nils-herrmann/clean_scirepeval huggingface dataset:
A structured framework to assess the business application landscape of blockchain technologies

Blockchain is emerging as a game changing technology in many industries. Although it is increasingly capturing the business community’s attention, a comprehensive overview of commercially available applications is lacking to date. This paper aims to fill this gap. Firstly, we propose a structured approach to assess the application landscape of blockchain technologies. To build our framework, we relied on largely accepted classifications of blockchains, based on protocols, consensus mechanisms and ownership, as well as on the most cited application areas emerging from the literature. Secondly, we applied the framework on a database of 460 released blockchains. The analysis confirms a dominance of applications for cryptocurrencies, financial transactions and certification purposes, with a prevalence of permissionless platforms. We also found new application fields that go far beyond the seven initial areas addressed by the current body of knowledge, leading to some interesting takeaways for both practitioners and IS researchers.
Image Patch Matching Using Convolutional Descriptors with Euclidean Distance

In this work we propose a neural network based image descriptor suitable for image patch matching, which is an important task in many computer vision applications. Our approach is influenced by recent success of deep convolutional neural networks (CNNs) in object detection and classification tasks. We develop a model which maps the raw input patch to a low dimensional feature vector so that the distance between representations is small for similar patches and large otherwise. As a distance metric we utilize L2 norm, i.e. Euclidean distance, which is fast to evaluate and used in most popular hand-crafted descriptors, such as SIFT. According to the results, our approach outperforms state-of-the-art L2-based descriptors and can be considered as a direct replacement of SIFT. In addition, we conducted experiments with batch normalization and histogram equalization as a preprocessing method of the input data. The results confirm that these techniques further improve the performance of the proposed descriptor. Finally, we show promising preliminary results by appending our CNNs with recently proposed spatial transformer networks and provide a visualisation and interpretation of their impact.
Camallanus tridentatus (Drasche) (Nematoda: Camallanidae): new taxonomically important morphological data.

Camallanus tridentatus is redescribed on the basis of the examination of specimens obtained from the stomach, caeca and intestine of the naturally infected arapaima Arapaima gigas (Schinz) from the Mexiana Island, Amazon River Delta, Brazil. Data on the surface morphology of adults inferred from confocal laser scanning and scanning electron microscopical observations are also provided. The study revealed some taxonomically important, previously unreported morphological features in this species, such as the presence of the poorly sclerotized left spicule and deirids. C. tridentatus distinctly differs from other congeneric species parasitizing freshwater fishes in South America mainly in the structure of the buccal capsule and the female caudal end. C. maculatus Martins, Garcia, Piazza and Ghiraldelli is considered a junior synonymm of Camallanus cotti Fujita.
Variable stiffness strip with strain sensing for wearable robotics

Variable stiffness technologies help to solve the conflicting requirements of strength, comfort, and safety in soft exoskeletons. Here, we propose a modular variable stiffness strip based on layer jamming as a mechanical solution to constrain users' degrees of freedom and/or to implement wearable anchor points. Equipped with integrated capacitive strain sensors in the longitudinal direction, the strip is 30 mm wide and 3 mm thick and has a modular design that enables its adaptability to different morphologies and force requirements. If negative pressure in the order of tens of KPa is applied, the strip undergoes a stiffness change by a factor of 600, thus ranging between stretchability typical of elastane and inextensibility up to a maximum resisting force of 60 N. The versatility of the strip is shown by integration in an upper body exosuit.
Alpha's standard error (ASE): an accurate and precise confidence interval estimate.

This research presents the inferential statistics for Cronbach's coefficient alpha on the basis of the standard statistical assumption of multivariate normality. The estimation of alpha's standard error (ASE) and confidence intervals are described, and the authors analytically and empirically investigate the effects of the components of these equations. The authors then demonstrate the superiority of this estimate compared with previous derivations of ASE in a separate Monte Carlo simulation. The authors also present a sampling error and test statistic for a test of independent sample alphas. They conclude with a recommendation that all alpha coefficients be reported in conjunction with standard error or confidence interval estimates and offer SAS and SPSS programming codes for easy implementation.
Creating a cyber moving target for critical infrastructure applications using platform diversity

