nils-herrmann/clean_scirepeval huggingface dataset:
Promoting Student Engagement Using Social Media Technologies
Year: 2018

Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students' satisfaction and engagement. Results show that using social media enhances students' engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
Feasibility study of the sterilization of pork and human skin surfaces by atmospheric pressure plasmas
Year: 2009

Atmospheric pressure radio frequency (rf) glow discharge characteristics were studied aiming for the plasma treatment of living tissues such as pork and human skin. Electrical and optical measurements of the plasma gave reasonable values at low current of 4 mA and gas temperature of 60 degC at 100 W. The colorimetric measurement of the treated pork sample demonstrated negligible thermal effect from the plasma. In addition, the sterilization efficiency of Escherichia coli inoculated on pork surface was comparable with that of a conventional UV sterilizer. The experimental results promise that the atmospheric pressure rf plasma can be safely applied to human skin treatment without electrical and thermal damage.
Small Traffic Sign Detection Through Selective Feature Fusion Based Faster R CNN With Arc Softmax Loss
Venue: IWCTS@SIGSPATIAL
Year: 2019

Traffic signs are basic and important elements in maps. They are related to traffic regulations, profoundly affecting/managing the travel mode of human beings and efficiency of vehicle running. Traffic sign mining technology is applied in many research fields such as traditional map update, high precision map establishment and automatic driving. Image based traffic sign identification technology has the advantages of low cost and high efficiency over manual processing mode, and traffic sign detection has thus become a significant task with the pacing advancement of autonomous driving. However, many common object detection methods cannot be directly applied to this task, as the size of traffic signs are very small yet they vary considerably. Due to such characteristics, features of traffic signs are difficult to capture, and are harder to discriminate between classes. To address this problem, we proposed a selective feature fusion based Faster R CNN with Arc Softmax loss, which optimizes the detection performance from the two following ways: network structure and loss function. We discover that each Faster R CNN layer is only capable of detecting targets within a certain size range. By carefully selecting and combining different layers' feature maps, we can extract features that effectively represent traffic signs of various sizes. Then, Arc Softmax loss penalizes the angular distances between the feature vectors of different signs, and their corresponding weight vectors of the last fully connected layers, thereby encouraging intra class compactness and inter class separability between learned features. Extensive analysis and experiments on the challenging Tsinghua Tencent 100K benchmark demonstrate the superiority and implementation simplicity of our proposed method. Code will be made publicly available.
"Children of the Great Mexican Family" Anglo American Immigration to Texas and the Making of the American Empire, 1820 1861
Year: 2015

This dissertation examines the thousands of Anglo Americans who immigrated to Mexican Texas during the years following its independence from Spain. Long assumed to be the forbears of Manifest Destiny, it argues instead that these immigrants demonstrated a sincere desire to become Mexican citizens, that they were attracted to that country as much for its political promise as for its natural resources, and that they in fact shared more with their northern Mexican neighbors than with their compatriots in the northeastern United States. Drawing chiefly from the personal papers, diplomatic correspondence, and newspapers of Anglo settlers and their Mexican allies, this dissertation exposes a political irony at the heart of the United States' imperial rise that it had to do with that country's early political weakness, rather than Mexico's, and that the people most responsible for it were in fact trying to escape US dominion, not perpetuate it. It argues that Mexico offered a viable and attractive alternative to the US. Rather than seeing Mexico's commitment to regional sovereignty and local autonomy as its chief failure, this project argues that it was precisely what attracted these immigrants to Mexico and formed the basis of their loyalty. Yet, if Mexico's weak central government was its strength in the 1820's, it would be the source of conflict and secession by the 1830's and 1840's. But Mexico was not unique in this regard. Indeed, this project recasts the US Civil War as part of a longer and more expansive experiment in extreme federalism by arguing that Texans seceded from Mexico for many of the same reasons that they and the rest of the South would ultimately secede from the United States. Thus, throughout the early part of the nineteenth century, the dominant geopolitical arrangement of the northwestern hemisphere was not primarily national. Rather, the southern United States and northern Mexico formed a semi autonomous region united by its inhabitants' shared commitment to regional sovereignty, martial citizenship, forced labor, and free trade; and one that presented the possibility of a geopolitical arrangement very different from that which ultimately emerged. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group History First Advisor Stephanie McCurry
Communication Efficient Distributed Deep Learning with GPU FPGA Heterogeneous Computing
Venue: 2020 IEEE Symposium on High Performance Interconnects (HOTI)
Year: 2020

