nils-herrmann/clean_scirepeval huggingface dataset:
Optimal DR Submodular Maximization and Applications to Provable Mean Field Inference
Venue: ICML 2019
Year: 2018

Mean field inference in probabilistic models is generally a highly nonconvex problem. Existing optimization methods, e.g. coordinate ascent algorithms, can only generate local optima. In this work we propose provable mean filed methods for probabilistic log submodular models and its posterior agreement (PA) with strong approximation guarantees. The main algorithmic technique is a new Double Greedy scheme, termed DR DoubleGreedy, for continuous DR submodular maximization with box constraints. It is a one pass algorithm with linear time complexity, reaching the optimal 1/2 approximation ratio, which may be of independent interest. We validate the superior performance of our algorithms against baseline algorithms on both synthetic and real world datasets.
Image Steganography Using Fuzzy Logic and Chaotic for Large Payload and High Imperceptibility
Venue: Int. J. Fuzzy Syst.
Year: 2018

Information security is one of the most important processes and methodologies which have to be considered when any information is communicated secretly between two parties. Cryptography and steganography are the two important practices used for this purpose. Cryptography changes the secret message into unintelligible text; however, it reveals the existence of the secret message. Steganography technique on the other hand hides the secret message such that no one can notice the presence of it. Current paper presents an image steganographic technique exploiting fuzzy logic edge detection and chaotic method. The fuzzy logic approach for image processing allows defining the degree to which a pixel belongs to an edge. Least significant bit substitution approach is used for data hiding in the carrier image. The combination of chaotic theory with steganography is emerging and gets attracted by researchers. Chaotic method is applied to the original message to generate the random sequence of binary equivalent secret message which prevents the secret message from attackers. The proposed method hides large amount of data with good quality of stage image of the human visual system and guarantees the confidentiality in the communication. The experimental results shows better peak signal to noise ratio, image quality index and payload for evaluating the stego image compared to the existing methods.
Business Analytics and Performance Management: A Small Data Example Combining TD ABC and BSC for Simulation and Optimization
Year: 2017

The purpose of this paper is twofold: first, it discuss the potentials of combining performance management with the concept and methodology of business analytics. The inspiration for this stems from the intensified discussions and use of business analytics and performance in organizations by both academics and professionals. Second, the paper demonstrates and evaluates the idea of business analytics on a numerical example combining the balanced scorecard and TD ABC. Four different scenarios are analysed by means of spreadsheet Monte Carlo simulation and optimization to show how such a combination can be made and assess the consequences. The paper shows that in order to be able to fulfill future jobs descriptions; accountants must not only have specific quantitative skills but also creativity and imagination. In addition, the results of the simulation scenarios show the complicated nature of decision making and the careful considerations needed before actually making a decision. For practical implications, the paper establishes some basic ideas for integrating the business analytics methodology for selecting and evaluating accounting problems and it therefore paves the way for analytics performance management approaches.
Multiple Intelligences, the Mozart Effect, and Emotional Intelligence: A Critical Review
Year: 2006

This article reviews evidence for multiple intelligences theory, the Mozart effect theory, and emotional intelligence theory and argues that despite their wide currency in education these theories lack adequate empirical support and should not be the basis for educational practice. Each theory is compared to theory counterparts in cognitive psychology and cognitive neuroscience that have better empirical support. The article considers possible reasons for the appeal of these 3 theories and concludes with a brief rationale for examining theories of cognition in the light of cognitive neuroscience research findings.
Stress and anxiety detection using facial cues from videos
Venue: Biomed. Signal Process. Control.
Year: 2017

This study develops a framework for the detection and analysis of stress/anxiety emotional states through video recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non voluntary and semi voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye related events, mouth activity, head motion parameters and heart rate estimated through camera based photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.
Building safety in the offshore petroleum industry: Development of risk based major hazard risk indicators at a national level
Venue: Process Safety and Environmental Protection
Year: 2019

