nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Dispatch Model of Wind Power Accommodation in Combined Heat and Power System Based on Unit Commitment
Venue: 2019 IEEE Innovative Smart Grid Technologies Asia (ISGT Asia)
Year: 2019

The principle of power determined by heat and the anti peak regulation characteristics of wind power output cause the difficulty of peak regulation in the power grid along with the production of the abandon wind. By analyzing the topological differences between power system and thermal system, establishing positive and negative spinning reserve constraints of the district heating network, and considering both the hot and the cold start up cost of the units as well as the thermoelectric coupling of the CHP units, a unit commitment model of wind power accommodation in combined heat and power system whose objective function is to minimize the total coal consumption and environmental cost of the units is presented in this paper. In this model, unit commitment and optimized dispatch are combined, the electric load is shared by all the units, and the heat load is balanced by the local CHP plants respectively. Finally, the optimization results show that while meeting power and heat load demand, the model of this paper can timely start up or shut down units according to load change and spinning reserve demand, reduce total minimum technical output of operating units, and further improve wind power accommodation rate.
A High Performance Parallel Hardware Architecture of SHA 256 Hash in ASIC
Venue: 2020 22nd International Conference on Advanced Communication Technology (ICACT)
Year: 2020

The SHA 256 algorithm is used to ensure the integrity and authenticity of data in order to achieve a good security thus is playing an important role in various applications, such as e transactions and bitcoins. The SHA 256 computation capacity is a main research direction of Hashing Algorithm. In order to improve the computation capacity of hardware, the proposed design first uses pipeline principle and circuitry of timing prediction to find a most efficient architecture for implementation. Then it is optimized with hash function and hardware characteristics to give a high performance hardware architecture of SHA 256 hash. Three pipelines are used to replace the critical path in the round functions which can shorten the timing path, and divide the computation chain into independent steps. Multi computation of SHA 256 is working in parallel pipelines, indicating that the computation capacity can be 3 times of that with standard SHA 256 implementation. The proposed SHA 256 hardware architecture has been implemented and synthesized with Intel 14nm technology. Simulation and synthesis results show the proposed SHA 256 hashing throughput can be improved by 3 times with 50.7% power reduction, at an area cost of 2.9 times compared to that of the standard implementation.
The effects of hemispheric dominance, literacy acquisition, and handedness on the development of visuospatial attention: A study in preschoolers and second graders.
Venue: Journal of experimental child psychology
Year: 2020

A tendency to over attend the left side of the space (i.e. pseudoneglect) has been repeatedly reported in Western adult populations and is supposed to reflect a right hemisphere dominance in the control of visuospatial attention. This neurobiological hypothesis has been partially challenged by growing evidence showing that pseudoneglect is profoundly triggered by cultural practices such as reading and writing habits. Accordingly, more recent theoretical accounts suggest a strict coupling between nature and nurture dimensions at the origins of such bias. To further explore this possibility, here we first administered a digitized cancellation task to right handed Western children before and after literacy acquisition. Results showed an incremental leftward shift of attention in the cancellation of the first target and an increasing preference for a left to right visual search from preschoolers to second graders. Yet, despite these differences, the overall distribution of visuospatial attention was biased to the left in both groups. To explore the role of handedness in visuospatial asymmetries, we also tested a group of left handed second graders. Results showed an impact of handedness on visuospatial performance, with an accentuated rightward oriented visual search for left handed children, although the overall distribution of attention was again biased to the left hemispace. Taken together, these findings do not provide support to a pure neurobiological view of visuospatial biases. Rather, our study indicates that the control of visuospatial attention is mediated by a dynamic interplay among biological (i.e. right hemisphere dominance) biomechanical (i.e. hand dominance) and cultural (i.e. reading habits) factors.
Cryptanalysis of a Symmetric Fully Homomorphic Encryption Scheme
Venue: IEEE Transactions on Information Forensics and Security
Year: 2018

