nils-herrmann/clean_scirepeval huggingface dataset:
Occupancy forecasting methods and the use of expert judgement in hotel revenue management

This dissertation presents two studies of the forecast of occupancy in the United States’ hotel industry. The first is a quantitative study of the forecast accuracy performance of moving average, simple exponential smoothing, additive, and multiplicative Holt-Winters method, and Box-Jenkins forecasting procedures on weekly aggregated occupied room data from 10 geographic markets in the United States. In addition, this researcher also examined the performance of combined forecasts. The additive Holt-Winters method was found to be the most accurate in forecasting in seven of the 10 markets, even though it was not the most accurate in the training set. In three of the markets, the seasonal autoregressive integrated moving average method produced the highest level of accuracy. The second study is a qualitative study designed to understand how the sample of revenue management experts uses their tacit knowledge of future demand in specific markets to modify statistically based forecasts of hotel occupancy. The researcher interviewed revenue managers. Four of these were working on a revenue management team, which supported groups of franchised hotels for a major global brand. These managers worked directly with the multiple hotels they supported in their assigned geographies. The remaining six revenue managers were located on the property they supported. Two of these managers also supported one or more properties in their geographic area in addition to their property. Marriott International, Hilton Worldwide, Starwood Hotels and Resorts Worldwide, Intercontinental Hotels Group, and Wyndham Hotels and Resorts were in the sample. The revenue managers oversaw the revenue management function in the limited and select service, full service, and luxury quality tiers.
Seven-point checklist of dermoscopy revisited.

Most dermoscopic algorithms to diagnose melanoma were established more than 10 years ago and have been tested primarily on clear-cut melanomas and excised melanocytic naevi.



To assess the diagnostic performance of pattern analysis and seven-point checklist on lesions that reflect the current clinical setting, compared with a revised seven-point checklist with a lower threshold for excision.



Eight experienced dermatologists viewed dermoscopic images of 100 excised melanomas, 100 excised naevi and 100 monitored naevi. Each lesion was evaluated by pattern analysis and scored as naevus, melanoma or lesion to be excised. Images were then evaluated using the seven-point criteria, with both standard and revised thresholds for excision.



Pooled data using the pattern analysis algorithm showed that 82% of melanomas and 87·5% of monitored naevi were correctly scored as lesion to be excised and benign naevus, respectively. Using the standard and revised thresholds for the seven-point checklist, excision was recommended for 77·9% and 87·8% of the lesions in the melanoma set, respectively. The standard threshold produced 'no excision' recommendations for 85·6% of the monitored naevi, compared with 74·5% using the revised threshold. Pattern analysis, standard seven-point and revised seven-point algorithms resulted in recommendations of 'excision' for 63·6%, 60·3% and 72·0% of the excised naevi, respectively.



The diagnostic approach to naevi and melanoma should be adapted to the current clinical setting, in which patients may present with early-stage melanomas and multiple atypical naevi. To increase sensitivity, a revised seven-point checklist with a lower threshold for excision should be used.
SOKOBAN and other motion planning problems

We consider a natural family of motion planning problems with movable obstacles and obtain hardness results for them. Some members of the family are shown to be PSPACE-complete thus improving and extending (and also simplifying) a previous NP-hardness result of Wilfong. The family considered includes a motion planning problem which forms the basis of a popular computer game called SOKOBAN. The decision problem corresponding to SOKOBAN is shown to be NP-hard. The motion planning problems considered are related to the \warehouseman's problem" considered by Hopcroft, Schwartz and Sharir, and to geometric versions of the motion planning problem on graphs considered
Control Unit Design of a 16-bit Processor Using VHDL

This paper describes the design and implementation of control unit of a 16-bit processor that is implemented in Spartan-II FPGA device. The CPU (Central Processing Unit) is the “brain” of the computer. Its function is to execute the programs stored in the main memory by fetching their instructions, examining them, and executing them one after another. The CPU is composed of several distinct parts, like data path, control path and memory units. For operating the data path CONTROL UNIT is needed to generate the control signals automatically at each clock cycle. The proposed architecture illustrates behavioral and structural description styles of a 16-bit
Replacing the Data Processing Computer with Brain-like Learning Machines : a new Communication and Computing Paradigm based on the Autosophy Information Theory

