nils-herrmann/clean_scirepeval huggingface dataset:
Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G

With the severe spectrum shortage in conventional cellular bands, large-scale antenna systems in the mmWave bands can potentially help to meet the anticipated demands of mobile traffic in the 5G era. There are many challenging issues, however, regarding the implementation of digital beamforming in large-scale antenna systems: complexity, energy consumption, and cost. In a practical large-scale antenna deployment, hybrid analog and digital beamforming structures can be important alternative choices. In this article, optimal designs of hybrid beamforming structures are investigated, with the focus on an N (the number of transceivers) by M (the number of active antennas per transceiver) hybrid beamforming structure. Optimal analog and digital beamforming designs in a multi-user beamforming scenario are discussed. Also, the energy efficiency and spectrum efficiency of the N × M beamforming structure are analyzed, including their relationship at the green point (i.e., the point with the highest energy efficiency) on the energy efficiency-spectrum efficiency curve, the impact of N on the energy efficiency performance at a given spectrum efficiency value, and the impact of N on the green point energy efficiency. These results can be conveniently utilized to guide practical LSAS design for optimal energy/ spectrum efficiency trade-off. Finally, a reference signal design for the hybrid beamform structure is presented, which achieves better channel estimation performance than the method solely based on analog beamforming. It is expected that large-scale antenna systems with hybrid beamforming structures in the mmWave band can play an important role in 5G.
Resource allocation for multiclass service in IoT uplink communications

The Internet of Things has drawn lots of research attention as the growing number of devices connected to the Internet. Long Term Evolution-Advanced (LTE-A) is a promising technology for wireless communication and it's also promising for IoT. The main challenge of incorporating IoT devices into LTE-A is a large number of IoT devices attempting to access the network in a short period which will greatly reduce the network performance. In order to improve the network utilization, we adopted a hierarchy architecture using a gateway for connecting the devices to the eNB and proposed a multiclass resource allocation algorithm for LTE based IoT communication. Simulation results show that the proposed algorithm can provide good performance both on data rate and latency for different QoS applications both in saturated and unsaturated environment.
Review of fall risk assessment in geriatric populations using inertial sensors

Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population. Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes. Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%). Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.
A Technical Assessment of IOT for Indian Agriculture Sector

Agriculture is one of the most important aspects of human civilization. The usages of information and communication technologies (ICT) have significantly contributed in the area in last two decades. Internet of things (IOT) is a technology, where real life physical objects (e.g. sensor nodes) can work collaboratively to create an information based and technology driven system to maximize the benefits (e.g. improved agricultural production) with minimized risks (e.g. environmental impact). Implementation of IOT based solutions, at each phase of the area, could be a game changer for whole agricultural landscape, i.e. from seeding to selling and beyond. This article presents a technical review of IOT based application scenarios for agriculture sector. The article presents a brief introduction to IOT, IOT framework for agricultural applications and discusses various agriculture specific application scenarios, e.g. farming resource optimization, decision support system, environment monitoring and control systems. The article concludes with the future research directions in this area.
A hybrid packet-circuit switched on-chip network based on SDM

In this paper, we propose a novel on-chip communication scheme by dividing the resources of a traditional packet-switched network-on-chip between a packet-switched and a circuit-switched sub-network. The former directs packets according to the traditional packet-switching mechanism, while the latter forwards packets over circuits which are directly established between two non-adjacent nodes by bypassing the intermediate routers. A packet may switch between the subnetworks several times to reach its destination. The circuits are set up using a low-latency and low-cost setup-network. The network resources are split between the two sub-networks using Spatial-Division Multiplexing (SDM). The work aims to improve the power and performance metrics of Network-on-Chip (NoC) architectures and benefits from the power and scalability advantage of packet-switched NoCs and superior communication performance of circuit-switching. The evaluation results show a significant reduction in power and latency over a traditional packet-switched NoC.
Homotopical patch theory

Homotopy type theory is an extension of Martin-Löf type theory, based on a correspondence with homotopy theory and higher category theory. In homotopy type theory, the propositional equality type becomes proof-relevant, and corresponds to paths in a space. This allows for a new class of datatypes, called higher inductive types, which are specified by constructors not only for points but also for paths. In this paper, we consider a programming application of higher inductive types. Version control systems such as Darcs are based on the notion of patches - syntactic representations of edits to a repository. We show how patch theory can be developed in homotopy type theory. Our formulation separates formal theories of patches from their interpretation as edits to repositories. A patch theory is presented as a higher inductive type. Models of a patch theory are given by maps out of that type, which, being functors, automatically preserve the structure of patches. Several standard tools of homotopy theory come into play, demonstrating the use of these methods in a practical programming context.
A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory.

Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health.
Approximate SRAMs With Dynamic Energy-Quality Management

In this paper, approximate SRAMs are explored in the context of error-tolerant applications, in which energy is saved at the cost of the occurrence of read/write errors (i.e., signal quality degradation). This analysis investigates variation-resilient techniques that enable dynamic management of the energy-quality tradeoff down to the bit level. In these techniques, the different impacts of errors on quality at different bit positions are explicitly considered as key enabler of energy savings that are far larger than a simple voltage scaling. The analysis is based on the experimental results in an energy-quality scalable 28-nm SRAM and the extrapolation to a wide range of conditions through the models that combine the individual energy contributions. Results show that the joint adoption of multiple bit-level techniques provides substantially larger energy gains than individual techniques. Compared with the simple voltage scaling at isoquality, the joint adoption of these techniques can provide more than $2\times $ energy reduction at negligible area penalty. Energy savings turn out to be highly sensitive to the choice of joint techniques, thus showing the crucial importance of dynamic energy-quality management in approximate SRAMs.
Detecting annotation noise in automatically labelled data

We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
Implicit Dimension Identification in User-Generated Text with LSTM Networks

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user’s knowledge, intent or belief that may be based on writer’s moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.  IN USER-GENERATED TEXT 3 Implicit Dimension Identification in User-Generated Text with LSTM Networks
Assisted repeated reading with an advanced-level Japanese EFL reader : A longitudinal diary study

Reading fluency has attracted the attention of reading researchers and educators since the early 1970s and has become a priority issue in English as a first language (L1) settings. It has also become a critical issue in English as a second or foreign language (L2) settings because the lack of fluency is considered a major obstacle to developing independent readers with good comprehension skills. Repeated Reading (RR) was originally devised by Samuels (1979) in order to translate Automaticity Theory (LaBerge & Samuels, 1974) into a pedagogical approach for developing English L1 readers’ fluency. Extensive research has been conducted to show the positive effects of RR in English L1 settings. A growing number of L2 reading researchers have demonstrated that RR may be a promising approach for building fluency and comprehension in L2 settings. However, while L1 research has demonstrated a robust correlation between improved reading fluency and enhanced comprehension, L2 fluency research has not yet shown such a strong correlation. In addition, most studies on reading fluency in L2 settings have used quantitative approaches and only a few of them have explored the “inside of L2 readers' brain,” that is, what is actually happening while they engage in RR. The present study attempts to reveal the inner process of L2 reading fluency development through RR for an advanced-level L2 reader who is articulate in describing her metacognitive processes. Using a diary study approach comprising more than 70 RR sessions over the course of 14 weeks, the current study investigated an L2 reader with good comprehension skills Taguchi et al.: Assisted repeated reading 31 Reading in a Foreign Language 24(1) engaging in RR. This study was designed to investigate specifically how her reading fluency developed and how her comprehension changed during the course of the treatment. Based on the study findings, some issues are discussed for better RR program implementation.
Altered metabolite levels in cancer: implications for tumour biology and cancer therapy

Altered cell metabolism is a characteristic feature of many cancers. Aside from well-described changes in nutrient consumption and waste excretion, altered cancer cell metabolism also results in changes to intracellular metabolite concentrations. Increased levels of metabolites that result directly from genetic mutations and cancer-associated modifications in protein expression can promote cancer initiation and progression. Changes in the levels of specific metabolites, such as 2-hydroxyglutarate, fumarate, succinate, aspartate and reactive oxygen species, can result in altered cell signalling, enzyme activity and/or metabolic flux. In this Review, we discuss the mechanisms that lead to changes in metabolite concentrations in cancer cells, the consequences of these changes for the cells and how they might be exploited to improve cancer therapy.
Performance analysis of single relay selection in rayleigh fading

We provide closed-form expressions for the outage and bit error probability (BEP) of uncoded, threshold-based opportunistic relaying (OR) and selection cooperation (SC), at arbitrary signal to noise ratios (SNRs) and number of available relays, assuming decode-and-forward relays and Rayleigh fading channels. Numerical results demonstrate that SC performs slightly better in terms of outage probability; in terms of BEP, both systems may outperform one another, depending on the SNR threshold that determines the set of relays that participate in the forwarding process.
The Art of Signaling: Fifty Years of Coding Theory

