Reviewerly/exHarmony huggingface dataset:
APLIKASI PENYEWAAN FASILITAS UMUM PADA BALAI PENGEMBANGAN PENDIDIKAN ANAK USIA DINI DAN PENDIDIKAN MASYARAKAT PROVINSI KALIMANTAN TENGAH BERBASIS WEBSITE

Department of Early Childhood Education Development and Community Education (BP PAUD and Dikmas) Central Kalimantan Province is the Technical Implementation Unit (UPT) of the Directorate General of Early Childhood Education and Community Education (PAUD and Dikmas) of the Ministry of Education and Culture. Several facilities can be rented out to the public at the agency, such as halls, dormitories, and guest houses. There are still many people who do not know this public facility that can be used. The rental process is still done manually by coming directly or sending a letter of application. In terms of bookkeeping, sometimes there are some data recording errors. Therefore, a website was developed that can help provide information on what public facilities can be used for the public and used to carry out the rental process to help the community make rentals without having to come directly to BP PAUD and Dikmas. Developing this website using the Waterfall software development methodology has stages, namely Requirements Definition, which is done by making Flowcharts. System and Software Design is carried out by making Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD), Implementation and Unit Testing with the programming languages used, namely HTML, PHP, CSS, Bootstrap, Javascript and MySQL and Integration and System Testing. The testing method used is the Blackbox method. Based on the test results, it can be concluded that this website can run according to its function. The final result of this program also shows the achievement of the objectives that have been implemented, such as the presence of several features that are used appropriately according to their use.
A Semiotic Analysis Of Emoticon Symbols on Whatsapp Messenger In Students’ Chat Activies

The purpose of this study was to find out more about the meaning of semiotic and the use of emoticon symbols and how students view the use of emoticon symbols on WhatsApp messenger. This study uses a qualitative method. To collect data and determine research subjects, the authors used a questionnaire and observation as research instruments.&#x0D; The results of this study prove that most students mean the meaning of each emoticon symbol on WhatsApp and often use emoticon symbols in chat activities on WhatsApp messenger. They often express feelings and expressions through the symbol emoticon. Two main points are discussed in this thesis. First, the semiotic meaning and use of emoticon symbols, and the second, students' views on the use of emoticon symbols on WhatsApp messenger.
CFA BASED UNIVERSAL BIQUAD FILTER

This paper is a review of the organizational structure and challenges that a technological enterprise faces during scaling, from the perspective of the engineering management. After expanding operations internationally, the distributed engineering teams developing hardware and software for internet of things applications, face issues regarding culture, communication, leadership, project planning and management. Relevant literature addressing these issues is presented. We elaborate on already implemented measures for improving the overall efficiency and propose further processes introduced in recent literature that can be beneficial for further optimizing the new product development. Potential knock-on effects by such implementation are also discussed. A universal biquad filter using current feedback amplifiers (CFA) as active devices and a canonical number of capacitors is presented. It realizes the classical feedforward structure through the use of grounded capacitors and MOS transistors, which permit a full integrability. The proposed circuit has attractive characteristics, as the possibility to electronically control the transfer function coefficients and the possibility to be directly inserted within cascaded configurations. The theoretical analysis of the circuit is followed by some PSPICE simulation results in agreement with the theoretical findings. In this paper a universal fully integrable biquad using CFAs as active devices and a canonical number of capacitors is presented. It realizes the classical feedforward structure [3] through the use of grounded capacitors and MOS transistors. The presence of grounded capacitors makes the proposed circuit absorb shunt parasitic capacitances. All the possible kinds of responses can be realized, while in the biquad reported in [2] only the low-pass and band-pass responses can be obtained. The numerator and denominator coefficients of the voltage transfer function of the proposed biquad can be electronically controlled by acting on the gate voltages of the MOS transistors of the circuit. Moreover the presented structure has a low-impedance output and then it can be cascaded without additional buffers.
Spatial Data Partitioning Method Based on Spatio-Temporal and Semantic Features

