alphard by developing and verifying a data structure definition and a program that uses it. It shows how each language construct contributes to the development of the abstraction and discusses the way the language design and the verification methodology were tailored to each other. It serves not only as an introduction to Alphard, but also as an example of the symbiosis between verification and methodology in language design. The strategy of program structuring, illustrated for Alphard, is also applicable to most of the “data abstraction” mechanisms now appearing.
Nepenthes Honeypots based Botnet Detection

the numbers of the botnet attacks are increasing day by day and the detection of botnet spreading in the network has become very challenging. Bots are having specific characteristics in comparison of normal malware as they are controlled by the remote master server and usually don’t show their behavior like normal malware until they don’t receive any command from their master server. Most of time bot malware are inactive, hence it is very difficult to detect. Further the detection or tracking of the network of theses bots requires an infrastructure that should be able to collect the data from a diverse range of data sources and correlate the data to bring the bigger picture in view.In this paper, we are sharing our experience of botnet detection in the private network as well as in public zone by deploying the nepenthes honeypots. The automated framework for malware collection using nepenthes and analysis using antivirus scan are discussed. The experimental results of botnet detection by enabling nepenthes honeypots in network are shown. Also we saw that existing known bots in our network can be detected.
Big Data and the SP Theory of Intelligence

This paper is about how the SP theory of intelligence and its realization in the SP machine may, with advantage, be applied to the management and analysis of big data. The SP system-introduced in this paper and fully described elsewhere-may help to overcome the problem of variety in big data; it has potential as a universal framework for the representation and processing of diverse kinds of knowledge, helping to reduce the diversity of formalisms and formats for knowledge, and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualization of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.
Chinese-English Phone Set Construction for Code-Switching ASR Using Acoustic and DNN-Extracted Articulatory Features

This study proposes a data-driven approach to phone set construction for code-switching automatic speech recognition (ASR). Acoustic and context-dependent cross-lingual articulatory features (AFs) are incorporated into the estimation of the distance between triphone units for constructing a Chinese-English phone set. The acoustic features of each triphone in the training corpus are extracted for constructing an acoustic triphone HMM. Furthermore, the articulatory features of the "last/first" state of the corresponding preceding/succeeding triphone in the training corpus are used to construct an AF-based GMM. The AFs, extracted using a deep neural network (DNN), are used for code-switching articulation modeling to alleviate the data sparseness problem due to the diverse context-dependent phone combinations in intra-sentential code-switching. The triphones are then clustered to obtain a Chinese-English phone set based on the acoustic HMMs and the AF-based GMMs using a hierarchical triphone clustering algorithm. Experimental results on code-switching ASR show that the proposed method for phone set construction outperformed other traditional methods.
DENDRAL and Meta-DENDRAL: Roots of Knowledge Systems and Expert System Applications

During AFs first decade (1956-1966), the task environments in which AI scientists investigated their basic science issues were generally idealized "clean" task environments, such as propositional calculus theorem proving and puzzle solving. After the mid-19605, a bolder and more applied inclination to choose complex real-world problems as task environments became evident. These efforts were both successful and exciting, in two ways. First, the AI programs were achieving high levels of competence at solving certain problems that human specialists found challenging (the excitement was that our AI techniques were indeed powerful and that we were taking the first steps toward the dream of the very smart machine). Second, these complex real-world task environments were proving to be excellent at stimulating basic science questions for the AI science, in knowledge representation, problem solving, and machine learning. To recognize and illuminate this trend, the Artificial Intelligence Journal in 1978 sponsored a special issue on applications of artificial intelligence.
Applying N-gram alignment entropy to improve feature decay algorithms

Data Selection is a popular step in Machine Translation pipelines. Feature Decay Algorithms (FDA) is a technique for data selection that has shown a good performance in several tasks. FDA aims to maximize the coverage of n-grams in the test set. However, intuitively, more ambiguous n-grams require more training examples in order to adequately estimate their translation probabilities. This ambiguity can be measured by alignment entropy. In this paper we propose two methods for calculating the alignment entropies for n-grams of any size, which can be used for improving the performance of FDA. We evaluate the substitution of the n-gramspecific entropy values computed by these methods to the parameters of both the exponential and linear decay factor of FDA. The experiments conducted on German-to-English and Czechto-English translation demonstrate that the use of alignment entropies can lead to an increase in the quality of the results of FDA.
Circularly polarized textile antenna for 2.45 GHz

