Reviewerly/exHarmony huggingface dataset:
Optimization of growth of oyster mushroom mycelium (Pleurotus sp.) from Tasikmalaya on several kinds of cereal medium

Pleurotus sp. or oyster mushroom is one type of edible mushroom that contains essential nutrients for the human body. This mushroom has been widely cultivated in Indonesia, one of which is Pleurotus sp. from Tasikmalaya. The key to the success of oyster mushroom cultivation is the seeds and growing medium or baglog, while the factors that influence the quality of the seeds include the type of medium. This study aims to determine 1) the effect of the type of seed medium from the type of cereals used on the growth of mycelium Pleurotus sp. from Tasikmalaya, 2) determine the type of seed medium that has the best mycelium growth. This study uses an experimental method with a Completely Randomized Design (CRD). The parameters measured include the main parameters, namely the rate of growth of mycelium Pleurotus sp. from Tasikmalaya on the seed medium. Supporting parameters are the quality of mycelium growth in the seed medium, the proximate value of cereals used for the seed medium. The results of the analysis showed that the medium of cereals used as seeds ready for cultivation could affect the quality of the seeds produced. The seedling medium from milled corn has the best growth of mycelium.
cpmViz: A Web-Based Visualization Tool for Uncertain Spatiotemporal Data

The goal of the VAST challenge 2019 Mini Challenge 2 was to visualize radioactive contaminations measured by mobile and static sensors and their changes over time, allowing city officials to determine the severity of the leakage at the city's nuclear power plant.We propose cpmViz, a web-based tool that allows for interactive data exploration of the sensor readings in both of the spatial and temporal dimensions.The tool consists out of three views that are connected via linking and scrolling.We visualize static sensor uncertainty by introducing Voronoi cells to illustrate how much space is covered by an individual measurement unit.For mobile sensors, we showcase their activity periods and introduce the concept of sensor streaks as periods of uninterrupted recordings as a temporal uncertainty measure.As for spatial uncertainty, we color individual districts based on the amount of data that was recorded inside the user's selected time window.Using our system, we were able to easily spot major events like the city's initial earthquake in the sensor readings.Certain southern districts are clearly visible as areas of concern that we consider in need of more static sensors.Furthermore, we were also able to identify static as well as moving contaminations.
Voice Synthesis System Based on Recursive Functions Designed by Graphs

A recursive function based on the use of graphs is presented.The recursive equation is based on mathematical morphology; A written text divided into syllables is related to phonemes of spoken Spanish in Mexico.The algorithm is implemented in a PIC and an integrated circuit that stores the sounds in memory locations.Each space in memory becomes a code that is related to a phoneme.Thus, the recursive function receives as input a vector that is the structure of a word; and outputs the corresponding memory locations.Obtaining intelligibility of more than 84%.The development of the recursive function in graphs is proposed, which allows an easy description of the sequence because it is handled as a black box with input and outputs; and the equation that governs the system can even be a transfer function.
A Search Strategy for Motion Planning of Unmanned Crawler Crane

The motion planning for unmanned crawler crane (UCC) whose initial and goal described accurately in workspace was challenging. The difficulty dues to the fact that the initial and goal produce low-dimensional self-motion manifolds, which rendered the search of planners bypassing the self-motion manifolds. In this study, a new concept of the neighbor-hoods of self-motion manifold was introduced, and the corresponding search strategy of bias was developed towards extending the nodes within the neighborhoods of self-motion manifolds so as to decrease the probability of the tree nodes walking by the self-motion manifolds. Then this strategy was used to improve the performance of BiMRRTs proposed in the previous study. Finally, several simulation experiments were implemented to demonstrate the effectiveness of the proposed search strategy of the neighborhood of self-motion manifold. The results showed that the proposed search strategy was able to dramatically decrease the planning time and the path length simultaneously.
Design of Weight Matrices for Echo State Networks With Truncated Singular Values

As a special recurrent neural network, Echo State Network (ESN) has succeed best in the field of nonlinear time series prediction. The key part of ESN is a sparsely and randomly connected recurrent layer (reservoir) which leads to a large and sparse random weight matrix. However, sparse random weight matrices have too small singular values, which may cause the poor performance of ESNs. In order to solve the mentioned problem, this paper proposed a way to construct the reservoir weight matrices for ESNs using the truncated singular values. First, a sparse random weight matrix is generated under some given probability distribution. Second, replace the smaller singular values of the reservoir weight matrix with some larger ones. Third, the weight matrix is changed to the new one with the truncated singular values. Finally, the proposed method is valued on benchmark problems and the simulation results shows its super performance.
Fraud Detection on Social Media using Data Analytics