As the volume of spatial data is increasing, distributed storage has become an effective storage management strategy for large amounts of data. The core of distributed storage is the data partitioning method, which partitions massive data onto different nodes evenly. Based on Geohash and Hilbert curve, this paper proposes a spatial data partitioning method of spatial data, which considers the characteristics of spatiotemporal and semantics, and then stores the partitioned spatio-temporal data onto each node to balance the data volume of each node. Experiments show that the spatial data partitioning method proposed in this paper can effectively take into account the balance of data volume, spatio-temporal proximity and semantic similarity. Compared with traditional data partitioning methods, the proposed method achieves the balance of data onto various nodes, and its data blocks have better spatio-temporal proximity and semantic similarity at the same time.
PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers

Syntactic parsing technologies have become significantly more robust thanks to advancements in their underlying statistical and Deep Neural Network (DNN) techniques: most modern syntactic parsers can produce a syntactic parse tree for almost any sentence, including ones that may not be strictly grammatical. Despite improved robustness, such parsers still do not reflect the alternatives in parsing that are intrinsic in syntactic ambiguities. Two most notable such ambiguities are prepositional phrase (PP) attachment ambiguities and pronoun coreference ambiguities. In this paper, we discuss PatchComm, which uses commonsense knowledge to help resolve both kinds of ambiguities. To the best of our knowledge, we are the first to propose the general-purpose approach of using external commonsense knowledge bases to guide syntactic parsers. We evaluated PatchComm against the state-of-the-art (SOTA) spaCy parser on a PP attachment task and against the SOTA NeuralCoref module on a coreference task. Results show that PatchComm is successful at detecting syntactic ambiguities and using commonsense knowledge to help resolve them.
Using the Semantic Information G Measure to Explain and Extend Rate-Distortion Functions and Maximum Entropy Distributions

In the rate-distortion function and the Maximum Entropy (ME) method, Minimum Mutual Information (MMI) distributions and ME distributions are expressed by Bayes-like formulas, including Negative Exponential Functions (NEFs) and partition functions. Why do these non-probability functions exist in Bayes-like formulas? On the other hand, the rate-distortion function has three disadvantages: (1) the distortion function is subjectively defined; (2) the definition of the distortion function between instances and labels is often difficult; (3) it cannot be used for data compression according to the labels’ semantic meanings. The author has proposed using the semantic information G measure with both statistical probability and logical probability before. We can now explain NEFs as truth functions, partition functions as logical probabilities, Bayes-like formulas as semantic Bayes’ formulas, MMI as Semantic Mutual Information (SMI), and ME as extreme ME minus SMI. In overcoming the above disadvantages, this paper sets up the relationship between truth functions and distortion functions, obtains truth functions from samples by machine learning, and constructs constraint conditions with truth functions to extend rate-distortion functions. Two examples are used to help readers understand the MMI iteration and to support the theoretical results. Using truth functions and the semantic information G measure, we can combine machine learning and data compression, including semantic compression. We need further studies to explore general data compression and recovery, according to the semantic meaning.
Design of integrated fire protection system for building electrical fire based on multi-sensor data fusion

Aiming at the problems of untimely fire warning and unreliable warning information in traditional systems, this paper designs a building electrical fire integrated fire protection system based on multi-sensor data fusion. This paper designs a multi-sensor data fusion fire-fighting system hardware module, and realizes data processing through environmental brightness sensor, smoke sensor, temperature sensor, and infrared flame sensor. At the same time, a data fusion algorithm is designed, and the number of nodes in the input layer is set to 4.At the same time, in order to reduce the data fusion error, the average value of the white noise variance is solved, so as to realize the integrated fire protection system of building electrical fire. Experimental results show that the fastest warning time of the designed system is 0.22s, and the predicted fire probability is more similar to the actual fire probability, indicating that the system has higher practicability.
A Quantitative Study of Simplification Strategies in Adapted Texts for L2 Learners of Russian

Nowadays there has been a growing interest in the topic of Russian text adaptation, both in theoretical aspects of intralingual translation into Simple and Plain Russian, and in practical tasks like automatic text simplification.Therefore, it is important to study the characteristics that make an adapted text more accessible.In this paper, we aim to investigate the strategies that human experts employ when simplifying texts, particularly when the texts are being adapted for learners of Russian as a foreign language.The main data source for this research is the RuAdapt parallel corpus, which consists of Russian literature texts adapted for the learners of RaaFL and the original versions of these texts.We study the changes that occur during the adaptation process on lexical, morphological, and syntax level, and compare them to the methods usually described in methodological recommendations for teaching RaaFL.
Cryptography: A Quantitative Analysis of the Effectiveness of Various Password Storage Techniques

