nils-herrmann/clean_scirepeval huggingface dataset:
Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This computing utility, like all other four existing utilities, will provide the basic level of computing service that is considered essential to meet the everyday needs of the general community. To deliver this vision, a number of computing paradigms have been proposed, of which the latest one is known as Cloud computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries, along with our current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload.We also describe ametanegotiation infrastructure to establish global Cloud exchanges and markets, and illustrate a case study of harnessing ‘Storage Clouds’ for high performance content delivery. Finally, we conclude with the need for convergence of competing IT paradigms to deliver our 21st century vision.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
A Cost-Aware Elasticity Provisioning System for the Cloud

In this paper we present Kingfisher, a {\em cost-aware} system that provides efficient support for elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimizes the cost. We have implemented a prototype of Kingfisher and have evaluated its efficacy on a laboratory cloud platform. Our experiments with varying application workloads demonstrate that Kingfisher is able to (i) decrease the cost of virtual server resources by as much as $24\%$ compared to the current cost-unaware approach, (ii) reduce by an order of magnitude the time to transition to a new configuration through multiple elasticity mechanisms in the cloud, and (iii), illustrate the opportunity for design alternatives which trade-off the cost of server resources with the time required to scale the application.
Role of Plant Growth Promoting Rhizobacteria in Agricultural Sustainability—A Review

Plant growth promoting rhizobacteria (PGPR) shows an important role in the sustainable agriculture industry. The increasing demand for crop production with a significant reduction of synthetic chemical fertilizers and pesticides use is a big challenge nowadays. The use of PGPR has been proven to be an environmentally sound way of increasing crop yields by facilitating plant growth through either a direct or indirect mechanism. The mechanisms of PGPR include regulating hormonal and nutritional balance, inducing resistance against plant pathogens, and solubilizing nutrients for easy uptake by plants. In addition, PGPR show synergistic and antagonistic interactions with microorganisms within the rhizosphere and beyond in bulk soil, which indirectly boosts plant growth rate. There are many bacteria species that act as PGPR, described in the literature as successful for improving plant growth. However, there is a gap between the mode of action (mechanism) of the PGPR for plant growth and the role of the PGPR as biofertilizer-thus the importance of nano-encapsulation technology in improving the efficacy of PGPR. Hence, this review bridges the gap mentioned and summarizes the mechanism of PGPR as a biofertilizer for agricultural sustainability.
To Bond or Not to Bond: An Optimal Channel Allocation Algorithm for Flexible Dynamic Channel Bonding in WLANs

To meet rapidly increasing data rate requirements in WLANs, one important technique adopted in 802.11ac is the channel bonding (CB) scheme that combines multiple 20MHz channels for a single transmission in 5GHz band. In order to effectively access channel after a series of contention operations, 802.11ac specifies two different CB operations: Dynamic Channel Bonding (DCB) and Static Channel Bonding (SCB). This paper proposes an optimal channel allocation algorithm to achieve maximal throughputs in DCB WLANs. Specifically, we first adopt a continuous-time Markov Chain (CTMC) model to analyze the equilibrium throughputs. Based on the throughput analysis, we then construct an integer nonlinear programming (INLP) model with the target of maximizing system throughputs. By solving the INLP model, we then propose an optimal channel allocation algorithm based on the Branch-and-Bound Method (BBM). Simulations show the proposed algorithm can achieve the maximal system throughput under various network settings. Importantly, it turns out that the maximal throughput performance can be achieved under the channel allocation scheme with the least overlapped channels among WLANs, which brings new insights into the design and optimization of future WLANs, especially for those adopting CB technique.
Self-Attention with Relative Position Representations

Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graphlabeled inputs.
Definitions of groove and hollowness of the infraorbital region and clinical treatment using
soft-tissue filler

Clarification is needed regarding the definitions and classification of groove and hollowness of the infraorbital region depending on the cause, anatomical characteristics, and appearance. Grooves in the infraorbital region can be classified as nasojugal grooves (or folds), tear trough deformities, and palpebromalar grooves; these can be differentiated based on anatomical characteristics. They are caused by the herniation of intraorbital fat, atrophy of the skin and subcutaneous fat, contraction of the orbital part of the orbicularis oculi muscle or squinting, and malar bone resorption. Safe and successful treatment requires an optimal choice of filler and treatment method. The choice between a cannula and needle depends on various factors; a needle is better for injections into a subdermal area in a relatively safe plane, while a cannula is recommended for avoiding vascular compromise when injecting filler into a deep fat layer and releasing fibrotic ligamentous structures. The injection of a soft-tissue filler into the subcutaneous fat tissue is recommended for treating mild indentations around the orbital rim and nasojugal region. Reducing the tethering effect of ligamentous structures by undermining using a cannula prior to the filler injection is recommended for treating relatively deep and fine indentations. The treatment of mild prolapse of the intraorbital septal fat or broad flattening of the infraorbital region can be improved by restoring the volume deficiency using a relatively firm filler.
The Targeting of Advertising

