nils-herrmann/clean_scirepeval huggingface dataset:
Depression and anxiety symptoms after lower limb amputation: the rise and fall.

To examine the time course of anxiety and depressive symptoms over a three year period after amputation.



A prospective study in inpatients admitted to a rehabilitation ward after lower limb amputation.



Successive admissions over a one-year period of whom 68 were alive at follow-up, 2-3 years later.



Nil.



Hospital Anxiety and Depression Scale (HADS) on admission and discharge from inpatient rehabilitation and at a 2.7(SD=0.4) year mean follow-up period with correlation to demographic and patient features.



Of the 68 responding patients, 12 (17.6%) and 13 (19.1%) had symptoms of depression and anxiety respectively. This compared to an original incidence of 16 (23.5%) for both on admission and 2 (2.9%) on discharge. This rise in incidence from time of discharge was highly significant for both depression (P<0.001) and anxiety (P<0.001). Depression at follow-up was correlated to depressive symptoms at admission (P=0.03) and to having other significant comorbidities (P=0.02). Anxiety symptoms were commoner in younger patients (P=0.03). There was no association with age, gender, living in isolation, vascular cause for amputation, wearing a limb prosthesis or length of original inpatient stay.



Depression and anxiety are common after lower limb amputation but resolve during inpatient rehabilitation. The incidence then rises again after discharge.
Continuous Auditing in ERP System Environments: The Current State and Future Directions

Recent research has focused heavily on the practicality and feasibility of alternative architectures for supporting continuous auditing. In this paper, we explore the alternative architectures for continuous auditing that have been proposed in both the research and practice environments. We blend a focus on the practical realities of the current technological options and ERP structures with the emerging theory and research on continuous assurance models. The focus is on identifying the strengths and weaknesses of each architectural form as a basis for forming a research agenda that could allow researchers to contribute to the future evolution of both ERP system designs and auditor implementation strategies. There are substantial implications and insights that should be of interest to both researchers and practitioners interested in exploring continuous audit feasibility, capability, and organizational impact.
Wireless Flexible Smart Bandage for Continuous Monitoring of Wound Oxygenation

Current methods in treating chronic wounds have had limited success in large part due to the open loop nature of the treatment. We have created a localized 3D-printed smart wound dressing platform that will allow for real-time data acquisition of oxygen concentration, which is an important indicator of wound healing. This will serve as the first leg of a feedback loop for a fully optimized treatment mechanism tailored to the individual patient. A flexible oxygen sensor was designed and fabricated with high sensitivity and linear current output. With a series of off-the-shelf electronic components including a programmable-gain analog front-end, a microcontroller and wireless radio, an integrated electronic system with data readout and wireless transmission capabilities was assembled in a compact package. Using an elastomeric material, a bandage with exceptional flexibility and tensile strength was 3D-printed. The bandage contains cavities for both the oxygen sensor and the electronic systems, with contacts interfacing the two systems. Our integrated, flexible platform is the first step toward providing a self-operating, highly optimized remote therapy for chronic wounds.
Exploring LoRa and LoRaWAN A suitable protocol for IoT weather stations ?

Svenska Sjöräddningssällskapet (SSRS) maintains a mobile-phone application that provides up-to-date weather information to seafarers in Sweden. In order to increase the granularity of the weather data that powers the application, they wish to place simple weather stations at popular sailing destinations in the archipelagos surrounding Sweden. In this thesis we examine a new radio protocol called LoRa and the accompanying low power wide area network protocol LoRaWAN. The aim of the thesis is to evaluate if and how these protocols can be used for the purpose of transmitting weather data from simple IoT weather stations. Furthermore, we wish to discuss and present a specification to extend the effective range of the network. The LoRa protocol is examined, along with the theory behind the chirp spread spectrum modulation, which LoRa exploits. The network layer protocol LoRaWAN and its structure is presented and shortly explained. We discuss how this structure can be utilized for testing of the protocol and for our use-case. Furthermore, packet error rate testing is performed between an RN2483 transceiver and a Kerlink gateway. Utilizing the results from this testing, we discuss and create a specification for network range extending intermediate-nodes. In addition to the specification, we provide insight into suitable placement of the IoT weather stations and intermediatenodes for good network coverage. The LoRa protocol and the accompanying LoRaWAN network protocol is found to be useful for the intended IoT weather stations. Furthermore, we find that our suggested network range extending specification is a good fit for the intended weather station network, but the intermediate-nodes introduce some limitations to the network when compared to gateways.
Collaborative Tracking Control of UAV-UGV

