Miracle-dz/UniFAR-FTUs huggingface dataset:
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information

Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the units are dropped randomly according to the same probability, we aim to use explicit instructions based on global information of the dataset to guide the training process. With GI-Dropout, the model is supposed to pay more attention to inapparent features or patterns. Experiments demonstrate the effectiveness of the dropout with global information on seven text classification tasks, including sentiment analysis and topic classification.
EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for n = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA). Using this technique, EEG topographies can be substituted with microstate labels by competitive fitting based on spatial correlation, resulting in a symbolic, non-metric time series, the microstate sequence. Microstate topographies and symbolic time series are further analyzed statistically, including static and dynamic properties. Static properties, which do not contain information about temporal dependencies of the microstate sequence include the maximum similarity of microstate maps within and between the tested clustering algorithms, the global explained variance and the Shannon entropy of the microstate sequences. Dynamic properties are sensitive to temporal correlations between the symbols and include the mixing time of the microstate transition matrix, the entropy rate of the microstate sequences and the location of the first local maximum of the autoinformation function. We also test the Markov property of microstate sequences, the time stationarity of the transition matrix and detect periodicities by means of time-lagged mutual information. Finally, possible long-range correlations of microstate sequences are assessed via Hurst exponent estimation. We find that while static properties partially reflect properties of the clustering algorithms, information-theoretical quantities are largely invariant with respect to the clustering method used. As each clustering algorithm has its own profile of computational speed, ease of implementation, determinism vs. stochasticity and theoretical underpinnings, our results convey a positive message concerning the free choice of method and the comparability of results obtained from different algorithms. The invariance of these quantities implies that the tested properties are algorithm-independent, inherent features of resting state EEG derived microstate sequences.
Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents

This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.
Towards Machine Learning-Based Optimal HAS

Mobile video consumption is increasing and sophisticated video quality adaptation strategies are required to deal with mobile throughput fluctuations. These adaptation strategies have to keep the switching frequency low, the average quality high and prevent stalling occurrences to ensure customer satisfaction. This paper proposes a novel methodology for the design of machine learning-based adaptation logics named HASBRAIN. Furthermore, the performance of a trained neural network against two algorithms from the literature is evaluated. We first use a modified existing optimization formulation to calculate optimal adaptation paths with a minimum number of quality switches for a wide range of videos and for challenging mobile throughput patterns. Afterwards we use the resulting optimal adaptation paths to train and compare different machine learning models. The evaluation shows that an artificial neural network-based model can reach a high average quality with a low number of switches in the mobile scenario. The proposed methodology is general enough to be extended for further designs of machine learning-based algorithms and the provided model can be deployed in on-demand streaming scenarios or be further refined using reward-based mechanisms such as reinforcement learning. All tools, models and datasets created during the work are provided as open-source software.
A Fast and Simple Qualitative Method for Screening Oleaginous Yeasts on Agar

Finding new oleaginous yeasts is of great interest due to their many important applications. Currently available screening procedures are time-consuming, and most of these require liquid cultures. In this work, a new, fast, economical, and simple qualitative method for screening oleaginous yeasts was developed. The fluorescent dye, Rhodamine B, was selected because its fluorescence is directly correlated to lipid content, and no additional steps or special equipment are needed. This method only requires growing the yeasts on dyed agar plates. Under visible light, it is easy to observe that nonpigmented oleaginous yeasts become colored, whereas non-oleaginous yeasts remain uncolored. The developed method is also useful for improving medium composition in specific applications. Moreover, it was also adapted to use alternative carbon sources, such as lignocellulosic materials and glycerol. The developed method was applied to screen 124 recently isolated nonpigmented yeasts on three different carbon sources, namely, glucose, glycerol, and agave bagasse hydrolysate. Five strains were selected as good lipid producers on all tested carbon sources and accumulated over 48% lipids. Furthermore, the assay was adapted to screen reddish-pigmented yeasts. Considering all the above, the developed method has a wide range of applications in the field of microbial oils.
Determining the factors influencing the intimate relationship between sexual satisfaction and dyadic adjustment in postmenopausal women

