nils-herrmann/clean_scirepeval huggingface dataset:
Automated image captioning with deep neural networks
Year: 2020

Generating natural language descriptions of the content of an image automatically is a complex task. Though it comes naturally to humans, it is not the same when making a machine do the same. But undoubtedly, achieving this feature would remarkably change how machines interact with us. Recent advancement in object recognition from images has led to the model of captioning images based on the relation between the objects in it. In this research project, we are demonstrating the latest technology and algorithms for automated caption generation of images using deep neural networks. This model of generating a caption follows an encoder decoder strategy inspired by the language translation model based on Recurrent Neural Networks (RNN) The language translation model uses RNN for both encoding and decoding, whereas this model uses a Convolutional Neural Networks (CNN) for encoding and an RNN for decoding. This combination of neural networks is more suited for generating a caption from an image. The model takes in an image as input and produces an ordered sequence of words, which is the caption.
Real Time Station Grouping under Dynamic Traffic for IEEE 802.11ah
Venue: Sensors
Year: 2017

IEEE 802.11ah, marketed as Wi Fi HaLow, extends Wi Fi to the sub 1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW) focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT) and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF) the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real life IoT networks.
Improving DDPG via Prioritized Experience Replay
Year: 2019

Experience replay plays an important role in reinforcement learning. It reuses previous experiences to prevent the input data from being highly correlated. Recently, a deep reinforcement learning algorithm with experience replay, called deep deterministic policy gradient (DDPG) has achieved good performance in many continuous control tasks. However, it assumes the experiences are of equal importance and samples them uniformly, which obviously neglects the difference in the value of each individual experience. To improve the efficiency of experience replay in DDPG method, we propose to replace the original uniform experience replay with prioritized experience replay. We test the algorithms in five tasks in the OpenAI Gym, a testbed for reinforcement learning algorithms. In the experiment, we find that DDPG with prioritized experience replay mechanism significantly outperforms that with uniform sampling in terms of training time, training stability and final performance. We also find that our algorithm can achieve better performance in three tasks compared with some state of the art algorithms like Q prop, which directly proves the effectiveness of our proposed scheme. Our weekly report and codes are available at https:/github.com/cardwing/Codes for RL Project.
Emerging Research Paradigm for Infant Drug Exposure Through Breast Milk.
Venue: Current pharmaceutical design
Year: 2019

Information on drug secretion into milk is insufficient due to the exclusion of lactating women from clinical trials and drug development processes. As a result, non adherence to the necessary drug therapy and discontinuation of breastfeeding occur, even if the predicted level of infant exposure is low. In contrast, inadvertent infant exposure to drugs in breast milk continues to happen due to lack of rational risk assessment, resulting in serious toxicity cases including death. This problem is multifactorial, but one of the key elements is the lack of pharmacokinetic information on drug secretion into milk and resultant infant exposure levels, the first line of evidence for risk assessment.  Basic PK principles in drug excretion into milk were explained. The literature was scanned to identify approaches for PK data acquisition in this challenging field.  This review describes the feasibility to develop such approaches, and the knowledge gaps that still exist. A combination of population pharmacokinetics approach (to estimate averages and variations of drug concentration profiles in milk) and physiologically based pharmacokinetics modeling of infants (to predict the population profiles of infant drug exposure levels) appears useful.  In order to facilitate participant enrollment and PK data acquisition in a timely manner, networks of investigators become crucial.
Repeatedly heated palm kernel oil induces hyperlipidemia, atherogenic indices and hepatorenal toxicity in rats: Beneficial role of virgin coconut oil supplementation.
Year: 2017

The literature reports that the health benefits of vegetable oil can be deteriorated by repeated heating, which leads to lipid oxidation and the formation of free radicals. Virgin coconut oil (VCO) is emerging as a functional food oil and its health benefits are attributed to its potent polyphenolic compounds. We investigated the beneficial effect of VCO supplementation on lipid profile, liver and kidney markers in rats fed repeatedly heated palm kernel oil (HPO) METHODS Rats were divided into four groups (n 5) The control group rats were fed with a normal diet; group 2 rats were fed a 10% VCO supplemented diet; group 3 administered 10 ml HPO/kg b.w. orally; group 4 were fed 10% VCO 10 ml HPO/kg for 28 days. Subsequently, serum markers of liver damage (ALT, AST, ALP and albumin) kidney damage (urea, creatinine and uric acid) lipid profile and lipid ratios as cardiovascular risk indices were evaluated.  HPO induced a significant increase in serum markers of liver and kidney damage as well as con comitant lipid abnormalities and a marked reduction in serum HDL C. The lipid ratios evaluated for atherogenic and coronary risk indices in rats administered HPO only were remarkably higher than control. It was observed that VCO supplementation attenuated the biochemical alterations, including the indices of cardiovascular risks.  VCO supplementation demonstrates beneficial health effects against HPO induced biochemical alterations in rats. VCO may serve to modulate the adverse effects associated with consumption of repeatedly heated palm kernel oil.
Transferable Perturbations of Deep Feature Distributions
Venue: ICLR
Year: 2020

Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the modeling and exploitation of class wise and layer wise deep feature distributions. We achieve state of the art targeted blackbox transfer based attack results for undefended ImageNet models. Further, we place a priority on explainability and interpretability of the attacking process. Our methodology affords an analysis of how adversarial attacks change the intermediate feature distributions of CNNs, as well as a measure of layer wise and class wise feature distributional separability/entanglement. We also conceptualize a transition from task/data specific to model specific features within a CNN architecture that directly impacts the transferability of adversarial examples.
VisualWord2Vec (Vis W2V) Learning Visually Grounded Word Embeddings Using Abstract Scenes
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016

We propose a model to learn visually grounded word embeddings (vis w2v) to capture visual notions of semantic relatedness. While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world. For instance, although "eats" and "stares at" seem unrelated in text, they share semantics visually. When people are eating something, they also tend to stare at the food. Grounding diverse relations like "eats" and "stares at" into vision remains challenging, despite recent progress in vision. We note that the visual grounding of words depends on semantics, and not the literal pixels. We thus use abstract scenes created from clipart to provide the visual grounding. We find that the embeddings we learn capture fine grained, visually grounded notions of semantic relatedness. We show improvements over text only word embeddings (word2vec) on three tasks: common sense assertion classification, visual paraphrasing and text based image retrieval. Our code and datasets are available online.
Gamification to motivate the development of autonomous tasks in the university Case study: Central University of Ecuador
Venue: 2020 9th International Conference On Software Process Improvement (CIMPS)
Year: 2020

Combine face to face classes with a gamified e Learning platform, is a proposal in higher education that increases the commitment and motivation in the development of autonomous tasks. The present research shows the results of the application in the subject Programming I (P I) of the Faculty of Engineering, Physical Sciences and Mathematics (FEPSM) of the Central University of Ecuador (CUE) For this, an explanatory study, with a quasi experimental design with groups: experimental (gamified methodology) and control (traditional methodology) is carried out. These results are discussed in seven semesters, during the years 2015 and 2019. In addition, surveys are applied to students who completed the gamification course to evaluate the gamification strategies applied. To determine if the proposal allows improving the academic performance of the students, differences between groups were evaluated using the t Student test in the case of normality and the non parametric U Mann Whitney test in the case of non normality. The nine experimental groups who used the gamified methodology are compared using the Kruskal Wallis test and the medium for k samples. It is concluded that the implementation of gamification strategies on a virtual learning platform not only influenced students' motivation in the development of autonomous tasks but also allowed better academic performance.
pH sensitive (halochromic) smart packaging films based on natural food colorants for the monitoring of food quality and safety
Year: 2020

The recent rise in awareness of safe food and changing consumer attitudes have brought many innovations in packaging technology. Consumers are increasingly demanding natural food colorants such as carotenoids, betaines, anthocyanins and chlorophylls rather than synthetic dyes for food applications. As a result, smart packaging based on natural colorants and biopolymers has been introduced as the latest technology in the food packaging field. Smart products not only protect food from environmental hazards, but also send real time signals (colorimetric, chemical, or electrical) to consumers for changes in the packaging environment and food quality.  This paper reviewed the pH sensitive smart packaging films based on natural food colorants and biopolymers and key applications in the food industry. Technological aspects associated with the natural colorant based pH responsive packaging films, biopolymer based smart packaging, and the importance of natural colorants compared with synthetic dyes are discussed. The most commonly used colorimetric indicators indicating changes in pH, the application of smart food packaging films in real food systems, and the trend of future research were investigated.  Based on natural food colorants, smart packaging monitors the qualitative properties of foods in real time, including freshness, microbial growth, and chemical changes in the product, through visual changes and quantification of pH changes in the food. It provides a fast and reliable inline assessment of food quality and safety. Nevertheless, future studies should consider commercially sustainable key points such as consumer acceptance and trust, cost, regulatory considerations (i.e. labeling) and multifunctional performance of smart films.
CZTSSe Formation Mechanism Using a Cu/Zn/SnS Stacked Precursor: Origin of Triple CZTSSe Layer Formation.
Venue: ACS applied materials interfaces
Year: 2020

