nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Blocking Probability in a Relay-based Cellular OFDMA Network

Relay deployment in Orthogonal Frequency Division Multipl e Access (OFDMA) based cellular networks helps in coverage extension and/or capacity improvement. To quantify capacity improvement, blocking probability of voice traffic is typically calculated using Erlang B formula. This calculation is based on the assumption that all users require same amount of resourc es to satisfy their rate requirement. However, in an OFDMA system, each user requires different number of su bcarriers to meet its rate requirement. This resource requirement depends on the Signal to Interference Ratio (SIR) experienced by a user. Therefore, the Erlang B formula can not be employed to compute blocking p robability in an OFDMA network.In this paper, we determine an analytical expression to comput e the blocking probability of relay based cellular OFDMA network. We determine an expression of the probability distribution of the user’s resource requirement based on its experienced SIR. Then, we classify the users into various classes depending upon their subcarrier requirement. We consider the system to be a multi-dimensional system with different classes and evaluate the blocking probabili ty of system using the multi-dimensional Erlang loss formulas. This model is useful in the performance evaluation, design, planning of resources and call admission control of relay based cellular OFDMA networks like LTE.
Alcohol addiction and the attachment system: an empirical study of attachment style, alexithymia, and psychiatric disorders in alcoholic inpatients.

This study aims at investigating alcoholic inpatients' attachment system by combining a measurement of adult attachment style (AAQ, Hazan and Shaver, 1987. Journal of Personality and Social Psychology, 52(3): 511-524) and the degree of alexithymia (BVAQ, Bermond and Vorst, 1998. Bermond-Vorst Alexithymia Questionnaire, Unpublished data). Data were collected from 101 patients (71 men, 30 women) admitted to a psychiatric hospital in Belgium for alcohol use-related problems, between September 2003 and December 2004. To investigate the research question, cluster analyses and regression analyses are performed. We found that it makes sense to distinguish three subgroups of alcoholic inpatients with different degrees of impairment of the attachment system. Our results also reveal a pattern of correspondence between the severity of psychiatric symptoms-personality disorder traits (ADP-IV), anxiety (STAI), and depression (BDI-II-Nl)-and the severity of the attachment system's impairment. Limitations of the study and suggestions for further research are highlighted and implications for diagnosis and treatment are discussed.
Fast unfolding of community hierarchies in large networks

The typical size of large networks such as social network services, mobile phone networks or the web now counts in millions when not billions of nodes and these scales demand new methods to retrieve comprehensive information from their structure. A promising approach consists in decomposing the networks into communities of strongly connected nodes, with the nodes belonging to different communities only sparsely connected. Finding exact optimal partitions in networks is known to be computationally intractable, mainly due to the explosion of the number of possible partitions as the number of nodes increases. It is therefore of high interest to propose algorithms to find reasonably “good” solutions of the problem in a reasonably “fast” way. One of the fastest algorithms consists in optimizing the modularity of the partition in a greedy way (Clauset et al, 2004), a method that, even improved, does not allow to analyze more than a few millions nodes (Wakita et al, 2007).
The Effect of Reflexology on the Pain-Insomnia-Fatigue Disturbance Cluster of Breast Cancer Patients During Adjuvant Radiation Therapy.

To evaluate the effects of reflexology treatment on quality of life, sleep disturbances, and fatigue in breast cancer patients during radiation therapy.



A total of 72 women with breast cancer (stages 1-3) scheduled for radiation therapy were recruited.



Women were allocated upon their preference either to the group receiving reflexology treatments once a week concurrently with radiotherapy and continued for 10 weeks or to the control group (usual care).



The Lee Fatigue Scale, General Sleep Disturbance Scale, and Multidimensional Quality of Life Scale Cancer were completed by each patient in both arms at the beginning of the radiation treatment, after 5 weeks, and after 10 weeks of reflexology treatment.



The final analysis included 58 women. The reflexology treated group demonstrated statistically significant lower levels of fatigue after 5 weeks of radiation therapy (p < 0.001), compared to the control group. It was also detected that although the quality of life in the control group deteriorated after 5 and 10 weeks of radiation therapy (p < 0.01 and p < 0.05, respectively), it was preserved in the reflexology group, which also demonstrated a significant improvement in the quality of sleep after 10 weeks of radiation treatment (p < 0.05). Similar patterns were obtained in the assessment of the pain levels experienced by the patients.



