Miracle-dz/UniFAR-FTUs huggingface dataset:
Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition

Part 1 The need for a theory of cognitive evolution: mental architecture as an emergent phenomenon culture as evidence for cognitive structure the organization of this book. Part 2 Darwin's thesis: continuity and discontinuity pre-Darwinian theories Darwin on animal intelligence Darwin's thesis on language origins conclusion - a multidisciplinary puzzle. Part 3 Wernicke's machine: modular and unitary models of language neuropsychological aspects of evolution the case of Brother John the brain without language conclusion - Wernicke's machine in evolution. Part 4 The chronology of anatomical and cultural change: markers in the chronology of change bipedalism - the Australopithecines encephalization - the hominids speech - a recent innovation conclusion - the question of cognitive stages. Part 5 Primate cognition - episodic culture: ape skill - an overview episodic culture - a definition event perception in apes culture and evolution conclusion - the episodic mind. Part 6 First transition - from episodic to mimetic culture: mimetic culture - the missing link human cognition without language mimetic skill social consequences of mimetic representation modalities of mimetic expression neuroanatomical considerations conclusion - mimetic culture as a survival strategy. Part 7 Second transition - from mimetic to mythic culture: language and the rise of human culture symbolic invention the speech adaptation what is localized? conclusion - mythic culture and the uses of language. Part 8 Third transition - external symbolic storage and theoretic culture: from mythic to theoretic governance visuographic invention external memory devices - a hardware change the emergence of theoretic culture conclusion -the hybrid modern mind. Part 9 Consciousness and indeterminacy: transcending the episodic model of mind localization after the third transition conclusion - exuberant materialism.
Positioning of Distribution Network Commodities Using the Kraljic Portfolio Matrix

Kraljic portfolio matrix has been widely used in purchasing as an effective diagnostic tool. Despite its popularity, the matrix has been criticized for subjective in categorizing commodities. In this paper, a quantitative model to position distribution network commodities with Kraljic portfolio matrix is proposed. Attributes for supply risk and profit impact are identified first. Questionnaire survey is then conducted to get the relative weight of every attribute and the performance score. To deal with experts' qualitative assessments, triangular fuzzy number is applied to convert experts' linguistic assessment into accurate numbers. As a numerical illustration, 12 kinds of distribution network commodities with the largest purchasing amount of Hubei Grid (from year 2013 to 2017) are selected for positioning.
A Novel Plug-and-Play SAR Reconstruction Framework Using Deep Priors

The reconstruction of synthetic aperture radar (SAR) images from phase history data is an ill-posed inverse problem. Existing reconstruction methods use regularization to tackle the ill-posed nature of the imaging task, while assuming a forward model or trying to learn one. In either case, these methods do not decouple the sensing model and the priors used as regularizers. Recently emerging plug-and-play (PnP) priors is a flexible framework that allows forward models of imaging systems to be matched with the state-of-the-art prior models. Inspired by this, in this work, we propose a novel PnP SAR reconstruction framework. This framework decouples the forward model and the prior model, therefore allows us to replace either of them without affecting the other. In this paper, we demonstrate the use of a convolutional neural network (CNN) based prior model for the reconstruction of synthetic SAR scenes and compare the results with FFT-based and non-quadratic regularization based reconstruction methods. Experimental results show that our framework performs on par or better than with these methods in the majority of the scenarios considered.
Subblock Design aided OFDM with Adaptive Subblock Lengths

In recent researches, index modulation (IM) aided OFDM schemes are proposed to achieve diversity gain compared to classical OFDM. In this paper, high diversity order subblock design aid OFDM with adaptive subblock lengths (SD-OFDM-ASL) is proposed, which aims to adapt the different requirements of low computational complexity or high diversity gains by the changes of subblock length. With such arrangement, diversity gains under Rayleigh fading channel can be further improved by the long subblock length, whilst for shorter subblock length, high spectral efficiency with low computational complexity can be obtained.
Detecting the Manipulation of Demand via IoT

This article presents a research work on contrasting the Manipulation of Demand figures via IoT networks (MadIoT). MadIoT is a new example of large-scale coordinated attacks on the power grid that targets high-wattage electric appliances. By altering the concentration in time of switching on-off certain number of appliances, the stability of the energy distribution network as a whole it can be compromised. An issue is the fact that no individual consumer can detect MadIoT autonomously. Current energy business scenario uses interval-energy meters capillary deployed to remote users. Current meters operate in the timescale of minutes, which makes undetectable the MadIoT attacks. The authors propose using next-generation event-based real-time demand-oriented meters in order to collect true data about the timing of switching on-off devices. The above-mentioned big data stream enables newly introduced Security Agents to perform machine-reasoning over timing patterns against an unexpected probability density of events in certain neighborhood(s).
Classification of Japanese Fagaceae Wood Based on Microscopic Image Analysis

