nils-herrmann/clean_scirepeval huggingface dataset:
Competency Assessment Toolkit for Professional Psychology

A “toolkit” for professional psychology to assess student and practitioner competence is presented. This toolkit builds on a growing and long history of competency initiatives in professional psychology, as well as those in other health care disciplines. Each tool is a specific method to assess competence, appropriate to professional psychology. The methods are defined and described; information is presented about their best use, psychometrics, strengths and challenges; and future directions are outlined. Finally, the implications of professional psychology’s current shift to a “culture of competency,” including the challenges to implementing ongoing competency assessment, are discussed.
INFLUENCE OF SOCIAL NETWORKING ON THE STUDY HABITS AND PERFORMANCE OF STUDENTS IN A STATE UNIVERSITY

This study aimed to ascertain the influence of social networking on the study habits and academic performance of tertiary students of the West Visayas State University (WVSU) System. It likewise aimed to determine the significant differences on the extent of influence of social networking on the study habits and academic performance of the students when they were grouped as to age, sex, socio economic status and educational attainment of parents; as well as to ascertain the significant relationships among the extent of influence of social networking and students’ study habits and their academic performance. This study utilized the descriptive-correlational method in describing how social networking influenced the study habits and academic performance of the students. Two hundred thirty five (235) graduating students taking Bachelor of Science in Information Technology (BS InfoTech) at WVSU were utilized as respondents of the study. Researcher-made and duly-validated instruments such as questionnaire checklists that described the influence of social networking and the status of students’ study habits and the WVSU terminal competencies assessment to measure their academic performance were used to gather data. Means and standard deviations were used to describe the influence of social networking on the study habits and academic performance of the students. The t-test and ANOVA were used to assess the significant differences on the influence of social networking on the respondents’ study habits and academic performance and Pearson’sr correlation was used to test the significant relationships among the extent of influence of social networking, students’ study habits and their academic performance. Results revealed a high extent of influence of social networking on the respondents regardless of age, sex, socio economic status, and educational attainment of their parents. The status of the students’ study habits was also high while their level of academic performance was basic. There were significant differences in the level of academic performance of students when classified as to age, socio economic status and educational attainment of parent while significant relationship existed between the extent of influence of social networking and the status of the study habits of the respondents and between the respondents’ extent of influence of social networking and the level of their academic performance.
Artistic Data Visualization: Beyond Visual Analytics

Information visualization is traditionally viewed as a tool for data exploration and hypothesis formation. Because of its roots in scientific reasoning, visualization has traditionally been viewed as an analytical tool for sensemaking. In recent years, however, both the mainstreaming of computer graphics and the democratization of data sources on the Internet have had important repercussions in the field of information visualization. With the ability to create visual representations of data on home computers, artists and designers have taken matters into their own hands and expanded the conceptual horizon of infovis as artistic practice. This paper presents a brief survey of projects in the field of artistic information visualization and a preliminary examination of how artists appropriate and repurpose “scientific” techniques to create pieces that actively guide analytical reasoning and encourage a contextualized reading of their subject matter.
The missing link between BPM and accounting: Using event data for accounting in process-oriented organizations

The purpose of this paper is to integrate BPM and accounting on a conceptual level in order to account for the economic implications of process-state changes in process design time and process run-time.  The paper adopts a design science research paradigm. The research, grounded in an " events " approach to accounting theory, builds on the REA accounting model that has been adapted for the design of a process accounting model (PAM).  The paper presents a process accounting model (PAM) that can be used to structure event records in process-aware information systems (PAIS) to enable process-oriented accounting. The PAM is specified as a light weight data structure that is intended for the integration of PAIS and accounting information systems.  As this paper is technical in nature, more research is needed to evaluate more thoroughly its approach in naturalistic settings.  The PAM can support traditional accounting approaches, and because of the adopted events approach, it readily supports use cases related to real-time analyt-ics in BPM and accounting.  The process accounting model presents a novel approach to integrating BPM and accounting. The novelty of this approach lies in its use of event records to document flows of economic resources.
Image matching as a diffusion process: an analogy with Maxwell's demons

In this paper, we present the concept of diffusing models to perform image-to-image matching. Having two images to match, the main idea is to consider the objects boundaries in one image as semi-permeable membranes and to let the other image, considered as a deformable grid model, diffuse through these interfaces, by the action of effectors situated within the membranes. We illustrate this concept by an analogy with Maxwell's demons. We show that this concept relates to more traditional ones, based on attraction, with an intermediate step being optical flow techniques. We use the concept of diffusing models to derive three different non-rigid matching algorithms, one using all the intensity levels in the static image, one using only contour points, and a last one operating on already segmented images. Finally, we present results with synthesized deformations and real medical images, with applications to heart motion tracking and three-dimensional inter-patients matching.
SKEW DETECTION IN IMAGE PROCESSING

Many researchers proposed different methodologies for the text skew estimation in binary images/gray scale images. They have been used widely for the skew identification of the printed text. There exist so many ways algorithms for detecting and correcting a slant or skew in a given document or image. Some of them provide better accuracy but are slow in speed, others have angle limitation drawback. So a new technique for skew detection in the paper, will reduce the time and cost.  Document image processing, Skew detection, Nearest-neighbour approach, Moments, Hough transformation.
A State-of-the-art Review of Job-Shop Scheduling Techniques

A great deal of research has been focused on solving the job-shop problem (ΠJ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve ΠJ as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive. In addition this work suggests guide-lines on features that should be incorporated to create a good ΠJ system. Finally the possible direction for future work is highlighted so that current barriers within ΠJ maybe surmounted as we approach the 21st Century.
Association of Cytokines and Chemokines in Pathogenesis of Breast Cancer.

