nils-herrmann/clean_scirepeval huggingface dataset:
A Memory-Network Based Solution for Multivariate Time-Series Forecasting

Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Timeseries network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Addtionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.
Distributed Coordinate Descent Method for Learning with Big Data

In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data. We initially partition the coordinates (features) and assign each partition to a different node of a cluster. At every iteration, each node picks a random subset of the coordinates from those it owns, independently from the other computers, and in parallel computes and applies updates to the selected coordinates based on a simple closed-form formula. We give bounds on the number of iterations sufficient to approximately solve the problem with high probability, and show how it depends on the data and on the partitioning. We perform numerical experiments with a LASSO instance described by a 3TB matrix.
A Comparative Experiment of Several Shape Methods in Recognizing Plants

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.
EKG-based key agreement in Body Sensor Networks

Preserving a person's privacy in an efficient manner is very important for critical, life-saving infrastructures like body sensor networks (BSN). This paper presents a novel key agreement scheme which allows two sensors in a BSN to agree to a common key generated using electrocardiogram (EKG) signals. This EKG-based key agreement (EKA) scheme aims to bring the "plug-n-play" paradigm to BSN security whereby simply deploying sensors on the subject can enable secure communication, without requiring any form of initialization such as pre-deployment. Analysis of the scheme based on real EKG data (obtained from MIT PhysioBank database) shows that keys resulting from EKA are: random, time variant, can be generated based on short-duration EKG measurements, identical for a given subject and different for separate individuals.
Beyond basic faceted search

This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved
Precision SAR processing using chirp scaling

Abstmct-A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical rangelDoppler (RID) algorithm or related frequency domain techniques. In general, interpolation requires significant computation time, and leads to loss of image quality, especially in the complex image. The new chirp scaling algorithm avoids interpolation, yet performs range cell migration correction accurately. The algorithm requires only complex multiplies and Fourier transforms to implement, is inherently phase preserving, and is suitable for wide-swath, largebeamwidth, and large-squint applications. This paper describes the chirp scaling algorithm, summarizes simulation results, presents imagery processed with the algorithm, and reviews quantitative measures of its performance. Based on quantitative comparison, the chirp scaling algorithm provides image quality equal to or better than the precision rangel Doppler processor. Over the range of parameters tested, image quality results approach the theoretical limit, as defined by the system bandwidth.
The Big-Data-Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions, and Future Trends

The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.
Flow Wars: Systemizing the Attack Surface and Defenses in Software-Defined Networks

Emerging software defined network SDN stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.
Blockchain as a Service (BaaS): Providers and Trust

Distributed ledger technologies (DLTs) are receiving much attention. As discussion focuses on the potential applications of DLTs, Blockchain-as-a-Service (BaaS) offerings are emerging to provide the underlying supporting infrastructure. BaaS entails a service provider supplying and managing aspects of a DLT infrastructure to facilitate and bring efficiencies regarding the development, experimentation, deployment, and the ongoing management of DLT applications. However, much of the interest in DLTs stems from their potential to decentralise, disintermediate, and enable ‘trustless’ interactions. At first sight, BaaS – being offered by a provider – appears to run counter to this. In practice, whether BaaS raises substantive trust concerns depends on the nature of the offering, the application's specifics, and the participants' goals and risk appetite. This paper elaborates the nature of BaaS and explores the trust considerations it raises, particularly regarding the role of providers as part of a wider infrastructure.
Stress Recognition Using Wearable Sensors and Mobile Phones

In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
Cluster based rank query over multidimensional data streams

Many data stream monitoring applications involve rank queries and hence a number of efficient evaluation algorithms are proposed recently. Most of these techniques assume that rank queries are executed directly over the whole data space. However, we observe that many applications often require to perform clustering over the data streams before rank queries are run on each cluster.
 To address the problem, we propose a novel algorithm for integral clustering and ranking processing and we refer to such integrated queries as cluster-based rank queries. The algorithm includes two phases, namely the online phase which maintains the required data structures and statistics, and the query phase which uses these data structures to process queries. Extensive experiments indicate that the proposed algorithm is efficient in both space consumption and query processing.
Peer-supported storytelling for grieving pediatric oncology nurses.

Telling stories about deceased patients to supportive peers is frequently mentioned as an activity used for meaning-making in anecdotal reports of clinical practice and the literature addressing nurses' experiences caring for dying children. This study examines peer-supported storytelling for grieving pediatric oncology nurses using a mixed methods single-group descriptive repeated measures design. Participants were 6 registered nurses from a tertiary care pediatric hospital inpatient oncology unit who self-identified as experiencing grief. Participants met in self-selected dyads for 2 storytelling sessions. Questionnaires were completed at baseline, midpoint, and study end. Sessions were audio-recorded. Participants reported (1) receiving and providing support during sessions; (2) that sessions had an impact on their grief; (3) that sessions had an impact on their meaning-making, and the explicit session focus on making sense of and identifying benefit in their experiences was particularly helpful. There was a significant positive correlation between participant report of number of special patient deaths during career and impact of sessions on grief.
An End-to-End Signal Strength Model for Underwater Optical Communications

In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.
Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charging into the existing wireless communication systems also brings along a series of challenging issues with regard to implementation, scheduling, and power management. In this paper, we present a comprehensive overview of wireless charging techniques, the developments in technical standards, and their recent advances in network applications. In particular, with regard to network applications, we review the static charger scheduling strategies, mobile charger dispatch strategies and wireless charger deployment strategies. Additionally, we discuss open issues and challenges in implementing wireless charging technologies. Finally, we envision some practical future network applications of wireless charging.
Statistical Text Analysis for Social Science

