nils-herrmann/clean_scirepeval huggingface dataset:
Data Quality: Concepts, Methodologies and Techniques

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading data quality concepts methodologies and techniques as one of the reading material to finish quickly.
Bayesian neural networks for nonlinear time series forecasting

In this article, we apply Bayesian neural networks (BNNs) to time series analysis, and propose a Monte Carlo algorithm for BNN training. In addition, we go a step further in BNN model selection by putting a prior on network connections instead of hidden units as done by other authors. This allows us to treat the selection of hidden units and the selection of input variables uniformly. The BNN model is compared to a number of competitors, such as the Box-Jenkins model, bilinear model, threshold autoregressive model, and traditional neural network model, on a number of popular and challenging data sets. Numerical results show that the BNN model has achieved a consistent improvement over the competitors in forecasting future values. Insights on how to improve the generalization ability of BNNs are revealed in many respects of our implementation, such as the selection of input variables, the specification of prior distributions, and the treatment of outliers.
DDS Based on Pipelined CORDIC Algorithm with Communication System

– The theoretical analysis gives an overview of the functioning of DDS, especially with respect to noise and spurs. Different spur reduction techniques are studied in detail. Four ICs, which were the circuit implementations of the DDS, were designed. One programmable logic device implementation of the CORDIC based quadrature amplitude modulation (QAM) modulator was designed with a separate D/A converter IC. For the realization of these designs some new building blocks, e.g. a new tunable error feedback structure and a novel and more cost-effective digital power ramp generator, were developed. Implementing a DDS on an FPGA using Xilinx’s ISE software. IndexTerms—CORDIC, DDS, NCO, FPGA, SFDR. ________________________________________________________________________________________________________
Cloud computing security considerations

Cloud computing is a new way of delivering computing resources and is not a new technology. It is an internet based service delivery model which provides internet based services, computing and storage for users in all markets including financial health care and government. This new economic model for computing has found fertile ground and is attracting massive global investment. Although the benefits of cloud computing are clear, so is the need to develop proper security for cloud implementations. Cloud security is becoming a key differentiator and competitive edge between cloud providers. This paper discusses the security issues that arise in a cloud computing frame work. It focuses on technical security issues arising from the usage of cloud services and also provides an overview of key security issues related to cloud computing with the view of a secure cloud architecture environment.
Midbrain dopamine neurons bidirectionally regulate CA3-CA1 synaptic drive

Dopamine (DA) is required for hippocampal-dependent memory and long-term potentiation (LTP) at CA1 Schaffer collateral (SC) synapses. It is therefore surprising that exogenously applied DA has little effect on SC synapses, but suppresses CA1 perforant path (PP) inputs. To examine DA actions under more physiological conditions, we used optogenetics to release DA from ventral tegmental area inputs to hippocampus. Unlike exogenous DA application, optogenetic release of DA caused a bidirectional, activity-dependent modulation of SC synapses, with no effect on PP inputs. Low levels of DA release, simulating tonic DA neuron firing, depressed the SC response through a D4 receptor–dependent enhancement of feedforward inhibition mediated by parvalbumin-expressing interneurons. Higher levels of DA release, simulating phasic firing, increased SC responses through a D1 receptor–dependent enhancement of excitatory transmission. Thus, tonic-phasic transitions in DA neuron firing in response to motivational demands may cause a modulatory switch from inhibition to enhancement of hippocampal information flow.
SU 2 : an Open-Source Suite for Multiphysics Simulation and Design

Economon, T., et al., "SU2: An Open-Source Suite for Multi-Physics Simulation and Design," AIAA Journal, 2016. doi:10.2514/1.J053813. Palacios, F., et al., “Stanford University Unstructured (SU2 ): Open-Source Analysis and Design Technology for Turbulent Flows,” AIAA Paper 2014-0243, 2014. Palacios, F., et al., , “Stanford University Unstructured (SU2): An openSource Integrated Computational Environment for Multi-Physics Simulation and Design,” AIAA Paper 2013-0287, 2013. Computational analysis tools have revolutionized the way we design aerospace systems, but most established codes are proprietary, unavailable, or prohibitively expensive for many users. The SU2 team is changing this, making computational analysis and design freely available as open-source software and involving everyone in its creation and development.
A wearable device for fall detection elderly people using tri dimensional accelerometer