Fully homomorphic encryption supports meaningful computations on encrypted data, and hence, is widely used in cloud computing and big data environments. Recently, Li et al. constructed an efficient symmetric fully homomorphic encryption scheme and utilized it to design a privacy preserving outsourced association rule mining scheme. Their proposal allows multiple data owners to jointly mine some association rules without sacrificing the data privacy. The security of the homomorphic encryption scheme against the known plaintext attacks was established by examining the hardness of solving nonlinear systems. However, in this paper, we illustrate that the security of Li et al.'s homomorphic encryption is overvalued. First, we show that we can recover the first part of the secret key from several known plaintext/ciphertext pairs with the continued fraction algorithm. Second, we find that we can retrieve the second part of the secret key through the Euclidean algorithm for the greatest common divisor problem. Experiments on the suggested parameters demonstrate that in case of more than two homomorphic multiplications, all the secret keys of the randomly instantiated Li et al.'s encryption schemes can be very efficiently recovered, and the success probability is at least 98% for one homomorphic multiplication.
Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering
Venue: IEEE Transactions on Cybernetics
Year: 2019

Attributed graphs have attracted much attention in recent years. Different from conventional graphs, attributed graphs involve two different types of heterogeneous information, i.e. structural information, which represents the links between the nodes, and attribute information on each of the nodes. Clustering on attributed graphs usually requires the fusion of both types of information in order to identify meaningful clusters. However, most of existing works implement the combination of these two types of information in a "global" manner by treating all nodes equally and learning a global weight for the information fusion. To address this issue, this paper proposed a novel weighted {K} means algorithm with "local" learning for attributed graph clustering, called adaptive fusion of structural and attribute information (Adapt SA) and analyzed the convergence property of the algorithm. The key advantage of this model is to automatically balance the structural connections and attribute information of each node to learn a fusion weight, and get densely connected clusters with high attribute semantic similarity. Experimental study of weights on both synthetic and real world data sets showed that the weights learned by Adapt SA were reasonable, and they reflected which one of these two types of information was more important to decide the membership of a node. We also compared Adapt SA with the state of the art algorithms on the real world networks with varieties of characteristics. The experimental results demonstrated that our method outperformed the other algorithms in partitioning an attributed graph into a community structure or other general structures.
Evaluation of Existing Solid Waste Management System in Una Town, India
Venue: Sustainable Waste Management: Policies and Case Studies
Year: 2019

Municipal solid waste management is an important function of the urban local body (ULB) of any city or town in India. The present study reports the existing prevalent MSW management system being carried out in Una, Himachal Pradesh. The total waste generation in Himachal Pradesh is about 350 tonnes per day (TPD) of which the total waste generation in Una town is about 6 TPD with per capita generation of 0.42 kg/day of which 5.5 TPD is disposed in an open dumpsite in Lalsinghi about 5 km from town with a collection efficiency of about 90% The study uses the "wasteaware" benchmark indicators for evaluating the existing municipal solid waste (MSW) management practices. The "wasteaware" benchmark parameters incorporate both quantitative and qualitative methods for determining the competency of the existing systems. Further, a matrix method has been utilised to complement the "wasteaware" benchmark indicators in evaluating the existing techniques. From the results, it was observed that the collection efficiency in the study area is good categorised as M/H, while transportation, disposal and recycling facilities are lacking (low) The overall score for the Una town is 38% after evaluation from matrix method. Thus, the study showed that there is an immediate need in the improvement of the existing MSW management practices and certain suggestions have been provided in this context.
ENHANCEMENT OF FLOW JET COMBINED BOILING HEAT TRANSFER OF FC 72 OVER MICRO PIN FINNED SURFACES
Year: 2012

For highly efficient solving of the power dissipation problem of electronic components with high heat flux, experimental study of the flow jet combined boiling heat transfer on silicon chips was conducted. Micro pin fins with dimensions of 30x60 mm and 30x120 mm (thickness x height) were fabricated on the chip surfaces by the dry etching technique. The experiments were made at three different cross flow velocities Vc (0.5, 1.0, 1.5 m/s) and different jet velocities Vj (0 2 m/s) with two liquid subcooling Tsub (25*C and 35*C) A smooth surface was also tested for comparison. The micro pin fins showed a considerable heat transfer enhancement due to an increase in effective heat transfer surface area caused by micro convection around micro pin fins. The maximum allowable heat flux qmax is increased and the wall superheat is decreased greatly. For a given Vc, the heat transfer enhancement is more significant as Vj increases, but the qmax increment decreases as Vc increases. The fin efficiency decreases as Vc or/and Vj increases or the heat flux q increases. The maximum qmax can reach 161 W/cm at Vc 1.5 m/s, Vj 2 m/s, Tsub 35*C for chip PF30 120.
BEHRT: Transformer for Electronic Health Records
Venue: Scientific Reports
Year: 2020

Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR) capable of simultaneously predicting the likelihood of 301 conditions in one's future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0 13.2% (in terms of average precision scores for different tasks) over the existing state of the art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g. diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre )training results in disease and patient representations can be useful for future studies (i.e. transfer learning)
Robotics training program: evaluation of the satisfaction and the factors that influence success of skills training in a resident robotics curriculum.
Venue: Journal of endourology
Year: 2011

We present our experience of training residents in a weekend robotic training program to assess its effectiveness and perceived usefulness.  Bimonthly training sessions were arranged such that residents could sign up for hour long, weekend training sessions. They are required to complete four training sessions. Five tasks were scored for time and accuracy: Peg Board, checkerboard, string running, pattern cutting, and suturing. Participants completed surveys (5 point Likert scale) regarding program utility, ease of attendance, and interest in future weekend training sessions.  Mean number of trials completed by 19 residents was >4, and 16 completed the trials within an average of 13.7+ 8.1 mos. Significant improvements (P<0.05) were seen in final trials for Peg Board accuracy (95.8% vs 79.0% checkerboard deviation (4.8% vs 18.2% and time (293 s vs 404 s) pattern cutting time (257 s vs 399 s) and suture time (203 s vs 305 s) Time to previous session correlated with relative improvement in Peg Board and pattern cutting time (r=0.300 and 0.277, P=0.021 and 0.041) but no specific training interval was predictive of improvement. Residents found the course easy to attend (3.6) noted skills improvement (4.1) and found it useful (4.0) CONCLUSION Training in the weekend sessions improved performance of basic tasks on the robot. Training interval had a modest effect on some exercises and may be more important for difficult tasks. This training program is a useful supplement to resident training and would be easy to implement in most programs.
Sub Nyquist computational ghost imaging with deep learning.
Venue: Optics express
Year: 2020

We propose a deep learning computational ghost imaging (CGI) scheme to achieve sub Nyquist and high quality image reconstruction. Unlike the second order correlation CGI and compressive sensing CGI, which use lots of illumination patterns and a one dimensional (1 D) light intensity sequence (LIS) for image reconstruction, a deep neural network (DAttNet) is proposed to restore the target image only using the 1 D LIS. The DAttNet is trained with simulation data and retrieves the target image from experimental data. The experimental results indicate that the proposed scheme can provide high quality images with a sub Nyquist sampling ratio and performs better than the conventional and compressive sensing CGI methods in sub Nyquist sampling ratio conditions (e.g. 5.45% The proposed scheme has potential practical applications in underwater, real time and dynamic CGI.
Institutional racism embedded in policies or societal institutional practices Differential treatment Differential access to resources Interpersonal racism embedded in interpersonal interactions Differential treatment Differential access to resources Socioeconomic status Disadvantaged neighborhood
Year: 2016

Fifteen studies met criteria for review. The majority of the studies found a significant relationship between racial discrimination and low birth weight, preterm birth, and small for gestational age. Each of the studies that examined more proximal variables related to birth outcomes such as entry into prenatal care, employment opportunities, neighborhood characteristics, or inflammatorymarkers found significant associations between the specific variables examined and racial discrimination. Participants in qualitative studies discussed experiences of institutional racism with regard to several components of prenatal care including access and quality of care.
Light Activated Metal Oxide Gas Sensors: A Review
Venue: Micromachines
Year: 2017

Conductometric gas sensors facilitated by photons have been investigated for decades. Light illumination may enhance device attributes including operational temperature, sensing sensitivity and selectivity. This paper aims to provide an overview on the progress of light activated gas sensors, with a specific focus on sensors based on metal oxides. The material systems that have been studied include pure metal oxides, heterostructures of semiconductor metal oxides and metal metal oxides, and metal oxides with dopant. Other reported works on the use of different nanostructures such as one dimensional and porous nanostructures, study of sensing mechanisms and the interplay between various factors are also summarized. Possible directions for further improvement of sensing properties, through optimizing the size of nanomaterials, film thickness, light intensity and wavelength are discussed. Finally, we point out that the main challenge faced by light activated gas sensors is their low optical response, and we have analyzed the feasibility of using localized surface plasmon resonance to solve this drawback. This article should offer readers some key and instructive insights into the current and future development of light activated gas sensors.
Adhesive systems used in the European particleboard, MDF and OSB industries*
Year: 2018

