nils-herrmann/clean_scirepeval huggingface dataset:
Sensitivity analysis of multi-attribute decision making methods in Clinical Group Decision Support System

A development of a clinical group decision support system (CGDSS) has been carried out for diagnosing both neurosis and personality disorders. The knowledge, stored in the knowledge base, were generated from the aggregated preferences given by decision makers. Two types of preferences used here, i.e. the preferences of a mental evidence by a mental condition; and the preferences of a mental disorder by mental condition. Ordered weighted averaging operator was adopted to aggregate those preferences. This aggregation process was carried out after transforming the selected subset to fuzzy preference relation format. Then the Bayesian theorem was adopted to compute the probability of evidence given a particular disorder. After developing the knowledge base, the next step is to develop an inference engine. The method used for developing an inference engine is multiattribute decision making concept, this is because of the system was directed to choose the best disorder when a particular condition was given. Many methods have been developed to solve MADM problem, however only the SAW, WP, and TOPSIS were appropriate to solve problem here. In this knowledge base, the relation between each disorder and evidence were represented X matrix (m x n) that consist of probability value. Where the Xij was probability of jth mental evidence given ith mental disorder; i=1,2,...,m; and j=1,2,...,n. Sensitivity analysis process was to compute the sensitivity degree of each attribute to the ranking outcome in each method. The sensitivity analysis was aimed to determine the degree of sensitivity of each attribute to the ranking outcome of each method. This degree implies that there were a relevant between an attribute and a ranking outcome. This relevant attribute can be emitted by influence degree of attribute Cj to ranking outcome fj. Then, relation between sensitivity degree and influence degree for each attribute, can be found by computing the Pearsonpsilas correlation coefficient. The biggest correlation coefficient shows as the best result. This research shows that TOPSIS method always has the highest correlation coefficient, and it is getting higher if the change of the ranking is increased. The experimental results shows that that TOPSIS is the appropriate method for the clinical group decision support system for the above purposes.
Variation of Boyer-Moore String Matching Algorithm : A Comparative Analysis

String matching plays an important role in field of Computer Science and there are many algorithm of String matching, the important aspect is that which algorithm is to be used in which condition. BM(Boyer-Moore) algorithm is standard benchmark of string matching algorithm so here we explain the BM(Boyer-Moore) algorithm and then explain its improvement as BMH (Boyer-Moore-Horspool), BMHS (Boyer-Moore-Horspool-Sundays), BMHS2 (Boyer-MooreHorspool-Sundays 2), improved BMHS( improved BoyerMoore-Horspool-Sundays) ,BMI (Boyer-Moore improvement) and CBM (composite Boyer-Moore).And also analyze and compare them using a example and find which one is better in which conditions. String Matching: BM; BMH; BMHS; BMHS2; improved BMHS; BMI; CBM
Consensus algorithms are input-to-state stable

In many cooperative control problems, a shared knowledge of information provides the basis for cooperation. When this information is different for each agent, a state of noncooperation can result. Consensus algorithms ensure that after some time the agents would agree on the information critical for coordination, called the coordination variable. In this paper we show that if the coordination algorithm is input-to-state stable where the input is considered to be the discrepancy between the coordination variable known to each vehicle, then cooperation is guaranteed when a consensus scheme is used to synchronize information. A coordinated timing example is shown in simulation to illustrate the notions of stability when a coordination algorithm is augmented with a consensus strategy.
Consumer credit-risk models via machine-learning algorithms q

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.
Evaluation of MPEG-7 shape descriptors against other shape descriptors

Shape is an important image feature - it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape descriptors are evaluated against other shape descriptors, and the two shape descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.
Governance in Social Media: A case study of the Wikipedia promotion process

Social media sites are often guided by a core group of committed users engaged in various forms of g vernance. A crucial aspect of this type of governance is deliberation, in which such a group reaches decisions on issues of importance to the site. Despite its crucial — though subtle — role in how a number of prominent social media sites function, there has been relatively little investigation of the deliberative a spects of social media governance. Here we explore this issue, investigating a particular deli berative process that is extensive, public, and recorded: the p romotion of Wikipedia admins, which is determined by elections that engage committed members of the Wikipedia community. We find that the group decision-making at the heart of this process exhibits several fundamental forms of relative assessment. First we observe that the chance that a voter will support a candidate is strongly dependent on the relationsh ip between characteristics of the voter and the candidate. Sec ond we investigate how both individual voter decisions and overall election outcomes can be based on models that take into account the sequential, public nature of the voting.
Dynamic Pricing for Hotel Revenue Management Using Price Multipliers

1 Abd El-Moniem M. Bayoumi is a graduate TA at the Department of Computer Engineering, Cairo University. He received his BS degree in from Cairo University in 2009. He is currently an RA, working for a research project on developing an innovative revenue management system for the hotel business. He was awarded the IEEE CIS Egypt Chapter’s special award for his graduation project in 2009. Bayoumi is interested to research in machine learning and business analytics; and he is currently working on his MS on stock market prediction.
A multi-stream ASR framework for BLSTM modeling of conversational speech