A fall detection device is needed to provide information to paramedics or family members when an elderly is falling. Helping for elderly falling can avoid fatal injuries or loss of life. In order for the falling device comfortably taken by the elderly, we proposed a wearable device that lightweight, using battery for power supply, and a low-energy consumption. Our proposed device consists of: 3-dimensional accelerometer as a sensor, a microcontroller and a communication device. The sensor provides accelerations of elderly body movements. Then, the microcontroller identifies position body and a falling from three-axis accelerations. We use parameter threshold in our proposed fall detection as a method that has success detect 75% in fall forward and 95% in fall backward. The proposed device also has a 100% success in providing information on normal activities, such as: standing or sitting, supine, face down, left and right, while the success rate for the e-health device by cooking hack is 92%.
InTime: A Machine Learning Approach for Efficient Selection of FPGA CAD Tool Parameters

FPGA CAD tool parameters controlling synthesis optimizations, place and route effort, mapping criteria along with user-supplied physical constraints can affect timing results of the circuit by as much as 70% without any change in original source code. A correct selection of these parameters across a diverse set of benchmarks with varying characteristics and design goals is challenging. The sheer number of parameters and option values that can be selected is large (thousands of combinations for modern CAD tools) with often conflicting interactions. In this paper, we present InTime, a machine-learning approach supported by a cloud-based (or cluster-based) compilation infrastructure for automating the selection of these parameters effectively to minimize timing costs. InTime builds a database of results from a series of preliminary runs based on canned configurations of CAD options. It then learns from these runs to predict the next series of CAD tool options to improve timing results. Towards the end, we rely on a limited degree of statistical sampling of certain options like placer and synthesis seeds to further tighten results. Using our approach, we show 70% reduction in final timing results across industrial benchmark problems for the Altera CAD flow. This is 30% better than vendor-supplied design space exploration tools that attempts a similar optimization using canned heuristics.
A Novel Cyclodextrin Glycosyltransferase from Alkaliphilic Amphibacillus sp. NPST-10: Purification and Properties

Screening for cyclodextrin glycosyltransferase (CGTase)-producing alkaliphilic bacteria from samples collected from hyper saline soda lakes (Wadi Natrun Valley, Egypt), resulted in isolation of potent CGTase producing alkaliphilic bacterium, termed NPST-10. 16S rDNA sequence analysis identified the isolate as Amphibacillus sp. CGTase was purified to homogeneity up to 22.1 fold by starch adsorption and anion exchange chromatography with a yield of 44.7%. The purified enzyme was a monomeric protein with an estimated molecular weight of 92 kDa using SDS-PAGE. Catalytic activities of the enzyme were found to be 88.8 U mg(-1) protein, 20.0 U mg(-1) protein and 11.0 U mg(-1) protein for cyclization, coupling and hydrolytic activities, respectively. The enzyme was stable over a wide pH range from pH 5.0 to 11.0, with a maximal activity at pH 8.0. CGTase exhibited activity over a wide temperature range from 45 °C to 70 °C, with maximal activity at 50 °C and was stable at 30 °C to 55 °C for at least 1 h. Thermal stability of the purified enzyme could be significantly improved in the presence of CaCl(2). K(m) and V(max) values were estimated using soluble starch as a substrate to be 1.7 ± 0.15 mg/mL and 100 ± 2.0 μmol/min, respectively. CGTase was significantly inhibited in the presence of Co(2+), Zn(2+), Cu(2+), Hg(2+), Ba(2+), Cd(2+), and 2-mercaptoethanol. To the best of our knowledge, this is the first report of CGTase production by Amphibacillus sp. The achieved high conversion of insoluble raw corn starch into cyclodextrins (67.2%) with production of mainly β-CD (86.4%), makes Amphibacillus sp. NPST-10 desirable for the cyclodextrin production industry.
Arthroscopic Transtibial Pullout Repair for Posterior Medial Meniscus Root Tears: A Systematic Review of Clinical, Radiographic, and Second-Look Arthroscopic Results.

