nils-herrmann/clean_scirepeval huggingface dataset:
OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining

Information extraction relevant to the user queries is the challenging task in the ontology environment due to data varieties such as image, video, and text. The utilization of appropriate semantic entities enables the content-based search on annotated text. Recently, the automatic extraction of textual content in the audio-visual content is an advanced research area in a multimedia (MM) environment. The annotation of the video includes several tags and comments. This paper proposes the Collaborative Tagging (CT) model based on the Block Acquiring Page Segmentation (BAPS) method to retrieve the tag-based information. The information extraction in this model includes the Ontology-Based Information Extraction (OBIE) based on the single ontology utilization. The semantic annotation phase in the proposed work inserts the metadata with limited machine-readable terms. The insertion process is split into two major processes such as database uploading to server and extraction of images/web pages based on the results of semantic phase. Novel weight-based novel clustering algorithms are introduced to extract knowledge from MM contents. The ranking based on the weight value in the semantic annotation phase supports the image/web page retrieval process effectively. The comparative analysis of the proposed BAPS-CT with the existing information retrieval (IR) models regarding the average precision rate, time cost, and storage space rate assures the effectiveness of BAPS-CT in OMIR.
Papillary syncytial metaplasia of fallopian tube endometriosis: a potential pitfall in the diagnosis of serous tubal intraepithelial carcinoma.

Histopathologic diagnosis of tubal intraepithelial carcinoma (TIC) has emerged as a significant challenge in the last few years. The avoidance of pitfalls in the diagnosis of TIC is crucial if a better understanding of its natural history and outcome is to be achieved. Herein, we present a case of a 52-year-old woman who underwent a risk-reducing salpingo-oophorectomy procedure. Histologic examination of a fallopian tube demonstrated a focus of atypical epithelial proliferation, which was initially considered to be a TIC. Complete study of the case indicated that the focus was, in fact, papillary syncytial metaplasia of tubal mucosal endometriosis. Papillary syncytial metaplasia may resemble TIC and should be considered in cases of proliferative lesions of the tubal epithelium.
IDENTIFICATION OF OUTLIERS : A SIMULATION STUDY

This paper compares two approaches in identifying outliers in multivariate datasets; Mahalanobis distance (MD) and robust distance (RD). MD has been known suffering from masking and swamping effects and RD is an approach that was developed to overcome problems that arise in MD. There are two purposes of this paper, first is to identify outliers using MD and RD and the second is to show that RD performs better than MD in identifying outliers. An observation is classified as an outlier if MD or RD is larger than a cut-off value. Outlier generating model is used to generate a set of data and MD and RD are computed from this set of data. The results showed that RD can identify outliers better than MD. However, in non-outliers data the performance for both approaches are similar. The results for RD also showed that RD can identify multivariate outliers much better when the number of dimension is large.
A Mixed Methods Approach to Technology Acceptance Research

The aim of this paper is to discuss the significance and potential of a mixed methods approach in technology acceptance research. After critically reviewing the dominance of the quantitative survey method in TAMbased research, this paper reports a mixed methods study of user acceptance of emergency alert technology in order to illustrate the benefits of combining qualitative and quantitative techniques in a single study. The main conclusion is that a mixed methods approach provides opportunities to move beyond the vague conceptualizations of “usefulness” and “ease of use” and to advance our understanding of user acceptance of technology in context.
Hierarchical Hyperdimensional Computing for Energy Efficient Classification

Brain-inspired Hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors rather than traditional numerical values. In HD, an encoder maps inputs to high dimensional vectors (hypervectors) and combines them to generate a model for each existing class. During inference, HD performs the task of reasoning by looking for similarities of the input hypervector and each pre-stored class hypervector However, there is not a unique encoding in HD which can perfectly map inputs to hypervectors. This results in low HD classification accuracy over complex tasks such as speech recognition. In this paper we propose MHD, a multi-encoder hierarchical classifier, which enables HD to take full advantages of multiple encoders without increasing the cost of classification. MHD consists of two HD stages: a main stage and a decider stage. The main stage makes use of multiple classifiers with different encoders to classify a wide range of input data. Each classifier in the main stage can trade between efficiency and accuracy by dynamically varying the hypervectors' dimensions. The decider stage, located before the main stage, learns the difficulty of the input data and selects an encoder within the main stage that will provide the maximum accuracy, while also maximizing the efficiency of the classification task. We test the accuracy/efficiency of the proposed MHD on speech recognition application. Our evaluation shows that MHD can provide a 6.6× improvement in energy efficiency and a 6.3× speedup, as compared to baseline single level HD.
Surface Rendering Versus Volume Rendering In Medical Imaging: Techniques And Applications