Data Analytics is one of the newest and emerging technologies.An application of artificial intelligence (AI), data analytics provides the ability to learn and improve systems without being programmed but from experience automatically.It centers around the advancement of computer programs that can get to information and use it learn for themselves.Machine learning is used in number of areas.One of major area is detecting fake news on social media.Internet based life for news utilization is a twofold edged sword.From one perspective, its ease, simple access, and fast spread of data lead individuals to search out and expend news from online networking.Then again, it empowers the wide spread of fake news, i.e., low quality news with purposefully bogus data.The broad spread of phony news has the potential for very negative effects on people and society.In this way, counterfeit news identification via web-based networking media has as of late become a developing examination that is drawing in gigantic consideration.Counterfeit news recognition via web-based networking media presents remarkable attributes and difficulties that make existing discovery calculations from conventional news media in active or not relevant.To start with, counterfeit news is purposefully composed to deceive users to accept bogus data, which makes it troublesome and nontrivial to identify dependent on news content.We directed this study to additionally encourage explore on the issue.In this overview, we present counterfeit news portrayals on brain research and social speculations, existing calculations from an information mining point of view, assessment measurements and delegate datasets.
Research and Teaching Practice on Experiment Course of STM32-Embedded Microcontroller

The experiment course of STM32 embedded microcontroller is very comprehensive and practical.The traditional teaching method can no longer meet the requirements of the course, and it is not conducive to cultivating applicationoriented and innovative talents.According to the features of STM32 embedded microcontroller experiment course, the authors set up a comprehensive experimental equipment platform to effectively carry out basic experiments, design experiments and comprehensive experiments.In this paper, several improved teaching methods are put forward, including the effective use of multimedia and network, the introduction of project-based teaching, and the improvement of assessment methods.Practices show that these measures improve the initiative of students' learning and effectively improve the quality of teaching.
From squeezing to Gaussian entanglement via beamsplitters

We investigate relations between squeezing of separable input states and entanglement of output states generated by a beamsplitter, both of which are valuable nonclassical resources for quantum information processing. Motivated by the seminal work of Asboth et al. [Phys. Rev. Lett. 94, 173602 (2005)], where a systematic study of nonclassicality (including squeezing) and beamsplitter-generated entanglement is conducted, we consider the situation when the two input states are all Gaussian and identify certain constraints on the squeezing parameters to generate entanglement. Contrary to naive expectation, large squeezing does not necessarily guarantee large entanglement. Depending on the phase relations between the input states and the beamsplitters, the conditions to generate entanglement vary, which may be regarded as a manifestation of interference. In two typical cases, it is the difference or sum of the squeezing amplitudes that plays a key role in generating entanglement. Analytic expressions of entanglement in terms of the difference or sum of the squeezing amplitudes are obtained, which are further shown to be monotonic in the relevant parameters.
Self-organization and Autonomous Network Survey

PurposeThe autonomic network gathers several aspects of Self-organization, which is depicted, into different autonomous function such as the Self-configuration, the Self-optimization, the Self-repair, the Selfprotection, and the Self-cure.The latter is considered as one of the autonomous functions wished of a system network, which could be described by autonomous behavior is realized by structures of the control loops and loop of control. IssueThese various aspects require an architectural framework, which integrates several protocols of telecommunication as well as programs proactive and reagents for the prevention and the resolution of the unforeseen problems.To ensure a reduction of the loops of the operator, we need good management per policy.An autonomic vision must guarantee the safety and the availability of the resources for the endusers by respecting the constraints of QoS and the deadlines for replies. GoalsDepict the challenges, the interests, and the anomalies and compare the autonomic approaches of the networks which consist of discovering neighbor's in the self-organizing wireless network.These points are considered the cornerstone of extending autonomous coverage and improving performance.However, the objective is to detect the hidden node in the neighbor discovery procedure based on the simple flood caused by the uncoordinated transmission, which can cause an unnecessary loss of energy.
CoT: A Secure Consensus of Trust with Delegation Mechanism in Blockchains