In this study, to control the formation of non uniformly distributed large voids and Cu Sn alloy agglomeration, which leads to local compositional misfit and secondary phase formation, a SnS compound precursor was applied instead of metal Sn to avoid compositional non uniformity. Using a Cu/Zn/SnS stacked precursor, a temperature tracking experiment was conducted to confirm the formation controllability of the void and the secondary phase. According to the results of this temperature profile tracking experiment, it was confirmed that the large void was successfully controlled; however, an additional ZnSSe secondary phase layer was formed in the middle of the CZTSSe upper layer and small voids were distributed relatively uniformly in the bottom CZTSSe layer. An efficiency of approximately 8% was obtained when the Cu/Zn/SnS stacked precursor was used. The origins of the low short circuit current and fill factor are posited to be caused by the increase of the energy bandgap of the CZTSSe layer due to the SnS precursor, the thin top CZTSSe layer (around 600 nm) of the triple CZTSSe layer, and the diffusion length extension of the minor carriers caused by bypassing the ZnSSe phase.
Online Library Management System
Year: 2012

The paper titled Library Management System is Library management software for monitoring and controlling the transactions in a library .The paper "Library Management System" is developed in java which mainly focuses on basic operations in a library like adding new member, new books, and updating new information, searching books and members and facility to borrow and return books. "Library Management System" is a windows application written for 32 bit Windows operating systems, designed to help users maintain and organize library. This software is easy to use for both beginners and advanced users.  The purpose of the application is automation of library ,it provide facilities to student or member to search for the required books and it allows the administrator or librarian to Issue return books to students and can create delete membership of students. The software Library Management System has four main modules.
Output feedback control of the longitudinal flight dynamics using adaptative backstepping
Venue: IEEE Conference on Decision and Control and European Control Conference
Year: 2011

An adaptive backstepping approach is used to design an output feedback control law for the longitudinal dynamics of an Unmanned Air Vehicle (UAV) The resulting nonlinear controller makes the system to follow references in the aerodynamic velocity and flight path angle, using elevator deflections and thrust as actuators. Only measurable quantities are used in the control and adaptation laws. The proposed strategy allows to design an explicit controller without any knowledge of the aerodynamic coefficients or the trim angle of attack, which also depends on the aerodynamic coefficients; only well known qualitative physical properties from aerodynamics are used. Simulations are included for a realistic UAV model including actuator saturation.
Excitatory neuronal connectivity in the barrel cortex
Venue: Front. Neuroanat.
Year: 2012

Neocortical areas are believed to be organized into vertical modules, the cortical columns, and the horizontal layers 1 6. In the somatosensory barrel cortex these columns are defined by the readily discernible barrel structure in layer 4. Information processing in the neocortex occurs along vertical and horizontal axes, thereby linking individual barrel related columns via axons running through the different cortical layers of the barrel cortex. Long range signaling occurs within the neocortical layers but also through axons projecting through the white matter to other neocortical areas and subcortical brain regions. Because of the ease of identification of barrel related columns, the rodent barrel cortex has become a prototypical system to study the interactions between different neuronal connections within a sensory cortical area and between this area and other cortical as well subcortical regions. Such interactions will be discussed specifically for the feed forward and feedback loops between the somatosensory and the somatomotor cortices as well as the different thalamic nuclei. In addition, recent advances concerning the morphological characteristics of excitatory neurons and their impact on the synaptic connectivity patterns and signaling properties of neuronal microcircuits in the whisker related somatosensory cortex will be reviewed. In this context, their relationship between the structural properties of barrel related columns and their function as a module in vertical synaptic signaling in the whisker related cortical areas will be discussed.
Revisiting the Netflix Prize the Decade After Category General Machine Learning
Year: 2017