This paper presents wood identification method on microscopic image of Japanese Fagaceae wood by applying Daubechies Wavelet (DW) and Local Binary Pattern (LBP) algorithms. The main idea for this identification is to extract as much as possible the characteristics of wood to improve accuracy. The color intensity of microscopic wood image can be one of the characteristics of wood. Therefore, before extracting features with DW and LBP, we add color extraction to separate the color intensity in the Red, Green, and Blue (RGB) channels from the image. In addition, we compared several values of the LBP parameter for kernel type original, var, uniform, and ror to find the optimal value. The results of this work performed the accuracy of wood identification on microscopic image of Japanese Fagaceae wood with the Support Vector Machine (SVM) classifier is obtained 95.2% by setting P and R parameters at 8 and 2. Moreover in this paper, the most optimal LBP parameter value occurs when P = 16 and R = 1 for the original kernel type, which reaches 100% accuracy.
A Digital Controller IC for High-Frequency DC-DC Switching Converters

A digital controller IC for DC-DC converters with high switching frequency is described and implemented by CSMC 0.18um CMOS process in this paper. The designed digital controller IC consists of a reference-voltage-programmable delay-line window type ADC, a current-mode compensator using Adjacent Cycle Sampling (ACS) control strategy, and a hybrid digital pulse-width modulator (DPWM) using the structure of counter comparator and delay-locked-loop delay line. The simulation results indicated that, a Buck DC-DC switching converters using the designed controller IC can convert an input from 5V to 1.8 V with 1Mhz switching frequency. The slew rate of the transient responses as load variation of 500 mA is 20mV/μs, at most.
Virtual reality training system for upper limb rehabilitation

In recent years, with the development of medicine, the survival rate of stroke has been greatly improved, but the number of patients with motor dysfunction caused by stroke is increasing now. Rehabilitation training is the most effective medical means. Virtual reality technology provides three technical means for rehabilitation training: repetition, feedback and motivation. Studies have shown that virtual reality has unique advantages in restoring motor function, and can achieve good therapeutic effect. Patients can learn many practical motor skills in virtual environment. This paper designs a virtual rehabilitation training scene for upper limb rehabilitation and combines it with an upper limb rehabilitation robot. The immersive virtual reality system, which realizes three-dimensional display of virtual scene, could increase the immersion feeling of patients. Patients observe the virtual scene 360 degrees with HMD, and interact with the virtual scene through rehabilitation robot. The virtual reality rehabilitation training system could satisfy various training modes, and patients could carry out targeted rehabilitation treatment.
Experimental investigation on aerodynamics of nonplanar rotor pairs in a multi-rotor UAV

This paper presents a new configuration of multi-rotor unmanned aerial vehicle (UAV) with non-planar rotor pairs and investigates the aerodynamics by experimental measurements with a test rig and wind tunnel. Two types of adjacent non-planar rotor pairs, face to face (F-F) and back to back (B-B), are investigated at different disk plane tilt angles ranging from 0° to 50° and different transverse rotor spacing ranging from 1.0 to 1.8 times rotor diameter. In addition, the effect of wind on two types of non-planar rotor pairs is also investigated in wind tunnel with various wind speeds ranging from 0 to 4m/s. Experimental results show that two adjacent non-planar rotors have strong aerodynamic interference which affects their overall aerodynamic performance significantly, but the aerodynamic performance can be improved with an optimal combination of tilt angle and rotor spacing. It's observed that smaller rotor spacing reduces the aerodynamic performance significantly, while higher tilt angle and large rotor spacing usually contribute to the total thrust of the rotor pairs. Wind also has strong influence on the aerodynamic performance of non-planar rotor pairs, making the thrust and efficiency reduced significantly. B-B rotor pair is more sensitive to the wind disturbance than F-F rotor pair, the aerodynamic performance of B-B rotor pair reduced more significantly with wind speed.
A Novel Bidirectional T-source DC Circuit Breaker for DC Microgrids