Breast cancer touches women's life worldwide. Expected outcome is not achieved due to molecular heterogeneity and complex biology despite substantial advancement in diagnosis, prevention and treatment of breast cancer. Patients with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (Her2) positive tumors receive hormone ablation and Her2 directed therapy. While patients diagnosed with triple-negative breast cancer receive chemotherapy in both the early and advanced stages. However, chemotherapeutic efficacies are not the same in every patient, which has fostered a major effort to identify new targets to treat breast cancer. Positive therapeutic outcome after immune checkpoint inhibitors emphasizes the significance of the host immune system in breast cancer. Cancer develops in immune competent host wherein cytokines, while shaping the immune system, also serve as growth signals for cancer cells. The dynamics of cross talk between immune system and cancer cells mediated by cytokines and chemokines changes during cancer initiation, progression, and therapeutic interventions. Hence, better understanding of molecular footprint of cancer cells, as well as crosstalk between cancer cells and host immune system is needed to develop patient specific treatment and management of breast cancer.
Automatic feature learning for vulnerability prediction

Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, information loss, or system failure. A variety of approaches have been developed to try and detect the most likely locations of such code vulnerabilities in large code bases. Most of them rely on manually designing features (e.g. complexity metrics or frequencies of code tokens) that represent the characteristics of the code. However, all suffer from challenges in sufficiently capturing both semantic and syntactic representation of source code, an important capability for building accurate prediction models. In this paper, we describe a new approach, built upon the powerful deep learning Long Short Term Memory model, to automatically learn both semantic and syntactic features in code. Our evaluation on 18 Android applications demonstrates that the prediction power obtained from our learned features is equal or even superior to what is achieved by state of the art vulnerability prediction models: 3%–58% improvement for within-project prediction and 85% for cross-project prediction.
Business Model Innovation

This chapter offers a broad review of the literature at the nexus between Business Models and innovation studies and examines the notion of Business Model Innovation in three different situations: Business Model Design in newly formed organizations, Business Model Reconfiguration in incumbent firms, and Business Model Innovation in the broad context of sustainability. Tools and perspectives to make sense of Business Models and support managers and entrepreneurs in dealing with Business Model Innovation are reviewed and organized around a synthesizing meta-framework. The framework elucidates the nature of the complementarities across various perspectives. Finally, the use of business model-related ideas in practice is discussed, and critical managerial challenges as they relate to Business Model Innovation and managing business models are identified and examined.
Polarization-Insensitive Single- and Broadband Switchable Absorber/Reflector and Its Realization Using a Novel Biasing Technique

In this communication, a switchable absorber/reflector based on active frequency-selective surface (AFSS) has been presented for single- as well as broadband applications. The FSS comprises periodic patterns of square loops connected among themselves through p-i-n diodes, which exhibit switchable performances. The novelty of the proposed design lies in its symmetric configuration and biasing network, which makes the structure polarization insensitive unlike the earlier reported AFSSs. A single-band switchable absorber/reflector has initially been realized, which is characterized through an equivalent circuit model to derive the circuit parameters. Later, surface-mount resistors have been implemented in the design to realize a wideband switchable absorber/reflector aimed for C-band applications. Both the structures have used a novel biasing methodology to provide the bias voltage to semiconductor switches without disturbing the original resonance pattern. Furthermore, the fabricated samples, while measuring in an anechoic chamber, show good agreement with the simulated responses under normal incidence as well as for different polarization angles.
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation

We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3Dmulti-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture. Rather than simply projecting color data into a volumetric grid and operating solely in 3D – which would result in insufficient detail – we first extract feature maps from associated RGB images. These features are then mapped into the volumetric feature grid of a 3D network using a differentiable backprojection layer. Since our target is 3D scanning scenarios with possibly many frames, we use a multi-view pooling approach in order to handle a varying number of RGB input views. This learned combination of RGB and geometric features with our joint 2D-3D architecture achieves significantly better results than existing baselines. For instance, our final result on the ScanNet 3D segmentation benchmark increases from 52.8% to 75% accuracy compared to existing volumetric architectures.  2 A. Dai and M. Nießner
Shopping mode choice : Physical store shopping versus e-shopping

