nils-herrmann/clean_scirepeval huggingface dataset:
Software Defined Network (SDN) Based Internet of Things (IoT): A Road Ahead

Internet of Things (IoT) and Software Defined Network (SDN) are two emerging technologies. The IoT aims to connect objects over the Internet and the SDN provides orchestration for network management by decoupling the control plane and the data plane. The number of connected objects is in billions, and its management and control is a complex task for a large distributed network. The SDN provides flexibility and programmability in the IoT network without troubling underlying architecture of existing implementations. In this paper, we study different IoT application and domains, and the generalized IoT-SDN solutions over the period of 2012-2016. Furthermore, this paper provides a critical view of the IoT and the SDN technologies, current trends in research and the futuristic contributing factors. A comparative analysis of the existing solutions of SDN-based IoT implementations provides an easy and concise view of the emerging trends.
Are memory complaints predictive for dementia? A review of clinical and population-based studies.

To review studies that have reported on the prevalence of memory complaints and the relationship between memory complaints and impairment or decline (dementia) in elderly individuals.



All publications in the English language relating to memory complaints, memory impairment, cognitive disorder and dementia in MEDLINE, PSYCHLIT and EMBASE computerized databases, together with a search of relevant citations.



The prevalence of memory complaints, defined as everyday memory problems, shows a large variation of approximately 25 - 50%. A high age, female gender and a low level of education are generally associated with a high prevalence of memory complaints. In community-based samples of elderly subjects an association has been found between memory complaints and memory impairment, after adjustment for depressive symptomatology. Memory complaints predict dementia after a follow-up of at least 2 years, in particular in those with mild cognitive impairment, defined as Mini Mental State Examination (MMSE) > 23. Memory complaints in highly educated elderly subjects may be predictive of dementia even when there is no indication of cognitive impairment on short cognitive screen tests. The shift in methodology which is noticeable in the recently published major studies is discussed as a possible explanation for the established association between memory complaints and decline in memory (or dementia) in elderly subjects. Three methodological factors, in particular, are responsible for the results: community-based sampling, longitudinal design and the treatment of variables such as depression, cognitive impairment and level of education.



Memory complaints in elderly people should no longer be considered merely as an innocent age-related phenomenon or a symptom of depression. Instead, these complaints deserve to be taken seriously, at least as a possible early sign of dementia.
Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability

Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.
A pipeline architecture for computing the Euler number of a binary image

Euler number of a binary image is a fundamental topological feature that remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. In this work, a run-based method for computing Euler number is formulated and a new hardware implementation is described. Analysis of time complexity and performance measure is provided to demonstrate the efficiency of the method. The sequential version of the proposed algorithm requires significantly fewer number of pixel accesses compared to the existing methods and tools based on bit-quad counting or quad-tree, both for the worst case and the average case. A pipelined architecture is designed with a single adder tree to implement the algorithm on-chip by exploiting its inherent parallelism. The architecture uses O(N) 2-input gates and requires O(N logN) time to compute the Euler number of an N · N image. The same hardware, with minor modification, can be used to handle arbitrarily large pixel matrices. A standard cell based VLSI implementation of the architecture is also reported. As Euler number is a widely used parameter, the proposed design can be readily used to save computation time in many image processing applications.
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.
Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning

In any non-deterministic environment, unexpected events can indicate true changes in the world (and require behavioural adaptation) or reflect chance occurrence (and must be discounted). Adaptive behaviour requires distinguishing these possibilities. We investigated how humans achieve this by integrating high-level information from instruction and experience. In a series of EEG experiments, instructions modulated the perceived informativeness of feedback: Participants performed a novel probabilistic reinforcement learning task, receiving instructions about reliability of feedback or volatility of the environment. Importantly, our designs de-confound informativeness from surprise, which typically co-vary. Behavioural results indicate that participants used instructions to adapt their behaviour faster to changes in the environment when instructions indicated that negative feedback was more informative, even if it was simultaneously less surprising. This study is the first to show that neural markers of feedback anticipation (stimulus-preceding negativity) and of feedback processing (feedback-related negativity; FRN) reflect informativeness of unexpected feedback. Meanwhile, changes in P3 amplitude indicated imminent adjustments in behaviour. Collectively, our findings provide new evidence that high-level information interacts with experience-driven learning in a flexible manner, enabling human learners to make informed decisions about whether to persevere or explore new options, a pivotal ability in our complex environment.
Detection of glaucoma using Neuroretinal Rim information

