nils-herrmann/clean_scirepeval huggingface dataset:
Bank Customer Credit Scoring by Using Fuzzy Expert System

Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
Empathy hurts: Compassion for another increases both sensory and affective components of pain perception

Recent studies demonstrate that some brain structures activated by pain are also engaged when an individual observes someone else in pain, and that these empathy-related responses are modulated as a function of the affective link between the empath and the individual in pain. In this study we test the hypothesis that empathy-evoked activation in the pain network leads to heightened pain perception. After inducing in half of our subjects a state of high empathy for an actor and in the other half a state of low empathy towards him, we measured the sensitivity to heat stimuli of various intensities in healthy participants while they watched the actor being exposed to similar stimuli. Participants in the "high-empathy" group rated painful (but not non-painful) stimuli applied to themselves as more intense and unpleasant than did those in the "low-empathy" group. Positive correlations between state empathy scores and pain ratings further suggest that this perceptual phenomenon depends on the magnitude of empathic response induced in the participants. The effects were observed when subjects watched the model receiving either neutral or painful stimuli, suggesting that it is empathy itself that alters pain perception, and not necessarily the observation of pain behaviors.
The questionable contribution of medical measures to the decline of mortality in the United States in the twentieth century.

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Non-Invasive Control Solution inside Higher-Level OPC UA based Wrapper for Optimizing Groups of Wastewater Systems

Interoperability is a key attribute of systems nowadays in the industry. It enables local devices towards integration into higher-level supervisory/management structures and assures communication among same level systems. In the wastewater industry, local automation and SCADA structures are functioning independently in most situations and sometimes implemented using proprietary protocols causing higher-level integration and scalability/extensibility problems. With interdependences at the process level but without correlation in a higher-level supervisory algorithm, some real scenarios are causing undesired behaviors (e.g. wastewater overflows, untreated wastewater directed through bypasses into outlet). The current paper proposes a higher-level control solution (HLCS) that is modularly implemented inside a OPC UA based wrapper. The solution is designed for wastewater pumping stations and wastewater treatment plant to optimize their behavior as a group. Besides achieving interoperability of structures, the noninvasive character of the solution eliminates specific and frequent problems of interference in local functioning systems.
Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach

This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.
Is endoscopic polypectomy an adequate therapy for malignant colorectal adenomas? Presentation of 114 patients and review of the literature.

This study was designed to evaluate the outcome of endoscopic polypectomy of malignant polyps with and without subsequent surgery based on histologic criteria.



Consecutive patients with invasive carcinoma in colorectal polyps endoscopically removed between 1985 and 1996 were retrospectively studied. Patients with complete resection, grading G1 or G2, and absence of vascular invasion were classified as "low risk." The other patients were classified "high risk." Available literature was reviewed by applying similar classification criteria.



A total of 114 patients (59 males; median age, 70 (range, 20-92) years) were included. Median polyp size was 2.5 (0.4-10) cm. After polypectomy, of 54 patients with low-risk malignant polyps, 13 died of unrelated causes after a median of 76 months, 5 had no residual tumor at surgery, and 33 were alive and well during a median follow-up of 69 (range, 9-169) months. Of 60 patients with high-risk malignant polyps, 52 had surgery (residual carcinoma 27 percent). Five of eight patients not operated had an uneventful follow-up of median 57 (range, 47-129) months. Patients in the high-risk group were significantly more likely to have an adverse outcome than those in the low-risk group (P < 0.0001). Review of 20 studies including 1,220 patients with malignant polyps revealed no patient with low-risk criteria with an adverse outcome.



For patients with low-risk malignant polyps, endoscopic polypectomy alone seems to be adequate. In high-risk patients, the risk of adverse outcome should be weighed against the risk of surgery.
Application of information technology: MedEx: a medication information extraction system for clinical narratives

Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.
Power/freedom on the dark web: A digital ethnography of the Dark Web Social Network

This essay is an early ethnographic exploration of the Dark Web Social Network (DWSN), a social networking site only accessible to Web browsers equipped with The Onion Router. The central claim of this essay is that the DWSN is an experiment in power/freedom, an attempt to simultaneously trace, deploy, and overcome the historical conditions in which it finds itself: the generic constraints and affordances of social networking as they have been developed over the past decade by Facebook and Twitter, and the ideological constraints and affordances of public perceptions of the dark web, which hold that the dark web is useful for both taboo activities and freedom from state oppression. I trace the DWSN’s experiment with power/freedom through three practices: anonymous/social networking, the banning of child pornography, and the productive aspects of techno-elitism. I then use these practices to specify particular forms of power/freedom on the DWSN.
Two-Phase Malicious Web Page Detection Scheme Using Misuse and Anomaly Detection

Misuse detection method and anomaly detection method are widely used for the detection of malicious web pages. Both are based on machine learning. Misuse detection can detect known malicious web pages, but it cannot detect new ones. In contrast, anomaly detection can detect unknown malicious web pages, but it has a high false positive rate. In order to achieve a high detection rate through precisely detecting known and unknown malicious web pages, we propose a two-phase detection scheme. In the first phase, the misuse detection model is built based on the C4.5 decision tree algorithm, which allows known malicious web pages to be detected. In the second phase, the anomaly detection model with a one-class support vector machine is used to detect new types of malicious web pages. The experimental results show that our proposed method has significantly higher malicious web page detection rate than conventional ones with the expense of slightly higher false positive rate.
An area efficient 64-bit square root carry-select adder for low power applications