Despite the significant effort that often goes into securing critical infrastructure assets, many systems remain vulnerable to advanced, targeted cyber attacks. This paper describes the design and implementation of the Trusted Dynamic Logical Heterogeneity System (TALENT), a framework for live-migrating critical infrastructure applications across heterogeneous platforms. TALENT permits a running critical application to change its hardware platform and operating system, thus providing cyber survivability through platform diversity. TALENT uses containers (operating-system-level virtualization) and a portable checkpoint compiler to create a virtual execution environment and to migrate a running application across different platforms while preserving the state of the application (execution state, open files and network connections). TALENT is designed to support general applications written in the C programming language. By changing the platform on-the-fly, TALENT creates a cyber moving target and significantly raises the bar for a successful attack against a critical application. Experiments demonstrate that a complete migration can be completed within about one second.
Flatness-based nonlinear control strategies for trajectory tracking of quadcopter systems

This paper proposes several nonlinear control strategies for trajectory tracking of a quadcopter system based on the property of differential flatness. Its originality is twofold. Firstly, it provides a flat output for the quadcopter dynamics capable of creating full flat parametrization of the states and inputs. Moreover, B-splines characterizations of the flat output and their properties allow for optimal trajectory generation subject to way-point constraints. Secondly, several control strategies based on computed torque control and feedback linearization are presented and compared. The advantages of flatness within each control strategy are analyzed and detailed through extensive simulation results.
The Effect of Probiotics on Childhood Constipation: A Randomized Controlled Double Blind Clinical Trial

Inconsistent data exist about the role of probiotics in the treatment of constipated children. The aim of this study was to investigate the effectiveness of probiotics in childhood constipation.  In this placebo controlled trial, fifty-six children aged 4-12 years with constipation received randomly lactulose plus Protexin or lactulose plus placebo daily for four weeks. Stool frequency and consistency, abdominal pain, fecal incontinence, and weight gain were studied at the beginning, after the first week, and at the end of the 4th week in both groups.  Forty-eight patients completed the study. At the end of the fourth week, the frequency and consistency of defecation improved significantly (P = 0.042 and P = 0.049, resp.). At the end of the first week, fecal incontinence and abdominal pain improved significantly in intervention group (P = 0.030 and P = 0.017, resp.) but, at the end of the fourth week, this difference was not significant (P = 0.125 and P = 0.161, resp.). A significant weight gain was observed at the end of the 1st week in the treatment group.  This study showed that probiotics had a positive role in increasing the frequency and improving the consistency at the end of 4th week.
Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays

Since the last decade, several complex-valued neural networks have been developed and applied in various research areas. As an extension of real-valued recurrent neural networks, complex-valued recurrent neural networks use complex-valued states, connection weights, or activation functions with much more complicated properties than real-valued ones. This paper presents several sufficient conditions derived to ascertain the existence of unique equilibrium, global asymptotic stability, and global exponential stability of delayed complex-valued recurrent neural networks with two classes of complex-valued activation functions. Simulation results of three numerical examples are also delineated to substantiate the effectiveness of the theoretical results.
The far-reaching effects of believing people can change: implicit theories of personality shape stress, health, and achievement during adolescence.

The belief that personality is fixed (an entity theory of personality) can give rise to negative reactions to social adversities. Three studies showed that when social adversity is common-at the transition to high school--an entity theory can affect overall stress, health, and achievement. Study 1 showed that an entity theory of personality, measured during the 1st month of 9th grade, predicted more negative immediate reactions to social adversity and, at the end of the year, greater stress, poorer health, and lower grades in school. Studies 2 and 3, both experiments, tested a brief intervention that taught a malleable (incremental) theory of personality--the belief that people can change. The incremental theory group showed less negative reactions to an immediate experience of social adversity and, 8 months later, reported lower overall stress and physical illness. They also achieved better academic performance over the year. Discussion centers on the power of targeted psychological interventions to effect far-reaching and long-term change by shifting interpretations of recurring adversities during developmental transitions.
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules require a multiply and accumulate operation for each synaptic weight, which is challenging to implement compactly using CMOS. In this paper, a method for performing these update operations simultaneously (incremental outer products) using memristor-based arrays is proposed. The method is based on the fact that, approximately, given a voltage pulse, the conductivity of a memristor will increment proportionally to the pulse duration multiplied by the pulse magnitude if the increment is sufficiently small. The proposed method uses a synaptic circuit composed of a small number of components per synapse: one memristor and two CMOS transistors. This circuit is expected to consume between 2% and 8% of the area and static power of previous CMOS-only hardware alternatives. Such a circuit can compactly implement hardware MNNs trainable by scalable algorithms based on online gradient descent (e.g., backpropagation). The utility and robustness of the proposed memristor-based circuit are demonstrated on standard supervised learning tasks.
160 Gb/s Bidirectional Polymer-Waveguide Board-Level Optical Interconnects Using CMOS-Based Transceivers