The consensus algorithm is a key part of blockchains, which significantly influences the performance of security and efficiency. The PoW consensus guarantees the security of decentralized systems by competing to solve a puzzle, while with serious energy waste and low throughout. Follow-up consensus algorithms adopt delegation mechanisms to improve throughput and scalability. However, these delegation mechanisms, which are essentially partly decentralized, have security risks. This paper presents a consensus algorithm based on trust relationship between nodes, called Consensus of Trust (CoT), and introduces real-time credit of nodes into the delegation mechanism of the blockchain system. Firstly, CoT quantifies the trust relationship between nodes based on interactive transactions and generates the corresponding credit graph and matrix. It then uses the iterative algorithm, a variant of PageRank, to calculate the credit value of each node from the trust matrix. The nodes with high credit value are selected as the delegated nodes to participate in the block generation. We finally analyze the security performance that CoT can tolerant more than 33% of nodes to be malicious. We also prove the effectiveness and consistency in CoT.
Constructing Field Theories Using Quantum Simulators

Quantum simulators can help researchers extract the key parameters of a quantum field theory from experiments.
Image Patch Extraction in Text Area of Balinese Palm Leaf Manuscripts with Gabor Filters

In an effort to build a word spotting and indexation system for the Balinese palm leaf manuscripts, text area detection and image patch extraction in the text area must be done effectively.However, there is no space between words in writing with Balinese scripts.This paper aimed at describing a complete scheme to detect the text area in a Balinese palm-leaf manuscript image and to extract all possible image patches in the manuscript.Gabor filters provide initial information about the document's of text textures.A sliding window algorithm is proposed and is optimized to be able to optimally extract the image patches only in the text line area of the Gabor filtered images.The results show that the combination of Gabor filter with the optimized sliding window algorithm is effectively able to detect and to extract image patches from the text area of the Balinese palm leaf manuscripts.
Hyperspectral Band Selection for Face Recognition Based on a Structurally Sparsified Deep Convolutional Neural Networks

Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spectrum has been exploited to improve face recognition performance over conventional broad band face images. In this paper, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands. Specifically, in this method, all the bands are fed to a CNN and the convolutional filters in the first layer of the CNN are then regularized by employing a group Lasso algorithm to zero out the redundant bands during the training of the network. Contrary to other methods which usually select the bands manually or in a greedy fashion, our method selects the optimal spectral bands automatically to achieve the best face recognition performance over all the spectral bands. Moreover, experimental results demonstrate that our method outperforms state of the art band selection methods for face recognition on several publicly-available hyperspectral face image datasets.
The 3rd wave AI requirements

We review 3 rd Waves of Artificial Intelligence (AI).The 1st Wave is 5 decades ago MIT Marvin Minsky with Seymour Papert proposed rule based "Perception": "If so so, Then so so" system against the brain base "Perception" by Cornell Frank Rosenblat (1928-1971).The 2nd Wave is circa March 15 2016 "learn-able Rule Based system" with supervised learning with labeled data "from A to B" that Alpha-Go Brain had beat human (Korean genius Lee Sedol) in Go Chess Games (democratic black-white territorial game) 4 to 1.The revolution is due to (1) Massive Parallel Distributed (MPD) Compute (e.g.miniaturized GPU, as PC backplane), (2) matching MPD software (without the need of slow-down inner do-loop, Python: Tensor Flow, e.g.Stanford Course RA, MIT AGI), and (3) immense amount training data (e.g. in the Cloud, or Tesla Model 3-new Electrek collecting real world driving data from owners paid inexpensively at $35K).The paper describes 3rd Wave of AI that has human Fuzzy Linguistic Thinking using Unsupervised Learning of Homo sapiens at a constant temperature in the sensory pairs called the power of pairs, e.g."Agree, the Signal; Disagree, Noise" in terms of two LIDAR's, RADAR's, Videos' pairs operated by their relaxation toward the Minimum Helmholtz Free Energy.
Perceiving the World with Hearing in Virtual Reality

Although vision is usually considered to be the most important of the five senses, sound design of an inferior quality may still heavily influence a user's feeling of presence in virtual reality. In this study, a 3D sound system for virtual reality based on ambisonics was built to explore the influence of audio upon users' feeling of presence in a virtual reality environment. The research participants wore an HTC Vive device with a two-channel headset to perceive the virtual reality. They were asked to identify the location and the origin of a detected sound in terms of direction and distance and to perceive the size of a space through hearing. For comparison, visual hints were provided to determine whether they affect the participants' audio perception. The results of the experiment are analyzed to provide practical suggestions on interactive sound system design for virtual reality games or training systems in scenarios where hearing is crucial.
Performance evaluation of SIFT under low light contrast