Glaucoma is one of the most common causes of blindness in the world. The vision lost due to glaucoma cannot be regained. Early detection of glaucoma is thus very important. The Optic Disk(OD), Optic Cup(OC) and Neuroretinal Rim(NRR) are among the important features of a retinal image that can be used in the detection of glaucoma. In this paper, a computer-assisted method for the detection of glaucoma based on the ISNT rule is presented. The OD and OC are segmented using watershed transformation. The NRR area in the ISNT quadrants is obtained from the segmented OD and OC. The method is applied on the publicly available databases HRF, Messidor, DRIONS-DB, RIM-ONE and a local hospital database consisting of both normal and glaucomatous images. The proposed method is simple, computationally efficient and achieves a sensitivity of 91.82% and an overall accuracy of 94.14%.
Neural Headline Generation with Minimum Risk Training

Automatic headline generation is an important research area within text summarization and sentence compression. Recently, neural headline generation models have been proposed to take advantage of well-trained neural networks in learning sentence representations and mapping sequence to sequence. Nevertheless, traditional neural network encoder utilizes maximum likelihood estimation for parameter optimization, which essentially constraints the expected training objective within word level instead of sentence level. Moreover, the performance of model prediction significantly relies on training data distribution. To overcome these drawbacks, we employ minimum risk training strategy in this paper, which directly optimizes model parameters with respect to evaluation metrics and statistically leads to significant improvements for headline generation. Experiment results show that our approach outperforms state-of-the-art systems on both English and Chinese headline generation tasks.
Beyond Usability: Evaluation Aspects of Visual Analytic Environments

A new field of research, visual analytics, has been introduced. This has been defined as "the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas and Cook, 2005). Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation, and dissemination. As researchers begin to develop visual analytic environments, it is advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work has on the users who work in such environments. This paper presents five areas or aspects of visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as the five evaluation areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined
Microbial Electrosynthesis

Electrobiosynthesis conducted by microorganisms represents a new technology with great potential. This review considers mechanisms of direct electron transfer from cathode to bacterial cell and a number of anaerobic processes catalyzed with such transport: the biosynthesis of hydrogen, methane, and multicarbon compounds. The possibilities for the use of electrolysis hydrogen to grow hydrogen oxidizing bacteria are also considered, as well as some examples of electricity that influence the reductive and oxidative processes occurring during fermentation. Realization of the electric biosynthesis potential would require deep fundamental research on the mechanisms of extracellular electron transport and the coupling of electric and metabolic processes. Work would be required to reorganize microbial genomes to intensify their metabolism and broaden the repertoire of synthesized metabolites. Progress in these technologies would depend not only on improvements in microorganisms but also on the successful creation of effective biocompatible electrodes and the designing of highly productive reactors.
Need and disparities in primary care management of patients with diabetes

An aging population means that chronic illnesses, such as diabetes, are becoming more prevalent and demands for care are rising. Members of primary care teams should organize and coordinate patient care with a view to improving quality of care and impartial adherence to evidence-based practices for all patients. The aims of the present study were: to ascertain the prevalence of diabetes in an Italian population, stratified by age, gender and citizenship; and to identify the rate of compliance with recommended guidelines for monitoring diabetes, to see whether disparities exist in the quality of diabetes patient management.



A population-based analysis was performed on a dataset obtained by processing public health administration databases. The presence of diabetes and compliance with standards of care were estimated using appropriate algorithms. A multilevel logistic regression analysis was applied to assess factors affecting compliance with standards of care.