What can text corpora tell us about society? How can automatic text analysis algorithms efficiently and reliably analyze the social processes revealed in language production? This work develops statistical text analyses of dynamic social and news media datasets to extract indicators of underlying social phenomena, and to reveal how social factors guide linguistic production. This is illustrated through three case studies: first, examining whether sentiment expressed in social media can track opinion polls on economic and political topics (Chapter 3); second, analyzing how novel online slang terms can be very specific to geographic and demographic communities, and how these social factors affect their transmission over time (Chapters 4 and 5); and third, automatically extracting political events from news articles, to assist analyses of the interactions of international actors over time (Chapter 6). We demonstrate a variety of computational, linguistic, and statistical tools that are employed for these analyses, and also contribute MiTextExplorer, an interactive system for exploratory analysis of text data against document covariates, whose design was informed by the experience of researching these and other similar works (Chapter 2). These case studies illustrate recurring themes toward developing text analysis as a social science methodology: computational and statistical complexity, and domain knowledge and linguistic assumptions.
A simple circularly polarized loop tag antenna for increased reading distance

A simple circularly polarized (CP) loop tag antenna is proposed to overcome the polarization mismatch problem between reader antenna and tag antenna. Good CP radiation can be achieved by introducing an open gap onto the square loop. Also, the technique of loading a matching stub across the RFID chip is applied to the proposed loop tag antenna, and desirable impedance matching between the antenna and RFID chip can be realized by tuning the matching stub. The proposed loop tag antenna shows good reading distance performance, and its measured reading distance reaches to 17.6 m.
DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data

Triple stores are the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations. In this paper, we propose a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triple stores and, thus, settled on measuring performance against a relational database which had been converted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more useful to compare existing triple stores and provide results for the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the performance of triple stores is by far less homogeneous than suggested by previous benchmarks.
Controlled Language for Multilingual Document Production: Experience with Caterpillar Technical English 1

Caterpillar Inc., a heavy equipment manufacturing company headquartered in Peoria IL, supports world-wide distribution of a large number of products and parts. Each Caterpillar product integrates several complex subsystems (engine, hydraulic system, drive system, implements, electrical, etc.) for which a variety of technical documents must be produced (operations and maintenance, testing and adjusting, disassembly and assembly, specifications, etc.). To support consistent, high-quality authoring and translation of these documents from English into a variety of target languages, Caterpillar uses Caterpillar Technical English (CTE), a controlled English system developed in conjunction with CarnegieMellon University’s Center forMachine Translation (CMT) andCarnegie Group Incorporated (CGI).
Word Sense Disambiguation with Recurrent Neural Networks

This paper presents a neural network architecture for word sense disambiguation (WSD). The architecture employs recurrent neural layers and more specifically LSTM cells, in order to capture information about word order and to easily incorporate distributed word representations (embeddings) as features, without having to use a fixed window of text. The paper demonstrates that the architecture is able to compete with the most successful supervised systems for WSD and that there is an abundance of possible improvements to take it to the current state of the art. In addition, it explores briefly the potential of combining different types of embeddings as input features; it also discusses possible ways for generating ”artificial corpora” from knowledge bases – for the purpose of producing training data and in relation to possible applications of embedding lemmas and word senses in the same space.
FPGA implementation of AWGN noise generator using Box-Muller method

In this study, AWGN generator is implemented on FPGA by using Box-Muller method. Firstly, AWGN generator is designed with floating point in C++ language. Then fixed-point model is developed in C++. The means and standard deviations of the designs with fixed point and floating point are tested and it is proved that designs have normal distribution. After this step, the fixed-point design is modelled in Verilog HDL. The design in Verilog HDL is matched with C++ model and then the Verilog HDL model is implemented on the Xilinx Virtex-7 FPGA. The design implemented on FPGA has 110 MHz clock frequency, and 7.04 Gbps data speed. The AWGN generator designed in C++ has 39 Mbps data speed and it is confirmed that the AWGN generator implemented on FGPA is working 200 times faster than C++ model.
Optimal Parallel Algorithms for Computing the Sum, the Prefix-Sums, and the Summed Area Table on the Memory Machine Models

The main contribution of this paper is to show optimal parallel algorithms to compute the sum, the prefix-sums, and the summed area table on two memory machine models, the Discrete Memory Machine (DMM) and the Unified Memory Machine (UMM). The DMM and the UMM are theoretical parallel computing models that capture the essence of the shared memory and the global memory of GPUs. These models have three parameters, the number p of threads, and the width w of the memory, and the memory access latency l. We first show that the sum of n numbers can be computed in O(n w + nl p + l log n) time units on the DMM and the UMM. We then go on to show that Ω(n w + nl p + l log n) time units are necessary to compute the sum. We also present a parallel algorithm that computes the prefix-sums of n numbers in O( n w + nl p + l log n) time units on the DMM and the UMM. Finally, we show that the summed area table of size √ n × √n can be computed in O( n w + nl p + l log n) time units on the DMM and the UMM. Since the computation of the prefix-sums and the summed area table is at least as hard as the sum computation, these parallel algorithms are also optimal. key words: Memory machine models, prefix-sums computation, parallel algorithm, GPU,
A Communication Theoretical Analysis of Multiple-Access Channel Capacity in Magneto-Inductive Wireless Networks

Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.