We have developed parallel optical interconnect technologies designed to support terabit/s-class chip-to-chip data transfer through polymer waveguides integrated in printed circuit boards (PCBs). The board-level links represent a highly integrated packaging approach based on a novel parallel optical module, or Optomodule, with 16 transmitter and 16 receiver channels. Optomodules with 16 Tx+16 Rx channels have been assembled and fully characterized, with transmitters operating at data rates up to 20 Gb/s for a 27-1 PRBS pattern. Receivers characterized as fiber-coupled 16-channel transmitter-to-receiver links operated error-free up to 15 Gb/s, providing a 240 Gb/s aggregate bidirectional data rate. The low-profile Optomodule is directly surface mounted to a circuit board using convention ball grid array (BGA) solder process. Optical coupling to a dense array of polymer waveguides fabricated on the PCB is facilitated by turning mirrors and lens arrays integrated into the optical PCB. A complete optical link between two Optomodules interconnected through 32 polymer waveguides has been demonstrated with each unidirectional link operating at 10 Gb/s achieving a 160 Gb/s bidirectional data rate. The full module-to-module link provides the fastest, widest, and most integrated multimode optical bus demonstrated to date.
Distributed Turboelectric Propulsion for Hybrid Wing Body Aircraft

Meeting future goals for aircraft and air traffic system performance will require new airframes with more highly integrated propulsion. Previous studies have evaluated hybrid wing body (HWB) configurations with various numbers of engines and with increasing degrees of propulsion-airframe integration. A recently published configuration with 12 small engines partially embedded in a HWB aircraft, reviewed herein, serves as the airframe baseline for the new concept aircraft that is the subject of this paper. To achieve high cruise efficiency, a high lift-to-drag ratio HWB was adopted as the baseline airframe along with boundary layer ingestion inlets and distributed thrust nozzles to fill in the wakes generated by the vehicle. The distributed powered-lift propulsion concept for the baseline vehicle used a simple, high-lift-capable internally blown flap or jet flap system with a number of small high bypass ratio turbofan engines in the airframe. In that concept, the engine flow path from the inlet to the nozzle is direct and does not involve complicated internal ducts through the airframe to redistribute the engine flow. In addition, partially embedded engines, distributed along the upper surface of the HWB airframe, provide noise reduction through airframe shielding and promote jet flow mixing with the ambient airflow. To improve performance and to reduce noise and environmental impact even further, a drastic change in the propulsion system is proposed in this paper. The new concept adopts the previous baseline cruise-efficient short take-off and landing (CESTOL) airframe but employs a number of superconducting motors to drive the distributed fans rather than using many small conventional engines. The power to drive these electric fans is generated by two remotely located gas-turbine-driven superconducting generators. This arrangement allows many small partially embedded fans while retaining the superior efficiency of large core engines, which are physically separated but connected through electric power lines to the fans. This paper presents a brief description of the earlier CESTOL vehicle concept and the newly proposed electrically driven fan concept vehicle, using the previous CESTOL vehicle as a baseline.
Learning the unlearnable: the role of missing evidence

Syntactic knowledge is widely held to be partially innate, rather than learned. In a classic example, it is sometimes argued that children know the proper use of anaphoric one, although that knowledge could not have been learned from experience. Lidz et al. [Lidz, J., Waxman, S., & Freedman, J. (2003). What infants know about syntax but couldn't have learned: Experimental evidence for syntactic structure at 18 months. Cognition, 89, B65-B73.] pursue this argument, and present corpus and experimental evidence that appears to support it; they conclude that specific aspects of this knowledge must be innate. We demonstrate, contra Lidz et al., that this knowledge may in fact be acquired from the input, through a simple Bayesian learning procedure. The learning procedure succeeds because it is sensitive to the absence of particular input patterns--an aspect of learning that is apparently overlooked by Lidz et al. More generally, we suggest that a prominent form of the "argument from poverty of the stimulus" suffers from the same oversight, and is as a result logically unsound.
Augmented borders: Big Data and the ethics of immigration control

