nils-herrmann/clean_scirepeval huggingface dataset:
Impact of land disposal of reject brine from desalination plants on soil and groundwater

The impact of reject brine chemical composition and disposal from inland desalination plants on soil and groundwater in the eastern region of Abu Dhabi Emirate, namely Al Wagan, Al Quaa and Um Al Zumool, was evaluated. Twenty five inland BWRO desalination plants (11 at Al Wagan, 12 at Al Quaa, and 2 at Um Al Zumool) have been investigated. The study indicated that average capacity of these plants varied between 26,400 G/d (99.93 m/d) to 61,000 G/d (230.91 m/d). The recovery rate varied from 60 to 70% and the reject brine accounted for about 30–40% of the total water production. The electrical conductivity of feed water and rejects brine varied from 4.61 to 14.70 and 12.90–30.30 (mS/cm), respectively. The reject brine is disposed directly into surface impoundment (unlined pits) in a permeable soil with low clay content, cation exchange capacity and organic matter content. The groundwater table 1ies at a depth of 100–150 m. The average distance between feed water intake and the disposal site is approximately 5 km. A survey has been conducted to gather basic information, determine the type of chemicals used, and determine if there is any current and previous monitoring program. The chemical compositions of the feed, product, reject, and pond water have been analyzed for major, minor and trace constituents. Most of the water samples (feed, product, reject and pond water) showed the presence of major, minor and trace constituents. Some of these constituents are above the Gulf Cooperation Council (GCC) and Abu-Dhabi National Oil Company (ADNOC) Standards for drinking water and effluents discharged into the desert. Total Petroleum Hydrocarbon (TPH) was also analyzed and found to be present, even in product water samples, in amount that exceed the GCC standards for organic chemical constituents in drinking water (0.01 mg/l).
Certified Defenses against Adversarial Examples

While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most = 0.1 can cause more than 35% test error.
The significance of the aerobic-anaerobic transition for the determination of work load intensities during endurance training

Anaerobic and aerobic-anaerobic threshold (4 mmol/l lactate), as well as maximal capacity, were determined in seven cross country skiers of national level. All of them ran in a treadmill exercise for at least 30 min at constant heart rates as well as at constant running speed, both as previously determined for the aerobic-anaerobic threshold. During the exercise performed with a constant speed, lactate concentration initially rose to values of nearly 4 mmol/l and then remained essentially constant during the rest of the exercise. Heart rate displayed a slight but permanent increase and was on the average above 170 beats/min. A new arrangement of concepts for the anaerobic and aerobic-anaerobic threshold (as derived from energy metabolism) is suggested, that will make possible the determination of optimal work load intensities during endurance training by regulating heart rate.
How the LEGO Group Is Embarking on Architectural Path Constitution to Transform Its Information Infrastructure into a Digital Platform

Traditional companies are increasingly turning towards platform strategies to gain speed in the development of digital value propositions and prepare for the challenges arising from digitalization. This paper reports on the digitalization journey of the LEGO Group to elaborate how brick-and-mortar companies can break away from a drifting information infrastructure and trigger its transformation into a digital platform. Conceptualizing information infrastructure evolution as path-dependent process, the case study explores how mindful deviations by Enterprise Architects guide installed base cultivation through collective action and trigger the creation of a new ‘platformization’ path. Additionally, the findings portrait Enterprise Architecture management as a process of socio-technical path constitution that is equally shaped by deliberate human interventions and emergent forces through path dependencies.
The role of autophagy in modulation of neuroinflammation in microglia

Microglia have multiple functions in regulating homeostasis in the central nervous system (CNS), and microglial inflammation is thought to play a role in the etiology of the neurodegenerative diseases. When endogenous or exogenous stimuli trigger disorders in microenvironmental homeostasis in CNS, microglia critically determine the fate of other neural cells. Recently, it was reported that autophagy might influence inflammation and activation of microglia. Though the interaction between autophagy and macrophages has been reported and reviewed in length, the role of autophagy in microglia has yet to be reviewed. Herein, we will highlight recent advances on the emerging role of autophagy in microglia, focusing on the regulation of autophagy during microglial inflammation, and the possible mechanism involved.
Service-Generated Big Data and Big Data-as-a-Service: An Overview

With the prevalence of service computing and cloud computing, more and more services are emerging on the Internet, generating huge volume of data, such as trace logs, QoS information, service relationship, etc. The overwhelming service-generated data become too large and complex to be effectively processed by traditional approaches. How to store, manage, and create values from the service-oriented big data become an important research problem. On the other hand, with the increasingly large amount of data, a single infrastructure which provides common functionality for managing and analyzing different types of service-generated big data is urgently required. To address this challenge, this paper provides an overview of service-generated big data and Big Data-as-a-Service. First, three types of service-generated big data are exploited to enhance system performance. Then, Big Data-as-a-Service, including Big Data Infrastructure-as-a-Service, Big Data Platform-as-a-Service, and Big Data Analytics Software-as-a-Service, is employed to provide common big data related services (e.g., accessing service-generated big data and data analytics results) to users to enhance efficiency and reduce cost.
An Efficient Explanation of Individual Classifications using Game Theory