There has been an important controversy over whether the series of major accidents at Chinese Bohai Bay in 2011, i.e. the Penglai 19 3 and Suizhong 36 1 oil spills, are a sign of systematic safety problems in the Chinese offshore petroleum industry or a casual result of fortuities. It is hard to obtain the answer unless the national risk level of the offshore petroleum industry is monitored and measured. This paper describes an effort to propose and discuss an analytical approach for the development of major hazard risk indicators that can be used for monitoring, measuring and predicting national risk levels in the offshore petroleum industry. This study focuses on major hazards on offshore installations, hence personal safety hazards that affect individuals are not covered. Firstly, a risk based approach for developing major hazard risk indicators on offshore installations is developed. Both leading and lagging major hazard risk indicators on offshore installations are suggested. After that, the proposed analytical approach is tested by the risk assessment results of the Norwegian Continental Shelf (NCS) in the latest ten years (2007 2017) This is followed by a discussion on suitability and challenges of the proposed risk based approach. It has been demonstrated that the results of this study can provide a realistic and jointly agreed major hazard risk picture in the offshore petroleum industry.
Facial expression recognition using active contour based face detection, facial movement based feature extraction, and non linear feature selection
Venue: Multimedia Systems
Year: 2014

Knowledge about people's emotions can serve as an important context for automatic service delivery in context aware systems. Hence, human facial expression recognition (FER) has emerged as an important research area over the last two decades. To accurately recognize expressions, FER systems require automatic face detection followed by the extraction of robust features from important facial parts. Furthermore, the process should be less susceptible to the presence of noise, such as different lighting conditions and variations in facial characteristics of subjects. Accordingly, this work implements a robust FER system, capable of providing high recognition accuracy even in the presence of aforementioned variations. The system uses an unsupervised technique based on active contour model for automatic face detection and extraction. In this model, a combination of two energy functions: Chan Vese energy and Bhattacharyya distance functions are employed to minimize the dissimilarities within a face and maximize the distance between the face and the background. Next, noise reduction is achieved by means of wavelet decomposition, followed by the extraction of facial movement features using optical flow. These features reflect facial muscle movements which signify static, dynamic, geometric, and appearance characteristics of facial expressions. Post feature extraction, feature selection, is performed using Stepwise Linear Discriminant Analysis, which is more robust in contrast to previously employed feature selection methods for FER. Finally, expressions are recognized using trained HMM(s) To show the robustness of the proposed system, unlike most of the previous works, which were evaluated using a single dataset, performance of the proposed system is assessed in a large scale experimentation using five publicly available different datasets. The weighted average recognition rate across these datasets indicates the success of employing the proposed system for FER.
Caring for Frail Older Adults During COVID 19: integrating public health ethics into clinical practice
Venue: Journal of the American Geriatrics Society
Year: 2020

During the COVID 19 pandemic, principles from both clinical and public health ethics cue clinicians and health care administrators to plan alternatives for frail older adults who prefer to avoid critical care, and for when critical care is not available due to crisis triaging. This paper will explore the COVID 19 Ethical Decision Making Framework, published in British Columbia, Canada, to familiarize clinicians and policy makers with how ethical principles can guide systems change, in the service of frail older adults. In British Columbia, the health care system has launched resources to support clinicians in proactive advance care planning discussions, and is providing enhanced supportive and palliative care options to residents of long term care facilities. If the pandemic truly overwhelms the healthcare system, frailty, but not age alone, provides a fair and evidence based means of triaging patients for critical care and could be included into ventilator allocation frameworks. This article is protected by copyright. All rights reserved.
Joint Downlink Uplink Throughput optimization in Wireless Powered Communication Networks
Venue: 2019 IEEE/CIC International Conference on Communications in China (ICCC)
Year: 2019

Wireless power transfer has attracted a great deal of attentions recently, due to its ability to extend the lifetime of IoT devices. In this paper, a joint downlink (DL) and uplink (UL) optimization for sum throughput maximization in a two user wireless powered communication network (WPCN) is considered. During DL, a hybrid access point (H AP) sequentially broadcasts energy and transmits information to each user, and during the UL, each user transmits information to the H AP, and harvests energy from other users' UL information transmissions. Each user exploits the total harvested energy during both DL and UL to support its circuit power consumption for the DL information receiving and decoding and WET in the UL as well as information transmit power in the UL. Under this setting, we maximize the weighted sum throughput over both DL and UL by optimizing transmit power and time allocations at H AP and users, subject to the H AP's and each user's transmit power constraint, time allocation constraint and energy harvesting constraints at each user. However, this problem is found to be a non convex optimization problem. We transform this problem into a convex optimization problem, and obtain the optimal solutions using an efficient algorithm.
Simultaneous saccharification and fermentation of Spirulina sp. and corn starch for the production of bioethanol and obtaining biopeptides with high antioxidant activity.
Venue: Bioresource technology
Year: 2019