A important question that firms face in advertising is developing effective media strategy. Major improvements in the quality of consumer information and the growth of targeted media vehicles allow firms to precisely target advertising to consumer segments within a market. This paper examines advertising strategy when competing firms can target advertising to different groups of consumers within a market. With targeted advertising, we find that firms advertise more to consumers who have a strong preference for their product than to comparison shoppers who can be attracted to the competition. Advertising less to comparison shoppers can be seen as a way for firms to endogenously increase differentiation in the market. In addition, targeting allows the firm to eliminate “wasted” advertising to consumers whose preferences do not match a product’s attributes. As a result, the targeting of advertising increases equilibrium profits. The model demonstrates how advertising strategies are affected by firms being able to target pricing. Target advertising leads to higher profits, regardless of whether or not the firms have the ability to set targeted prices, and the targeting of advertising can be more valuable for firms in a competitive environment than the ability to target pricing.
Automatic Topic Labeling Using Ontology-Based Topic Models

Topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. Topic labeling, which aims to assign meaningful labels for discovered topics, has recently gained significant attention. In this paper, we argue that the quality of topic labeling can be improved by considering ontology concepts rather than words alone, in contrast to previous works in this area, which usually represent topics via groups of words selected from topics. We have created: (1) a topic model that integrates ontological concepts with topic models in a single framework, where each topic and each concept are represented as a multinomial distribution over concepts and over words, respectively, and (2) a topic labeling method based on the ontological meaning of the concepts included in the discovered topics. In selecting the best topic labels, we rely on the semantic relatedness of the concepts and their ontological classifications. The results of our experiments conducted on two different data sets show that introducing concepts as additional, richer features between topics and words and describing topics in terms of concepts offers an effective method for generating meaningful labels for the discovered topics.
Intelligent appliances controller using Raspberry Pi

Using the inexpensive Raspberry Pi to automate the tasks at home such as switching appliances on & off over Wi-Fi (Wireless Fidelity) or LAN(Local Area Network) using a personal computer or a mobile or a tablet through the browser. This can also be done by using the dedicated Android application. The conventional switch boards will be added with a touch screen or replaced with a touch screen to match the taste of the user's home decor. PIR (Passive Infrared Sensor) sensor will be used to detect human detection and automate the on and off functionality.
Frequence: interactive mining and visualization of temporal frequent event sequences

Extracting insights from temporal event sequences is an important challenge. In particular, mining frequent patterns from event sequences is a desired capability for many domains. However, most techniques for mining frequent patterns are ineffective for real-world data that may be low-resolution, concurrent, or feature many types of events, or the algorithms may produce results too complex to interpret. To address these challenges, we propose Frequence, an intelligent user interface that integrates data mining and visualization in an interactive hierarchical information exploration system for finding frequent patterns from longitudinal event sequences. Frequence features a novel frequent sequence mining algorithm to handle multiple levels-of-detail, temporal context, concurrency, and outcome analysis. Frequence also features a visual interface designed to support insights, and support exploration of patterns of the level-of-detail relevant to users. Frequence's effectiveness is demonstrated with two use cases: medical research mining event sequences from clinical records to understand the progression of a disease, and social network research using frequent sequences from Foursquare to understand the mobility of people in an urban environment.
LSTM-based Deep Learning Models for Answer Ranking

The learning problem of ranking arises in many tasks, including the question answering, information retrieval, and movie recommendation. In these tasks, the ordering of the answers, documents or movies returned is a critical aspect of the system. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task. We aim to solve the answer ranking problem in practical question answering system with deep learning approaches. In this paper, we define a composite representation for questions and answers by combining convolutional neural network (CNN) with bidirectional long short-term memory (biLSTM) models, and learn a similarity function to relate them in a supervised way from the available training data. Considering the limited training data, we propose a hypernym strategy to get more general text pairs and test the effectiveness of different strategies. Experimental results on a public benchmark dataset from TREC demonstrate that our system outperforms previous work which requires syntactic features and some deep learning models.
Self-Driving Cars - An AI-Robotics Challenge