The aim of this paper is to consider some of the issues in light of the application of Big Data in the domain of border security and immigration management. Investment in the technologies of borders and their securitisation continues to be a focal point for many governments across the globe. This paper is concerned with a particular example of such technologies, namely, “Big Data” analytics. In the past two years, the technology of Big Data has gained a remarkable popularity within a variety of sectors, ranging from business and government to scientific and research fields. While Big Data techniques are often extolled as the next frontier for innovation and productivity, they are also raising many ethical issues.  The author draws on the example of the new Big Data solution recently developed by IBM for the Australian Customs and Border Protection Service. The system, which relies on data collected from Passenger Name Records, aims to facilitate and automate mechanisms of profiling enable the identification of “high-risk” travellers. It is argued that the use of such Big Data techniques risks augmenting the function and intensity of borders.  The main concerns addressed here revolve around three key elements, namely, the problem of categorisation, the projective and predictive nature of Big Data techniques and their approach to the future and the implications of Big Data on understandings and practices of identity.  By exploring these issues, the paper aims to contribute to the debates on the impact of information and communications technology-based surveillance in border management.
Uni-Planar Dual-Band Monopole Antenna for 2.4/5 GHz WLAN Operation in the Laptop Computer

A novel uni-planar dual-band monopole antenna capable of generating two wide bands for 2.4/5 GHz WLAN operation is presented. The antenna has a simple structure consisting of a driven strip and a coupled shorted strip. The antenna occupies a small area of 6 times 20 mm2 on an FR4 substrate. The small area allows the antenna to be easily employed in the narrow space between the top edge of the display panel and the casing of the laptop computer to operate as an internal antenna. It is believed that the size of the antenna is about the smallest among the existing uni-planar internal laptop antennas for 2.4/5 GHz WLAN operation.
Exploring Small Extracellular Vesicles for Precision Medicine in Prostate Cancer

Tumor microenvironment constitutes a complex network in which tumor cells communicate among them and with stromal and immune cells. It has been shown that cancer cells are able to exchange genetic materials through small extracellular vesicles (EVs), a heterogeneous group of vesicles with different size and shape, cargo content, and function. The importance to investigate populations of circulating EVs would be of great importance as prostate cancer (PCa) biomarkers. In several neoplasms as well as in PCa, nanometer-sized EVs of endosomal origin are implicated in supporting tumor growth and metastatic spread by both altering local stroma cells and creating a protumor environment that favors the formation of pre-metastatic niches. Several techniques are applicable for the isolation and analysis of PCa-derived small EVs and are illustrated in this article. Due to the high sensitivity and specificity of these techniques, small EVs have become ideal candidates for early diagnosis. Moreover, we discuss the role of small EVs during PCa carcinogenesis, as well as in modulating the development of drug resistance to hormonal therapy and chemotherapy, thus underlining the potential of EV-tailored strategies in PCa patients.
Message Queuing Pat terns for Middleware-Mediated Transact ions

Many enterprise applications require the use of object-oriented middleware and message-oriented middleware in combination. Middleware-mediated transactions have been proposed as a transaction model to address reliability of such applications; they extend distributed object transactions to include messageoriented transactions. In this paper, we present three message queuing patterns that we have found useful for implementing middleware-mediated transactions. We discuss and show how the patterns can be applied to support guaranteed compensation in the engineering of transactional enterprise applications.
A Review of Sentiment Analysis Research in Chinese Language

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This review paper aims to study past, present, and future of Chinese sentiment analysis from both monolingual and multilingual perspectives. The constructions of sentiment corpora and lexica are first introduced and summarized. Following, a survey of monolingual sentiment classification in Chinese via three different classification frameworks is conducted. Finally, sentiment classification based on the multilingual approach is introduced. After an overview of the literature, we propose that a more human-like (cognitive) representation of Chinese concepts and their inter-connections could overcome the scarceness of available resources and, hence, improve the state of the art. With the increasing expansion of Chinese language on the Web, sentiment analysis in Chinese is becoming an increasingly important research field. Concept-level sentiment analysis, in particular, is an exciting yet challenging direction for such research field which holds great promise for the future.
Collaborative localization in visual sensor networks

Collaboration in visual sensor networks is essential not only to compensate for the limitations of each sensor node but also to tolerate inaccurate information generated by faulty sensors. This article focuses on the design of a collaborative target localization algorithm that is resilient to sensor faults. We first develop a distributed solution to fault-tolerant target localization based on a so-called certainty map. To tolerate potential sensor faults, a voting mechanism is adopted and a threshold value needs to be specified which is the key to the realization of the distributed solution. Analytical study is conducted to derive the lower and upper bounds for the threshold such that the probability of faulty sensors negatively impacts the localization performance is less than a small value. Second, we focus on the detection and correction of one type of sensor faults, error in camera orientation. We construct a generative image model in each camera based on the detected target location to estimate camera's orientation, detect inaccuracies in camera orientations and correct them before they cascade. Based on results obtained from both simulation and real experiments, we show that the proposed method is effective in localization accuracy as well as fault detection and correction performance.