Data processing computers may soon be eclipsed by a next generation of brain-like learning machines based on the "Autosophy" information theory. This will have a profound impact on communication and computing applications. Data processing computers are essentially adding or calculating machines that cannot find "meaning" as our own brains obviously can. No matter the speed of computation or the complexity of the software, computers will not evolve into brain-like machines. All that can be achieved are mere simulations. The basic problem can be traced back to an outdated (Shannon) information theory that treats all data items (such as ASCII characters or pixels) as "quantities" in meaningless bit streams. In 1974 Klaus Holtz developed a new Autosophy information theory, which treats all data items as "addresses." The original Autosophy research explains the functioning of self-assembling natural structures, such as chemical crystals or living trees. The same natural laws and principles can also produce self-assembling data structures, which grow like data crystals or data trees in electronic memories, without computing or programming. Replacing the programmed data processing computer with brain-like, self-learning, failure-proof "autosopher" promises a true paradigm shift in technology, resulting in system architectures with true "learning" and eventually true Artificial Intelligence.
MATHEMATICS ANXIETY AND ACHIEVEMENT AMONG SECONDARY SCHOOL STUDENTS

Research has shown that mathematics achievement in students is influenced by psychological factors suc h as mathematics anxiety. Weaknesses among students in l earning mathematics in particular will affect the e fforts of various sectors in making Malaysia a fully develope d nation by 2020. The purpose of this study was to determine mathematics anxiety and mathematics achievement amo ng secondary school students in Selangor, Malaysia. The research examined the differences in mathematics an xiety according to gender as well as the difference s in mathematics achievement of students based on the le vel of mathematics anxiety. The study involved195 F orm Four students (86 male and 109 female). The instrum ent used to measure differences was adapted from th e Fennema-Sherman Mathematics Attitudes Scale. The da ta was analyzed using Statistical Package for the S ocial Sciences (SPSS) to determine the mean, frequency, t -test and one-way ANOVA. The findings of the study indicated that there is mathematics anxiety among s econdary school students. The t-test showed that th e mean difference between mathematics anxiety and gender i s not significant. The ANOVA test showed that there were significant differences in achievement based on the lev l of mathematics anxiety. Thus, math anxiety i s one factor that affects student achievement. Therefore, teache rs should strive to understand mathematics anxiety and implement teaching and learning strategies so that s udents can overcome their anxiety.
Anatomical study of phrenic nerve using ultrasound

To demonstrate whether ultrasonography may allow a precise assessment of the course and relations of the phrenic nerve in its cervical portion. This study, initially undertaken in five cadavers, was followed by high-resolution ultrasonographic study in 20 volunteers (40 nerves) by two radiologists in consensus. The location, course and relations of the phrenic nerve to the adjacent anatomical structures were analysed. The phrenic nerve could be nicely identified through the majority of its course by high-resolution ultrasonography. Useful anatomic landmarks for the detection of the nerve could be defined; these include the transverse cervical and ascending cervical arteries. Some anatomical variations were observed. Knowledge of the nerve’s precise location, which may demonstrate individual variations, may have useful clinical applications.
Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Mobility-Assisted Localization in Wireless Sensor Networks

In many applications of wireless sensor networks (WSNs), node location is required to locate the monitored event once occurs. Mobility-assisted localization has emerged as an efficient technique for node localization. It works on optimizing a path planning of a location-aware mobile node, called mobile anchor (MA). The task of the MA is to traverse the area of interest (network) in a way that minimizes the localization error while maximizing the number of successful localized nodes. For simplicity, many path planning models assume that the MA has a sufficient source of energy and time, and the network area is obstacle-free. However, in many real-life applications such assumptions are rare. When the network area includes many obstacles, which need to be avoided, and the MA itself has a limited movement distance that cannot be exceeded, a dynamic movement approach is needed. In this paper, we propose two novel dynamic movement techniques that offer obstacle-avoidance path planning for mobility-assisted localization in WSNs. The movement planning is designed in a real-time using two swarm intelligence based algorithms, namely grey wolf optimizer and whale optimization algorithm. Both of our proposed models, grey wolf optimizer-based path planning and whale optimization algorithm-based path planning, provide superior outcomes in comparison to other existing works in several metrics including both localization ratio and localization error rate.
Links: Web Programming Without Tiers

Links is a programming language for web applications that generates code for all three tiers of a web application from a single source, compiling into JavaScript to run on the client and into SQL to run on the database. Links supports rich clients running in what has been dubbed ‘Ajax’ style, and supports concurrent processes with statically-typed message passing. Links is scalable in the sense that session state is preserved in the client rather than the server, in contrast to other approaches such as Java Servlets or PLT Scheme. Client-side concurrency in JavaScript and transfer of computation between client and server are both supported by translation into continuation-passing style.
Intelligent Parkinson Disease Prediction Using Machine Learning Algorithms

— Diagnosis of the Parkinson disease through machine learning approache provides better understanding from PD dataset in the present decade. Orange v2.0b and weka v3.4.10 has been used in the present experimentation for the statistical analysis, classification, Evaluation and unsupervised learning methods. Voice dataset for Parkinson disease has been retrieved from UCI Machine learning repository from Center for Machine Learning and Intelligent Systems. The dataset contains name, attributes. The parallel coordinates shows higher variation in Parkinson disease dataset. SVM has shown good accuracy (88.9%) compared to Majority and k-NN algorithms. Classification algorithm like Random Forest has shown good accuracy (90.26) and Naïve Bayes has shown least accuracy (69.23. Higher number of clusters in healthy dataset in Fo and less number in diseased data has been predicted by Hierarchal clustering and SOM.
Airway mucosa: secretory cells, mucus and mucin genes.