High resolution imaging modalities. combined with advances in computer technology has prompted renewed interest and led to significant progress in volumetric reconstruction of medical images. Clinical assessment of this technique and whether it can provide enhanced diagnostic interpretation is currently under investigation by various medical and scientific groups. The purpose of this panel is to evaluate the clinical utility of two major 3D rendering techniques that allow the user to “fly through” and around medical data-sets.
Calibrated alar base excision: A 20-year experience

Conflicting guidelines for excisions about the alar base led us to develop calibrated alar base excision, a modification of Weir's approach. In approximately 20% of 1500 rhinoplasties this technique was utilized as a final step. Of these patients, 95% had lateral wallexcess (“tall nostrils”), 2% had nostril floor excess (“wide nostrils”), 2% had a combination of these (“tall-wide nostrils”), and 1% had thick nostril rims. Lateral wall excess length is corrected by a truncated crescent excision of the lateral wall above the alar crease. Nasal floor excess is improved by an excision of the nasal sill. Combination noses (e.g., tall-wide) are approached with a combination alar base excision. Finally, noses with thick rims are improved with diamond excision. Closure of the excision is accomplished with fine simple external sutures. Electrocautery is unnecessary and deep sutures are utilized only in wide noses. Few complications were noted. Benefits of this approach include straightforward surgical guidelines, a natural-appearing correction, avoidance of notching or obvious scarring, and it is quick and simple.
Scar revision techniques: z-plasty, w-plasty, and geometric broken line closure.

This article addresses the use of scar revision surgery as it relates to the use of Z-plasty, W-plasty, and geometric broken line closure. Each of these techniques is discussed in detail and the author provides perspectives regarding the indications, advantages, and limitations of each procedure. The surgeon should be experienced with each of these and apply these methods as appropriate. As with any technique, careful preoperative planning along with meticulous execution will lead to optimal results.
LEARNING DEEP MODELS: CRITICAL POINTS AND LOCAL OPENNESS

With the increasing interest in deeper understanding of the loss surface of many non-convex deep models, this paper presents a unifying framework to study the local/global optima equivalence of the optimization problems arising from training of such non-convex models. Using the local openness property of the underlying training models, we provide simple sufficient conditions under which any local optimum of the resulting optimization problem is globally optimal. We first completely characterize the local openness of matrix multiplication mapping in its range. Then we use our characterization to: 1) show that every local optimum of two layer linear networks is globally optimal. Unlike many existing results in the literature, our result requires no assumption on the target data matrix Y , and input data matrixX . 2) develop almost complete characterization of the local/global optima equivalence of multi-layer linear neural networks. We provide various counterexamples to show the necessity of each of our assumptions. 3) show global/local optima equivalence of non-linear deep models having certain pyramidal structure. Unlike some existing works, our result requires no assumption on the differentiability of the activation functions and can go beyond “full-rank” cases.
Recent improvements of efficiency and power density of DC-DC converters for automotive applications

Automotive industry is considered to be one of the main contributors to environmental pollution and global warming. Therefore, many car manufacturers are in near future planning to introduce hybrid electric vehicles (HEV), fuel cell electric vehicles (FCEV) and pure electric vehicles (EV) to make our cars more environmentally friendly. These new vehicles require highly efficient and small power converters. In recent years, considerable improvements were made in designing such converters. In this paper, an approach based on so called Snubber Assisted Zero Voltage and Zero Current Switching topology otherwise also know as SAZZ is presented. This topology has evolved to be one of the leaders in the field of highly efficient converters with high power densities. Evolution and main features of this topology are briefly discussed. Capabilities of the topology are demonstrated on two case study prototypes based on different design approaches. The prototypes are designed to be fully bi-directional for peak power output of 30 kW. Both designs reached efficiencies close to 99 % in wide load range. Power densities over 40 kW/litre are attainable in the same time. Combination of MOSFET technology and SAZZ topology is shown to be very beneficial to converters designed for EV applications.
Learner support in distance and networked learning environments: Ten

