Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF CONSUMER ETHICAL BELIEFS ON GREEN BUYING INTENTION: SOCIAL DILEMMA AS A MEDIATING VARIABLE

This study uses an ethical aprroach to explain green buying intentions. The results of the study so far have shown inconsistency. Costumer ethics will trigger their reasoning and lead to dilemma situations. To reduce a dilemma situations, consumer will do something good, one of them is by making an intention to buy green products. The purpose of this study is to examine the influence of consumer ethical beliefs on green buying intention with social dilemma as a mediating variable. The sample in this study is 126 respondents. The analysis technique used path analysis. The result of this studi is social dilemma indicate that social dilemma does not mediate the effect of consumer ethical beliefs on green buying intention. Keywords: green consumerism, ethical beliefs, social dilemma, green purchase intentiton
Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text

An author’s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neural character n-gram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks. We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution.
Evaluation of Quality Parameters of Chicken Eggs Stored at Different Temperatures

This study was carried out to evaluate the effect of temperature and time on the internal and external quality of chicken eggs stored at room and fridge temperature for 28 days. A total of 176 eggs of Lohmann Brown (LSL) laying hens were used for the study. The eggs from the same batch were stored at fridge (5°C) and room temperature (25°C), internal and external quality parameters and microbiological analyzes of eggs were evaluated on days 0, 1, 8, 18, 21 and 28. No coliform bacteria, E. coli and Salmonella spp. were detected. At the end of the evaluation, the eggs stored at room temperature were found to have been negatively affected in terms of albumen index and Haugh unit parameters from day 8 onwards. When a comparison was made between the eggs kept in the room and the fridge temperature, significant differences were found in terms of albumen index, Haugh unit, egg yolk index and egg yolk height parameters (P<0.05). As a result, it is predicted that the storage temperature and duration play an important role in preserving the freshness of the eggs and the eggs should be kept under the cold chain, especially from day 8 onwards after the egg laying date.
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach

Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4% (F1-measure).
An equational theory for weak bisimulation via generalized parameterized coinduction

Coinductive reasoning about infinitary structures such as streams is widely applicable. However, practical frameworks for developing coinductive proofs and finding reasoning principles that help structure such proofs remain a challenge, especially in the context of machine-checked formalization. This paper gives a novel presentation of an equational theory for reasoning about structures up to weak bisimulation. The theory is both compositional, making it suitable for defining general-purpose lemmas, and also incremental, meaning that the bisimulation can be created interactively. To prove the theory’s soundness, this paper also introduces generalized parameterized coinduction, which addresses expressivity problems of earlier works and provides a practical framework for coinductive reasoning. The paper presents the resulting equational theory for streams, but the technique applies to other structures too. All of the results in this paper have been proved in Coq, and the generalized parameterized coinduction framework is available as a Coq library.
Pooled variable scaling for cluster analysis

MOTIVATION Many popular clustering methods are not scale invariant because they are based on Euclidean distances. Even methods using scale invariant distances such as the Mahalanobis distance lose their scale invariance when combined with regularization and/or variable selection. Therefore, the results from these methods are very sensitive to the measurement units of the clustering variables. A simple way to achieve scale invariance is to scale the variables before clustering. However, scaling variables is a very delicate issue in cluster analysis: A bad choice of scaling can adversely affect the clustering results. On the other hand, reporting clustering results that depend on measurement units is not satisfactory. Hence, a safe and efficient scaling procedure is needed for applications in bioinformatics and medical sciences research. RESULTS We propose a new approach for scaling prior to cluster analysis based on the concept of pooled variance. Unlike available scaling procedures such as the standard deviation and the range, our proposed scale avoids dampening the beneficial effect of informative clustering variables. We confirm through an extensive simulation study and applications to well known real data examples that the proposed scaling method is safe and generally useful. Finally, we use our approach to cluster a high dimensional genomic dataset consisting of gene expression data for several specimens of breast cancer cells tissue obtained from human patients. AVAILABILITY An R-implementation of the algorithms presented is available at https://wis.kuleuven. be/statdatascience/robust/software. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application, such as proximity-based services (PBSs), and location-based services (LBSs). For these systems to perform, it is essential to have reliable hardware and predict a user’s position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize received signal strength indicators (RSSIs) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this article, Bluetooth low energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a nonparametric information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of mean absolute error (MAE) and root mean squared error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30% in comparison with traditional filtering, when the beacon and the receiver are within 3 m.
LiftTiles: Constructive Building Blocks for Prototyping Room-scale Shape-changing Interfaces