A bidirectional DC breaker based on T-source series circuit is presented in this paper, which can work in the application where require bi-directional power flow between different nodes of microgrid and loads. When a short-circuit fault occurs, the proposed topology utilizes a T-source transformer to naturally commutate off the thyristor(SCR) by coupling and reducing the current to zero through the thyristor, thus avoiding circuit breaking under high current operating condition, furthermore, this topology allows a response to faults on either the source or load side only using a single controlled switch, no additional passive components are required compared with the unidirectional series T-source circuit breaker topology. The analysis is performed to find the fault conditions that cause the bi-directional circuit breaker to trip when operating in a single load power system, the transfer function of this breaker circuit is deduced and circuit parameters is designed. Finally, the operation principle of the bi-directional circuit breaker is evaluated for both source and load side faults by using a simulation platform PSIM.
An Heuristic Feature Selection Algorithm to Evaluate Academic Performance of Students

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student’s performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.
Slice Management Mechanism based on Dynamic Weights for Service Guarantees in Smart Grid

With the rapid development of the power grid, the variety of power services has become more and more diverse, resulting in different service needs. As one of the important technologies of 5G, network slicing aims to accommodate different services over the same physical network. In this paper, after briefly analyzing the background of smart grid, we investigate the related research of network slicing. Each kind of services in smart grid has its own bandwidth, reliability and delay tolerance requirements. Therefore, for the purpose of service guarantees in smart grid, we propose a slice management mechanism for resource allocation based on 5G network slicing technology. Due to the good self-learning and adaptive capabilities, we use BP(back-propagation) neural network to train the appropriate weights. Correspondingly, a heuristic algorithm for dynamic weights based on BP neural network to solve the optimization problem is proposed. Finally, simulation results demonstrate the effectiveness of our algorithm within the mechanism.
Hybrid frog-leap algorithm for job shop scheduling

An improved frog-leap algorithm for job-shop scheduling is proposed. The algorithm forms frog individuals via process-based coding and utilizes the switched local search strategy to make frog individuals quickly find the minimum fitness function, thereby improving scheduling efficiency of the job shop. The proposed algorithm offers advantages, such as few parameters, simple implementation, strong global optimization ability, and dynamic balance between local and global searches. It offers means to effectively solve problems associated with job-shop scheduling.
Improving the disease awareness: how a communication campaign brings hidradenitis suppurativa to the light

Hidradenitis suppurativa is an inflammatory skin disease that presents a recurrence of painful and suppurating lesions in the apocrine gland‐bearing regions, with a strong impact on the patients’ life quality. Despite its peculiar presentation, early forms are often underestimated by patients and this would inevitably result in late diagnosis and delayed therapy.
Distributed Neurodynamic Optimization for Coordination of Redundant Robots

In this paper, the robot coordination system is composed of a number of redundant manipulators. An optimization model is formulated as a convex problem with globally coupled constraints, which includes equality constraints derived for path tracking, inequality constraints derived for obstacle avoidance, and convex sets built for joint physical limits. A novel distributed neurodynamic-based algorithm is developed for solving the complex problem in real time, so that there is no need for having a center coordinator in the robot system. To verify the established model and the developed algorithm, a two redundant manipulators system is simulated to carry a rigid object following desired trajectories with obstacles considered.
Self-Citation Analysis on Google Scholar Dataset for H-Index Corrections

For the recent decades, self-citations have been extensively studied by the academia, therein Web of Science (WoS) citations count and h-index are being considered as the benchmark parameters. The WoS is used to determine citations based on the Institute for Scientific Information’s (ISI) master list. Towards this end, Google Scholar maintains a broad source of the research articles. However, Google Scholar does not exclude self-citations from the list of citations of one particular journal, author or co-author. The Google Scholar citation statistics are, therefore, not regarded as highly accurate. In this paper, we propose an updated h-index for Google Scholar by first quantifying and thereafter excluding the self-citations from the h-index. We target the two aspects of Google Scholar that belong to the evaluation of the quality of Google Scholar sources and the self-citation’ records available in the citation lists. In our analysis, we have taken two datasets. The first dataset is composed of scientists awarded by the Scientometrics, which is recorded from the Google Scholar. According to this dataset, 28 scientists have been awarded as best researchers on the basis of their maximum citations and the h-indexes. The second dataset includes 16 non-award winner scientists. Both datasets include records falling in the period of 1984 to 2017. Based on analysis of award winner scientists’ data, the aggregated journal self-citations are observed as 3.95%, whereas author and coauthor self-citations are found as 2.86% and 3.33%, respectively. In contrast, non-award winner scientists have average journal biased citation as 1.22%, author biased citations as 0.41% and co-author biased citations as 0.90%. We consider three types of the self-citations, i.e., journal, author, and coauthor for each scientist to cumulatively calculate the revised h-index. We obtained a new ranking of the scientists, which is based on a more accurate updated h-index. The updated h-index for the Google Scholar can be used for a more accurate academic ranking of the authors and the research articles.
Pharmacology of the ATM Inhibitor AZD0156: Potentiation of Irradiation and Olaparib Responses Preclinically