This paper suggests a fast and stable Target Tracking system in collaborative control of UAV and UGV. Wi-Fi communication range is limited in collaborative control of UAV and UGV. Thus, to secure a stable communications, UAV and UGV have to be kept within a certain distance from each other. But existing method which uses UAV Vertical Camera to follow the motion of UGV is likely to lose a target with a sudden movement change. Eventually, UGV has disadvantages that it could only move at a low speed and not make any sudden change of direction in order to keep track of the target. Therefore, we suggest utilizing AR Drone UAV front camera to track fast-moving and Omnidirectional Mecanum Wheel UGV. Collaborative control, UAV, UGV, Target Tracking.
Fusion Proteins for Half-Life Extension of Biologics as a Strategy to Make Biobetters

The purpose of making a “biobetter” biologic is to improve on the salient characteristics of a known biologic for which there is, minimally, clinical proof of concept or, maximally, marketed product data. There already are several examples in which second-generation or biobetter biologics have been generated by improving the pharmacokinetic properties of an innovative drug, including Neulasta® [a PEGylated, longer-half-life version of Neupogen® (filgrastim)] and Aranesp® [a longer-half-life version of Epogen® (epoetin-α)]. This review describes the use of protein fusion technologies such as Fc fusion proteins, fusion to human serum albumin, fusion to carboxy-terminal peptide, and other polypeptide fusion approaches to make biobetter drugs with more desirable pharmacokinetic profiles.
Learner and Instructional Factors Influencing Learning Outcomes within a Blended Learning Environment

Among the many studies focusing on the effect of learner and instructional variables on learning outcomes, few studies have investigated the effect of these variables and their mediating mechanisms influencing students’ learning within a blended learning environment. This study examined the influence of instructional and learner variables on learning outcomes for a blended instruction course offered for undergraduate students. Data analysis indicated that age, prior experiences with distance learning opportunities, preference in delivery format, and average study time are those learner antecedents differentiating learning outcomes among groups of college students. From a regression analysis, the influence of learner, instructional, and motivational variables on learning outcomes found to be consolidated around one variable in learning application.
Security and Privacy Implications of NFC-enabled Contactless Payment Systems

Nowadays, contactless payments are becoming increasingly common as new smartphones, tablets, point-of-sale (POS) terminals and payment cards (often termed "tap-and-pay" cards) are designed to support Near Field Communication (NFC) technology. However, as NFC technology becomes pervasive, there have been concerns about how well NFC-enabled contactless payment systems protect individuals and organizations from emerging security and privacy threats. In this paper, we examine the security of contactless payment systems by considering the privacy threats and the different adversarial attacks that these systems must defend against. We focus our analysis on the underlying trust assumptions, security measures and technologies that form the basis on which contactless payment cards and NFC-enabled mobile wallets exchange sensitive transaction data with contactless POS terminals. We also explore the EMV and ISO standards for contactless payments and disclose their shortcomings with regards to enforcing security and privacy in contactless payment transactions. Our findings shed light on the discrepancies between the EMV and ISO standards, as well as how card issuing banks and mobile wallet providers configure their contactless payment cards and NFC-enabled mobile wallets based on these standards, respectively. These inconsistencies are disconcerting as they can be exploited by an adversary to compromise the integrity of contactless payment transactions.
CBT for depression: a pilot RCT comparing mobile phone vs. computer

This paper reports the results of a pilot randomized controlled trial comparing the delivery modality (mobile phone/tablet or fixed computer) of a cognitive behavioural therapy intervention for the treatment of depression. The aim was to establish whether a previously validated computerized program (The Sadness Program) remained efficacious when delivered via a mobile application.