Recently, there has been a rise in impactful data breaches releasing billions of people’s online accounts and financial data into the public domain. The result is an increased importance of effective cybersecurity measures, especially regarding the storage of user passwords. Strong password storage security means that an actor cannot use the passwords in vectors such as credential-stuffing attacks despite having access to breached data. It will also limit user exposure to threats such as unauthorized account charges or account takeovers. This research evaluates the effectiveness of different password storage techniques. The storage techniques to be tested are: BCRYPT Hashing, SHA-256 Hashing, SHA-256 with Salt, and SHA-256 with MD5 Chaining. Following the National Institute of Standards and Technology (NIST) guidelines on password strength, both a weak and robust password will be passed through the stated techniques. Reversal of each of the results will be attempted using Rainbow Tables and dictionary attacks. The study results show that pairing a strong password that has not been exposed in a data breach with the BCRYPT hashing algorithm results in the most robust password security. However, SHA-256 hashing with a salt results in a very similar level of security while maintaining better performance. While plain SHA-256 hashing or chaining multiple hashing algorithms together is theoretically as secure, in practice, they are easily susceptible to simple attacks and thus should not be used in a production environment. Requiring strong password which have not been exposed in previous data breaches was also found to greatly increase security.
Extreme Learning Machines with feature selection using GA for effective prediction of fetal heart disease: A Novel Approach

Heart disease is considered to be the most life-threatening ailment in the entire world and has been a major concern of developing countries. Heart disease also affects the fetus, which can be detected by cardiotocography tests conducted on the mother during her pregnancy. This paper analyses the presence of heart disease in the foetus by optimizing the Extreme Learning Machine with a novel activation function (roots). The accuracy of predicting the heart condition of the foetus is measured and compared with other activation functions like sigmoid, Fourier, tan hyperbolic, and a user-defined function, called “roots”. The best features from the Cardiotocography data set are selected by applying the Genetic Algorithm (GA). ELM with activation functions sigmoid, Fourier, tan hyperbolic, and roots (a novel function), have been measured and compared on accuracy, sensitivity, specificity, precision, F-score, area under the curve (AUC), and computation time metrics. The GA uses three types of regression: linear, lasso, and ridge, for cross-validation of the features. ELM with user-defined activation function shows comparable performance with sigmoid and hyperbolic tangent functions. Features selected from linear and lasso produce better results in ELM than those selected from the ridge. It gives an accuracy of 96.45% as compared to 94.56% and 94.56% respectively with the best features selected from both linear and lasso. The roots activation function also takes 2.50 seconds computation time versus 3.27 seconds and 2.67 seconds for sigmoid and hyperbolic tangent respectively and scores better on all other metrics in designing an efficient model to classify fetal heart disease.
Tourism Information Systems with Grouping using Google Maps

During travelling, travelers usually have some sort of travels preferences such as its purposes, types or chosen categories and its destination/attractions.To ensure fully indulge travel, tourist usually needed instruction or guidance and travel map during those trips.A map is used to show the precise location of each destination.Some travelers travels without knowing the exact location of the places such as museums, restaurants, park or shopping district that yet hasn't been known to them before, therefore in need of a tourism map.As a fact, tourism maps are one of the most common groups of cartographic documents since it has lots of diversity in contents, subject and publication title due to the increasing popularity and diverse forms of tourism activities.In this research, coordinates values were taken and saved in a database using ddmm'ss" (degree, minutes, seconds) format whereas Google maps reads value in the format of latitude and longitude which led the system to implement automatic conversions in order to be functional with Google Maps.PHP programming, JavaScript, MySQL database with other assisting software suitable in extracting and converting coordinates value to latitude, longitudes value was used in making this program.This system really helps travelers knowing the exact location and assist in determining the chosen destination.
MODEL-ORIENTED PROGRAMMING

The problem of complex multi-component system processing arises in many fields of science and engineering. A system can be described in terms of its components, behavior, and interaction. This work proposes a new declarative Turing complete “model-oriented” programming paradigm based on the concept of “model-component” - a complex structure with well-defined characteristics and behavior, and no external methods. The set of model-components is closed under the union operation of model-components into “model-complex”. The proposed approach allows the program to describe the complex system and behavior of its components in a declarative way, possesses a higher level of encapsulation than the object-oriented paradigm, involves a reduced amount of imperative programming, and is naturally focused on parallel computations.
Performance Analysis of a Dynamic Virtual Machine Management Method Based on the Power-Aware Integral Estimation