How do educators and instructional designers assess the effectiveness of the learning environments they design? One important means of ensuring the effectiveness of instruction in distance and face-to-face settings is through provision of learner support. Increasingly, as learners utilize the World Wide Web for collaborative learning, support systems contribute to the processes of learning and assist the learner in developing competencies and con dence in self-regulated learning and social interaction. Originating in the socio-cultural perspective of Vygotskyan theory, the term scaffolding refers to learning support based on social constructivist models of learning. As the World Wide Web becomes increasingly integrated into the delivery of learning experiences at primary, tertiary and secondary levels, the concept of scaffolding needs to be reconsidered because it is not readily translated into contexts where the teacher is not present, such as in online learning environments. The aim of this paper is to offer a conceptualization of the term scaffolding in distance learning, to provide examples of how learners can be supported in the processes of constructivist inquiry in a range of learning settings, and to offer principles for the design of learning support that can be applied across a range of instructional settings. Why Do We Need Learning Supports? Educators are the  rst to admit that not all learners are willing to execute the tasks and activities that lead to successful learning, and that learners need support and structured learning experiences (Collis, 1998; Laurillard, 2002). The recognition that teaching has a supportive dimension has long been recognized (Biggs, 1999). Scaffolding is the term widely used to describe effective learning support with an interesting history. Wood, Bruner and Ross (1976) originally coined the term scaffolding as a metaphor to describe the effective intervention by a peer, adult or competent person in the learning of another person. The term can be traced to Vygotsky’s concept of the zone of proximal development, which refers to a learner’s optimal developmental potential, if assistance that is timely and appropriate is provided by another person (Vygotsky, 1978). The appeal of the concept of scaffolding lies in the fact that it directs attention to the need for support in the learning process, and does so in a way that emphasizes that good teaching is necessarily responsive to the state of understanding achieved by particular learners. In distance learning settings, the metaphor of scaffolding is appealing in principle, yet elusive and problematic. With reference to distance learning, Garrison and Baynton (1987) ISSN 0158-7919 print; 1475-0198 online/02/020149 2002 Open and Distance Learning Association of Australia, Inc. DOI: 10.1080/0158791022000009178
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However, the learned CNNs are often overfitted and do not generalize well to unseen subjects due to limited AU-coded training images. We proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into the CNN via an incremental boosting layer that selects discriminative neurons from the lower layer and is incrementally updated on successive mini-batches. In addition, a novel loss function that accounts for errors from both the incremental boosted classifier and individual weak classifiers was proposed to fine-tune the IB-CNN. Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. The improvement is more impressive for the AUs that have the lowest frequencies in the databases.
Palm Uses in Northwestern South America: A Quantitative Review

A thorough review concerning palm uses in tropical rainforests of north-western South America was carried out to understand patterns of palm use throughout ecoregions (Amazonia, Andes, Chocó), countries (Colombia, Ecuador, Peru, Bolivia), and among the different human groups (indigenous, mestizos, afroamericans, colonos) that occur there. A total of 194 useful palm species, 2,395 different uses and 6,141 use-reports were recorded from 255 references. The Amazon had the highest palm use, whereas fewer, but similar uses were recorded for the Andes and Chocó. Ecuador was the most intensively studied country. Most palms were used for human food, utensils and tools, construction, and cultural purposes. Indigenous people knew more palm uses than mestizos, afroamericans and colonos. The use of palms was not random and the main uses were the same throughout the studied ecoregions and countries. Palms satisfy basic subsistence needs and have great importance in traditional cultures of rural indigenous and peasant populations in our study area. Arecaceae is probably the most important plant family in the Neotropics, in relation to use diversity and abundance. Se realizó una revisión exhaustiva de los usos de las palmeras en los bosques tropicales lluviosos del noroeste de América del Sur para comprender los patrones de uso de las palmeras por ecorregiones (Amazonia, Andes, Chocó), países (Colombia, Ecuador, Perú, Bolivia) y entre los diferentes grupos humanos (indígenas, mestizos, afroamericanos, colonos) existentes. Se registraron 194 especies de palmeras útiles, 2,395 usos distintos y 6,141 registros de uso a partir de 255 referencias. La Amazonia tuvo el uso más alto de palmeras, mientras que en los Andes y el Chocó se encontraron menores usos aunque similares. Ecuador fue el país que se estudió más intensamente. La mayoría de las especies se usaron para alimentación humana, utensilios y herramientas, construcción y usos culturales. Los indígenas conocieron más usos de palmeras que los mestizos, afroamericanos y colonos. El uso de las palmeras no fue al azar y los usos principales fueron los mismos en todas las ecorregiones y países estudiados. Las palmeras cubren necesidades básicas de subsistencia y tienen una gran importancia en las culturas tradicionales de las poblaciones indígenas y campesinas rurales en nuestra área de estudio. Arecaceae es probablemente la familia de plantas más importante del Neotrópico, en relación a su diversidad y abundancia de usos.
Movie Analysis Based on Roles' Social Network

