nils-herrmann/clean_scirepeval huggingface dataset:
Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning

Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs’ parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead.
Regenerative endodontic treatment of perforated internal root resorption: a case report.

To present the regenerative endodontic treatment procedure of a perforated internal root resorption case and its clinical and radiographic findings after 2 years.



A 14-year-old female patient was referred complaining of moderate pain associated with her maxillary left lateral incisor. After radiographic examination, a perforated internal resorption lesion in the middle third of tooth 22 was detected. Under local anaesthesia and rubber dam isolation, an access cavity was prepared and the root canal was shaped using K-files under copious irrigation with 1% NaOCl, 17% EDTA and distilled water. At the end of the first and second appointments, calcium hydroxide (CH) paste was placed in the root canal using a lentulo. After 3 months, the CH paste was removed using 1% NaOCl and 17% EDTA solutions and bleeding in the root canal was achieved by placing a size 20 K-file into the periapical tissues. Mineral trioxide aggregate was then placed over the blood clot. The access cavity was restored using glass-ionomer cement and resin composite. After 2 years, the tooth was asymptomatic and radiographic examination revealed hard tissue formation in the perforated resorption area and remodelling of the root surface.



Regenerative endodontic treatment procedures are an alternative approach to treat perforated internal root resorption lesions. Calcium hydroxide was effective as an intracanal medicament in regenerative endodontic treatment procedures.
Addictive Personality and Problematic Mobile Phone Use

Mobile phone use is banned or regulated in some circumstances. Despite recognized safety concerns and legal regulations, some people do not refrain from using mobile phones. Such problematic mobile phone use can be considered to be an addiction-like behavior. To find the potential predictors, we examined the correlation between problematic mobile phone use and personality traits reported in addiction literature, which indicated that problematic mobile phone use was a function of gender, self-monitoring, and approval motivation but not of loneliness. These findings suggest that the measurements of these addictive personality traits would be helpful in the screening and intervention of potential problematic users of mobile phones.
Anomaly detection on the edge

Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices. Traditionally, the observations are sent to centralized servers where large-scale systems perform analytics on the data gathered from all devices. However, the compute capability of these small form factor devices is ever increasing with annual improvements to hardware. A new model, known as edge computing, takes advantage of this compute capability and performs local analytics on the distributed devices. This paper presents an approach to anomaly detection that uses autoencoders, specialized deep learning neural networks, deployed on each edge device, to perform analytics and identify anomalous observations in a distributed fashion. Simultaneously, the autoencoders learn from the new observations in order to identify new trends. A centralized server aggregates the updated models and distributes them back to the edge devices when a connection is available. This architecture reduces the bandwidth and connectivity requirements between the edge devices and the central server as only the autoencoder model and anomalous observations must be sent to the central servers, rather than all observation data.
E-COMMERCE AND SUPPLY CHAIN MANAGEMENT

Current technologies have revolutionized the manner in which retailers operate. The early stages of the dot com era allowed businesses to experiment with different business models. On-line retailers focused on front-end systems, overlooking the importance of back-end operations such as supply chain management. Retailers have the option to distribute their products via the traditional method, drop-shipping, or a mix of traditional and drop-shipping strategies also known as the hybrid technique. Retailers have to account for stockouts and act accordingly to not lose sales or customers. This paper revisits current trends in inventory management covered in a paper by Snyder and Hamdan (2009)  The emergence of the Internet has been the greatest technological advancement since industrial age. It is estimated that 44% of US Internet users are using the Internet to pay bills, and 33% of Internet users are purchasing products online. The number is expected to grow from time as the Internet becomes more prevalent and secure. The rise of the Internet created opportunities for entrepreneurs, destroyed many businesses, and most importantly changed the business structure of e-commerce. The dot com era was a time period where entrepreneurs experimented with new business models (O'Brien, 2008).
Metric Factorization: Recommendation beyond Matrix Factorization.

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as Metric Factorization. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.
T HE UNREASONABLE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS

We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books (“Anonymous audio book service”), Mobile Apps, Video and Music. It produces recommendations based on user consumption history: purchases, listens or watches. Our key contribution is to formulate recommendation problem as a model that encodes historical behavior to predict the future behavior using soft data split, combining predictor and autoencoder models. We introduce convolutional layer for learning the importance (time decay) of the purchases depending on their purchase date and demonstrate that the shape of the time decay function can be well approximated by a parametrical function. We present offline experimental results showing that neural networks with two hidden layers can capture seasonality changes, and at the same time outperform other modeling techniques, including our recommender in production. Most importantly, we demonstrate that our model can be scaled to all digital categories. Finally, we show online A/B test results, discuss key improvements to the neural network model, and describe our production pipeline.
Multi-attention Recurrent Network for Human Communication Comprehension

Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand faceto-face communication, however, comprehending this form of communication remains a significant challenge for Artificial Intelligence (AI). AI must understand each modality and the interactions between them that shape human communication. In this paper, we present a novel neural architecture for understanding human communication called the Multiattention Recurrent Network (MARN). The main strength of our model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent component called the Long-short Term Hybrid Memory (LSTHM). We perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis, speaker trait recognition and emotion recognition. MARN shows state-of-the-art performance on all the datasets.
A Novel Methodological Framework for the Design of Sustainable Rural Microgrid for Developing Nations

Sustainable electrification planning for remote locations especially in developing countries is very complex in nature while considering different traits such as social, economic, technical, and environmental. To address these issues related to current energy needs depending upon the end user requirements, a coherent, translucent, efficient, and rational energy planning framework has to be identified. This paper presents a comprehensive generalized methodological framework based on the synergies of decision analysis and optimization models for the design of a reliable, robust, and economic microgrid system based on locally available resources for rural communities in developing nations. The framework consists of three different stages. First, decision analysis considering various criterions (technical, social, economic, and environmental) for the selection of suitable energy alternative for designing the microgrid considering multiple scenarios are carried out. Second, the optimal sizing of the various energy resources in different microgrid structures is illustrated. Third, hybrid decision analysis methods are used for selection of the best sustainable microgrid energy system. Finally, the framework presented is then utilized for the design of a sustainable rural microgrid for a remote community located in the Himalayas in India to illustrate its effectiveness. The results obtained show that decision analysis tools provide a real-time solution for rural electrification by binding the synergy between various criteria considering different scenarios. The feasibility analysis using proposed multiyear scalable approach shows its competence not only in determining the suitable size of the microgrid, but also by reducing the net present cost and the cost of electricity significantly.
Building Customer Value in the Hospitality Industry: Towards the Definition of a Customer-Centric Information System

Increasing occupancy rates and revenue by improving customer experience is the aim of modern hospitality organizations. To achieve these results, hotel managers need to have a deep knowledge of customers’ needs, behavior, and preferences and be aware of the ways in which the services delivered create value for the customers and then stimulate their retention and loyalty. In this article a methodological framework to analyze the guest–hotel relationship and to profile hotel guests is discussed, focusing on the process of designing a customer information system and particularly the guest information matrix on which the system database will be built.
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (e.g., atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.
Advances in Game Accessibility from 2005 to 2010

The research in the area of game accessibility has grown significantly since the last time it was examined in 2005. This paper examines the body of work between 2005 and 2010. We selected a set of papers on topics we felt represented the scope of the field, but were not able to include all papers on the subject. A summary of the research we examined is provided, along with suggestions for future work in game accessibility. It is hoped that this summary will prompt others to perform further research in this area.
Development and validation of the Self-Regulation of Eating Behaviour Questionnaire for adults

Eating self-regulatory capacity can help individuals to cope with the obesogenic environment and achieve, as well as maintain, a healthy weight and diet. At present, there is no comprehensive, reliable and valid questionnaire for assessing this capacity and measuring change in response to self-regulation interventions in adults. This paper reports the development of the Self-regulation of Eating Behaviour Questionnaire (SREBQ) for use in UK adults, and presents evidence for its reliability and construct validity. The development of the SREBQ involved generation of an item pool, followed by two pilot studies (Samples 1 and 2) and a test of the questionnaire's underlying factor structure (Sample 3). The final version of the SREBQ was then assessed for reliability and construct validity (Sample 4).



Development of the SREBQ resulted in a 5-item questionnaire. The face validity was satisfactory, as assessed by the pilot studies. The factor structure analysis (Sample 3) suggested that it has a single underlying factor, which was confirmed in a second sample (Sample 4). The SREBQ had strong construct validity, showing a positive correlation with general measures of self-regulation. It was also positively correlated with motivation and behavioural automaticity, and negatively correlated with food responsiveness and emotional over-eating (p < 0.001). It showed good discriminant validity, as it was only weakly associated with satiety responsiveness, food fussiness and slowness in eating.