To systematically review the results of arthroscopic transtibial pullout repair (ATPR) for posterior medial meniscus root tears.



A systematic electronic search of the PubMed database and the Cochrane Library was performed in September 2014 to identify studies that reported clinical, radiographic, or second-look arthroscopic outcomes of ATPR for posterior medial meniscus root tears. Included studies were abstracted regarding study characteristics, patient demographic characteristics, surgical technique, rehabilitation, and outcome measures. The methodologic quality of the included studies was assessed with the modified Coleman Methodology Score.



Seven studies with a total of 172 patients met the inclusion criteria. The mean patient age was 55.3 years, and 83% of patients were female patients. Preoperative and postoperative Lysholm scores were reported for all patients. After a mean follow-up period of 30.2 months, the Lysholm score increased from 52.4 preoperatively to 85.9 postoperatively. On conventional radiographs, 64 of 76 patients (84%) showed no progression of Kellgren-Lawrence grading. Magnetic resonance imaging showed no progression of cartilage degeneration in 84 of 103 patients (82%) and showed reduced medial meniscal extrusion in 34 of 61 patients (56%). On the basis of second-look arthroscopy and magnetic resonance imaging in 137 patients, the healing status was rated as complete in 62%, partial in 34%, and failed in 3%. Overall, the methodologic quality of the included studies was fair, with a mean modified Coleman Methodology Score of 63.



ATPR significantly improves functional outcome scores and seems to prevent the progression of osteoarthritis in most patients, at least during a short-term follow-up. Complete healing of the repaired root and reduction of meniscal extrusion seem to be less predictable, being observed in only about 60% of patients. Conclusions about the progression of osteoarthritis and reduction of meniscal extrusion are limited by the small portion of patients undergoing specific evaluation (44% and 35% of the study group, respectively).
Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors

As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company’s user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.
Co-design of Interdisciplinary Projects as a Mechanism for School Capacity and Teacher Professional Community Growth

This paper examines the use of interdisciplinary project co-design, as a mechanism for increasing the capacity of a school, and promoting the growth of teachers' professional practice in an urban high school setting. Changing teaching practices and the professional culture within a school can be extremely difficult. Simply providing resources about novel strategies can be ineffective. In fact, in some school cultures, suggestions for classroom practice change can be received with hostility, being viewed by some teachers as acts questioning their professional competence. This study describes how a strategically chosen task, interdisciplinary project co-design, was used by external consultants as a productive, non-threatening mechanism for instructional improvement, by simultaneously enhancing classroom practices and cultivating the growth of professional school community and organizational practices.
Suicide by the intraoral blast of firecrackers — experimental simulation using a skull simulant model

Suicides committed by intraorally placed firecrackers are rare events. Given to the use of more powerful components such as flash powder recently, some firecrackers may cause massive life-threatening injuries in case of such misuse. Innocuous black powder firecrackers are subject to national explosives legislation and only have the potential to cause harmless injuries restricted to the soft tissue. We here report two cases of suicide committed by an intraoral placement of firecrackers, resulting in similar patterns of skull injury. As it was first unknown whether black powder firecrackers can potentially cause serious skull injury, we compared the potential of destruction using black powder and flash powder firecrackers in a standardized skull simulant model (Synbone, Malans, Switzerland). This was the first experiment to date simulating the impacts resulting from an intraoral burst in a skull simulant model. The intraoral burst of a “D-Böller” (an example of one of the most powerful black powder firecrackers in Germany) did not lead to any injuries of the osseous skull. In contrast, the “La Bomba” (an example of the weakest known flash powder firecrackers) caused complex fractures of both the viscero- and neurocranium. The results obtained from this experimental study indicate that black powder firecrackers are less likely to cause severe injuries as a consequence of intraoral explosions, whereas flash powder-based crackers may lead to massive life-threatening craniofacial destructions and potentially death.
PARADOXES, DUALITIES AND DILEMMAS: UNDERSTANDING SUSTAINABILITY IN HRM