The aim was to produce bioethanol by the simultaneous saccharification and fermentation (SSF) of Spirulina sp. LEB 18 biomass and corn starch, increasing the process scale and obtaining biopeptides from bioethanol residue. Different temperatures of SSF and biomass/starch concentrations were tested, and the best conditions were chosen to scale up the bioethanol production. The biopeptides were obtained enzymatically with a protease. The antioxidant capacity, molecular structure, thermal stability and mass loss of the biopeptides were evaluated. A total of 73 g L 1 bioethanol was obtained during scale up, and the residue presented a high protein content with a degree of hydrolysis of 86% The biopeptides showed 32% ABTS radical inhibition with high thermal stability. This study showed the possibility of the biorefinery concept being able to produce bioethanol by Spirulina, and the biopeptides from the bioethanol residue presented high antioxidant capacity and can be used in many areas of the food industry.
Secreted Phosphoprotein 1 (SPP1) Contributes to Second Generation EGFR Tyrosine Kinase Inhibitor Resistance in Non Small Cell Lung Cancer
Venue: Oncology research
Year: 2019

Second generation irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) afatinib, has been approved for treating EGFR mutant lung cancer patients, but the mechanism of acquired resistance to afatinib has not been well studied. In this study, we established afatinib acquired resistant cell lines. Gene array technology was used to screen changes in gene expression between afatinib resistant lung cancer cells and parental cells. Our results showed that secreted phosphoprotein 1 (SPP1) was significantly increased in afatinib resistant lung cancer cells. To study the effect of SPP1 on afatinib resistance, siSPP1 was used to knock down SSP1 in afatinib resistant lung cancer cells. Then sensitivity to afatinib and invasive ability were studied. We found that knockdown of SPP1 increased sensitivity of lung cancer cells to afatinib and decrease the ability of invasion. Of clinical significance, we found that SSP1 was upregulated in lung cancer tissues compared with adjacent normal tissues, and low level of SSP1 was strongly associated with better overall survival. Our results suggest that SPP1 enhanced the second generation EGFR TKI resistance in lung cancer, and inhibiting SPP1 might be a therapeutic target to overcome afatinib resistance.
A multi agent hybrid cognitive architecture with self awareness for homecare robot
Venue: 2014 9th International Conference on Computer Science Education
Year: 2014

This paper presents a novel anthropomorphic multi agent hybrid architecture with abilities of self awareness and cognitive control for homecare robots. The paper presents the details of the robot memory (knowledge/data base) self awareness model, learning method for weight updating, and the decision making algorithm. The developed architecture will facilitate a robot to adapt to an uncertain and dynamic home environment and improve its intelligence, reliability and flexibility for effectively performing homecare tasks.
Blended computation of machine learning with the recurrent neural network for intra day stock market movement prediction using a multi level classifier
Venue: International Journal of Computers and Applications
Year: 2019

Intra day stock market forecasting is one of the challenging tasks as its nature is difficult to predict due to the non volatility of the stock market. Therefore, the building block concepts of machine learning techniques with the recurrent neural network have been used. These techniques are very supportive for developing the long short term memory (LSTM) network. This network is mainly used for sequential learning while performing classification. This paper presents research on applied computing to assess the efficacy of a multi level classifier over single classifier. The multi level classifier is hybrid of machine learning techniques (logistic regression classifier, decision tree, support vector machine) and recurrent neural network (LSTM) aiming to maximize the predictive ability, increases the interpretability, minimizes the complexity, reduced the training time, and provides the generalized platform with an optimistic forecasting model. The developed model has the potential to act as an intelligent system with the ability to adapt the market behavior present in the dataset which analyzes the best performing feature subset based on model accuracy. The comparative results clearly reveal that the blended computation of multi level classifiers outperforms the existing works and it also leads to more precise model development with the maximum predictive ability, approximately 10 12% increment in accuracy.
Neural Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
Venue: Data Science and Engineering
Year: 2019