Distributed deep learning has an inevitable bottleneck in communication between GPUs. This bottleneck is due to the difference in the data transfer latency of internode and intra node communication. Here, we propose a GPU FPGA (field programmable gate array) heterogeneous computing system and a hardware specific communication module with parameter based computation/communication overlap. In our system, we configure a network interface card (NIC) with a data aggregation function. To reduce the load on CPUs, we developed a device driver for remote direct memory access between GPUs and FPGAs. The hardware and software can be run with TensorFlow and Horovod. For comparison, we compared our system with the conventional GPUDirect RDMA system. Results of the measurement show Allreduce latency is reduced to 1/4 by offloading Allreduce to FPGA NICs. Our hardware/software co design can also conceal about 90% of the communication overhead and improve scalability by 20% The end to end time consumed for training in distributed deep learning with ResNet 50 and ImageNet is reduced to 87.3% without any degradation in validation accuracy.
The Hippocratic Oath and the Declaration of Geneva: legitimisation attempts of professional conduct
Venue: Medicine, health care, and philosophy
Year: 2019

The Hippocratic Oath and the Declaration of Geneva of the World Medical Association are compared in terms of content and origin. Their relevance for current medical practice is investigated. The status which is ascribed to these documents will be shown and the status which they can reasonably claim to have will be explored. Arguments in favor of the Hippocratic Oath that rely on historical stability or historical origin are being examined. It is demonstrated that they get caught up in paradoxes. Should doctors swear the Hippocratic Oath or the Declaration of Geneva? The Hippocratic Oath is a remarkable historic document, which contains important elements still relevant for medical ethics today. Its interpretation as a timeless, still valid medical code is unfounded. The historical arguments, that should justify its validity, are untenable. The Declaration of Geneva, and not the Hippocratic Oath, can legitimately claim to come close to representing the most important principles of professional medical conduct in today's globalised world.
Climate, landscape history and management drive Eurasian steppe biodiversity
Year: 2020

Palaearctic steppes are among the largest continuous terrestrial natural habitats of the world with high biodiversity at multiple scales. Steppe grasslands and adjacent forest steppes are characteristic landscape elements from Central and Eastern Europe to Northern China across the whole temperate zone of Eurasia with some floristically similar regions in North Africa, Anatolia, and Iran. The origin and evolution of the landscape history of the Eurasian steppes started in the lower Miocene and passed through widening and diminishing of the steppe area. This history is mirrored by the phylogeography of typical steppe species. Today, the area of typical steppes is highly reduced by various activities of humans. Because steppes are often characterised by fertile soils, they are subjected to large scale degradation and area loss by intensive crop production, or other forms of overuse especially in the Western part of their distribution zone. Steppes are among the most threatened and least protected habitats globally, and therefore, the conservation and restoration of steppe biodiversity, especially in agriculture dominated landscapes, are key priorities for research and practice. Effective biodiversity conservation and restoration depend, however, on knowledge of ecological properties and processes that are responsible for the sustainment of crucial ecological functions and services in pristine steppes. In this special issue we aimed to give emphasis on the most recent and novel research in steppe biodiversity and ecology, highlighting the enormous levels of biodiversity at multiple scales shaped by a complex interaction of a long term evolutionary history, macroclimate and local factors including disturbances such as overgrazing. Fine tuned management, for example in a form of low intensity and extensive grazing is urgently needed for maintaining and/or restoring steppe resistance and resilience to climate change extremities.
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
Year: 2016

Machine learning techniques are applied to make prediction models of the {G}_{0}{W}_{0} band gaps for 270 inorganic compounds using Kohn Sham (KS) band gaps, cohesive energy, crystalline volume per atom, and other fundamental information of constituent elements as predictors. Ordinary least squares regression (OLSR) least absolute shrinkage and selection operator, and nonlinear support vector regression (SVR) methods are applied with two levels of predictor sets. When the KS band gap by generalized gradient approximation of Perdew Burke Ernzerhof (PBE) or modified Becke Johnson (mBJ) is used as a single predictor, the OLSR model predicts the {G}_{0}{W}_{0} band gap of randomly selected test data with the root mean square error (RMSE) of 0.59 eV. When KS band gap by PBE and mBJ methods are used together with a set of predictors representing constituent elements and compounds, the RMSE decreases significantly. The best model by SVR yields the RMSE of 0.24 eV. Band gaps estimated in this way should be useful as predictors for virtual screening of a large set of materials.
SWAP Project: Beyond the State of the Art on Harvested Energy Powered Wireless Sensors Platform Design
Venue: 2011 IEEE Eighth International Conference on Mobile Ad Hoc and Sensor Systems
Year: 2011