As an excellent method for extracting distinctive invariant features from images, SIFT (scale-invariant feature transform) can effectively resist affine transformation such as translation and rotation of images, and theoretically has better resistance to illumination changes <sup>[1]</sup>. However, in practical applications the performance of SIFT is always affected by the contrast reduction caused by illumination changes. In this paper, the performance of SIFT under different contrasts is systematically analyzed and evaluated, and a reasonable explanation is given for the reason of SIFT performance change under different illumination conditions. And a SIFT fast matching method based on contrast compression is proposed.
Data Conversion Method Based on Prior Knowledge Template Updating

In order to convert S1000D standard data into SCORM standard data, this paper modeled interactive electronic manual data (IETP) from two dimensions of SNS and PM, and generated SNS-IETP and PM-IETP models. The data of electronic training system was modeled to generate SCORMP model. Three models were compared and analyzed according to the business scenarios, and the PM-IETP model was selected as the conversion input to build the mapping model between PM-IETP and SCORMP. The common elements of SCORM standard are extracted to form a prior knowledge template, based on which a data conversion model is constructed and a dynamic update algorithm is designed. Through practice, this data conversion method is feasible and effective, which can solve the one-way conversion between the two standard data.
Data Mining untuk Klasifikasi Penderita Kanker Payudara Berdasarkan Data dari University Medical Center Menggunakan Algoritma Naïve Bayes

The breast cancer sufferers data from the University Medical Center is data about patients suffering from breast cancer based on certain characteristics. This data has abundant information so that data mining can be done with the aim of digging deeper information which of course can be useful in the future. The data class itself is divided into 2 groups: recurrence and non-relapse classes. The technique used in this study is classification using the Naive Bayes algorithm. Naive Bayes is a simple probabilistic prediction technique based on the implementation of Bayes rules with a strong assumption of independence on features. The tool used to find accuracy values is RapidMiner 9.3. Data attributes consist of Class, Age, Menopause, Tumor-Size, Inv-Nodes, Node-Caps, Deg-Malig, Breast, Breast-Quad and Irradiant. In terms of methods, this study uses the CRISP-DM (Cross Industry Standard Process for Data Mining) method. This research is used as information in making decisions to determine policies taken in dealing with patients with breast cancer
Evolutionary Training of Deep Belief Networks for Handwritten Digit Recognition

Two of the most representative deep architectures are Deep Convolutional Neural Networks and Deep Belief Networks (DBNs).Both of these can be applied to the problem of pattern classification.Nevertheless, they differ in the training method: while the first is trained by backpropagation of the error through the whole network, the latter is typically pre-trained on a per-layer basis using an unsupervised algorithm known as Contrastive Divergence (CD), and then it is fine-tuned with a gradient descent algorithm.Although metaheuristic algorithms have been widely applied for hyperparameter tuning, little has been published regarding alternative methods to pre-train DBNs.In this work, we substitute the conventional pre-training method with an evolutionary optimization algorithm called the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).The pretraining is achieved by minimizing the so-called reconstruction error.This proposal is validated on the problem of MNIST digit recognition by training a Deep Belief Network, following the methodology described by Hinton and Salakhutdinov (Science, 2006).It is also compared against the well known Genetic Algorithm (GA).We provide evidence to show that, although the computational cost is significantly highern than CD, the use of CMA-ES leads to a significantly smaller reconstruction error than CD and the GA.
EVALUASI KINERJA TEKNOLOGI INFORMASI BERDASARKAN DOMAIN MONITOR AND EVALUATE MENGGUNAKAN COBIT 4.1 (STUDI KASUS: PT. GLOBAL INFOTECH SOLUTION)

PT Global Infotech Solution is a service, marketing and IT consulting company, among others, data solutions, security systems, cloud and managed services. In carrying out business processes there are several factors that must be considered by the company, not only focusing on the use of information technology that supports business processes, but the company must be able to focus on maintaining, monitoring, managing and ensuring that the company complies with applicable laws and regulations so that the company is able provide publishers with business objectives. Therefore, researchers use the Monitor and Evaluate domain. The purpose of this study is to measure the level of maturity of a company's information technology based on the process of maintenance, management and the extent to which companies obey the applicable laws and regulations. The research methodology is a qualitative method, with data collection through observation and interviews with informants. The results of this study are the level of maturity the company and provide advice to companies so that the application of information technology can be better, effective and efficient.