Large-scale shape-changing interfaces have great potential, but creating such systems requires substantial time, cost, space, and efforts, which hinders the research community to explore interactions beyond the scale of human hands. We introduce modular inflatable actuators as building blocks for prototyping room-scale shape-changing interfaces. Each actuator can change its height from 15cm to 150cm, actuated and controlled by air pressure. Each unit is low-cost (8 USD), lightweight (10 kg), compact (15 cm), and robust, making it well-suited for prototyping room-scale shape transformations. Moreover, our modular and reconfigurable design allows researchers and designers to quickly construct different geometries and to explore various applications. This paper contributes to the design and implementation of highly extendable inflatable actuators, and demonstrates a range of scenarios that can leverage this modular building block.
REST: A thread embedding approach for identifying and classifying user-specified information in security forums

How can we extract useful information from a security forum? We focus on identifying threads of interest to a security professional: (a) alerts of worrisome events, such as attacks, (b) offering of malicious services and products, (c) hacking information to perform malicious acts, and (d) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Novel approaches are needed to address the challenges in this domain: (a) the difficulty in specifying the “topics” of interest efficiently, and (b) the unstructured and informal nature of the text. We propose, REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above. The key novelty of the work is a multi-step weighted embedding approach: we project words, threads and classes in appropriate embedding spaces and establish relevance and similarity there. We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads. First, REST robustness to initial keyword selection can extend the user-provided keyword set and thus, it can recover from missing keywords. Second, REST categorizes the threads into the classes of interest with superior accuracy compared to five other methods: REST exhibits an accuracy between 63.3-76.9%. We see our approach as a first step for harnessing the wealth of information of online forums in a user-friendly way, since the user can loosely specify her keywords of interest.
The Importance of Phase in Complex Compressive Sensing

We consider the question of estimating a real low-complexity signal (such as a sparse vector or a low-rank matrix) from the phase of complex random measurements. We show that in this phase-only compressive sensing (PO-CS) scenario, we can perfectly recover such a signal with high probability and up to global unknown amplitude if the sensing matrix is a complex Gaussian random matrix and the number of measurements is large compared to the complexity level of the signal space. Our approach proceeds by recasting the (non-linear) PO-CS scheme as a linear compressive sensing model built from a signal normalization constraint, and a phase-consistency constraint imposing any signal estimate to match the observed phases in the measurement domain. Practically, stable and robust estimation of the signal direction is achieved from any instance optimal algorithm of the compressive sensing literature (such as basis pursuit denoising). This is ensured by proving that the matrix associated with this equivalent linear model satisfies with high probability the restricted isometry property under the above condition on the number of measurements. We finally observe experimentally that robust signal direction recovery is reached at about twice the number of measurements needed for signal recovery in compressive sensing.
Risk Factors of Obesity among 15-64 Yrs Age Group: Picture in a Village of West Bengal

Obesity, a result of unhealthy consumption, coupled lack of physical activity, is itself a serious health risk. The key to the control of epidemics of obesity is primary prevention. The basis of prevention is therefore identification of the major risk factors and their prevention and control. A Descriptive epidemiological study using cross sectional design was carried out among seven thirty subjects of 15-64 yr age group in a village with primary objective of finding out the prevalence of various risk factors and to see the association in between. Interview was taken using a schedule modified in line with IDSP questionnaire [11] taking into account the local needs and resources. Data was collected on socio demographic variables, risk factors of obesity by house to house visit after obtaining informed verbal consent. Study population consisted of 57% male, 70% Hindu, 20% illiterate and 35 % unemployed respondents. 22.9% gave smoked and smokeless tobacco use history, 75.2% current tobacco users, 21% current drinkers, 38.5% had sedentary lifestyle, nearly 50% added extra salt most of the times, 80% use unsaturated oil. Sixty subjects had BMI of more than 30.0. Significant association was found with age group, per capita income, tobacco use, alcohol consumption, physical activity, salt intake with food and intake of oils/fat. (P<.01). Finally motivation for improvement in life style is the million dollar suggestion.
Real galaxy mergers from galaxy pair catalogues