We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method’s initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and real-world data sets. The results demonstrate that the method is efficient and that the explanations are intuitive and useful.
Modeling Diabetes to Achieve Closed-loop Control of Glucose Levels : A Literature Review

With diabetes affecting 7% of the United States population, and 180 million people worldwide, there is a lot of interest in the development of more advanced and effective diabetes treatment methods. This paper presents a literature review of recently-published research that is working toward the development of a wearable closed-loop controller for glucose regulation. A variety of approaches to modeling diabetes and applying closed-loop control to this problem are presented and analyzed with respect to some of the major challenges that are involved. The major challenges discussed are the complexity of the glucose-insulin dynamics in the human body, the site-specific insulin dynamics, and the accuracy and precision of the technology involved. The approaches that are discussed include traditional PID controllers, an innovative neural network modeling technique, model reference adaptive control, robust parameter estimation, and a PID switching control strategy.
Edgecourier: an edge-hosted personal service for low-bandwidth document synchronization in mobile cloud storage services

Using cloud storage to automatically back up content changes when editing documents is an everyday scenario. We demonstrate that current cloud storage services can cause unnecessary bandwidth consumption, especially for office suite documents, in this common scenario. Specifically, even with incremental synchronization approach in place, existing cloud storage services still incur whole-file transmission every time when the document file is synchronized. We analyze the problem causes in depth, and propose EdgeCourier, a system to address the problem. We also propose the concept of edge-hosed personal service (EPS), which has many benefits, such as helping deploy EdgeCourier easily in practice. We have prototyped the EdgeCourier system, deployed it in the form of EPS in a lab environment, and performed extensive experiments for evaluation. Evaluation results suggest that our prototype system can effectively reduce document synchronization bandwidth with negligible overheads.
Incept-N: A Convolutional Neural Network based Classification Approach for Predicting Nationality from Facial Features

Nationality of a human being is a well-known identifying characteristic used for every major authentication purpose in every country. Albeit advances in application of Artificial Intelligence and Computer Vision in different aspects, its’ contribution to this specific security procedure is yet to be cultivated. With a goal to successfully applying computer vision techniques to predict a human’s nationality based on his facial features, we have proposed this novel method and have achieved an average of 93.6% accuracy with very low misclassification rate. KeywordsNationality, Artificial Intelligence,
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entityoriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.
Why People Continue to Use Social Networking Services: Developing a Comprehensive Model

Social networking (SNW) services such as Facebook and MySpace are growing exponentially. While many people are spending an increasing amount of their time on the services, others use them minimally or discontinue use after a short period of time. This research is asking the question: What are the salient factors influencing individuals to continue using and extending the range of SNW services after their initial acceptance? This research recognizes that long-term viability and the eventual success of these services depends on continued usage rather than initial acceptance, and usage continuance of SNW services at the individual level is fundamental to the survival of many social technology-empowered businesses and organizations. We look to the Expectation-Confirmation Model of information systems (IS) continuance and a series of social theories as the underlying theoretical foundations. We develop the Usage Continuance Model of SNW Services to investigate continued usage behavior and enduring impacts of SNW services. The model proposes that usage continuance behavior of SNW services is a joint function of individuals’ perceptions of (1) intrinsic flow experience of SNW services, expected instrumentality of SNW services in managing and improving informational and relational values, and social influence as the outgrowth of social capital, and (2) costs in informational risks and participative efforts of time and social exchanges. The joint function is moderated by individuals’ use history of SNW features. The proposed model and hypotheses offer a comprehensive framework for empirically extending the IS continuance research to the ever pervasive SNW context.
Analytical Approach to Study Effect of Shear Wall on Flat Slab & Two Way Slab

Slab directly supported on column termed as flat slab. The present objective of this work is to compare behavior of flat slab with old traditional two way slab along with effect of shear walls on their performance. The parametric studies comprise of maximum lateral displacement, storey drift and axial forces generated in the column. For these case studies we have created models for two way slabs with shear wall and flat slab with shear wall, for each plan size of 16X24 m and 15X25 m, analyzed with Staad Pro. 2006 for seismic zones III, IV and V with varying height 21m, 27 m , 33 m and 39 m. This investigation also told us about seismic behavior of heavy slab without end restrained. For stabilization of variable parameter shear wall are provided at corner from bottom to top for calculation. Results is comprises of study of 36 models, for each plan size, 18 models are analyzed for varying seismic zone.  Axial Forces, Flat Slab, Lateral Displacement, Seismic Zones, Shear Wall, Two way slab, Storey Drift.
Information Sharing for Supply Chain Management Based on Block Chain Technology