The Netflix Prize rewarded the top team with an incremental 10% improvement on their existing Cinematch algorithm with a million dollars. It turns out that improving the RMSE of predictions is difficult. Furthermore, the winning solution was cumbersome and infeasible for commercial use. We will study the Netflix problem in a simplified setting under discretization, and combine prior information calculated on the dataset to make predictions on each individual movie. This method achieves nearly the performance of KNN at 75% accuracy in the discretized setting but can be up to 420x faster.
Antimicrobial, cytotoxicity, mutagenicity and anti epileptic potential of ethanol extracts of a multipurpose medicinal plant Dalbergia sissoo
Venue: Biocatalysis and Agricultural Biotechnology
Year: 2019

Dalbergia sissoo (Shisham) is a medicinal plant with great medicinal impact on the nervous system. The present study was aimed to evaluate the phytochemical screening, antimicrobial, cytotoxicity, mutagenicity and anti epileptic potential of ethanol extracts of Dalbergia sissoo (bark and leaf ethanolic extracts) in epilepsy induced rats. The bark and leaves extract of D. sissoo were found to be rich in phytochemicals tannins, flavonoids, saponins, reducing sugars, terpenoids, glycosides, and proteins, and possessed both antibacterial and antifungal properties against various pathogenic bacterial and fungal species. In vitro cytotoxicity and mutagenicity profile evidenced that bark and leaf extracts were non toxic and non mutagenic, respectively. Finally, the anticonvulsant effect of these extracts was tested on Pilocarpine induced seizures in albino Wistar rats. In vivo studies revealed that both bark and leaf extracts of D. sissoo significantly reduced the intensity of the seizures, and duration of convulsions induced by Pilocarpine in albino Wistar rat models. The ethanolic extracts showed marked protective activities against Pilocarpine induced seizures in rat models. A dose of 500 mg/kg of D. sissoo leaf extract delayed the onset of seizures by 144 s versus the control Pilocarpine. In conclusion, this work supports the folkloric use of D. sissoo in Pakistan for the treatment of epilepsy in a more rational and patient friendly manner.
Assessing the Retest Reliability of Prefrontal EEG Markers of Brain Rhythm Slowing in the Eyes Closed Resting State
Venue: Clinical EEG and neuroscience
Year: 2020

We examined whether prefrontal lobe EEG markers of slower brain rhythms, which are correlated with functional brain aging, can reliably reflect those of other brain lobes, as measured by a multichannel device.  EEG measurements were taken of 112 healthy individuals aged 20 to 69 years in the eyes closed resting state. A 5 minute measurement was taken at 8 regions (Fp1, Fp2, F3, F4, T3, T4, O1, O2) Indices (median frequency [MDF] peak frequency [PF] that quantitatively reflect the characteristics of EEG slowing, and traditional commonly used spectral indices (absolute powers as delta, theta, alpha, beta, and relative power as alpha to theta ratio [ATR] were extracted from the EEG signals. For these indices, the differences between the prefrontal lobe and other areas were analyzed and the test retest reproducibility was investigated.  The EEG slowing indicators showed high conformity over all brain lobes and stable reproducibility. On the other hand, the typical EEG spectral indicators delta, theta, alpha, beta, and ATR differed between brain regions.  It was found that EEG slowing markers, which were used for assessing the aging or degeneration of brain functions, could be reliably extracted from a prefrontal EEG alone.  These findings suggest that EEG prefrontal markers may reflect markers of other brain regions when a multi channel device is used. Thus, this method may constitute a low cost, wearable, wireless, easily accessible, and noninvasive tool for neurological assessment that could be used in the early detection of cognitive decline and in the prevention of dementia.
Are complex energy system models more accurate? An intra model comparison of power system optimization models
Year: 2019

Today, optimization models are by far the most popular choice when analyzing energy systems. Impressive advances in computer and data sciences have allowed for a multitude of complex energy system optimization models. In our manuscript, we assess the hypothesis of a positive correlation between complexity of a model and accuracy of its results. For this, we propose a framework based on alternative model formulations and apply it in a case study with 160 different, more or less complex, implementations of power system optimization models for dispatch and investment. Our results indicate that a certain degree of complexity is indeed necessary to obtain sufficiently accurate results. However, a careful balancing is also necessary for acceptable use of computational resources. In terms of partial load efficiency of conversion processes, our findings indicate that (two) different formulations should be used for dispatch and for investment decisions. Furthermore, a model including simple grid constraints comes with only minor increases in complexity compared to a model assuming a copper plate. Moreover, the combination of multiple technical constraints for conversion processes leads to a significant gain in accuracy while the combination of multiple time coupling constraints is a major complexity driver.