Mergers of galaxies are extremely violent events shaping their evolution. Such events are thought to trigger starbursts and, possibly, black hole accretion. Nonetheless, it is still not clear how to know the fate of a galaxy pair from the data available at a given time, limiting our ability to constrain the exact role of mergers. In this paper we use the light-cone of the horizon-agn simulation, for which we know the fate of each pair, to test three selection processes aiming at identifying true merging pairs. We find that the simplest one (selecting objects within two thresholds on projected distance d and redshift difference Δz) gives similar results than the most complex one (based on a neural network analysing d, Δz, redshift of the primary, masses/star formation rates/aspect ratio of both galaxies). Our best thresholds are $d_\mathrm{th}\sim 100\, \mathrm{kpc}$ and Δzth ∼ 10−3, in agreement with recent results.
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware

We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the timing (using a hardware timer), and ensuring the data sent to hardware is safe and correct, where safety corresponds to safe operating voltage and current for the specified hardware. Much of the recent success of Reinforcement learning (RL) has been made possible with training and testing tools like OpenAI Gym and Deepmind Control Suite. Unfortunately, tools for quickly testing and transferring high-frequency RL algorithms from simulation to real hardware environment remain mostly absent. We present Blue River Controls, a tool that allows to train and test reinforcement learning algorithms on real-world hardware. It features a simple interface based on OpenAI Gym, that works directly on both simulation and hardware. We use Quanser's Qube Servo2-USB platform, an underactuated rotary pendulum as an initial testing device. We also provide tools to simplify training RL algorithms on other hardware. Several baselines, from both classical controllers and pretrained RL agents are included to compare performance across tasks. Blue River Controls is available at this https URL: this https URL
Diffuse radio emission in the galaxy cluster SPT-CL J2031−4037: a steep-spectrum intermediate radio halo?

The advent of sensitive low-frequency radio observations has revealed a number of diffuse radio objects with peculiar properties that are challenging our understanding of the physics of the intracluster medium. Here, we report the discovery of a steep-spectrum radio halo surrounding the central brightest cluster galaxy (BCG) in the galaxy cluster SPT-CL J2031−4037. This cluster is morphologically disturbed yet has a weak cool core, an example of a cool-core/non-cool-core transition system, which harbours a radio halo ∼0.7 Mpc in size. The halo emission detected at 1.7 GHz is less extended compared to that in the 325 MHz observation, and the spectral index of the part of the halo visible at the 325 MHz to 1.7 GHz frequencies was found to be −1.35 ± 0.07. Also, P1.4 GHz was found to be 0.77 × 1024 W Hz−1, which falls in the region where radio mini-haloes, halo upper limits and ultra-steep-spectrum (USS) haloes are found in the P1.4 GHz–LX plane. Additionally, simulations presented in the paper provide support for the scenario of the steep spectrum. The diffuse radio emission found in this cluster may be a steep-spectrum ‘intermediate’ or ‘hybrid’ radio halo that is transitioning into a mini-halo.
Impact of polarized galactic foreground emission on CMB lensing reconstruction and delensing of B-modes

Next generation CMB experiments such as CMB-S4 aim at measuring the CMB lensing potential at sub-percent precision where most of the constraining power will come from CMB polarization. We investigate the prospects of achieving this goal in the presence of large-scale, diffuse galactic foreground emission by using non-Gaussian sky simulations and exploit multi-frequency information to clean those. We show that, while prior to foreground cleaning, cosmological parameter estimates from the contaminated lensing potential estimation with the quadratic estimator can be significantly biased, these can be successfully mitigated by applying a parametric foreground cleaning approach. We further observe no significant additional bias in the delensed B-mode power spectrum after applying foreground cleaning and are therefore able to obtain an unbiased measurement of the tensor-to-scalar ratio, r, after delensing with the quadratic estimate of the lensing potential.
Learning to Encode and Classify Test Executions