Roles in a movie form a small society and their interrelationship provides clues for movie understanding. Based on this observation, we present a new viewpoint to perform semantic movie analysis. Through checking the co-occurrence of roles in different scenes, we construct a roles' social network to describe their relationships. We introduce the concept of social network analysis to elaborately identify leading roles and the hidden communities. With the results of community identification, we perform storyline detection that facilitates more flexible movie browsing and higher-level movie analysis. The experimental results show that the proposed community identification method is accurate and is robust to errors.
An Evaluation of DGA Classifiers

Domain Generation Algorithms (DGAs) are a popular technique used by contemporary malware for command-and-control (C&C) purposes. Such malware utilizes DGAs to create a set of domain names that, when resolved, provide information necessary to establish a link to a C&C server. Automated discovery of such domain names in real-time DNS traffic is critical for network security as it allows to detect infection, and, in some cases, take countermeasures to disrupt the communication and identify infected machines. Detection of the specific DGA malware family provides the administrator valuable information about the kind of infection and steps that need to be taken. In this paper we compare and evaluate machine learning methods that classify domain names as benign or DGA, and label the latter according to their malware family. Unlike previous work, we select data for test and training sets according to observation time and known seeds. This allows us to assess the robustness of the trained classifiers for detecting domains generated by the same families at a different time or when seeds change. Our study includes tree ensemble models based on human-engineered features and deep neural networks that learn features automatically from domain names. We find that all state-of-the-art classifiers are significantly better at catching domain names from malware families with a time-dependent seed compared to time-invariant DGAs. In addition, when applying the trained classifiers on a day of real traffic, we find that many domain names unjustifiably are flagged as malicious, thereby revealing the shortcomings of relying on a standard whitelist for training a production grade DGA detection system.
Fault Diagnosis of Networked Control Systems

Networked Control Systems (NCSs) deal with feedback control systems with loops closed via data communication networks. Control over a network has many advantages compared with traditionally controlled systems, such as a lower implementation cost, reduced wiring, simpler installation and maintenance and higher reliability. Nevertheless, the networkinduced delay, packet dropout, asynchronous behavior and other specificities of networks will degrade the performance of closed-loop systems. In this context, it is necessary to develop a new theory for systems that operate in a distributed and asynchronous environment. Research on Fault Detection and Isolation (FDI) for NCSs has received increasing attention in recent years. This paper reviews the state of the art in this topic.
Advances in Autism Systemic-attachment formulation for families of children with autism

Case formulation has gained increasing prominence as a guide to intervention across a range of clinical problems. It offers a contrasting orientation to diagnosis and its value is considered in the context of clinical work with autistic spectrum disorders (ASD). The purpose of this paper is to argue that case formulation integrating attachment, systemic and narrative perspectives offers a valuable way forward in assisting people with the diagnosis and their families.  The literature on ASD and related conditions is reviewed to examine levels of co-morbidity, consider the role of parental mental health difficulties and explore the issues inherent with current approaches to diagnosis.  ASD is found to have a high level of co-morbidity with other difficulties, such as anxiety and insecure attachment. Research findings, alongside the authors own clinical experience, are developed to suggest that formulation can allow the possibility of early intervention based on a holistic appraisal of the array of difficulties present prior to a diagnosis.  It is argued that the use of this systemic-attachment formulation approach could offset the exacerbation in ASD and related conditions, and deterioration in families’ mental health, whilst they face long waiting times for a diagnosis.
A Mode-Based Design Method for Dual-Band and Self-Diplexing Antennas Using Double T-Stubs Loaded Aperture

A mode-based design method for dual-band and self-deplexing antennas is presented. By analyzing the E-field distributions and cut-off frequencies of the individual modes in a T-stub loaded aperture, we selected two proper modes for the antenna designed for dual-frequency operations. The desired feature is that at the two different frequencies, the antenna may perform the same or similar radiation characteristics including the polarization, the pattern shape, and the gain level. A rectangular aperture loaded with double T-stubs was employed for the illustration basis. The proposed method was demonstrated to the dual-band antenna and self-diplexing antenna design with promising performances. The presented dual-band antenna performed a wide range of the frequency ratio from 1.3 to 3.0 under a fixed antenna size. The proposed self-diplexing antenna achieved good port isolation better than 26 dB in both operating bands. Both illustrated antenna designs were successfully verified with built prototypes of which the simulation and measurement results were quite consistent.