The results of the present study indicate that reflexology may have a positive effect on fatigue, quality of sleep, pain, and quality of life in breast cancer patients during radiation therapy. Reflexology prevented the decline in quality of life and significantly ameliorated the fatigue and quality of sleep of these patients. An encouraging trend was also noted in amelioration of pain levels.
Using REST based protocol to enable ABAC within IoT systems

With the development of the Internet of Things (IoT) and the usage of low-powered devices (sensors and effectors), a large number of people are using IoT systems in their homes and businesses to have more control over their technology. However, a key challenge of IoT systems is data protection in case the IoT device is lost, stolen, or used by one of the owner's friends or family members. The problem studied here is how to protect the access to data of an IoT system. To solve the problem, an attribute-based access control (ABAC) mechanism is applied to give the system the ability to apply policies to detect any unauthorized entry. Finally, a prototype was built to test the proposed solution. The evaluation plan was applied on the proposed solution to test the performance of the system.
Eliminating bias in randomized controlled trials: importance of allocation concealment and masking.

Randomization in randomized controlled trials involves more than generation of a random sequence by which to assign subjects. For randomization to be successfully implemented, the randomization sequence must be adequately protected (concealed) so that investigators, involved health care providers, and subjects are not aware of the upcoming assignment. The absence of adequate allocation concealment can lead to selection bias, one of the very problems that randomization was supposed to eliminate. Authors of reports of randomized trials should provide enough details on how allocation concealment was achieved so the reader can determine the likelihood of success. Fortunately, a plan of allocation concealment can always be incorporated into the design of a randomized trial. Certain methods minimize the risk of concealment failing more than others. Keeping knowledge of subjects' assignment after allocation from subjects, investigators/health care providers, or those assessing outcomes is referred to as masking (also known as blinding). The goal of masking is to prevent ascertainment bias. In contrast to allocation concealment, masking cannot always be incorporated into a randomized controlled trial. Both allocation concealment and masking add to the elimination of bias in randomized controlled trials.
Microbial Fuel Cells : Methodology and Technology †

Microbial fuel cell (MFC) research is a rapidly evolving field that lacks established terminology and methods for the analysis of system performance. This makes it difficult for researchers to compare devices on an equivalent basis. The construction and analysis of MFCs requires knowledge of different scientific and engineering fields, ranging from microbiology and electrochemistry to materials and environmental engineering. Describing MFC systems therefore involves an understanding of these different scientific and engineering principles. In this paper, we provide a review of the different materials and methods used to construct MFCs, techniques used to analyze system performance, and recommendations on what information to include in MFC studies and the most useful ways to present results.
Imaging retinotopic maps in the human brain

A quarter-century ago visual neuroscientists had little information about the number and organization of retinotopic maps in human visual cortex. The advent of functional magnetic resonance imaging (MRI), a non-invasive, spatially-resolved technique for measuring brain activity, provided a wealth of data about human retinotopic maps. Just as there are differences amongst non-human primate maps, the human maps have their own unique properties. Many human maps can be measured reliably in individual subjects during experimental sessions lasting less than an hour. The efficiency of the measurements and the relatively large amplitude of functional MRI signals in visual cortex make it possible to develop quantitative models of functional responses within specific maps in individual subjects. During this last quarter-century, there has also been significant progress in measuring properties of the human brain at a range of length and time scales, including white matter pathways, macroscopic properties of gray and white matter, and cellular and molecular tissue properties. We hope the next 25years will see a great deal of work that aims to integrate these data by modeling the network of visual signals. We do not know what such theories will look like, but the characterization of human retinotopic maps from the last 25years is likely to be an important part of future ideas about visual computations.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making. Our contribution is a practical system which is able to predict pixelwise class labels with a measure of model uncertainty using Bayesian deep learning. We achieve this by Monte Carlo sampling with dropout at test time to generate a posterior distribution of pixel class labels. In addition, we show that modelling uncertainty improves segmentation performance by 2-3% across a number of datasets and architectures such as SegNet, FCN, Dilation Network and DenseNet.
Approximations of fractional integrals and Caputo fractional derivatives

In this paper we propose two algorithms for numerical fractional integration and Caputo fractional differentiation. We present a modification of trapezoidal rule that is used to approximate finite integrals, the new modification extends the application of the rule to approximate integrals of arbitrary order a > 0. We then, using the new modification derive an algorithm to approximate fractional derivatives of arbitrary order a > 0, where the fractional derivative based on Caputo definition, for a given function by a weighted sum of function and its ordinary derivatives values at specified points. The study is conducted through illustrative examples and error analysis.
Emotions in robot psychology