The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing. The goal in this paper is to solve the test oracle problem in a way that is general, scalable and accurate. To achieve this, we use supervised learning over test execution traces. We label a small fraction of the execution traces with their verdict of pass or fail. We use the labelled traces to train a neural network (NN) model to learn to distinguish runtime patterns for passing versus failing executions for a given program. Our approach for building this NN model involves the following steps, 1. Instrument the program to record execution traces as sequences of method invocations and global state, 2. Label a small fraction of the execution traces with their verdicts, 3. Designing a NN component that embeds information in execution traces to fixed length vectors, 4. Design a NN model that uses the trace information for classification, 5. Evaluate the inferred classification model on unseen execution traces from the program. We evaluate our approach using case studies from different application domains: 1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Value pointer library and 6. Nine network protocols from Linux packet identifier, L7-Filter. We found the classification models for all subject programs resulted in high precision, recall and specificity, over 95%, while only training with an average 9% of the total traces. Our experiments show that the proposed neural network model is highly effective as a test oracle and is able to learn runtime patterns to distinguish passing and failing test executions for systems and tests from different application domains.
Radiation Damage and Thermal Recovery of Perovskite Superconductor Yttrium Barium Copper Oxide.

High temperature superconducting materials are being considered to generate the magnetic fields required for the confinement of plasma in fusion reactors. The present study aims to assess the microstructural degradation resulting from ion implantation at room temperature under two implantation conditions in Yttrium Barium Copper Oxide (YBCO) tapes. Xray Diffraction (XRD) and high resolution characterisation techniques including Atom Probe Tomography (APT) and Transmission Electron Microscopy (TEM) analyses were used to correlate alterations in superconducting behaviour measured using a Magnetic Properties Measurement System (MPMS) to amorphization and recovery caused by ion implantation. TEM analysis was performed to depth profile the degree of crystallinity (or lack thereof) on irradiated samples. SRIM predicted the damage depth at 900 nm below the sample surface of the 2 MeV Xe implanted sample and 450 nm beneath the surface of the 0.6 MeV Xe implanted sample. 2 MeV Xe implantation caused the superconducting temperature to decrease by 10 K and the critical current density to display a 10 fold reduction. Post irradiation heat treatments up to 600C caused recrystallisation of the irradiated layer, but also oxygen loss and alterations in grain size. The recrystallised grain orientation was random in TEM lamellae, however, bulk samples re-grew along the original crystal orientation provided that some of the original material was not amorphized (ie if they nucleated on crystalline YBCO). This is extremely promising for the thermal recovery of tokamak components.
Mean-field density of states of a small-world model and a jammed soft spheres model

We consider a class of random block matrix models in $d$ dimensions, $d \ge 1$, motivated by the study of the vibrational density of states (DOS) of soft spheres near the isostatic point. The contact networks of average degree $Z = z_0 + \zeta$ are represented by random $z_0$-regular graphs (only the circle graph in $d=1$ with $z_0=2$) to which Erdos-Renyi graphs having a small average degree $\zeta$ are superimposed. In the case $d=1$, for $\zeta$ small the shifted Kesten-McKay DOS with parameter $Z$ is a mean-field solution for the DOS. Numerical simulations in the $z_0=2$ model, which is the $k=1$ Newman-Watts small-world model, and in the $z_0=3$ model lead us to conjecture that for $\zeta \to 0$ the cumulative function of the DOS converges uniformly to that of the shifted Kesten-McKay DOS, in an interval $[0, \omega_0]$, with $\omega_0 < \sqrt{z_0-1} + 1$. For $2 \le d \le 4$, we introduce a cutoff parameter $K_d \le 0.5$ modeling sphere repulsion. The case $K_d=0$ is the random elastic network case, with the DOS close to the Marchenko-Pastur DOS with parameter $t=\frac{Z}{d}$. For $K_d$ large the DOS is close for small $\omega$ to the shifted Kesten-McKay DOS with parameter $t=\frac{Z}{d}$; in the isostatic case the DOS has around $\omega=0$ the expected plateau. The boson peak frequency in $d=3$ with $K_3$ large is close to the one found in molecular dynamics simulations for $Z=7$ and $8$.