1,948,622 Italians aged 16+ were included in the study. In this population, 105,987 subjects were identified as having diabetes on January 1st, 2009. The prevalence of diabetes was 5.43% (95% CI 5.33-5.54) overall, 5.87% (95% CI 5.82-5.92) among males, and 5.05% (95% CI 5.00-5.09) among females. HbA1c levels had been tested in 60.50% of our diabetic subjects, LDL cholesterol levels in 57.50%, and creatinine levels in 63.27%, but only 44.19% of the diabetic individuals had undergone a comprehensive assessment during one year of care. Statistical differences in diabetes care management emerged relating to gender, age, diagnostic latency period, comorbidity and citizenship.



Process management indicators need to be used not only for the overall assessment of health care processes, but also to monitor disparities in the provision of health care.
A parallel evolutionary algorithm for RNA secondary structure prediction using stacking-energies (INN and INN-HB)

This work presents a coarse-grained distributed genetic algorithm (GA) for RNA secondary structure prediction. This research builds on previous work and contains two new thermodynamic models, INN and INN-HB, which add stacking-energies using base pair adjacencies. Comparison tests were performed against the original serial GA on known structures that are 122, 543, and 784 nucleotides in length on a wide variety of parameter settings. The effects of the new models are investigated, the predicted structures are compared to known structures and the GA is compared against a serial GA with identical models. Both algorithms perform well and are able to predict structures with high accuracy for short sequences.
An Admission Control Scheme for Predictable Server Response Time for Web Accesses

The diversity in web object types and their resource requirements contributes to the unpredictability of web service provisioning. In this paper, an eÆcient admission control algorithm, PACERS, is proposed to provide di erent levels of services based on the server workload characteristics. Service quality is ensured by periodical allocation of system resources based on the estimation of request rate and service requirements of prioritized tasks. Admission of lower priority tasks is restricted during high load periods to prevent denial-of-services to high priority tasks. A doublequeue structure is implemented to reduce the e ects of estimation inaccuracy and to utilize the spare capacity of the server, thus increasing the system throughput. Response delays of the high priority tasks are bounded by the length of the prediction period. Theoretical analysis and experimental study show that the PACERS algorithm provides desirable throughput and bounded response delay to the prioritized tasks, without any signi cant impact on the aggregate throughput of the system under various workload.
Single Image Super-Resolution Using a Joint GMM Method

Single image super-resolution (SR) algorithms based on joint dictionaries and sparse representations of image patches have received significant attention in the literature and deliver the state-of-the-art results. Recently, Gaussian mixture models (GMMs) have emerged as favored prior for natural image patches in various image restoration problems. In this paper, we approach the single image SR problem by using a joint GMM learnt from concatenated vectors of high and low resolution patches sampled from a large database of pairs of high resolution and the corresponding low resolution images. Covariance matrices of the learnt Gaussian models capture the inherent correlations between high and low resolution patches, which are utilized for inferring high resolution patches from given low resolution patches. The proposed joint GMM method can be interpreted as the GMM analogue of joint dictionary-based algorithms for single image SR. We study the performance of the proposed joint GMM method by comparing with various competing algorithms for single image SR. Our experiments on various natural images demonstrate the competitive performance obtained by the proposed method at low computational cost.
Do we spontaneously form stable trustworthiness impressions from facial appearance?

It is widely assumed among psychologists that people spontaneously form trustworthiness impressions of newly encountered people from their facial appearance. However, most existing studies directly or indirectly induced an impression formation goal, which means that the existing empirical support for spontaneous facial trustworthiness impressions remains insufficient. In particular, it remains an open question whether trustworthiness from facial appearance is encoded in memory. Using the 'who said what' paradigm, we indirectly measured to what extent people encoded the trustworthiness of observed faces. The results of 4 studies demonstrated a reliable tendency toward trustworthiness encoding. This was shown under conditions of varying context-relevance, and salience of trustworthiness. Moreover, evidence for this tendency was obtained using both (experimentally controlled) artificial and (naturalistic varying) real faces. Taken together, these results suggest that there is a spontaneous tendency to form relatively stable trustworthiness impressions from facial appearance, which is relatively independent of the context. As such, our results further underline how widespread influences of facial trustworthiness may be in our everyday life.
Fiber-based tissue engineering: Progress, challenges, and opportunities.