In his famous thought experiments on synthetic vehicles, Valentino Braitenberg stipulated that simple stimulus-response reactions in an organism could evoke the appearance of complex behavior, which, to the unsuspecting human observer, may even appear to be driven by emotions such as fear, aggression, and even love (Braitenberg, Vehikel. Experimente mit künstlichen Wesen, Lit Verlag, 2004). In fact, humans appear to have a strong propensity to anthropomorphize, driven by our inherent desire for predictability that will quickly lead us to discern patterns, cause-and-effect relationships, and yes, emotions, in animated entities, be they natural or artificial. But might there be reasons, that we should intentionally “implement” emotions into artificial entities, such as robots? How would we proceed in creating robot emotions? And what, if any, are the ethical implications of creating “emotional” robots? The following article aims to shed some light on these questions with a multi-disciplinary review of recent empirical investigations into the various facets of emotions in robot psychology.
Design of Vending Machine using Finite State Machine and Visual Automata Simulator

Automata theory is dominating in many applications developed from the concept of finite state machine (FSM). This research will consider the design of vending machine (VM), which improves the books delivery service in the academic institution, illustrated by an application. An efficiency of VM is considered as a problem. In order to improve the efficiency, automata theory depends on the design of the FSM and visual automata simulator (VAS) are used as a method. Finally, the paper will construct FSM that uses fewer states. As a result, we will find that we can improve the efficiency and design cost of the VM.
ASR-based Features for Emotion Recognition: A Transfer Learning Approach

During the last decade, the applications of signal processing have drastically improved with deep learning. However areas of affecting computing such as emotional speech synthesis or emotion recognition from spoken language remains challenging. In this paper, we investigate the use of a neural Automatic Speech Recognition (ASR) as a feature extractor for emotion recognition. We show that these features outperform the eGeMAPS feature set to predict the valence and arousal emotional dimensions, which means that the audio-to-text mapping learned by the ASR system contains information related to the emotional dimensions in spontaneous speech. We also examine the relationship between first layers (closer to speech) and last layers (closer to text) of the ASR and valence/arousal.
The Shape Boltzmann Machine: A Strong Model of Object Shape

A good model of object shape is essential in applications such as segmentation, detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shapes can help where object boundaries are noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to parts of the objects. In general, the stronger the model of shape, the more performance is improved. In this paper, we use a type of deep Boltzmann machine (Salakhutdinov and Hinton, International Conference on Artificial Intelligence and Statistics, 2009) that we call a Shape Boltzmann Machine (SBM) for the task of modeling foreground/background (binary) and parts-based (categorical) shape images. We show that the SBM characterizes a strong model of shape, in that samples from the model look realistic and it can generalize to generate samples that differ from training examples. We find that the SBM learns distributions that are qualitatively and quantitatively better than existing models for this task.
Accelerating large scale centroid-based clustering with locality sensitive hashing

Most traditional data mining algorithms struggle to cope with the sheer scale of data efficiently. In this paper, we propose a general framework to accelerate existing algorithms to cluster large-scale datasets which contain large numbers of attributes, items, and clusters. Our framework makes use of locality sensitive hashing to significantly reduce the cluster search space. We also theoretically prove that our framework has a guaranteed error bound in terms of the clustering quality. This framework can be applied to a set of centroid-based clustering algorithms that assign an object to the most similar cluster, and we adopt the popular K-Modes categorical clustering algorithm to present how the framework can be applied. We validated our framework with five synthetic datasets and a real world Yahoo! Answers dataset. The experimental results demonstrate that our framework is able to speed up the existing clustering algorithm between factors of 2 and 6, while maintaining comparable cluster purity.
Securing Personal Health Records in Cloud Computing: Patient-Centric and Fine-Grained Data Access Control in Multi-owner Settings

Online personal health record (PHR) enables patients to manage their own medical records in a centralized way, which greatly facilitates the storage, access and sharing of personal health data. With the emergence of cloud computing, it is attractive for the PHR service providers to shift their PHR applications and storage into the cloud, in order to enjoy the elastic resources and reduce the operational cost. However, by storing PHRs in the cloud, the patients lose physical control to their personal health data, which makes it necessary for each patient to encrypt her PHR data before uploading to the cloud servers. Under encryption, it is challenging to achieve fine-grained access control to PHR data in a scalable and efficient way. For each patient, the PHR data should be encrypted so that it is scalable with the number of users having access. Also, since there are multiple owners (patients) in a PHR system and every owner would encrypt her PHR files using a different set of cryptographic keys, it is important to reduce the key distribution complexity in such multi-owner settings. Existing cryptographic enforced access control schemes are mostly designed for the single-owner scenarios. In this paper, we propose a novel framework for access control to PHRs within cloud computing environment. To enable fine-grained and scalable access control for PHRs, we leverage attribute based encryption (ABE) techniques to encrypt each patients’ PHR data. To reduce the key distribution complexity, we divide the system into multiple security domains, where each domain manages only a subset of the users. In this way, each patient has full control over her own privacy, and the key management complexity is reduced dramatically. Our proposed scheme is also flexible, in that it supports efficient and on-demand revocation of user access rights, and break-glass access under emergency scenarios.
Combining KLD-sampling with Gmapping proposal for grid-based Monte Carlo localization of a moving robot