We propose a novel multi-stream framework for continuous conversational speech recognition which employs bidirectional Long Short-Term Memory (BLSTM) networks for phoneme prediction. The BLSTM architecture allows recurrent neural nets to model long-range context, which led to improved ASR performance when combined with conventional triphone modeling in a Tandem system. In this paper, we extend the principle of joint BLSTM and triphone modeling to a multi-stream system which uses MFCC features and BLSTM predictions as observations originating from two independent data streams. Using the COSINE database, we show that this technique prevails over a recently proposed single-stream Tandem system as well as over a conventional HMM recognizer.
FUZZY ANALYSIS OF MACHINE REPAIR PROBLEM WITH SWITCHING FAILURE AND REBOOT

Multi-component machining systems are being used in every sphere of engineering sector such as job shops, flow lines, communication system, computer system, etc. This paper presents fuzzy analysis of availability characteristics of machining system comprising of multiactive units and multi-standby units. The Markov machine repair model has been developed by incorporating the concepts of switching failure and reboot. The life times of identical active units and identical standby units follow the fuzzified exponential distribution. The time-to-repair of failed unit is also governed by the fuzzified exponential distribution. The automatic switching of standby unit to replace the failed units may not be perfect in many realistic scenarios as we assume the switching failure probability . The system may reboot itself automatically if the active unit fails and available standby unit is not able to replace the failed unit perfectly. We employ the parametric non-linear program with -cut approach to establish the membership function of availability of the system and availability of both standbys. A numerical example is also provided to validate the suggested approach which facilitates more useful information for the designers and practitioners to examine general repairable system more accurately.
Visual Exploration of Semantic Relationships in Neural Word Embeddings

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.
Production of polyhydroxyalkanoate during treatment of tomato cannery wastewater.

Polyhydroxyalkanoate (PHA) production was achieved using tomato cannery waste coupled with a mixed microbial culture during wastewater treatment. The two-stage PHA production process comprised a sequencing batch reactor (SBR), operating under a periodic feast-famine regime, to accomplish simultaneously wastewater treatment and selection of PHA-accumulating microbes, followed by a batch reactor for the production of PHA-rich biomass. The SBRs were efficient at removing soluble carbon (84%), ammonia (100%), and phosphorus (76%). Meanwhile, PHA-accumulating microbes were enriched under the SBR operating conditions, and PHA content on a cell-weight basis was within the range 7 to 11% in nonfiltered wastewater and 2 to 8% in filtered wastewater. Subsequently, batch studies were implemented with varying loading rates, ranging from 0.4 to 3.2 food-to-microorganism ratios. A maximum 20% PHA content on a cell-weight basis was obtained. Based on the experimental results, a PHA biosynthesis-degradation kinetic model was developed to (1) aid in the design of a pilot- or full-scale PHA production process coupled with wastewater treatment and (2) determine optimal conditions for harvest of PHA-rich biomass.
Dynamic Security Design and Corporate Financing In preparation for the Handbook of Economics and Finance , Volume 2

This essay considers dynamic security design and corporate financing, with particular emphasis on informational micro-foundations. The central idea is that firm insiders must retain an appropriate share of firm risk, either to align their incentives with those of outside investors (moral hazard) or to signal favorable information about the quality of the firm’s assets. Informational problems lead to inevitable inefficiencies imperfect risk sharing, the possibility of bankruptcy, investment distortions, etc. The design of contracts that minimize these inefficiencies is a central question. This essay explores the implications of dynamic security design on firm operations and asset prices.
The effect of badges on the engagement of students with special educational needs: A case study

This paper addresses the perceived benefits from gamification in the context of special education. It presents the findings of a study evaluating the effects of a specific gamification element (badges) on the engagement of five students with special learning needs, through online courses developed on the Moodle Learning Management System (LMS). The results indicate that this particular gamification element yielded positive effects on students’ engagement and on their overall attitude towards the educational process in general.
Visual Interpretability forDeepLearning

This paper reviews recent studies in emerging directions of understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always an Achilles' heel of deep neural networks. At present, deep neural networks obtain a high discrimination power at the cost of low interpretability of their black-box representations. We believe that the high model interpretability may help people to break several bottlenecks of deep learning, e.g. learning from very few annotations, learning via human-computer communications at the semantic level, and semantically debugging network representations. In this paper, we focus on convolutional neural networks (CNNs), and we revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends of explainable arti cial intelligence.
On Social Delay-Tolerant Networking: Aggregation, Tie Detection, and Routing

Social-based routing protocols have shown their promising capability to improve the message delivery efficiency in Delay Tolerant Networks (DTNs). The efficiency greatly relies on the quality of the aggregated social graph that is determined by the metrics used to measure the strength of social connections. In this paper, we propose an improved metrics that leads to high-quality social graph by taking both frequency and duration of contacts into consideration. Furthermore, to improve the performance of social-based message transmission, we systematically study the community evolution problem that has been little investigated in the literation. Distributed algorithms based on the obtained social graph are developed such that the overlapping communities and bridge nodes (i.e., connecting nodes between communities) can be dynamically detected in an evolutionary social network. Finally, we take all the results above into our social-based routing design. Extensive trace-driven simulation results show that our routing algorithm outperforms existing social-based forwarding strategies significantly.
Effective Regression Test Case Selection: A Systematic Literature Review

Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results.
 The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible.
 There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.
Combination of drug therapy in acute lymphoblastic leukemia with a CXCR4 antagonist

The bone marrow (BM) stromal niche can protect acute lymphoblastic leukemia (ALL) cells against the cytotoxicity of chemotherapeutic agents and is a possible source of relapse. The stromal-derived factor-1 (SDF-1)/CXCR4 axis is a major determinant in the crosstalk between leukemic cells and BM stroma. In this study, we investigated the use of AMD11070, an orally available, small-molecule antagonist of CXCR4, as an ALL-sensitizing agent. This compound effectively blocked stromal-induced migration of human ALL cells in culture and disrupted pre-established adhesion to stroma. To examine how to optimally use this compound in vivo, several combinations with cytotoxic drugs were tested in a stromal co-culture system. The best treatment regimen was then tested in vivo. Mice transplanted with murine Bcr/Abl ALL cells survived significantly longer when treated with a combination of nilotinib and AMD11070. Similarly, immunocompromised mice transplanted with human ALL cells and treated with vincristine and AMD11070 had few circulating leukemic cells, normal spleens and reduced human CD19+ cells in the BM at the termination of the experiment. These results show that combined treatment with AMD11070 may be of significant benefit in eradicating residual leukemia cells at locations where they would otherwise be protected by stroma.
Qualitative Research Methods in Mental Health Dr

http://ebmh.bmj.com/content/13/2/35.full.html Updated information and services can be found at: These include: References http://ebmh.bmj.com/content/13/2/35.full.html#ref-list-1 This article cites 30 articles, 9 of which can be accessed free at: service Email alerting box at the top right corner of the online article. Receive free email alerts when new articles cite this article. Sign up in the Notes
A New Temperature Compensation Method for a 2 . 5 GHz Integrated VCO

A 2.5 GHz fully integrated voltage controlled oscillator (VCO) for wireless application has been designed in a 0.35μm CMOS technology. A method for compensating the effect of temperature on the carrier oscillation frequency has been presented in this work. We compare also different VCOs topologies in order to select one with low phase noise, low supply sensitivity and large tuning frequency. Good results are obtained with a simple NMOS –Gm VCO. This proposed VCO has a wide operating range from 300 MHz with a good linearity between the output frequency and the control input voltage, with a temperature coefficient of -5 ppm/°C from 20°C to 120°C range. The phase noise is about -135.2dBc/Hz at 1MHz from the carrier with a power consumption of 5mW.
Practice, instruction and skill acquisition in soccer: challenging tradition.

The acquisition of soccer skills is fundamental to our enjoyment of the game and is essential to the attainment of expertise. Players spend most of their time in practice with the intention of improving technical skills. However, there is a lack of scientific research relating to the effective acquisition of soccer skills, especially when compared with the extensive research base on physiological aspects of performance. Current coaching practice is therefore based on tradition, intuition and emulation rather than empirical evidence. The aim of this review is to question some of the popular beliefs that guide current practice and instruction in soccer. Empirical evidence is presented to dispel many of these beliefs as myths, thereby challenging coaches to self-reflect and critically evaluate contemporary doctrine. The review should inform sports scientists and practitioners as to the important role that those interested in skill acquisition can play in enhancing performance at all levels of the game.
Thermal degradation of PVC: A review.

This review summarized various chemical recycling methods for PVC, such as pyrolysis, catalytic dechlorination and hydrothermal treatment, with a view to solving the problem of energy crisis and the impact of environmental degradation of PVC. Emphasis was paid on the recent progress on the pyrolysis of PVC, including co-pyrolysis of PVC with biomass/coal and other plastics, catalytic dechlorination of raw PVC or Cl-containing oil and hydrothermal treatment using subcritical and supercritical water. Understanding the advantage and disadvantage of these treatment methods can be beneficial for treating PVC properly. The dehydrochlorination of PVC mainly happed at low temperature of 250-320°C. The process of PVC dehydrochlorination can catalyze and accelerate the biomass pyrolysis. The intermediates from dehydrochlorination stage of PVC can increase char yield of co-pyrolysis of PVC with PP/PE/PS. For the catalytic degradation and dechlorination of PVC, metal oxides catalysts mainly acted as adsorbents for the evolved HCl or as inhibitors of HCl formation depending on their basicity, while zeolites and noble metal catalysts can produce lighter oil, depending the total number of acid sites and the number of accessible acidic sites. For hydrothermal treatment, PVC decomposed through three stages. In the first region (T<250°C), PVC went through dehydrochlorination to form polyene; in the second region (250°C<T<350°C), polyene decomposed to low-molecular weight compounds; in the third region (350°C<T), polyene further decomposed into a large amount of low-molecular weight compounds.