This paper presents a circularly polarized antenna on thin and flexible Denim substrate for Industrial, Scientific and Medical (ISM) band and Wireless Body Area Network (WBAN) applications at 2.45 GHz. Copper tape is used as the conductive material on 1 mm thick Denim substrate. Circular polarization is achieved by introducing rectangular slot along diagonal axes at the center of the circular patch radiator. Bandwidth enhancement is done using partial and slotted ground plane. The measured impedance bandwidth of the proposed antenna is 6.4 % (2.42 GHz to 2.58 GHz) or 160 MHz. The antenna exhibits good radiation characteristics with gain of 2.25 dB. Simulated and measured results are presented to validate the operability of antenna within the proposed frequency bands.
FPGA implementation of the Lucy-Richardson algorithm for fast space-variant image deconvolution

The Lucy-Richardson algorithm is a very well-known method for non-blind image deconvolution. It can also deal with space-variant problems, but it is seldom used in these cases because of its iterative nature and complexity of realization. In this paper we show that exploiting the sparse structure of the deconvolution matrix, and utilizing a specifically devised architecture, the restoration can be performed almost in real-time on VGA-size images.
Caffe CNN-based classification of hyperspectral images on GPU

Deep learning techniques based on Convolutional Neural Networks (CNNs) are extensively used for the classification of hyperspectral images. These techniques present high computational cost. In this paper, a GPU (Graphics Processing Unit) implementation of a spatial-spectral supervised classification scheme based on CNNs and applied to remote sensing datasets is presented. In particular, two deep learning libraries, Caffe and CuDNN, are used and compared. In order to achieve an efficient GPU projection, different techniques and optimizations have been applied. The implemented scheme comprises Principal Component Analysis (PCA) to extract the main features, a patch extraction around each pixel to take the spatial information into account, one convolutional layer for processing the spectral information, and fully connected layers to perform the classification. To improve the initial GPU implementation accuracy, a second convolutional layer has been added. High speedups are obtained together with competitive classification accuracies.
PM-tree: Pivoting Metric Tree for Similarity Search in Multimedia Databases

In this paper we introduce the Pivoting M-tree (PM-tree), a metric access method combining M-tree with the pivot-based approach. While in M-tree a metric region is represented by a hyper-sphere, in PM-tree the shape of a metric region is determined by intersection of the hyper-sphere and a set of hyper-rings. The set of hyper-rings for each metric region is related to a fixed set of pivot objects. As a consequence, the shape of a metric region bounds the indexed objects more tightly which, in turn, significantly improves the overall efficiency of similarity search. We present basic algorithms on PM-tree and two cost models for range query processing. Finally, the PM-tree efficiency is experimentally evaluated on large synthetic as well as real-world datasets.
A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia

In this paper, we propose a probabilistic survival model derived from the survival analysis theory for measuring aspect novelty. The retrieved documents' query-relevance and novelty are combined at the aspect level for re-ranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval.
Reproductive health status and life skills of adolescent girls dwelling in slums in Chennai, India.

Adolescent girls dwelling in slums are vulnerable to poor reproductive health due to lack of awareness about reproductive health and low life skills. These girls are in a crucial stage of their life cycle and their health can impact the health of future generations. Despite adolescents comprising almost one-quarter of the Indian population they are ill served in terms of reproductive health.



This cross-sectional study was done among 130 slum-dwelling adolescent girls, aged 13-19 years, using multistage sampling method from five slums in Chennai, southern India. The reproductive and menstrual morbidity profile, personal and environmental menstrual hygiene was assessed to determine their reproductive health-seeking behaviour and life skills.



