Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory-Based Graph Networks

Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs. We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling. We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory. The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data.
A social norm nudge to save more: A field experiment at a retail bank

A large fraction of households have very little savings buffer and are therefore vulnerable to financial shocks. We examine whether a social norm nudge can stimulate such households to save more by running a small-scale survey experiment and a large-scale field experiment at a retail bank in the Netherlands. The survey experiment shows that a social norm nudge increases intended savings. In line with this, we find in our field experiment that households who are exposed to the social norm nudge click more often on a link to a personal webpage where they can start or adjust an automatic savings plan. However, analyzing detailed bank data, we find no treatment effect on actual savings, neither in the short run nor in the long run. Our null findings are quite precisely estimated.
Science Magazine

O n 13 November 2015, a group of Islamic jihadists launched a series of coordinated terror attacks across the city of Paris, France. Witness statements and police reports were almost unbearable to hear, but for people directly affected by the attacks, their traumatic experiences are unforgettable. How do people cope with the memories of such terrible experiences when reminders of the event are omnipresent? Selectively blocking memories of the event is a common coping strategy, but is it a good one? Clinicians would probably be skeptical about recommending this strategy because it is counterproductive for many patients who have experienced a traumatic event. On page 756 of this issue, Mary et al. (1) report the neural differences that control the retrieval of traumatic memories in 102 individuals who were affected by the Paris terror attacks but who dealt with these memories in different ways: 55 developed posttraumatic stress disorder (PTSD), and 47 did not. Mary et al. investigated the control of memory retrieval. They first asked all participants, including 73 healthy individuals who had never experienced a traumatic event in their lives, to learn cue-target pairs of words (cues) and pictures (targets), which were completely unrelated to the terror attack, until they recalled the targets automatically in response to the presentation of the cue. Participants were then instructed to suppress the recall for some targets but not for others while their brains were being scanned. On task completion, participants were given an implicit perceptual task in which they had to actively retrieve targets they had been suppressing. All participants were able to suppress the intrusive memories of targets, as instructed, and even improved performance during the task. In the perceptual task, PTSD participants retrieved the suppressed targets much more readily than the other two groups. This “readiness” suggests a weakness in regulatory control of memory in people with PTSD. Although all participants recruited the same neural networks during memory suppression, functional differences emerged when the authors investigated how these brain networks cooperated with one another. Successful suppression of intrusive memories was associated with increased functional INSIGHTS
Expression of genes involved in neurogenesis, and neuronal precursor cell proliferation and development: Novel pathways of human ovarian granulosa cell differentiation and transdifferentiation capability in vitro

The process of neural tissue formation is associated primarily with the course of neurogenesis during embryonic life. The source of neural-like cells is stem cells, which, under the influence of appropriate differentiating factors, may differentiate/transdifferentiate towards a neural-like lineage. The present study suggested that, under long-term in vitro culture conditions, human ovarian granulosa cells (GCs), obtained from granulosa-rich follicular fluid, acquired new properties and expressed genes characteristic of the ontological groups ‘neurogenesis’ (GO:0022008), ‘neuronal precursor cell proliferation’ (GO:0061351) and ‘nervous system development’ (GO:0007399), which are closely related to the formation of neurons. The present study collected GCs from 20 women referred for the procedure of in vitro fertilization. Cells were maintained in long-term in vitro culture for 30 days, and RNA was isolated after 1, 7, 15 and 30 days of culture. The expression profile of individual genes was determined using the Affymetrix microarray method. The 131 genes with the highest expression change in relation to day 1 of culture were then selected; the 10 most affected genes found to be primarily involved in nerve cell formation processes were chosen for consideration in this study: CLDN11, OXTR, DFNA5, ATP8B1, ITGA3, CD9, FRY, NANOS1, CRIM1 and NTN4. The results of the present study revealed that these genes may be considered potential markers of the uninduced differentiation potential of GCs. In addition, it was suggested that GCs may be used to develop a cell line showing neuronal characteristics after 30 days of cultivation. In addition, due to their potential, these cells could possibly be used in the treatment of neurodegenerative diseases, not only in the form of ‘cultured neurons’ but also as producers of factors involved in the regeneration of the nervous system.
An Error Analysis Method SPP-BEAM and a Construction Guideline of Nonconforming Finite Elements for Fourth Order Elliptic Problems