In this work, the adhesive systems used today in the European industries of particleboard, medium density fibreboard (MDF) and oriented strand board (OSB) are discussed. The structure of particleboard, MDF and OSB markets in Europe in relation to the types of adhesives and product specifications are presented as well. It is noticeable that new markets for wood based panels like particleboard and fibreboard, known as non furniture markets, are growing in Europe at a fast rate. It was concluded that most of the technological changes concerning the adhesive systems applied and additives have been realised from the need for niche panel products, the obligation to reach even lower formaldehyde emissions, and the necessity to decrease production costs due to the stringent competition in the market of wood based panels.
A descriptive research audit of the long term developmental outcomes of 30 infants with Hypoxic Ischemic Encephalopathy (HIE) who received hypothermia at a secondary Hospital in South Africa
Year: 2020

I ntroduction Hypothermia is a neuroprotective strategy proven to reduce death and disability in children presenting with Hypoxic Ischemic Encephalopathy (HIE) Little evidence exists regarding the long term developmental outcomes of infants who received hypothermia within a secondary hospital in South Africa.  Drawing on a descriptive research audit this paper describes the development (up to five years) of 30 infants with HIE who received hypothermia at the George hospital in South Africa. Developmental outcomes were measured at 3, 9, 12, 24 and 36 months using the Strive Towards Achieving Results Together (START) and at 48 and 60 months using the Early Childhood Developmental Criteria (ECDC) Summary data (variables considered risk factors) were collected retrospectively. The associations between this data and developmental outcomes are described.  Twenty children presented with typical development, eight presented with cerebral palsy (CP) and two were lost to follow up. A significant association between a severe HIE score and CP was found (p=0.0005) The children performed equally in fine motor, gross motor and cognitive abilities at four years with poorer outcomes in cognitive and fine motor abilities recorded at five years. The perceptual activity both age groups struggled with the most was directionality.  The results of the research audit support the need for long term occupational therapy follow up of children with HIE to monitor their development and provide specific interventions to promote their development and prevent barriers to learning. This paper also advocates the need for occupational therapists working in the public health sector to have access to standardised assessment tools which allow them to closely monitor the development of infants who present with HIE at birth.
MECHANICAL STRESS IN ABDOMINAL AORTIC ANEURYSMS USING ARTIFICIAL NEURAL NETWORKS
Year: 2015

Combination of numerical modeling and artificial intelligence (AI) in bioengineering processes are a promising pathway for the further development of bioengineering sciences. The objective of this work is to use Artificial Neural Networks (ANN) to reduce the long computational times needed in the analysis of shear stress in the Abdominal Aortic Aneurysm (AAA) by finite element methods (FEM) For that purpose two different neural networks are created. The first neural network (Mesh Neural Network, MNN) creates the aneurysm geometry in terms of four geometrical factors (asymmetry factor, aneurism diameter, aneurism thickness, aneurism length) The second neural network (Tension Neural Network, TNN) combines the results of the first neural network with the arterial pressure (new factor) to obtain the maximum stress distribution (output variable) in the aneurysm wall. The use of FEM for the analysis and design of bioengineering processes often requires high computational costs, but if this technique is combined with artificial intelligence, such as neural networks, the simulation time is significantly reduced. The shear stress obtained by the artificial neural models developed in this work achieved 95% of accuracy respect to the wall stress obtained by the FEM. On the other hand, the computational time is significantly reduced compared to the FEM.
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)
Venue: 2021 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2021

Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using secure practices, third party FPGA designers can be provided with no knowledge of initial and final classification layers. In this work, we demonstrate that hardware intrinsic attack (HIA) in such a "secure" design is still possible. Proposed HIA is inserted inside mathematical operations of individual layers of CNN, which propagates erroneous operations in all the subsequent CNN layers that leads to misclassification. The attack is non periodic and completely random, hence it becomes difficult to detect. Five different attack scenarios with respect to each CNN layer are designed and evaluated based on the overhead resources and the rate of triggering in comparison to the original implementation. Our results for two CNN architectures show that in all the attack scenarios, additional latency is negligible 0.61% increment in DSP, LUT, FF is also less than 2.36% Three attack scenarios does not require any additional BRAM resources, while in two scenarios BRAM increases, which compensates with the corresponding decrease in FF and LUTs. To the authors' best knowledge this work is the first to address the hardware intrinsic CNN attack with attacker does not have knowledge of the full CNN.