Ninety-five (73%) girls (95% CI 66.23-81.36) reported menstrual morbidity and 66 (51%; 95% CI 50.74-52.25) had symptoms suggestive of reproductive/urinary tract infection. Of the girls surveyed, 55 (42%) were married. Nearly 25% (95% CI 23.07-26.92) of the married girls had a history of abortion and 18% (95% CI 11.32-25.07) had self-treated with medications for the same. Contraceptive use among ever-married girls was 22.7% (95% CI 20.83-24.56). Even though 75% of respondents knew about HIV/AIDS, their knowledge of modes of transmission and prevention were low (39% and 19%, respectively). Almost 39% of respondents felt shame or insecurity as the key barrier for not seeking reproductive healthcare. About 52% had low life skill levels. On logistic regression, menstrual morbidity was high among those with low life skills, symptoms suggestive of reproductive/urinary tract infection were high among those who were married before 14 years of age and life skills were high among those who belonged to the scheduled caste community.



There is a high prevalence of menstrual/reproductive morbidity, self-treated abortion and low knowledge about modes of HIV transmission/prevention and use of contraceptives among adolescent girls in slums in Chennai. There is a need to initiate community-level life skill education, sex education and behaviour change communication.
Online communication, compulsive Internet use, and psychosocial well-being among adolescents: a longitudinal study.

The present study investigated the relationships between adolescents' online communication and compulsive Internet use, depression, and loneliness. The study had a 2-wave longitudinal design with an interval of 6 months. The sample consisted of 663 students, 318 male and 345 female, ages 12 to 15 years. Questionnaires were administered in a classroom setting. The results showed that instant messenger use and chatting in chat rooms were positively related to compulsive Internet use 6 months later. Moreover, in agreement with the well-known HomeNet study (R. Kraut et al., 1998), instant messenger use was positively associated with depression 6 months later. Finally, loneliness was negatively related to instant messenger use 6 months later.
Audio Based Disambiguation of Music Genre Tags

In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguating genre tags (identification of different labels for the same genre, tag translation from a tag system to another, inference of hierarchical taxonomies on these genre tags). These embeddings are built by training a deep convolutional neural network genre classifier with large audio datasets annotated with a flat tag system. We show empirically that they makes it possible to retrieve the original taxonomy of a tag system, spot duplicates tags and translate tags from a tag system to another.
Social Hash: An Assignment Framework for Optimizing Distributed Systems Operations on Social Networks

How objects are assigned to components in a distributed system can have a significant impact on performance and resource usage. Social Hash is a framework for producing, serving, and maintaining assignments of objects to components so as to optimize the operations of large social networks, such as Facebook’s Social Graph. The framework uses a two-level scheme to decouple compute-intensive optimization from relatively low-overhead dynamic adaptation. The optimization at the first level occurs on a slow timescale, and in our applications is based on graph partitioning in order to leverage the structure of the social network. The dynamic adaptation at the second level takes place frequently to adapt to changes in access patterns and infrastructure, with the goal of balancing component loads. We demonstrate the effectiveness of Social Hash with two real applications. The first assigns HTTP requests to individual compute clusters with the goal of minimizing the (memory-based) cache miss rate; Social Hash decreased the cache miss rate of production workloads by 25%. The second application assigns data records to storage subsystems with the goal of minimizing the number of storage subsystems that need to be accessed on multiget fetch requests; Social Hash cut the average response time in half on production workloads for one of the storage systems at Facebook.
False perception of motion in a patient who cannot compensate for eye movements

We are usually unaware of the motion of an image across our retina that results from our own movement. For instance, during slow-tracking eye movements we do not mistake the shift of the image projected onto the retina for motion of the world around us, but instead perceive a stable world. Following early suggestions by von Helmholtz, it is commonly believed that this spatial stability is achieved by subtracting the retinal motion signal from an internal reference signal, such as a copy of the movement command (efference copy). Object motion is perceived only if the two differ. Although this concept is widely accepted, its anatomical underpinning remains unknown. Here we describe the case of a patient with bilateral extrastriate cortex lesions, suffering from false perception of motion due to an inability to take eye movements into account when faced with self-induced retinal image slip. This is indicated by the fact that during smooth-pursuit eye movements, he perceives motion of the stationary world at a velocity that corresponds to the velocity of his eye movement; that is, he perceives the raw retinal image slip uncorrected for his own eye movements. We suspect that this deficiency reflects damage of a distinct parieto-occipital region that disentangles self-induced and externally induced visual motion by comparing retinal signals with a reference signal encoding eye movements and possibly ego-motion in general.