Abstract Under two hypotheses of nonconforming finite elements of fourth order elliptic problems, we present a side–patchwise projection based error analysis method (SPP–BEAM for short). Such a method is able to avoid both the regularity condition of exact solutions in the classical error analysis method and the complicated bubble function technique in the recent medius error analysis method. In addition, it is universal enough to admit generalizations. Then, we propose a sufficient condition for these hypotheses by imposing a set of in some sense necessary degrees of freedom of the shape function spaces. As an application, we use the theory to design a P3 second order triangular H 2 non-conforming element by enriching two P4 bubble functions and, another P4 second order triangular H nonconforming finite element, and a P3 second order tetrahedral H 2 non-conforming element by enriching eight P4 bubble functions, adding some more degrees of freedom.
Power Management of Wireless Sensor Nodes with Coordinated Distributed Reinforcement Learning

Energy Harvesting Wireless Sensor Nodes (EHWSNs) require adaptive energy management policies for uninterrupted perpetual operation in their physical environments. Contemporary online Reinforcement Learning (RL) solutions take an unrealistically long time exploring the environment to converge on working policies. Our work accelerates learning by partitioning the state-space for simultaneous exploration by multiple agents. We achieve this by using a novel coordinated e-greedy method and implement it via Distributed RL (DiRL) in an EHWSN network. Our simulation results show a four-fold increase in state-space penetration and reduction in time to achieve optimal operation by an order of magnitude (50x). Moreover, we also propose methods to reduce instances of disastrous outcomes associated with learning and exploration. This translates to reducing the downtimes of the nodes in simulations corresponding to a real-world scenario by one thirds.
A Reduced Features of Public Health Development Index by Using Clustering Methods

Public Health Development Index (PHDI) is an index that released by Ministry of Health of The Republic of Indonesia which is used to measure the health of Indonesian people. The PHDI is divided into seven categories and measured by using 30 features. There are 497 cities that contributed to PHDI 2013. The problem emerged is that not all the cities have those 30 features required by PHDI. The objective of this paper is to reduce PHDI features with various Feature Selection For Clustering (FSFC) method. The subset of features resulted from FSFC method is then used to generate two types of clusters, i.e. k-means and DBSCAN clustering. The clusters generated from k-means and DBSCAN techniques will be evaluated by using internal and external validation method. The subset of features is then compared with the original ranking of PHDI. Experimental results show that the results of each FSFC method producing a different rank compared to the original rank of PHDI. This result concludes that all the features that are used to measure PHDI are important to be included in the process of calculation.
System Identification-Based Dynamics Control for X-Y Pedestal Satellite Antenna

Subspace method-based system identification and six different transfer functions as product of this method have been developed and embedded in the motion control unit of the X-Y pedestal satellite antenna. Fit estimation data of these six transfer functions is changing from 95.46% to 99.99% with a RMSE (root mean square error) degree from 0.00502 to 0.00984, whereas the target RMSE degree value is 0.05.
Preliminary Design of a Waveguide-Fed Milimeter Wave Metasurface Antenna with LCD Controlled Array Factor for 5G User Equipment

The millimeter wave (mmW) band in 5G communication promises wide bandwidth for high data rate throughput, however the available area on cellular user equipment (UE) is quite limited, and 2 dimensional phased array occupies a large area and is fairly complicated. In this article we explore the possibility a waveguide-fed millimeter wave metasurface antenna for 28 GHz band. The element is a generic T-metasurface, and LCD control of array factor is suggested with a glass lens to focus the beam. A fan shape beam is generated from this antenna with maximum gain around 16.7 dB. This design may serve the 5G mmW communication with simpler structure to nearby small cell station.
Design of Weakly Coupled Two-Mode Hollow-Core Antiresonant Fiber With Low Loss