The main goal of the SWAP project is that of designing, implementing and ultimately testing a new breed of wireless sensor nodes with energy scavenging capabilities. Our design will include novel energy scavenging hardware as well as network protocols and algorithms. In this paper, we summarize the outcomes of the first year of the project as well as the way forward to the further phases. In particular, we analyze the state of the art in the main research areas: energy efficient communication protocol design, ultra low power hardware design and most advanced harvesting techniques. For what concerns the future phases of the project we elaborate on the adoption of statistical predictive models for the energy description, we account for game theoretic approaches for distributed optimization and we apply our considerations on the most modern standards for wireless sensor networks communication. We review the state of the art on hardware components, to provide a shortlist of the most efficient building blocks of the SWAP platforms as well as a draft version of the schematics of the module. Finally, we provide a brief overview on the latest energy harvester for miniature scale devices and we argue on the feasibility of a hybrid solar electromagnetic harvesting module.
Measurement of the total spectrum of electrons and positrons in the energy range of 300 1500 GeV in the PAMELA experiment with the aid of a sampling calorimeter and a neutron detector
Year: 2015

A method based on the use of a sampling calorimeter was developed for measuring the total energy spectrum of electrons and positrons from high energy cosmic rays in the PAMELA satellite borne experiment. This made it possible to extend the range of energies accessible to measurements by the magnetic system of the PAMELA spectrometer. Themethod involves a procedure for selecting electrons on the basis of features of a secondary particle shower in the calorimeter. The results obtained by measuring the total spectrum of cosmic ray electrons and positrons in the energy range of 300 1500 GeV by the method in question are presented on the basis of data accumulated over a period spanning 2006 and 2013.
Communicating art through interactive technology: new approaches for interaction design in art museums
Venue: NordiCHI
Year: 2008

This paper discusses new approaches to interaction design for communication of art in the physical museum space. In contrast to the widespread utilization of interactive technologies in cultural heritage and natural science museums it is generally a challenge to introduce technology in art museums without disturbing the domain of the art works. To explore the possibilities of communicating art through the use of technology, and to minimize disturbance of the artworks, we apply four main approaches in the communication: 1) gentle audio augmentation of art works; 2) conceptual affinity of art works and remote interactive installations; 3) using the body as an interaction device; 4) consistent audio visual cues for interaction opportunities. The paper describes the application of these approaches for communication of inspirational material for a Mariko Mori exhibition. The installations are described and argued for. Experiences with the interactive communication are discussed based on qualitative and quantitative evaluations of visitor reactions. It is concluded that the installations are received well by the visitors, who perceived exhibition and communication as a holistic user experience with a seamless interactive communication.
Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units
Venue: Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year: 2018

Embedding emergent parts in shape grammars is computationally challenging. The first challenge is the representation of shapes, which needs to enable reinterpretation of parts regardless of the creation history of the shapes. The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space time efficiently. In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization based algorithm to compute emergent parts. The execution time of the proposed method is improved substantially by dividing the search space into small parts and carrying out searches in each part concurrently using a graphics processing unit. The experiments show that the proposed implementation detects emergent parts accurately and time efficiently.
The Milk Thistle Seed Cakes and Hempseed Cakes are Potential Feed for Poultry
Venue: Animals an open access journal from MDPI
Year: 2020

In accordance with Commission Regulation (EC) No 726/2004, the use of antibiotics as stimulators of animal growth and performance has been banned in all European Union countries since 2006 (due to the elimination of antibiotic residues from the human food chain) Due to this ban, many scientists are studying alternative approaches to the use of various biologically active substances with a growth promoting effect. A promising direction is the use of alternative feeds containing bioactive compounds or mixtures of natural origin, or the use of phytoadditives or plant extracts, probiotics, prebiotics, symbiotics or oilseed by products, such as hempseed cakes and milk thistle seed cakes, in animal nutrition.  The aims of this work were to summarize the nutritional value of the milk thistle seed cakes and hempseed cakes and describe the influence on selected performance parameters, metabolism and animal health from inclusion of these non traditional feeds into diets. It seems more appropriate to apply the extract of the bioactive substances complex to the livestock diets than addition of expellers or other forms of plants processing. The seed expellers, etc. mostly worsened the chickens' performance parameters with higher doses in diets, while most of the work using the extract yields had positive results on animal performance.
Effect of substituting organic fraction of municipal solid waste with fruit and vegetable wastes on anaerobic digestion
Venue: Journal of Material Cycles and Waste Management
Year: 2019