The SREBQ is a reliable and valid measure for assessment of eating self-regulatory capacity in the general UK adult population.
Gardening is beneficial for health: A meta-analysis

There is increasing evidence that gardening provides substantial human health benefits. However, no formal statistical assessment has been conducted to test this assertion. Here, we present the results of a meta-analysis of research examining the effects of gardening, including horticultural therapy, on health. We performed a literature search to collect studies that compared health outcomes in control (before participating in gardening or non-gardeners) and treatment groups (after participating in gardening or gardeners) in January 2016. The mean difference in health outcomes between the two groups was calculated for each study, and then the weighted effect size determined both across all and sets of subgroup studies. Twenty-two case studies (published after 2001) were included in the meta-analysis, which comprised 76 comparisons between control and treatment groups. Most studies came from the United States, followed by Europe, Asia, and the Middle East. Studies reported a wide range of health outcomes, such as reductions in depression, anxiety, and body mass index, as well as increases in life satisfaction, quality of life, and sense of community. Meta-analytic estimates showed a significant positive effect of gardening on the health outcomes both for all and sets of subgroup studies, whilst effect sizes differed among eight subgroups. Although Egger's test indicated the presence of publication bias, significant positive effects of gardening remained after adjusting for this using trim and fill analysis. This study has provided robust evidence for the positive effects of gardening on health. A regular dose of gardening can improve public health.
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine

The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD ( http://www.hgmd.org ) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.
Frequency-Dependent Resistance of Planar Coils in Printed Circuit Board With Litz Structure

Printed circuit board (PCB) windings are convenient for many applications given their ease of manufacture, high repeatability, and low profile. In many cases, the use of multistranded litz wires is appropriate due to the rated power, frequency range, and efficiency constraints. This paper proposes a manufacturing technique and a semianalytical loss model for PCB windings using planar litz structure to obtain a similar ac loss reduction to that of conventional windings of round wires with litz structure. Different coil prototypes have been tested in several configurations to validate the proposal.
A Survey on Intrusion Detection Techniques in MANET

The mobile ad-hoc network (MANET) is a new wireless technology, having features like dynamic topology and self-configuring ability of nodes. The self configuring ability of nodes in MANET made it popular among the critical situation such as military use and emergency recovery. But due to open medium and broad distribution of nodes make MANET vulnerable to different attacks. So to protect MANET from various attacks, it is important to develop an efficient and secure system for MANET. Intrusion means any set of actions that attempt to compromise the integrity, confidentiality, or availability of a resource. Intrusion Prevention is the primary defense because it is the first step to make the systems secure from attacks by using passwords, biometrics etc. Even if intrusion prevention methods are used, the system may be subjected to some vulnerability. So we need a second wall of defense known as Intrusion Detection Systems (IDSs), to detect and produce responses whenever necessary. In this article we present a survey of various intrusion detection schemes available for ad hoc networks. We have also described some of the basic attacks present in ad hoc network and discussed their available solution.
A Nearly Optimal Contextual Bandit Algorithm

We investigate the contextual multi-armed bandit problem in an adversarial setting and introduce an online algorithm that asymptotically achieves the performance of the best contextual bandit arm selection strategy under certain conditions. We show that our algorithm is highly efficient and provides significantly improved performance with a guaranteed performance upper bound in a strong mathematical sense. We have no statistical assumptions on the context vectors and the loss of the bandit arms, hence our results are guaranteed to hold even in adversarial environments. We use a tree notion in order to partition the space of context vectors in a nested structure. Using this tree, we construct a large class of context dependent bandit arm selection strategies and adaptively combine them to achieve the performance of the best strategy. We use the hierarchical nature of introduced tree to implement this combination with a significantly low computational complexity, thus our algorithm can be efficiently used in applications involving big data. Through extensive set of experiments involving synthetic and real data, we demonstrate significant performance gains achieved by the proposed algorithm with respect to the state-of-the-art adversarial bandit algorithms.
Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis

Object tracking under complex circumstances is a challenging task because of background interference, obstacle occlusion, object deformation, etc. Given such conditions, robustly detecting, locating, and analyzing a target through single-feature representation are difficult tasks. Global features, such as color, are widely used in tracking, but may cause the object to drift under complex circumstances. Local features, such as HOG and SIFT, can precisely represent rigid targets, but these features lack the robustness of an object in motion. An effective method is adaptive fusion of multiple features in representing targets. The process of adaptively fusing different features is the key to robust object tracking. This study uses a multi-feature joint descriptor (MFJD) and the distance between joint histograms to measure the similarity between a target and its candidate patches. Color and HOG features are fused as the tracked object of the joint representation. This study also proposes a self-adaptive multi-feature fusion strategy that can adaptively adjust the joint weight of the fused features based on their stability and contrast measure scores. The mean shift process is adopted as the object tracking framework with multi-feature representation. The experimental results demonstrate that the proposed MFJD tracking method effectively handles background clutter, partial occlusion by obstacles, scale changes, and deformations. The novel method performs better than several state-of-the-art methods in real surveillance scenarios.