The carry-select method has deemed to be a good compromise between cost and performance in carry propagation adder design. However, the conventional carry-select adder (CSL) is still area-consuming due to the dual ripple-carry adder structure. The excessive area overhead makes CSL relatively unattractive but this has been circumvented by the use of an add-one circuit introduced recently. In this paper, an area efficient square root CSL scheme based on a new first zero detection logic is proposed. The proposed CSL witnesses a notable power-delay and area-delay performance improvement by virtue of proper exploitation of logic structure and circuit technique. For 64-bit addition, our proposed CSL requires 44% fewer transistors than the conventional one. Simulation results indicate that our proposed CSL can complete 64-bit addition in 1.50 ns and dissipate only 0.35 mW at 1.8V in TSMC 0.18 /spl mu/m CMOS technology.
Light-Weight Security and Data Provenance for Multi-Hop Internet of Things

Due to the limited resources and scalability, the security protocols for the Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for the multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of the secured data transfers. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is  $O(1)$ . The energy dissipation is calculated for the IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52–53 mJ for each IoT node and 313.626 mJ for the entire network. The RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance, and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.
Breast cancer intrinsic subtype classification, clinical use and future trends.

Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.
ITIL-based IT service support process reengineering

The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best practices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.
A Two-Parameter Calibration Technique Tracking Temperature Variations for Current Source Mismatch

This brief presents a foreground calibration technique for current source mismatch that is insensitive to temperature variations. Two calibration digital-to-analog converters (CAL DACs) provide correction currents to compensate two current source mismatch components caused by the threshold voltage mismatch and the current factor mismatch. Each CAL DAC has the same temperature dependence as its corresponding mismatch component. The sum current of the two CAL DACs automatically tracks the temperature variations of the current source mismatch with no need for temperature information. To obtain the CAL DAC input codes, two different bias current settings are used instead of calibrating at two separate temperatures. This scheme significantly reduces the calibration time and cost. A 16-bit DAC with this calibration technique is fabricated in a 65-nm CMOS process. The measurement results show that the matching of the most significant bit currents is improved from intrinsic 12 to 16 bits across the temperature range from −40 °C to 85 °C. The proposed calibration technique achieves superior temperature stability compared to the previously published schemes.
Bilingual FrameNet Dictionaries for Machine Translation

This paper describes issues surrounding the planning and design of GermanFrameNet (GFN), a counterpart to the English-based FrameNet project. The goals of GFN are (a) to create lexical entries for German nouns, verbs, and adjectives that correspond to existing FrameNet entries, and (b) to link the parallel lexicon fragments by means of common semantic frames and numerical indexing mechanisms. GFN will take a fine-grained approach towards polysemy that seeks to split word senses based on the semantic frames that underlie their analysis. The parallel lexicon fragments represent an important step towards capturing valuable information about the different syntactic realizations of frame semantic concepts across languages, which is relevant for information retrieval, machine translation, and language generation.
Ring-Shaped Substrate Integrated Waveguide Wilkinson Power Dividers/Combiners

This paper proposes and presents a class of substrate integrated waveguide (SIW) power dividers/combiners based on a modified Wilkinson architecture. The proposed design consists of a ring-type circuit of 1.5 λg length with three waveguide ports. Impedance variation without affecting cutoff frequency and integration of isolation resistance are the two major limitations in connection with the design of SIW Wilkinson structures. Central ring part with different thickness ensures different impedance in the first proposed scheme while the half-mode SIW in the second option. As for the second limitation, the optimal configuration of integrating the isolation resistance is studied. A version without resistance is also examined using a broad-wall series slot. To validate the proposed concept, a bilayered circuit with standard Printed circuit board prototype was fabricated and measured. A table summarizing performances of all the proposed circuits shows a bandwidth of around 25% at the 10 dB reference. A negligible additional insertion loss is observed. The proposed design techniques are found useful at millimeter-wave frequencies where reduced physical dimensions make a circuit configuration suitable for low-cost implementation. This concept is also valid for rectangular waveguide counterparts.
A Cross Tenant Access Control (CTAC) Model for Cloud Computing: Formal Specification and Verification

Sharing of resources on the cloud can be achieved on a large scale, since it is cost effective and location independent. Despite the hype surrounding cloud computing, organizations are still reluctant to deploy their businesses in the cloud computing environment due to concerns in secure resource sharing. In this paper, we propose a cloud resource mediation service offered by cloud service providers, which plays the role of trusted third party among its different tenants. This paper formally specifies the resource sharing mechanism between two different tenants in the presence of our proposed cloud resource mediation service. The correctness of permission activation and delegation mechanism among different tenants using four distinct algorithms (activation, delegation, forward revocation, and backward revocation) is also demonstrated using formal verification. The performance analysis suggests that the sharing of resources can be performed securely and efficiently across different tenants of the cloud.
Real-time neuroimaging and cognitive monitoring using wearable dry EEG

We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.  The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system.  Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time directdirected transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ± 0.09) and LCMV (0.72 ± 0.08) source localization. Cortical ERPbased classification was equivalent to ProxConn for cLORETA (0.74 ± 0.16) butsignificantlybetterforLCMV (0.82 ± 0.12).  We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from highdensity wearable dry EEG.  This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.
Issues and Challenges in Scrum Implementation

The aim of this research paper is to bring the challenges and issues in Scrum implementation to light and proposing solutions for these. For this, a survey is conducted in two companies named Digital Prodigy Limited (DPL) and Bentley Systems Pakistan. Participants include experienced and inexperienced scrum personals from both companies. The analysis of the survey results exposed several issues that affect scrum implementation directly or indirectly and resulting in violation of Scrum rules. Quality items pileup, module integration issues, code quality, disruption in team work, mature vs. immature scrum, sprint duration, lack of Scrum training , release process, backlog management, no technical practices, multiple teams, metrics, risk management, documentation and over idealistic are some examples of these issues. During this study, it has been observed that proper training of Scrum can eliminate half of the issues such as, disruption in team work, immature Scrum, sprint duration and backlog management. At the end, suggestions to address these issues are also provided.