AZD0156 is a potent and selective, bioavailable inhibitor of ataxia-telangiectasia mutated (ATM) protein, a signaling kinase involved in the DNA damage response. We present preclinical data demonstrating abrogation of irradiation-induced ATM signaling by low doses of AZD0156, as measured by phosphorylation of ATM substrates. AZD0156 is a strong radiosensitizer in vitro, and using a lung xenograft model, we show that systemic delivery of AZD0156 enhances the tumor growth inhibitory effects of radiation treatment in vivo. Because ATM deficiency contributes to PARP inhibitor sensitivity, preclinically, we evaluated the effect of combining AZD0156 with the PARP inhibitor olaparib. Using ATM isogenic FaDu cells, we demonstrate that AZD0156 impedes the repair of olaparib-induced DNA damage, resulting in elevated DNA double-strand break signaling, cell-cycle arrest, and apoptosis. Preclinically, AZD0156 potentiated the effects of olaparib across a panel of lung, gastric, and breast cancer cell lines in vitro, and improved the efficacy of olaparib in two patient-derived triple-negative breast cancer xenograft models. AZD0156 is currently being evaluated in phase I studies (NCT02588105).
Multi-illuminant Estimation Based on Spatial Fuzzy C-means Clustering with Application to Image White Balance

In most scenes, images captured by digital devices are greatly influenced by environmental illuminant. Because there are different illuminants in the scene, the color from objects even change and be similar to the illuminant color. It results that the objects cannot reflect the true color and this phenomenon is called color cast. In this thesis, by utilizing basic color science and experimental results, a dataset of color sample under different illuminants is constructed. Using the existing method of illuminant estimation to calculate plural color temperatures of the input image, called the initial estimated color temperature. Furthermore, utilizing clustering algorithm to execute illuminant classification to initial plural color temperatures, and calculating the multi-determined color temperatures of image. This study takes the lead in utilizing the spatial fuzzy c-means (SFCM) clustering algorithm to execute the illuminant classification. In traditional white balance, the correction configuration focuses on single illuminant scene, which is fixed correction value, whereas there are multi-illuminant scenes mostly in real scene, the performance of white balance is not good. In this thesis, the adopted SFCM algorithm revises the main illuminant individually and solves the color cast successfully.
Numerical and Experimental Analysis of Potential Causes Degrading Contact Resistances and Forces of Sensor Connectors for Vehicles

Faults in vehicle sensor connectors are crucial to the safety of a vehicle. Once the connector is loosened according to the reduction of its contact forces, its electrical contact resistance may be increased, causing unexpected errors in sensor systems. However, such error mechanisms have enormously diverse causes that have not yet been identified. This study proposes a process to analyze the causes influencing the connector contact force and resistance by employing a combination of numerical and experimental methods. Specifically, the causes of variation of contact force are numerically analyzed and their influence on the electrical contact resistance is experimentally studied. Precise 3D models of a commercial vehicle sensor connector are developed based on a 3D tomography, and a 3D finite element (FE) simulation is employed to estimate the connector contact force, considering plastic deformation. After classifying potential causes into three categories, two major factors are selected: manufacturing tolerance occurring during the connector manufacturing process and plastic deformation occurring during the vehicle maintenance process. The factors are observed to substantially reduce the contact force (by ~14.6 or 19%). The impact of the reduced contact force is validated in sequential experiments, and exhibits a nonlinear relationship between the contact force and corresponding contact resistance. In addition, the experiments also consider simulated manufacturing and maintenance factors, and demonstrate an increased contact resistance by the functions of the decreased contact force with minor measurement errors (< 7.5%).
Paraphrase Construction of Al Quran in Indonesian Language Translation

In order to better understand Quran one of the important things is to know the relevance and similarity of meaning between the translation sections in Quran. To carry out the process of identifying the similarities and interrelationships between texts, the construction of a paraphrase list is one of the supporting components. In this study, the technique of developing Quran paraphrase was developed using Monolingual and word Alignment. The features used in the study are identical words, word sequences, Align PPDB. The construction of this paraphrase list uses two different versions of the Quran translation which are used as input, then will produce a paraphrase database from the corpus used as the output. The system uses Precision, Recall, and F1 as evaluation metrics. The experimental results show that applying a combination of features can achieve F1 of 80%. Our source code and data are available at : https://github.com/AmaliaHutami/Paraphrase-Construction-of-Al-Quran-in-Indonesian-Language-Translation