The airway mucosa is lined by a continuous epithelium comprised of multiple cell phenotypes, several of which are secretory. Secretions produced by these cells mix with a variety of macromolecules, ions and water to form a respiratory tract fluid that protects the more distal airways and alveoli from injury and infection. The present article highlights the structure of the mucosa, particularly its secretory cells, gives a synopsis of the structure of mucus, and provides new information on the localization of mucin (MUC) genes that determine the peptide sequence of the protein backbone of the glycoproteins, which are a major component of mucus. Airway secretory cells comprise the mucous, serous, Clara and dense-core granulated cells of the surface epithelium, and the mucous and serous acinar cells of the submucosal glands. Several transitional phenotypes may be found, especially during irritation or disease. Respiratory tract mucins constitute a heterogeneous group of high molecular weight, polydisperse richly glycosylated molecules: both secreted and membrane-associated forms of mucin are found. Several mucin (MUC) genes encoding the protein core of mucin have been identified. We demonstrate the localization of MUC gene expression to a number of distinct cell types and their upregulation both in response to experimentally administered lipopolysaccharide and cystic fibrosis.
Backstepping control design of a DC-DC converter - DC machine association

This paper deals with the synthesis of a speed control strategy for a DC motor drive based on an output feedback backstepping controller. The backstepping method takes into account the non linearities of the system in the design control law and leads to a system asymptotically stable in the context of Lyapunov theory. Simulated results are displayed to validate the feasibility and the effectiveness of the proposed strategy.
Attention Based CLDNNs for Short-Duration Acoustic Scene Classification

Recently, neural networks with deep architecture have been widely applied to acoustic scene classification. Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) have shown improvements over fully connected Deep Neural Networks (DNNs). Motivated by the fact that CNNs, LSTMs and DNNs are complimentary in their modeling capability, we apply the CLDNNs (Convolutional, Long Short-Term Memory, Deep Neural Networks) framework to short-duration acoustic scene classification in a unified architecture. The CLDNNs take advantage of frequency modeling with CNNs, temporal modeling with LSTM, and discriminative training with DNNs. Based on the CLDNN architecture, several novel attention-based mechanisms are proposed and applied on the LSTM layer to predict the importance of each time step. We evaluate the proposed method on the truncated version of the 2016 TUT acoustic scenes dataset which consists of recordings from 15 different scenes. By using CLDNNs with bidirectional LSTM, we achieve higher performance compared to the conventional neural network architectures. Moreover, by combining the attention-weighted output with LSTM final time step output, significant improvement can be further achieved.
Lanthanide-Activated Phosphors Based on 4f-5d Optical Transitions: Theoretical and Experimental Aspects.

The synthesis of lanthanide-activated phosphors is pertinent to many emerging applications, ranging from high-resolution luminescence imaging to next-generation volumetric full-color display. In particular, the optical processes governed by the 4f-5d transitions of divalent and trivalent lanthanides have been the key to enabling precisely tuned color emission. The fundamental importance of lanthanide-activated phosphors for the physical and biomedical sciences has led to rapid development of novel synthetic methodologies and relevant tools that allow for probing the dynamics of energy transfer processes. Here, we review recent progress in developing methods for preparing lanthanide-activated phosphors, especially those featuring 4f-5d optical transitions. Particular attention will be devoted to two widely studied dopants, Ce3+ and Eu2+. The nature of the 4f-5d transition is examined by combining phenomenological theories with quantum mechanical calculations. An emphasis is placed on the correlation of host crystal structures with the 5d-4f luminescence characteristics of lanthanides, including quantum yield, emission color, decay rate, and thermal quenching behavior. Several parameters, namely Debye temperature and dielectric constant of the host crystal, geometrical structure of coordination polyhedron around the luminescent center, and the accurate energies of 4f and 5d levels, as well as the position of 4f and 5d levels relative to the valence and conduction bands of the hosts, are addressed as basic criteria for high-throughput computational design of lanthanide-activated phosphors.
Electrospun Mat of Poly(vinyl alcohol)/Graphene Oxide for Superior Electrolyte Performance.