Introduction Identifying and characterizing sexual dysfunction is crucial in order to improve the quality of the marital relationship in postmenopausal women. Regarding this, the present study aimed to determine the association of menopausal characteristics with sexual satisfaction and marital adjustment. Material and methods This cross-sectional study was conducted on 89 postmenopausal women chosen using an improbable randomized sampling method. A questionnaire that collects demographic characteristics, the Dyadic Adjustment Scale (DAS) and The Golombok Rust Inventory of Sexual Satisfaction (GRISS) index were used to collect relevant data during the survey. Results The mean age of postmenopausal women recruited to the present study was 54.6 ±6.7. The overall mean GRISS score and DAS score were 5.6 ±1.7 and 92.1 ±11.2 respectively. The mean GRISS scores were found to significantly increase as the ages of the women increased (p = 0.044). Mean DAS scores were lower in postmenopausal women that have menopause duration longer than 10 years (p = 0.035). Higher education level of the spouse is associated with lower GRISS scores (p = 0.024). No correlation was observed between marital adjustment and sexual satisfaction. Conclusions Although sexual satisfaction and marital adjustment levels are decreased in the postmenopausal period, these two parameters do not seem to be related to each other. Multidimensional factors must be taken into account when evaluating sexual and marital satisfaction in postmenopausal women.
Mitigation of jamming attacks in wireless networks

Wireless networks, intentional interference can be used as a launch pad for mounting Denial-of-Service in the network. Intentional interference (jamming) attacks can be performed by absorption of packets and discard them or drop the packets. Intrusion Detection System can be used to mitigate and prevent the jamming attack and the addition of cryptographic primitive used to preserve integrity of the packets. This paper proposes a mechanism for preventing jamming attacks on wireless networks-examine the detection efficiency of jamming attack and communication overhead of the wireless network using proactive and reactive protocols. RSA algorithm is used for providing data packets integrity information during wireless transmission. Through simulation and performance analysis, the implemented prevention mechanism and the integrity preservation provides higher packet delivery ratio in proactive routing protocol (OLSR) than reactive routing protocol (AODV).
Effects of Rivastigmine Combined with Reinhartdt and Sea Cucumber Capsule in Patients with Mild-to-Moderate Parkinson’s Disease Dementia: A Pilot Study

Many patients with Parkinson’s disease suffer cognitive impairment or dementia. Cholinesterase inhibitors (ChEIs) have positive effects on patients with Parkinson’s Disease Dementia (PDD). But it is only improve symptoms. There is no etiological cure for PDD. So, In order to achieve the best outcomes, the combination of ChEIs and other therapeutic strategies is needed to study. In the present study, we investigate the efficacy and safety of rivastigmine combined with Reinhartdt and Sea Cucumber Capsule (RSC) in patients with mild-to-moderate PDD, and its effect on thyroid function. There were 52 patients were randomly assigned to receive either rivastigmine (3 mg/ day) or rivastigmine plus RSC (2.7 g/day) treatment for 24 weeks. Efficacy was investigated by the change of the scores of Alzheimer’s Disease Assessment Scale cognitive subscale (ADAS-Cog), Activities of Daily Life (ADL) and Unified Parkinson’s Disease Rating Scale (UPDRS) part III (motor scale). Meanwhile, thyroid hormone levels were detected before and after 12 weeks, 24 weeks treatment in all patients. Results showed that the patients treated with rivastigmine plus RSC showed more improvement in the cognition and the daily life activities compared to those treated with rivastigmine alone. Significant difference was present after being treated for 12 weeks or more. However, no group difference was found on UPDRS part III, thyroid hormone level change and the incidence of adverse events (11.1% vs 16.0%) between the two groups of treatment. Adverse effects were nausea and vomiting which were the main reasons for the dropout. The finding suggests that Rivastigmine plus RSC may improves the treatment effects in cognition and the ADL of the patients with mild-to-moderate PDD, compared with the rivastigmine treatment alone. However, no effect was observed on the motor symptoms and thyroid hormone levels. In addition, this joint treatment is safe.
Developments in Pressurized Gyration for the Mass Production of Polymeric Fibers