This study aims to explore how consumers evaluate these time attributes; i.e., the value of time, when they are facing a shopping mode choice between physical store shopping and e-shopping. For this purpose, it conducts an experiment to acquire data on respondents’ stated preference choices between physical bookstore shopping and online bookstore shopping. It is finally found that the value of delivery time for a purchased book from an online bookstore to a consumer is approximately $0.53 per day, which means an online bookstore will have to lower a book’s price by $0.53 to attract a physical bookstore shopper if the delivery is delayed for one day. It is also found that in terms of monetary values, avoiding a shopping trip produces far more benefits than bearing waiting for the delivery of books for an online purchase.
A Reading Comprehension Style Question Answering Model Based On Attention Mechanism

In recent years, research on reading-compr question and answering has drawn intense attention in Language Processing. However, it is still a key issue to the high-level semantic vector representation of quest paragraph. Drawing inspiration from DrQA , wh question and answering system proposed by Facebook, tl proposes an attention-based question and answering 11 adds the binary representation of the paragraph, the par; attention to the question, and the question's attentioi paragraph. Meanwhile, a self-attention calculation m proposed to enhance the question semantic vector reption. Besides, it uses a multi-layer bidirectional Lon: Term Memory(BiLSTM) networks to calculate the h semantic vector representations of paragraphs and q Finally, bilinear functions are used to calculate the pr of the answer's position in the paragraph. The expe results on the Stanford Question Answering Dataset(SQl development set show that the F1 score is 80.1% and tl 71.4%, which demonstrates that the performance of the is better than that of the model of DrQA, since they inc 2% and 1.3% respectively.
Topology Preserving Parallel Smoothing for 3D Binary Images

This paper presents a new algorithm for smoothing 3D binary images in a topology preserving way. Our algorithm is a reduction operator: some border points that are considered as extremities are removed. The proposed method is composed of two parallel reduction operators. We are to apply our smoothing algorithm as an iterationby-iteration pruning for reducing the noise sensitivity of 3D parallel surface-thinning algorithms. An efficient implementation of our algorithm is sketched and its topological correctness for (26,6) pictures is proved.
Decoding Optimization for 5G LDPC Codes by Machine Learning

In this paper, we propose a generalized minimum-sum decoding algorithm using a linear approximation (LAMS) for protograph-based low-density parity-check (PB-LDPC) codes with quasi-cyclic (QC) structures. The linear approximation introduces some factors in each decoding iteration, which linearly adjust the check node updating and channel output. These factors are optimized iteratively using machine learning, where the optimization can be efficiently solved by a small and shallow neural network with training data produced by the LAMS decoder. The neural network is built according to the parity check matrix of a PB-LDPC code with a QC structure which can greatly reduce the size of the neural network. Since, we optimize the factors once per decoding iteration, the optimization is not limited by the number of the iterations. Then, we give the optimized results of the factors in the LAMS decoder and perform decoding simulations for PB-LDPC codes in fifth generation mobile networks (5G). In the simulations, the LAMS algorithm shows noticeable improvement over the normalized and the offset minimum-sum algorithms and even better performance than the belief propagation algorithm in some high signal-to-noise ratio regions.
Power-duration relationship: Physiology, fatigue, and the limits of human performance.

The duration that exercise can be maintained decreases as the power requirements increase. In this review, we describe the power-duration (PD) relationship across the full range of attainable power outputs in humans. We show that a remarkably small range of power outputs is sustainable (power outputs below the critical power, CP). We also show that the origin of neuromuscular fatigue differs considerably depending on the exercise intensity domain in which exercise is performed. In the moderate domain (below the lactate threshold, LT), fatigue develops slowly and is predominantly of central origin (residing in the central nervous system). In the heavy domain (above LT but below CP), both central and peripheral (muscle) fatigue are observed. In this domain, fatigue is frequently correlated with the depletion of muscle glycogen. Severe-intensity exercise (above the CP) is associated with progressive derangements of muscle metabolic homeostasis and consequent peripheral fatigue. To counter these effects, muscle activity increases progressively, as does pulmonary oxygen uptake (, with task failure being associated with the attainment of  see text] max. Although the loss of homeostasis and thus fatigue develop more rapidly the higher the power output is above CP, the metabolic disturbance and the degree of peripheral fatigue reach similar values at task failure. We provide evidence that the failure to continue severe-intensity exercise is a physiological phenomenon involving multiple interacting mechanisms which indicate a mismatch between neuromuscular power demand and instantaneous power supply. Valid integrative models of fatigue must account for the PD relationship and its physiological basis.
XNLI: Evaluating Cross-lingual Sentence Representations

State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in crosslingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.
Interdisciplinary Management Of Maxillary Lateral Incisors Agenesis With Mini Implant Prostheses : A Case Report

Orthodontic management for patients with single or bilateral congenitally missing permanent lateral incisors is a challenge to effective treatment planning. Over the last several decades, dentistry has focused on several treatment modalities for replacement of missing teeth. The two major alternative treatment options are orthodontic space closure or space opening for prosthetic replacements. For patients with high aesthetic expectations implants are one of the treatment of choices, especially when it comes to replacement of missing maxillary lateral incisors and mandibular incisors. Edentulous areas where the available bone is compromised to use conventional implants with 2,5 mm or more in diameter, narrow diameter implants with less than 2,5 mm diameter can be successfully used. This case report deals with managing a compromised situation in the region of maxillary lateral incisor using a narrow diameter implant.