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network) which is abundant in all real life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural Brane by solving the node classification and clustering tasks on four real world datasets. Experimental results demonstrate the superiority of our proposed method over the state of the art existing methods.
MicroRNA 495 enhances chondrocyte apoptosis, senescence and promotes the progression of osteoarthritis by targeting AKT1.
Venue: American journal of translational research
Year: 2019

Osteoarthritis (OA) is a common multifactorial degenerative articular disease among the aging population. The current investigation aimed to elucidate the function of microRNA 495 (miR 495) in the development of OA. We found that miR 495 was upregulated in the cartilage of OA patients. Transfection of a miR 495 mimic into rat primary chondrocytes, human chondrocytes (HC) and SW1353 chondrosarcoma cells inhibited AKT1 expression, proliferation and scratch wound closure and induced apoptosis. Transfection of a miR 495 inhibitor produced an opposite effect. Furthermore, the production of cartilage degeneration related substances was modified by miR 495. Luciferase reporter gene assay revealed that AKT1 is directly repressed by miR 495. Moreover, the levels of AKT1, p S6 and p mTOR diminished in chondrocytes overexpressing miR 495. AKT1 overexpression amplified p S6 and p mTOR levels as well as abolished miR 495 mimic induced apoptosis and inhibition of proliferation. In the surgically induced rat OA model, apoptosis of chondrocytes and cartilage degeneration were remedied by the administration of a miR 495 antagomir. Moreover, there was an increased expression of AKT1. These findings indicate that miR 495 induces OA by targeting AKT1 and regulating the AKT/mTOR pathway. Therefore, miR 495 may be a prospective target for OA treatment.
Efectos de dos tipos de dietas y un producto de levaduras en el rendimiento, digestibilidad de nutrientes, morfologia intestinal y composicion de la carcasa de cuyes (Cavia Porcellus)
Year: 2015

Resumen La investigacion consistio en evaluar los efectos de un producto de levadura (Celmanax) y dos tipos de dieta en el rendimiento, digestibilidad de nutrientes, morfologia intestinal, composicion de la carcasa y masa visceral de cuyes. Ochenta y ocho cuyes (PV= 453.21g 21.90g) fueron distribuidos en 24pozas durante un periodo de alimentacion de 69 d. Los tratamientos se dispusieron en un arreglo factorial 2x2 que incluyo una dieta a base de Concentrado (C) o una dieta mixta de concentrado y forraje (M) y la suplementacion de Celmanax (Ce, 0 o 1% de la dieta BS) No se registraron interacciones de dieta x Ce en las variables medidas (P> 0.157) El tratamiento M obtuvo mayor CMS, GDP y mejor CA (P< 0.001) La suplementacion de Ce aumento el CMS y mejoro la CA durante los primeros 49 dias (P< 0.048) En el rendimiento general no se registro efecto (P>=0.247) La digestibilidad de grasa fue mayor para C (P< 0.001) y la digestibilidad de fibra bruta se redujo (P= 0.004) La digestibilidad de nutrientes no fue alterada por la suplementacion Ce (P>=0.285) En el perfil de acidos grasos no se registraron diferencias entre tratamientos (P> 0.053) La relacion entre n 3: n 6 de acidos grasos fue mayor para los animales alimentados con M (P= 0.019) La altura de las microvellosidades y la profundidad de las criptas se incrementaron por la suplementacion Ce (P< 0.019) Los animales alimentados con la dieta M tuvieron una mayor profundidad de criptas (P= 0.05) Los resultados indican que la dieta mixta muestra mejores efectos en el rendimiento y en la composicion de la carcasa, la suplementacion con Celmanax mejora los aspectos relacionados con el rendimiento y la morfologia intestinal. Palabras Clave Cuyes; Tipo de dieta, Producto de levaduras