We first design and optimize a hollow-core conjoined-tube antiresonant fiber (CT-ARF) for weakly coupled <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{01}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{11}}$</tex-math></inline-formula> modes transmission. The critical core radii for the weak-coupling condition in the CT-ARFs with 4 to 8 tubes are obtained. Using numerical simulation, we demonstrate that by tuning the glass sheets positions in cladding tubes the confinement losses (CLs) of higher-order modes (HOMs) <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{21}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{02}}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{31}}$</tex-math></inline-formula> can be maximized through core-cladding mode coupling. Among the CT-ARFs with 4 to 8 tubes, the 6-tube one is proved to have the lowest CLs and bending losses of <inline-formula><tex-math notation="LaTeX">$\mathrm{LP}_{01}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP}_{11}$</tex-math></inline-formula> modes and the highest CL ratios between the undesired HOMs and the two guided modes. In the designed 6-tube CT-ARF, <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{01}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{11}}$</tex-math></inline-formula> modes have low CLs in the transmission band of 1.31–1.65 <inline-formula><tex-math notation="LaTeX">$\mu \text{m}$</tex-math></inline-formula>. The minimum CLs of <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{01}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{11}}$</tex-math></inline-formula> modes are <inline-formula><tex-math notation="LaTeX">$\text{1.7}\times \text{10}^{-4} \text{ dB/m}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{3.1} \times \text{10}^{-3} \text{ dB/m}$</tex-math></inline-formula> in the C-band (1.53–1.565 <inline-formula><tex-math notation="LaTeX">$\mu \text{m}$</tex-math></inline-formula>) respectively. A 10 cm bend has a weak influence on the two guided modes. The CL ratio between the lowest loss HOM and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP}_{11}$</tex-math></inline-formula> mode keeps <inline-formula><tex-math notation="LaTeX">$>\! \! 150$</tex-math></inline-formula> in the wavelength range from 1.25–1.65 <inline-formula><tex-math notation="LaTeX">$\mu \text{m}$</tex-math></inline-formula>, which ensures the high purity of <inline-formula><tex-math notation="LaTeX">$\mathrm{LP_{01}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{LP}_{11}$</tex-math></inline-formula> modes. The fabrication tolerances of the fiber are discussed.
Mobile Biometrics, Replay Attacks, and Behavior Profiling: An Empirical Analysis of Impostor Detection

The rise of mobile devices has contributed new biometric modalities which reflect behavioral tendencies as users interact with the device’s services. In this paper, we explore replay attacks against such systems and how a remote attack might affect authentication performance. There are few efforts that focus on replay attacks in mobile biometric systems, and none to our knowledge related to user-device interactions, such as the use of mobile apps. Instead, previous efforts have mainly considered spoofing attacks, which implicate that the attacker has learned their target’s behavior instead of obtaining a direct copy of logged behavior by theft. Here, we explore temporally-derived replay attacks that assume that application, Bluetooth, and Wi-Fi data has been captured remotely and then intelligently combined with some level of noise to avoid the replay of an exact copy of legitimate data. We study several factors that may affect replay attack detection, including the effects of varying the amount of data available during data collection, the number of samples used for training, and supervised and unsupervised learning on attack detection. In our analysis, false positive rates increased from 2.3% when using zero-effort attacks to over 40% as a result of replay attacks. However, our results also show that by contextualizing behavior in the feature representation, false positive rates decrease by over 25%.
A Data-Driven Vulnerability Evaluation Method in Grid Edge Based on Random Matrix Theory Indicators

In order to solve the problem of vulnerability assessment of complex power systems facing complex structures and large sizes, a novel data driven method based on random matrix theory is proposed in this paper. Firstly, with the use of phasor measurement units (PMUs) big data, evaluation matrices are constructed to extract statistical characteristics of power systems operation. Then, with the combination of random matrix theory and entropy theory, vulnerability evaluation index are constructed considering the degree of influence of some faults in power systems. With full use of big data, the model-free method is more accurate and comprehensive. Simulation results in IEEE 39-bus test system and a real-world power grid in China verify the effectiveness of the method.
The Intention to Adopt Electronic Document and Records Management System: Questionnaire Development Procedure

Electronic Document and Records Management System (EDRMS) is recognized to have a positive impact on the management of organization records. However, the system has not been optimally utilized because of rejection among the users. The adoption of the system is influenced by ten identified factors that are then used as a basis in questionnaire development. In this regard, the study aims to develop an authoritative questionnaire following stringent instrument development protocols. There were 72 indicators identified. After content validity measurement performed, seven indicators were rejected, and only 65 indicators were accepted to form the final questionnaire. The validated and evaluated questionnaire can be used to measure the level of EDRMS adoption in an organization.
Coding for Non-IID Sources and Channels: Entropic Approximations and a Question of Ahlswede

The theory of Verdú and Han provides a powerful framework to analyze and study general non-independent and identically distributed (non-i.i. d.) sources and channels. Already for simple non-i.i. d. sources and channels, this framework can result in complicated general capacity formulas. Ahlswede asked in his Shannon lecture if these general capacity formulas can be effectively, i.e., algorithmically, computed. In this paper, it is shown that there exist computable non-i.i. d. sources and channels, for which the capacity is a non-computable number. Even worse, it is shown that there are non-i.i. d. sources and channels for which the capacity is a computable number, i.e., the limit of the corresponding sequence of multi-letter capacity expressions is computable, but the convergence of this sequence is not effective. This answers Ahlswede’s question in a strong form, since in this case, the multi-letter capacity expressions for these sources and channels cannot be used to approximate the optimal performance algorithmically.
Pole Placement applied to drive a Truck and Trailer in backward. A didactic view