In recent years, all major automotive companies have launched initiatives towards cars that assist people in making driving decisions. The ultimate goal of all these efforts are cars that can drive themselves. The benefit of such a technology could be enormous. At present, some 42,000 people die every year in traffic accidents in the U.S., mostly because of human error. Self-driving cars could make people safer and more productive. Self-driving cars is a true AI challenge. To endow cars with the ability to make decisions on behalf of their drivers, they have to sense, perceive, and act. Recent work in this field has extensively built on probabilistic representations and machine learning methods. The speaker will report on past work on the DARPA Grand Challenge, and discuss ongoing work on the Urban Challenge, DARPA’s follow-up program on self-driving cars.
Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases

With the vigorous development of the World Wide Web, many large-scale knowledge bases (KBs) have been generated. To improve the coverage of KBs, an important task is to integrate the heterogeneous KBs. Several automatic alignment methods have been proposed which achieve considerable success. However, due to the inconsistency and uncertainty of large-scale KBs, automatic techniques for KBs alignment achieve low quality (especially recall). Thanks to the open crowdsourcing platforms, we can harness the crowd to improve the alignment quality. To achieve this goal, in this paper we propose a novel hybrid human-machine framework for large-scale KB integration. We rst partition the entities of different KBs into many smaller blocks based on their relations. We then construct a partial order on these partitions and develop an inference model which crowdsources a set of tasks to the crowd and infers the answers of other tasks based on the crowdsourced tasks. Next we formulate the question selection problem, which, given a monetary budget B, selects B crowdsourced tasks to maximize the number of inferred tasks. We prove that this problem is NP-hard and propose greedy algorithms to address this problem with an approximation ratio of 1--1/e. Our experiments on real-world datasets indicate that our method improves the quality and outperforms state-of-the-art approaches.
A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research

This paper introduces a framework for conducting and writing an effective literature review. The target audience for the framework includes information systems (IS) doctoral students, novice IS researchers, and other IS researchers who are constantly struggling with the development of an effective literature-based foundation for a proposed research. The proposed framework follows the systematic data processing approach comprised of three major stages: 1) inputs (literature gathering and screening), 2) processing (following Bloom’s Taxonomy), and 3) outputs (writing the literature review). This paper provides the rationale for developing a solid literature review including detailed instructions on how to conduct each stage of the process proposed. The paper concludes by providing arguments for the value of an effective literature review to IS research.
Replicated Softmax: an Undirected Topic Model

We introduce a two-layer undirected graphical model, calle d a “Replicated Softmax”, that can be used to model and automatically extract low -dimensional latent semantic representations from a large unstructured collec ti n of documents. We present efficient learning and inference algorithms for thi s model, and show how a Monte-Carlo based method, Annealed Importance Sampling, c an be used to produce an accurate estimate of the log-probability the model a ssigns to test data. This allows us to demonstrate that the proposed model is able to g neralize much better compared to Latent Dirichlet Allocation in terms of b th the log-probability of held-out documents and the retrieval accuracy.
Dirt Cheap Web-Scale Parallel Text from the Common Crawl

Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon’s Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about $500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data
BodyNet: Volumetric Inference of 3D Human Body Shapes

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them results in performance improvement as demonstrated by our experiments. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Besides achieving state-of-the-art performance, our method also enables volumetric bodypart segmentation.
Analysis of CNN-based speech recognition system using raw speech as input

Automatic speech recognition systems typically model the relationship between the acoustic speech signal and the phones in two separate steps: feature extraction and classifier training. In our recent works, we have shown that, in the framework of convolutional neural networks (CNN), the relationship between the raw speech signal and the phones can be directly modeled and ASR systems competitive to standard approach can be built. In this paper, we first analyze and show that, between the first two convolutional layers, the CNN learns (in parts) and models the phone-specific spectral envelope information of 2-4 ms speech. Given that we show that the CNN-based approach yields ASR trends similar to standard short-term spectral based ASR system under mismatched (noisy) conditions, with the CNN-based approach being more robust.
MQTT-Topics Management System for sharing of Open Data

At present, the sharing of open data in any city is importance. The shared data, e.g., flood monitoring data, earthquake data, or traffic data, can be used to improve the way of human life. MQTT, the lightweight messaging protocol based on the publish-subscribe concept, is the most popular protocols used to share these data. There are two elements of MQTT messaging for sharing of any data; topic and message. The first one is used to define which the subscriber are received which data and the data that we need to share are presented in the message element. To share the data with MQTT protocol, MQTT-Topic Naming Criteria of Open Data for Smart Cities (MTNC) was proposed in our previous work. In this paper, we propose MQTT-Topics Management System (MTMS) to manage the topics of the MQTT messages shared by any publisher based on MTNC. The experimental result shows that the proposed system get the high average score of usability, design and the benefits.