The paper contributes to the emerging literature linking sustainability as a concept to problems researched in HRM literature. Sustainability is often equated with social responsibility. However, emphasizing mainly moral or ethical values neglects that sustainability can also be economically rational. This conceptual paper discusses how the notion of sustainability has developed and emerged in HRM literature. A typology of sustainability concepts in HRM is presented to advance theorizing in the field of Sustainable HRM. The concepts of paradox, duality, and dilemma are reviewed to contribute to understanding the emergence of sustainability in HRM. It is argued in this paper that sustainability can be applied as a concept to cope with the tensions of shortvs. long-term HRM and to make sense of paradoxes, dualities, and dilemmas. Furthermore, it is emphasized that the dualities cannot be reconciled when sustainability is interpreted in a way that leads to ignorance of one of the values or logics. Implications for further research and modest suggestions for managerial practice are derived.
A Compact Low-loss Magic-T using Microstrip-Slotline Transitions

The design of a compact low-loss magic-T is proposed. The planar magic-T incorporates the compact microstrip-slotline tee junction and small microstrip-slotline transition area to reduce slotline radiation. The experimental results show that the magic-T produces broadband in-phase and out-of-phase power combiner/divider responses, has an average in-band insertion loss of 0.3 dB and small in-band phase and amplitude imbalance of less than plusmn 1.6deg and plusmn 0.3 dB, respectively.
Creativity Versus the Perception of Creativity in Computational Systems

We add to the discussion of how to assess the creativity of programs which generate artefacts such as poems, theorems, paintings, melodies, etc. To do so, we first review some existing frameworks for assessing artefact generation programs. Then, drawing on our experience of building both a mathematical discovery system and an automated painter, we argue that it is not appropriate to base the assessment of a system on its output alone, and that the way it produces artefacts also needs to be taken into account. We suggest a simple framework within which the behaviour of a program can be categorised and described which may add to the perception of creativity in the system.
Effects of a probiotic intervention in acute canine gastroenteritis--a controlled clinical trial.

To evaluate the effect of a probiotic product in acute self-limiting gastroenteritis in dogs.



Thirty-six dogs suffering from acute diarrhoea or acute diarrhoea and vomiting were included in the study. The trial was performed as a randomised, double blind and single centre study with stratified parallel group design. The animals were allocated to equal looking probiotic or placebo treatment by block randomisation with a fixed block size of six. The probiotic cocktail consisted of thermo-stabilised Lactobacillus acidophilus and live strains of Pediococcus acidilactici, Bacillus subtilis, Bacillus licheniformis and Lactobacillus farciminis.



The time from initiation of treatment to the last abnormal stools was found to be significantly shorter (P = 0.04) in the probiotic group compared to placebo group, the mean time was 1.3 days and 2.2 days, respectively. The two groups were found nearly equal with regard to time from start of treatment to the last vomiting episode.



The probiotic tested may reduce the convalescence time in acute self-limiting diarrhoea in dogs.
Motion dense sampling and component clustering for action recognition

In this paper, we propose Motion Dense Sampling (MDS) for action recognition, which detects very informative interest points from video frames. MDS has three advantages compared to other existing methods. The first advantage is that MDS detects only interest points which belong to action regions of all regions of a video frame. The second one is that it can detect the constant number of points even when the size of action region in an image drastically changes. The Third one is that MDS enables to describe scale invariant features by computing sampling scale for each frame based on the size of action regions. Thus, our method detects much more informative interest points from videos unlike other methods. We also propose Category Clustering and Component Clustering, which generate the very effective codebook for action recognition. Experimental results show a significant improvement over existing methods on YouTube dataset. Our method achieves 87.5 % accuracy for video classification by using only one descriptor.
Semantic-Aware Searching Over Encrypted Data for Cloud Computing

With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users’ retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.
Improved Face Detection and Alignment using Cascade Deep Convolutional Network

Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Recent studies have utilized the relation between face detection and alignment to make models computationally efficiency, but they ignore the connection between each cascade CNNs. In this paper, we combine detection, calibration and alignment in each Cascade CNN and propose an HEM method for End-to-End cascade network training, which give computers more space to automatic adjust weight parameter and accelerate convergence. Experiments on FDDB and AFLW demonstrate considerable improvement on accuracy and speed.