Driving a Truck and Trailer in backward is a challenging problem to address the control teaching of non-linear unstable systems. The paper shows techniques to formulate de feedback control problem, to find the equilibrium state, to linearize the system and to design the state feedback controllers with Luenberger observers tuned with pole placement techniques. Results show that the state feedback controller can stabilize the truck and trailer, moreover, the controlled system has good behaviour in following sinusoidal trajectories when driving in backward.
Multi-level approach for controlling architecture quality with Alloy

the software evolution process means the continuous control of functional and non –functional software characteristics by the change of its code without changing the properties of its quality neither the quality of its architecture. Therefore, the most important problem to be treated is how to limit the progressive shifting between the planned and the implemented architecture during the software evolution process. This phenomenon called the erosion of software architecture, often appears unexpectedly and must be controlled. In this paper, we have proposed an approach to detect erosion problems by representing tactical, strategic, and operational architectural constraints at different abstraction levels. These constraints are specified by the use of the Alloy constraint language in order to restore the deviations that may affect the description of the software architecture during the development steps. Our goal is to control architecture quality by limiting the occurrence and impact of erosion, which means the control of architecture conformance during system development and maintenance activities.
Input Filter Capacitance and Inductance Design for Photovoltaic Systems

This work selects a procedure to use a photovoltaic panel model that use only the datasheet data. After a model selection, the revised equations are presented and used to estimate the power reduction when pulsed input current and continuous input current converters are connected to photovoltaic panels. The ripple produced by these converters reduces the extracted power from the panels even if a maximum power point tracking technique is used. The study shows that on pulsed input current converters the capacitance filter is needed. For these converters there is a significant power reduction, or the maximum power point tracking is unfeasible in some situations. For panels connected to continuous input current converters the work shows that the worst situation is to design the inductor filter on small solar irradiance. The connection of photovoltaic panel with inverters is review and the produced ripple is related with the power reduction of extracted power. The contributions of this paper are providing guidelines, equations and graphs in order to design input filters to ensure that the connected converter can achieve its best operation point and the best maximum power point tracking control for many conditions.
IoT based Smart Attendance Monitoring System using RFID

Most of the institutional authorities are troubled with the cumbersome method of maintaining manual attendance of their employees. The manual process of signing on a paper is prolonged and insecure. An efficient attendance monitoring system needs to be enforced at such places. Radio Frequency Identification (RFID) based attendance system provides us with a solution that caters to issues like proxy attendance. This paper describes the design of an RFID based attendance monitoring system which uniquely identifies each employee/student based on their RFID tag which is attached to their ID card. This makes the mechanism of recording the attendance effortless, quicker and protected as compared to conventional method. This system is designed to be used at different educational institutions, corporate offices, government offices etc. The proposed system consist of both hardware and software components based on IoT Technology. The hardware component consists of RC522 RFID card reader and RFID tags/cards. The software component consists of the Web-based GUI for viewing the employee’s or student’s attendance, which is hosted on a web server and which stores the data in a database server. The employees or students just need to place their RFID card or tag on the reader and their attendance will be recorded for the day. Also, the attendance recorded will be more accurate as the system is synced with a real-time clock.
Automated Smart Home Controller Based on Adaptive Linear Neural Network

The purpose of this research is to model a smart home appliances control system using a neural network method. This control system consists of several components i.e. rain sensor, temperature sensor, light sensor, stepper motor and LCD on Arduino. Adaptive linear neural networks called Adaline algorithm is used for the training process of artificial neural networks. The inputs and targets are trained with a network that has been built to get the learning weights to be used as the basis for calculations on the next incoming training data. Adaline algorithm is used for the training process of Artificial Neural Networks. The results of the output of light intensity are 0, 150, 300, 400 with lux units and the results of the intensity of rain are 0, 250, 400, 700 in mm units of each water humidity simulated in light and rain sensors. The input obtained will be processed into an output that will move the motor to the position that has been set. Contribution of the results of this study, a proposed neural network-based multi-control system model based on criteria values of Adaline.