Particle filters using Gmapping proposal distribution has demonstrated their effectiveness in target tracking and robot self-localization. Due to the number of particles required in this approach, the computational demand is an issue associated with the Gmapping proposal distribution. The traditional approach is often ad hoc by setting a threshold for acceptance/rejection sampling to reduce the number of particles. However, the number of particles required in this approach is fixed and needs to be selected in advance which can be subjective and inefficient in representing a posterior distribution of various complexity. In parallel, the KLD-MCL algorithm has the capability to adaptively change the sample size of particles with an arbitrarily chosen proposal distribution. This paper combines the Gmapping proposal distribution with the KLD-MCL algorithm, resulting in an efficient particle filter which systematically adapts the number of particles. Simulation results demonstrate that the proposed approach has higher self-localization accuracy and requires a lower number of particles than the standard KLD-MCL algorithm.
Inductive Programming as Approach to Comprehensible Machine Learning

In the early days of machine learning, Donald Michie introduced two orthogonal dimensions to evaluate performance of machine learning approaches – predictive accuracy and comprehensibility of the learned hypotheses. Later definitions narrowed the focus to measures of accuracy. As a consequence, statistical/neuronal approaches have been favoured over symbolic approaches to machine learning, such as inductive logic programming (ILP). Recently, the importance of comprehensibility has been rediscovered under the slogan ‘explainable AI’. This is due to the growing interest in black-box deep learning approaches in many application domains where it is crucial that system decisions are transparent and comprehensible and in consequence trustworthy. I will give a short history of machine learning research followed by a presentation of two specific approaches of symbolic machine learning – inductive logic programming and end-user programming. Furthermore, I will present current work on explanation generation. Die Arbeitsweise der Algorithmen, die über uns entscheiden, muss transparent gemacht werden, und wir müssen die Möglichkeit bekommen, die Algorithmen zu beeinflussen. Dazu ist es unbedingt notwendig, dass die Algorithmen ihre Entscheidung begründen! Peter Arbeitsloser zu John of Us, Qualityland, Marc-Uwe Kling, 2017
Knowledge as a Service Framework for Disaster Data Management

Each year, a number of natural disasters strike across the globe, killing hundreds and causing billions of dollars in property and infrastructure damage. Minimizing the impact of disasters is imperative in today's society. As the capabilities of software and hardware evolve, so does the role of information and communication technology in disaster mitigation, preparation, response, and recovery. A large quantity of disaster-related data is available, including response plans, records of previous incidents, simulation data, social media data, and Web sites. However, current data management solutions offer few or no integration capabilities. Moreover, recent advances in cloud computing, big data, and NoSQL open the door for new solutions in disaster data management. In this paper, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM), with the objectives of 1) storing large amounts of disaster-related data from diverse sources, 2) facilitating search, and 3) supporting their interoperability and integration. Data are stored in a cloud environment using a combination of relational and NoSQL databases. The case study presented in this paper illustrates the use of Disaster-CDM on an example of simulation models.
A Drosophila model for TDP-43 proteinopathy.

Neuropathology involving TAR DNA binding protein-43 (TDP-43) has been identified in a wide spectrum of neurodegenerative diseases collectively named as TDP-43 proteinopathy, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar dementia (FTLD). To test whether increased expression of wide-type human TDP-43 (hTDP-43) may cause neurotoxicity in vivo, we generated transgenic flies expressing hTDP-43 in various neuronal subpopulations. Expression in the fly eyes of the full-length hTDP-43, but not a mutant lacking its amino-terminal domain, led to progressive loss of ommatidia with remarkable signs of neurodegeneration. Expressing hTDP-43 in mushroom bodies (MBs) resulted in dramatic axon losses and neuronal death. Furthermore, hTDP-43 expression in motor neurons led to axon swelling, reduction in axon branches and bouton numbers, and motor neuron loss together with functional deficits. Thus, our transgenic flies expressing hTDP-43 recapitulate important neuropathological and clinical features of human TDP-43 proteinopathy, providing a powerful animal model for this group of devastating diseases. Our study indicates that simply increasing hTDP-43 expression is sufficient to cause neurotoxicity in vivo, suggesting that aberrant regulation of TDP-43 expression or decreased clearance of hTDP-43 may contribute to the pathogenesis of TDP-43 proteinopathy.