In 1948 Shannon developed fundamental limits on the efficiency of communication over noisy channels. The coding theorem asserts that there are block codes with code rates arbitrarily close to channel capacity and probabilities of error arbitrarily close to zero. Fifty years later, codes for the Gaussian channel have been discovered that come close to these fundamental limits. There is now a substantial algebraic theory of error-correcting codes with as many connections to mathematics as to engineering practice, and the last 20 years have seen the construction of algebraic-geometry codes that can be encoded and decoded in polynomial time, and that beat the Gilbert–Varshamov bound. Given the size of coding theory as a subject, this review is of necessity a personal perspective, and the focus is reliable communication, and not source coding or cryptography. The emphasis is on connecting coding theories for Hamming and Euclidean space and on future challenges, specifically in data networking, wireless communication, and quantum information theory.
Comparison of the efficacy of chlorhexidine gluconate versus povidone iodine as preoperative skin preparation for the prevention of surgical site infections in clean-contaminated upper abdominal surgeries

To compare the efficacy of chlorhexidine-gluconate versus povidone iodine in preoperative skin preparation in the prevention of surgical site infections (SSIs) in clean-contaminated upper abdominal surgeries. This was a prospective randomized controlled trial conducted on patients undergoing clean-contaminated upper abdominal surgeries. A total of 351 patients 18–70 years old were randomized into two groups; chlorhexidine and povidone iodine skin preparation before surgery. The incidence of SSIs in the chlorhexidine group was 10.8 %, in comparison to 17.9 % in the povidone iodine group. The odds ratio was 0.6 in favor of chlorhexidine use, but the results were not statistically significant (P = 0.06). In the first postoperative week, SSIs developed in 7 % of patients in the chlorhexidine group and 14.1 % in the povidone iodine group (P = 0.03), and in the second postoperative week, SSIs were present in 4.1 % of the patients in the chlorhexidine group and 4.4 % in the povidone iodine group, which was not statistically significant (P = 0.88). The incidence of SSIs after clean-contaminated upper abdominal surgeries was lower with the use of chlorhexidine skin preparation than with povidone iodine preparation, although the results were not statistically significant. However, the odds ratio between the two groups favored the use of chlorhexidine over povidone iodine for preventing SSIs.
Transmission line fault detection and classification

Transmission line protection is an important issue in power system engineering because 85–87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on a transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved by application of evolutionary programming tools. PSCAD/EMTDC software is used to simulate different operating and fault conditions on high voltage transmission line, namely single phase to ground fault, line to line fault, double line to ground and three phase short circuit. The discrete wavelet transform (DWT) is applied for decomposition of fault transients, because of its ability to extract information from the transient signal, simultaneously both in time and frequency domain. The data sets which are obtained from the DWT are used for training and testing the SVM architecture. After extracting useful features from the measured signals, a decision of fault or no fault on any phase or multiple phases of a transmission line is carried out using three SVM classifiers. The ground detection task is carried out by a proposed ground index. Gaussian radial basis kernel function (RBF) has been used, and performances of classifiers have been evaluated based on fault classification accuracy. In order to determine the optimal parametric settings of an SVM classifier (such as the type of kernel function, its associated parameter, and the regularization parameter c), fivefold cross-validation has been applied to the training set. It is observed that an SVM with an RBF kernel provides better fault classification accuracy than that of an SVM with polynomial kernel. It has been found that the proposed scheme is very fast and accurate and it proved to be a robust classifier for digital distance protection.
An empirical comparison of influence measurements for social network analysis

The studying of social influence can be used to understand and solve many complicated problems in social network analysis such as predicting influential users. This paper focuses on the problem of predicting influential users on social networks. We introduce a three-level hierarchy that classifies the influence measurements. The hierarchy categorizes the influence measurements by three folds, i.e., models, types and algorithms. Using this hierarchy, we classify the existing influence measurements. We further compare them based on an empirical analysis in terms of performance, accuracy and correlation using datasets from two different social networks to investigate the feasibility of influence measurements. Our results show that predicting influential users does not only depend on the influence measurements but also on the nature of social networks. Our goal is to introduce a standardized baseline for the problem of predicting influential users on social networks.
The Effects of State Psychiatric Hospital Waitlist Policies on Length of Stay and Time to Readmission

This study examined the effects of a waitlist policy for state psychiatric hospitals on length of stay and time to readmission using data from North Carolina for 2004–2010. Cox proportional hazards models tested the hypothesis that patients were discharged “quicker-but-sicker” post-waitlist, as hospitals struggled to manage admission delays and quickly admit waitlisted patients. Results refute this hypothesis, indicating that waitlists were associated with increased length of stay and time to readmission. Further research is needed to evaluate patients’ clinical outcomes directly and to examine the impact of state hospital waitlists in other areas, such as state hospital case mix, local emergency departments, and outpatient mental health agencies.