Supply Chain Management systems provide information sharing and analysis to companies and support their planning activities. They are not based on the real data because there is asymmetric information between companies, then leading to disturbance of the planning algorithms. On the other hand, sharing data between manufacturers, suppliers and customers becomes very important to ensure reactivity towards markets variability. Especially, double marginalization is a widespread and serious problem in supply chain management. Decentralized systems under wholesale price contracts are investigated, with double marginalization effects shown to lead to supply insufficiencies, in the cases of both deterministic and random demands. This paper proposes a blockchain based solution to address the problems of supply chain such as Double Marginalization and Information Asymmetry etc.
FORECASTING INTERMITTENT DEMAND BY MARKOV CHAIN MODEL

The inventory control of the products which have intermittent demand is essential for many organizations since these items have a low lead time demand but a high price. Since the intermittent demand pattern is irregular, the estimation of the lead time demand is challenging. A modified Markov chain model (MMCM) has been proposed for modeling and estimating intermittent demand data, motivated by a case study. The performance of MMCM and the traditional methods have been compared by accuracy measures. The results reveal that the proposed method is a good competitor or even better than other methods.
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations

We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.
Agile Software Development: The People Factor

I n a previous article (" Agile Software Development: The Business of Innovation , " Computer, Sept. 2001, pp. 120-122), we introduced agile software development through the problem it addresses and the way in which it addresses the problem. Here, we describe the effects of working in an agile style. Over recent decades, while market forces, systems requirements, implementation technology, and project staff were changing at a steadily increasing rate, a different development style showed its advantages over the traditional one. This agile style of development directly addresses the problems of rapid change. A dominant idea in agile development is that the team can be more effective in responding to change if it can • reduce the cost of moving information between people, and • reduce the elapsed time between making a decision to seeing the consequences of that decision. To reduce the cost of moving information between people, the agile team works to • place people physically closer, • replace documents with talking in person and at whiteboards, and • improve the team's amicability—its sense of community and morale— so that people are more inclined to relay valuable information quickly. To reduce the time from decision to feedback, the agile team • makes user experts available to the team or, even better, part of the team and • works incrementally. Making user experts available as part of the team gives developers rapid feedback on the implications to the user of their design choices. The user experts, seeing the growing software in its earliest stages, learn both what the developers misunderstood and also which of their requests do not work as well in practice as they had thought. The term agile, coined by a group of people experienced in developing software this way, has two distinct connotations. The first is the idea that the business and technology worlds have become turbulent , high speed, and uncertain, requiring a process to both create change and respond rapidly to change. The first connotation implies the second one: An agile process requires responsive people and organizations. Agile development focuses on the talents and skills of individuals and molds process to specific people and teams, not the other way around. The most important implication to managers working in the agile manner is that it places more emphasis on people factors in the project: amicability, talent, skill, and communication. These qualities become a primary concern …
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data called HSI-CNN, which can also provides ideas for the processing of one-dimensional data. Firstly, the spectral-spatial feature is extracted from a target pixel and its neighbors. Then, a number of one-dimensional feature maps, obtained by convolution operation on spectral-spatial features, are stacked into a two-dimensional matrix. Finally, the two-dimensional matrix considered as an image is fed into standard CNN. This is why we call it HSI-CNN. In addition, we also implements two depth network classification models, called HSI-CNN+XGBoost and HSI-CapsNet, in order to compare the performance of our framework. Experiments show that the performance of hyperspectral image classification is improved efficiently with HSI-CNN framework. We evaluate the model's performance using four popular HSI datasets, which are the Kennedy Space Center (KSC), Indian Pines (IP), Pavia University scene (PU) and Salinas scene (SA). As far as we concerned, the accuracy of HSI-CNN has kept pace with the state-of-art methods, which is 99.28%, 99.09%, 99.57%, 98.97% separately.
Representation, Analysis, and Recognition of 3D Humans: A Survey

Computer Vision and Multimedia solutions are now offering an increasing number of applications ready for use by end users in everyday life. Many of these applications are centered for detection, representation, and analysis of face and body. Methods based on 2D images and videos are the most widespread, but there is a recent trend that successfully extends the study to 3D human data as acquired by a new generation of 3D acquisition devices. Based on these premises, in this survey, we provide an overview on the newly designed techniques that exploit 3D human data and also prospect the most promising current and future research directions. In particular, we first propose a taxonomy of the representation methods, distinguishing between spatial and temporal modeling of the data. Then, we focus on the analysis and recognition of 3D humans from 3D static and dynamic data, considering many applications for body and face.
Economic Incentives and Foster Child Adoption

Every year, a large number of children in the United States enter the foster care system. Many of them are eventually reunited with their biological parents or quickly adopted. A significant number, however, face long-term foster care, and some of these children are eventually adopted by their foster parents. The decision by foster parents to adopt their foster child carries significant economic consequences, including forfeiting foster care payments while also assuming responsibility for medical, legal, and educational expenses, to name a few. Since 1980, U.S. states have begun to offer adoption subsidies to offset some of these expenses, significantly lowering the cost of adopting a child who is in the foster care system. This article presents empirical evidence of the role that these economic incentives play in foster parents’ decision of when, or if, to adopt their foster child. We find that adoption subsidies increase adoptions through two distinct price mechanisms: by lowering the absolute cost of adoption, and by lowering the relative cost of adoption versus long-term foster care.