Tissue engineering aims to improve the function of diseased or damaged organs by creating biological substitutes. To fabricate a functional tissue, the engineered construct should mimic the physiological environment including its structural, topographical, and mechanical properties. Moreover, the construct should facilitate nutrients and oxygen diffusion as well as removal of metabolic waste during tissue regeneration. In the last decade, fiber-based techniques such as weaving, knitting, braiding, as well as electrospinning, and direct writing have emerged as promising platforms for making 3D tissue constructs that can address the abovementioned challenges. Here, we critically review the techniques used to form cell-free and cell-laden fibers and to assemble them into scaffolds. We compare their mechanical properties, morphological features and biological activity. We discuss current challenges and future opportunities of fiber-based tissue engineering (FBTE) for use in research and clinical practice.
Example-based expressive animation of 2D rigid bodies

We present a novel approach to facilitate the creation of stylized 2D rigid body animations. Our approach can handle multiple rigid objects following complex physically-simulated trajectories with collisions, while retaining a unique artistic style directly specified by the user. Starting with an existing target animation (e.g., produced by a physical simulation engine) an artist interactively draws over a sparse set of frames, and the desired appearance and motion stylization is automatically propagated to the rest of the sequence. The stylization process may also be performed in an off-line batch process from a small set of drawn sequences. To achieve these goals, we combine parametric deformation synthesis that generalizes and reuses hand-drawn exemplars, with non-parametric techniques that enhance the hand-drawn appearance of the synthesized sequence. We demonstrate the potential of our method on various complex rigid body animations which are created with an expressive hand-drawn look using notably less manual interventions as compared to traditional techniques.
Spectral Analysis for Detection of Leaks in Pipes Carrying Compressed Air

Pipe networks constitute the means of transporting fluids widely used nowadays. Increasing the operational reliability of these systems is crucial to minimize the risk of leaks, which can cause serious pollution problems to the environment and have disastrous consequences if the leak occurs near residential areas. Considering the importance in developing efficient systems for detecting leaks in pipelines, this work aims to detect the characteristic frequencies (predominant) in case of leakage and no leakage. The methodology consisted of capturing the experimental data through a microphone installed inside the pipeline and coupled to a data acquisition card and a computer. The Fast Fourier Transform (FFT) was used as the mathematical approach to the signal analysis from the microphone, generating a frequency response (spectrum) which reveals the characteristic frequencies for each operating situation. The tests were carried out using distinct sizes of leaks, situations without leaks and cases with blows in the pipe caused by metal instruments. From the leakage tests, characteristic peaks were found in the FFT frequency spectrum using the signal generated by the microphone. Such peaks were not observed in situations with no leaks. Therewith, it was realized that it was possible to distinguish, through spectral analysis, an event of leakage from an event without leakage.
A Hybrid Approach for Detection and Classification of the Defects on Printed Circuit Board

Printed Circuit Board (PCB) is the most important component of the electronic industry and thus, the electronic mass-production facilities make an attempt to achieve Printed Circuit Board with 100% quality assurance. To achieve Printed Circuit Board with 100% quality assurance i. e. to produce zero-defect Printed Circuit Board, inspection method is the vital process during manufacturing of PCB. This paper presents the Hybrid approach which helps in producing zero-defect PCB by detecting the defects. This approach not only detects the defect but also classifies and locates the defects. The hybrid approach uses referential and non-referential methods to analyze the Printed Circuit Board and this approach proposed an algorithm which involves Image Representation, Image Comparison, and Image Segmentation. The algorithm was tested with mass number of images of Printed Circuit Board. The experimental result shows that higher quality assurance is obtained than the previously implemented algorithms.