In this invited feature article, the invention of pressurized gyration in 2013 and its subsequent development into sister processes such as pressurized melt gyration, infusion gyration, and pressure-coupled infusion gyration is elucidated. The fundamentals of these processes are discussed, elucidating how these novel methods can be used to facilitate mass production of polymeric fibers and other morphologies. The effects of the main system parameters: rotational speed and gas pressure, are discussed along with the influence of solution parameters such as viscosity and polymer chain entanglement. The effect of flow of material into the gyrator in infused gyration is also illustrated. Examples of many polymers that have been subjected to these processes are discussed and the applications of resulting products are illustrated under several different research themes such as, tissue engineering, drug delivery, diagnostics, hydrogels, filtration, and wound healing.
A Forecasting Model Based on Multi-Valued Neutrosophic Sets and Two-Factor, Third-Order Fuzzy Fluctuation Logical Relationships

Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.
The role of net community production in air‐sea carbon fluxes at the North Pacific subarctic‐subtropical boundary region

The North Pacific subarctic‐subtropical boundary region is an important sink for atmospheric CO2, with high annual influx relative to surrounding waters and large seasonal variability. The location of the subarctic‐subtropical gyre boundary is relatively stable, but there is a large seasonal migration of the North Pacific Chlorophyll Front, between ~30°N and ~45°N. To determine the role of biological productivity on the seasonal CO2 flux, we measured sections of oxygen‐argon gas ratios on cruises across the transition zone in November 1997 (autumn) and September 2008 (summer). A simple upper ocean model was used with the O2:Ar data to estimate the net community production (NCP) in the euphotic zone. On both cruises the NCP was highest at the chlorophyll front with average values from 30°N to 45°N of 3.4 ± 2.0 and 8.1 ± 2.7 mmol C m−2 d−1 in autumn and summer, respectively. These values are sufficient to make biologically induced carbon export an important component of the CO2 drawdown in this region. Processes that control the relatively high NCP at this boundary are not certain, but it has been demonstrated that horizontal convergence of nitrate plays some role.
Dimension reduction for big data

Dimension reduction is aimed at reducing the dimension of a high dimensional vector-valued explanatory variables and simultaneously preserves its relationship with a univariate or low-dimensional real-valued response. As one of the oldest and most well-known dimension reduction approaches, principal component analysis (PCA) has been extensively used in high dimensional data analysis in applications. Classical PCA approaches cannot be applied to big data because of memory and storage barriers. Using a technique called scanning data by rows, the article proposes a new PCA approach. It shows that the proposed PCA approach can provide exact solutions when the size of observed data exceeds the memory size of a computing system.
Locating Velocity Changes in Elastic Media with Coda Wave Interferometry

In coda wave interferometry one uses the long wave paths of coda waves to detect minute changes in the velocity. When the relative velocity perturbation is constant in space, it is related to the travel time change δt by δv/v = −δt/t. But when the velocity change depends on space, the relation between the measured travel time change and the velocity change is more complicated. We show that in that case the estimation of velocity change can be formulated as a standard linear inverse problem. The sensitivity kernel that relates the travel time change to the velocity depends on the energy density of the coda waves in space. We derive these kernels for (1) diffusive acoustic waves, (2) acoustic waves that obey radiative transfer, and (3) diffusive elastic waves, and illustrate the theory with numerical examples for acoustic and elastic waves.
International Conference on Sustainable and Intelligent Manufacturing , RESIM 2016 , 14-17 December 2016 , Leiria , Portugal Green Marketing and Green Brand – The Toyota Case