The potential of replacing fruit and vegetable wastes (FVW) as remarkable sources of environmentally offensive biomass in metropolises with organic fraction of municipal solid waste (OFMSW) on biogas production was investigated. Components of OFMSW as a source separated MSW were prepared in 5 categories of fat and protein, starch, cellulose, fruit, and vegetable waste. Experiments were carried out in four FVW/OFMSW replacement ratios (wet basis) of 0, 15, 30 and 45% at two total solid (TS) concentrations of 8% and 15% at 37 degC. Main results indicated that co digestion is favorable at lower TS content (8% TS) and higher FVW/OFMSW ratios up to 30% Although methane yield did not improve significantly for these substitution ratios, the concentration of methane in the biogas enhanced up to 68% and about 92% of volatile solids' removal achieved. Kinetics study, based on lag phase of Gompetz model, indicated process rapidity increased proportionally to the FVW replacement ratios. Dedication up to 30% of feedstock capacity of ongoing OFMSW biogas plants in metropolises with FVW can suggest more revenue for plants through lowering HRT, increasing biodegradability of substrate, removing undesirable gas and supplying water needed for anaerobic digestion.
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
Venue: IEEE Access
Year: 2016

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of "smart transport," Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs) This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle to vehicle, vehicle to roadside, vehicle to infrastructure of cellular networks, vehicle to personal devices, and vehicle to sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.
Pathwise component descent method with MC+ penalty for low rank matrix recovery
Venue: Pattern Recognit. Lett.
Year: 2016

A new algorithm for low rank matrix recovery.Our algorithm can avoid getting trapped into a bad local minimum.Provide convergence guarantee for the proposed algorithm.Numerical results on matrix completion show superiority. We consider the low rank matrix recovery (LRM) problem, which recovers an unknown low rank matrix from very limited information. Although recent study has shown that non convex models for LRM can serve as better approximation of low rank regularization and outperform their convex counterpart, these models suffer from getting trapped into a bad local minimum. We propose an algorithm, named PIC LR, to address this problem. PIC LR is inspired by a recent algorithm, called SparseNet, which addresses analogical problem in the study of sparse optimization. Specifically, we take advantage of the properties of MC+ penalty and employ path following technique. We also generalize coordinate descent to better imitate SparseNet. In numerical experiment, we apply PIC LR to matrix completion problem, and the results show that PIC LR outperforms several state of the art solvers in terms of precision.
Introducing the Food Fraud Prevention Cycle (FFPC) A dynamic information management and strategic roadmap
Venue: Food Control
Year: 2019

Food fraud intentional deception for economic gain using food is an emerging research field due to increased awareness of the health hazard, increased regulatory scrutiny, increased awareness of the costs, a seemingly endless stream of identified incidents, and new compliance requirements. Beyond the laws and regulations that are being more routinely applied, commercial standards have expanded to address food fraud. Specifically, since January 2018 the Global Food Safety Initiative (GFSI) has required that Food Safety Management Systems include a Food Fraud Vulnerability Assessment (FFVA) and a Food Fraud Prevention Strategy (FFPS) When an entity begins to address the topic, the first consideration is what is food fraud and includes is food fraud really a problem, the focus then shifts to how to detect food fraud including the development of test methods. At that point, there are more fundamental and practical implementation questions such as how to start, what to do, and how much is enough, and how to measure success. The review of these questions has resulted in one unmet need for a method to manage the information and activities in the FFPS. Consideration of these concepts provided the realization that there was a need to connect the steps into an interconnected and dynamic system. The objective was to connect everything to everything. The Food Fraud Prevention Cycle (FFPC) was developed to meet this unmet need. This research paper includes four illustrative examples including a response to: new public policy priorities, a new food fraud incident, and a review of a new countermeasure or control system. The adoption of a holistic and all encompassing information management cycle will enable a harmonized approach and the sharing of best practices.