Here, we describe an electrospun mat of poly(vinyl alcohol) (PVA) and graphene oxide (GO) as a novel solid-state electrolyte matrix, which offers better performance retention upon drying after infiltrated with aqueous electrolyte. The PVA-GO mat overcomes the major issue of conventional PVA-based electrolytes, which is the ionic conductivity decay upon drying. After exposure to 45 ± 5% relative humidity at 25 °C for 1 month, its conductivity decay is limited to 38.4%, whereas that of pure PVA mat is as high as 84.0%. This mainly attributes to the hygroscopic nature of GO and the unique nanofiber structure within the mat. Monolithic supercapacitors have been derived directly on the mat via a well-developed laser scribing process. The as-prepared supercapacitor offers an areal capacitance of 9.9 mF cm-2 at 40 mV s-1 even after 1 month of aging under ambient conditions, with a high device-based volumetric energy density of 0.13 mWh cm-3 and a power density of 2.48 W cm-3, demonstrating great promises as a more stable power supply for wearable electronics.
Prior Knowledge based mutation prioritization towards causal variant finding in rare disease

How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutation loci focusing on protein structure features. The proposed structure-based features contain information about geometric, physicochemical, and functional information of mutation loci and those of structural neighbors of the loci. The performance of the structure-based features trained on 80% of HumDiv and tested on 20% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation’s pathogenic or benign effects: F score of 0.71 and 0.68 respectively using multi-layer perceptron. Combining structureand sequence-based feature further improve the accuracy: F score of 0.86 (HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in rare diseases cases showed that structure-based features are important in discerning importance of variant loci.
2 Variation in Working Memory Capacity as Variation in Executive Attention and Control

If, as many psychologists seem to believe, im­ mediate memory represents a distinct system or set of processes from long-term memory (L TM), then what might· it be for? This fundamental, functional question was surprisingly unanswer­ able in the 1970s, given the volume of research that had explored short-term memory (STM), and given the ostensible role that STM was thought to play in cognitive control (Atkinson & Shiffrin, 1971 ). Indeed, failed attempts to link STM to complex cognitive· functions, such as reading comprehension, loomed large in Crow­ der's (1982) obituary for the concept. Baddeley and Hitch ( 197 4) tried to validate immediate memory's functions by testing sub­ jects in reasoning, comprehension, and list­ learning tasks at the same time their memory was occupied by irrelevant material. Generally, small memory loads (i.e., three or fewer items) were retained with virtually no effect on the primary tasks, whereas memory loads of six items consistently impaired reasoning, compre­ hension, and learning. Baddeley and Hitch therefore argued that "working memory" (WM)
Report of an unsual case of anophthalmia and craniofacial cleft in a newborn with Toxoplasma gondii congenital infection

We present one unusual case of anophthalmia and craniofacial cleft, probably due to congenital toxoplasmosis only.



A two-month-old male had a twin in utero who disappeared between the 7th and the 14th week of gestation. At birth, the baby presented anophthalmia and craniofacial cleft, and no sign compatible with genetic or exposition/deficiency problems, like the Wolf-Hirschhorn syndrome or maternal vitamin A deficiency. Congenital toxoplasmosis was confirmed by the presence of IgM abs and IgG neo-antibodies in western blot, as well as by real time PCR in blood. CMV infection was also discarded by PCR and IgM negative results. Structures suggestive of T. gondii pseudocysts were observed in a biopsy taken during the first functional/esthetic surgery.



We conclude that this is a rare case of anophthalmia combined with craniofacial cleft due to congenital toxoplasmosis, that must be considered by physicians. This has not been reported before.
A Hybrid Approach for Web Document Clustering Using K-means and Artificial Bee Colony Algorithm

Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion. Document clustering is the solution for organising relevant documents together. In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively. The proposed algorithm uses the features of both K-means and Artificial Bee Colony (ABC) clustering algorithm. In this paper, ABC algorithm is employed as the global search optimizer and K-means is used for refining the solutions. Thus, the quality of the cluster is improved. The performance of WDC-KABC is analysed with four different datasets (webkb, wap, rec0 and 7sectors). The proposed algorithm is compared with existing algorithms such as K-means, Particle Swarm Optimization, Hybrid of Particle Swarm Optimization and K-means and Ant Colony Optimization. The experimental results of WDC-KABC are satisfactory, in terms of precision, recall, f-measure, accuracy and error rate.
Quasiresonant Control With a Dynamic Frequency Selector and Constant Current Startup Technique for 92% Peak Efficiency and 85% Light-Load Efficiency Flyback Converter

The proposed quasiresonant control scheme can be widely used in a dc-dc flyback converter because it can achieve high efficiency with minimized external components. The proposed dynamic frequency selector improves conversion efficiency especially at light loads to meet the requirement of green power since the converter automatically switches to the discontinuous conduction mode for reducing the switching frequency and the switching power loss. Furthermore, low quiescent current can be guaranteed by the constant current startup circuit to further reduce power loss after the startup procedure. The test chip fabricated in VIS 0.5 μm 500 V UHV process occupies an active silicon area of 3.6 mm 2. The peak efficiency can achieve 92% at load of 80 W and 85% efficiency at light load of 5 W.