The main aim of this study is to contextualize sustainability and call for its consideration in firms’ management, namely via green marketing and green brand. Although the concept of sustainable development appeared in the 1970s, only recently has it been incorporated by firms. Development, to be sustainable, has to satisfy the needs of current generations, without compromising the ability of future generations to satisfy theirs. This requires a balance in managing social, economic and environmental aims, both current and future. Given the increasing evidence of environmental problems, the awareness of the need for sustainability has been expanding, at the individual and at the corporate level. Environmentally responsible firms obtain multiple benefits, such as cost reduction (due to the lower resource consumption, such as water or energy), profit increase (from recycling and residuals reuse), production process enhancement (given the cleaner and more efficient technologies), corporate image upgrading, improvement of brand awareness and value as well as performance. Oppositely, firms associated to non-sustainable actions are impaired in those areas. In the scope of a green strategy, a green brand obtains attributes and benefits related to the reduction of the brand’s environmental impact. As such, it should promote the perception of an environmentally healthy brand and disclose such benefits to the more environmentally aware consumers. We present the case of Toyota, a brand with patent and acknowledged efforts in what regards sustainability. It is a worldwide reference of corporate responsibility and of a successful use of green marketing. The brand’s challenge to reach important environmental goals until 2050, demonstrates its commitment and endeavor in multiple crucial aspects of sustainability management. Therefore, Toyota is in a prominent position as a “global green brand”. © 2017 The Authors. Published by Elsevier B.V. * Corresponding author. Tel. : +351-244-843-313; fax: +351-244-820-310. E-mail address: ana.lisboa@ipleiria.pt © 2017 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the International Conference on Sustainable and Intelligent Manufacturing 184 Lídia Simão and Ana Lisboa / Procedia Manufacturing 12 ( 2017 ) 183 – 194
Refining the Model of Ionic Wind Propulsion by Analyzing Thrust Dependence on Electrode Geometry

Refining the Model of Ionic Wind Propulsion by Analyzing Thrust Dependence on Electrode Geometry Ryan Doel Department of Physics and Astronomy, BYU Bachelor of Science A single-stage parallel wire ionic wind device was investigated in an effort to further elucidate thrust dependencies. An attempt to measure the strength of the relationship between the geometrical constant C and the ratio of the emitter and collector diameters, C ∝ de dc , was undergone. Consequently a 1m long assembly was created wherein electrode gap distance, applied voltage, and diameter of emitter and collector wire were varied. We found that the collector size has a larger impact on thrust than emitter diameter. It was observed that the lengthened device reduced end effects witnessed by other researchers. The relationship C ∝ de dc was never quantified due to the propagation of errors in our results. In an effort to reduce these errors, future research should acquire force measurements at 30+ different voltages for each configuration. Also, further studies should combine 4+ collector sizes and 4+ emitter sizes, in order to paint a broader picture of the force dependence on electrode size.
Trade-off Analysis Tool for Interactive Nonlinear Multiobjective Optimization

In interactive methods, a decision maker (DM) directs the search for the most preferred Pareto optimal solution with his/her preferences. We propose a tool that can be used to support the DM. With this tool, the DM can conveniently learn about local trade-offs and judge whether they are worthwhile. The tool is based on an idea where the DM is able to vary a selected Pareto optimal objective vector. The varied vector is treated as a reference point which is then projected to the tangent hyperplane of the Pareto optimal set at the selected Pareto optimal solution. This information can be used to reflect what kind of Pareto optimal solutions and trade-offs are available in a local neighborhood of the selected solution. This tool is especially useful when trade-off analysis must be carried out fast and without increasing computation workload.
RFID Data-Driven Vehicle Speed Prediction via Adaptive Extended Kalman Filter

The traditional speed prediction generally utilizes the Global Position System (GPS) and video images, and thus, the prediction precision mainly depends on environmental factors (i.e., weather, ionosphere, troposphere, air, and electromagnetic waves). We study the Radio Frequency Identification (RFID) data-driven vehicle speed prediction and proposed an improved extended kalman filter (i.e., the adaptive extended kalman filter, AEKF) algorithm. Firstly, the on-board RFID reader equipped in the vehicle reads the information (i.e., current speed and time) from the tag deployed on the road. Secondly, the received information is transmitted to the on-board information processing unit, and it is demodulated and decoded into available information. Finally, based on the vehicle state space model, the AEKF algorithm is proposed to predict vehicle speed and improve the prediction results, so that the vehicle speed gradually approaches the actual vehicle speed. The simulation results show that compared with the conventional extended kalman filter (EKF) algorithm, our proposed AEKF algorithm improves the dynamic performance of the filtering and better suppresses the filtering divergence process. Moreover, the AEKF algorithm also improves the precision of the Mean Square Error (MSE) and Mean Absolute Error (MAE) by 57.4% and 32.4%, respectively.