35 participants were recruited with Major Depression (80% female) and randomly allocated to access the program using a mobile app (on either a mobile phone or iPad) or a computer. Participants completed 6 lessons, weekly homework assignments, and received weekly email contact from a clinical psychologist or psychiatrist until completion of lesson 2. After lesson 2 email contact was only provided in response to participant request, or in response to a deterioration in psychological distress scores. The primary outcome measure was the Patient Health Questionnaire 9 (PHQ-9). Of the 35 participants recruited, 68.6% completed 6 lessons and 65.7% completed the 3-months follow up. Attrition was handled using mixed-model repeated-measures ANOVA.



Both the Mobile and Computer Groups were associated with statistically significantly benefits in the PHQ-9 at post-test. At 3 months follow up, the reduction seen for both groups remained significant.



These results provide evidence to indicate that delivering a CBT program using a mobile application, can result in clinically significant improvements in outcomes for patients with depression.
Augmented Feedback in Semantic Segmentation Under Image Level Supervision

Training neural networks for semantic segmentation is data hungry. Meanwhile annotating a large number of pixel-level segmentation masks needs enormous human effort. In this paper, we propose a framework with only image-level supervision. It unifies semantic segmentation and object localization with important proposal aggregation and selection modules. They greatly reduce the notorious error accumulation problem that commonly arises in weakly supervised learning. Our proposed training algorithm progressively improves segmentation performance with augmented feedback in iterations. Our method achieves decent results on the PASCAL VOC 2012 segmentation data, outperforming previous image-level supervised methods by a large margin.
Exploiting linguistic and statistical knowledge in a text alignment system

Within machine translation, the alignment of corpora has evolved into a mature research area, aimed at providing training data for statistical or example-based machine translation systems. Moreover, the alignment information can be used for a variety of other purposes, including lexicography and the induction of tools for natural language processing. The alignment techniques used for these purposes fall roughly in two separate classes: sentence alignment approaches that often combine statistical and linguistic information, and word alignment models that are dominated by the statistical machine translation paradigm. Alignment approaches that use linguistic knowledge provided by corpus annotation are rare, as are as non-statistical word alignment strategies. Furthermore, parallel corpora are typically not aligned at all text levels simultaneously. Rather, a corpus is first sentence aligned, and in a subsequent step, the alignment information is refined to go below the sentence level. In this thesis, the distinction between the two alignment classes is withdrawn. Rather, a system is introduced that can simultaneously align at the paragraph, sentence, word, and phrase level. Furthermore, linguistic as well as statistical information can be combined. This combination of alignment cues from different knowledge sources, as well as the combination of the sentence and word alignment tasks, is made possible by the development of a modular alignment platform. Its main features are that it supports different kinds of linguistic corpus annotation, and furthermore aligns a corpus hierarchically, such that sentence and word alignments are cohesive. Alignment cues are not used within a global alignment model. Rather, different sub-models can be implemented and allowed to interact. Most of the alignment modules of the system have been implemented using empirical corpus studies, aimed at showing how the most common types of corpus annotation can be exploited for the alignment task.
Incentives for Mobile Crowd Sensing: A Survey

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.
Malware detection on Android smartphones using API class and machine learning

This paper proposes a (new) method to detect malware in Android smartphones using API (application programming interface) classes. We use machine learning to classify whether an application is benign or malware. Furthermore, we compare classification precision rate from machine learning. This research uses 51 APIs package classes from 16 APIs classes and employs cross validation and percentage split test to classify benign and malware using Random Forest, J48, and Support Vector Machine algorithms. We use 412 total application samples (205 benign, 207 malware). We obtain that the classification precision average is 91.9%.
Wideband substrate integrated waveguide Ku-band coupler

This paper deals with a 3 dB Ku-band coupler designed in substrate integrated waveguide (SIW) technology. A microstrip-SIW-transition is designed with a return loss (RL) greater than 20 dB. Rogers 4003 substrate is used for the SIW with a gold plated copper metallisation. The coupler achieves a relative bandwidth of 26.1% with an insertion loss (IL) lower than 2 dB, coupling balance smaller than 0.5 dB and RL and isolation greater than 15 dB.
Real-Time Scheduling via Reinforcement Learning

Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the need to perform more general tasks such as obstacle avoidance. This problem has been addressed by maintaining relative utilization of shared resources among tasks near a user-specified target level. Producing optimal scheduling strategies requires complete prior knowledge of task behavior, which is unlikely to be available in practice. Instead, suitable scheduling strategies must be learned online through interaction with the system. We consider the sample complexity of reinforcement learning in this domain, and demonstrate that while the problem state space is countably infinite, we may leverage the problem’s structure to guarantee efficient learning.
Intrarater and interrater reliability of three classifications for scapular dyskinesis in athletes

Clinical evaluation of scapular dyskinesis (SD) aims to identify abnormal scapulothoracic movement, underlying causal factors, and the potential relationship with shoulder symptoms. The literature proposes different methods of dynamic clinical evaluation of SD, but improved reliability and agreement values are needed. The present study aimed to evaluate the intrarater and interrater agreement and reliability of three SD classifications: 1) 4-type classification, 2) Yes/No classification, and 3) scapular dyskinesis test (SDT). Seventy-five young athletes, including 45 men and 30 women, were evaluated. Raters evaluated the SD based on the three methods during one series of 8-10 cycles (at least eight and maximum of ten) of forward flexion and abduction with an external load under the observation of two raters trained to diagnose SD. The evaluation protocol was repeated after 3 h for intrarater analysis. The agreement percentage was calculated by dividing the observed agreement by the total number of observations. Reliability was calculated using Cohen Kappa coefficient, with a 95% confidence interval (CI), defined by Kappa coefficient ±1.96 multiplied by the measurement standard error. The interrater analyses showed an agreement percentage between 80% and 95.9% and an almost perfect reliability (k>0.81) for the three classification methods in all the test conditions, except the 4-type and SDT classification methods, which had substantial reliability (k<0.80) in shoulder abduction. Intrarater analyses showed agreement percentages between 80.7% and 89.3% and substantial reliability (0.67 to 0.81) for both raters in the three classifications. CIs ranged from moderate to almost perfect categories. This indicates that the three SD classification methods investigated in this study showed high reliability values for both intrarater and interrater evaluation throughout a protocol that provided SD evaluation training of raters and included several repetitions of arm movements with external load during a live assessment.
Perspectives and limits for cement kilns as a destination for RDF.

RDF, the high calorific value fraction of MSW obtained by conventional separation systems, can be employed in technological plants (mainly cement kilns) in order to obtain a useful energy recovery. It is interesting and important to evaluate this possibility within the general framework of waste-to-energy solutions. The solution must be assessed on the basis of different aspects, namely: technological features and clinker characteristics; local atmospheric pollution; the effects of RDF used in cement kilns on the generation of greenhouse gases; the economics of conventional solid fuels substitution and planning perspectives, from the point of view of the destination of RDF and optimal cement kiln policy. The different experiences of this issue throughout Europe are reviewed, and some applications within Italy are also been considered. The main findings of the study are that the use of RDF in cement kilns instead of coal or coke offers environmental benefits in terms of greenhouse gases, while the formation of conventional gaseous pollutants is not a critical aspect. Indeed, the generation of nitrogen oxides can probably be lower because of lower flame temperatures or lower air excess. The presence of chlorinated micro-pollutants is not influenced by the presence of RDF in fuel, whereas depending on the quality of the RDF, some problems could arise compared to the substituted fuel as far as heavy metals are concerned, chiefly the more volatile ones.
Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data. ARTICLE HISTORY Received 8 April 2015
Using robots to investigate the evolution of adaptive behavior

In the field of evolutionary robotics, investigators evolve populations of autonomous machines to exhibit some desired behavior. The neurology, morphology, or both may be placed under evolutionary control, and different behaviors can be selected for. Results from this approach can generate unique and surprising hypotheses about why certain behaviors evolve, regardless of whether they emerge in organisms or machines. To illustrate this approach, I describe recent work on the evolution of modularity, morphological computation, and prospection.