The latest cloud resource management research has revealed that virtual machine (VM) consolidation allows for effectively managing the physical resources of cloud data centers. However, a tremendous waste of power and physical resources has been pointed as one of the research challenges related to the development of new methods for VM management in a cloud data center in order to deliver a wide range of IT services to clients effectively. This paper investigates a problem of power-aware VM consolidation under dynamic workloads, uncertainty, and a changing number of VMs. For this purpose, the authors propose a dynamic VM management method based on a beam search that takes into account four types of resources (CPU, memory, network throughput, and storage throughput) and six quality metrics. Optimal beam search algorithm parameters for the defined problem are determined using a new power-aware integral estimation method. The SLA violation minimization allows significant improvement of SLA quality metrics, accompanied by the decreased number of VM migrations and slight deterioration in the power consumption. The proposed method is evaluated using common widespread hardware configurations and Bitbrains workload traces. The experiments show that the proposed approach can ensure the efficient use of cloud resources in terms of SLA violation and the number of VM migrations.
Application of OMAT in HTCONDOR resource management

Conventional computing resource management systems use a system model to describe resources and a scheduler to control their allocation, computing resources are divided into isolated parts to provide computing services for different experiments.To improve resource utilization and reduce the deployment complexity of differentiated operating environments, our computing resources are configured to support a running environment of all HTC (high throughput computing) experiment jobs.To prevent experiments with few computing resources from occupying a large amount of extra computing resources for a long time, we configured the running jobs' quota of each experiment to ensure the fairness.The conventional computing resource management systems does not adapt well to the ever-expanding resources scale and complex scheduling strategies.Faced with these problems, we developed and implemented a new framework based on device management database and Open Maintain Analysis Tools (OMAT) [1] , a flexible and general approach to manage resources in a complex environment with that significantly reduces manual intervention.Novel aspects of the framework include a flexible configuration method for configuring the relationship between device, service, and experiment; alarm policies that quickly detect unallocated computing resources, and an operationally implementable way to quickly generate a scheduling policy and make it effective.This framework is robust, flexible, and scalable that can evolve with changes in resources and experiments.The framework was designed to solve real problems encountered in the deployment of HTCondor, a high throughput computing scheduler system at IHEP of Chinese Academy of Sciences.
Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance.The quality degradations further hiders the performances of imaging and computer vision applications.Therefore, the reconstruction and enhancement of uder-and over-exposed images is essential for various applications.Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years.Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy.The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details.However, the design of most DL architectures and objective functions don't take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent.Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions.For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario.Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.
Cops and robbers on graphs and hypergraphs

Cops and Robbers is a vertex-pursuit game played on a graph where a set of cops attempts to capture a robber. Meyniel's Conjecture gives as asymptotic upper bound on the cop number, the number of cops required to win on a connected graph. The incidence graphs of affine planes meet this bound from below, they are called Meyniel extremal. The new parameters mқ and Mқ describe the minimum orders of k-cop-win graphs. The relation of these parameters to Meyniel's Conjecture is discussed. Further, the cop number for all connected graphs of order 10 or less is given. Finally, it is shown that cop win hypergraphs, a generalization of graphs, cannot be characterized in terms of retractions in the same manner as cop win graphs. This thesis presents some small steps towards a solution to Meyniel's Conjecture.
A Secure Image Authentication Scheme with Tamper Localization and Recovery

This paper proposes a secure image authentication scheme that can locate the tampered regions, recover the lost contents and hide application-specific sensitive data. In this scheme, an encrypted watermark that comprises tamper localization, recovery and application-specific information is placed in the selected pixels, which is extracted and decrypted to identify the tampered regions, recover the tampered regions approximate to original image contents and extract the hidden data. The watermark is highly secure and sensitive to any modification in the image. The proposed scheme ensures lossless recovery of the original image and data from an untampered image. The experimental results show that this scheme generates watermarked image of high quality and has high resistance to copy-move, image splicing, vector quantization and collage attacks. As compared with state-of-the-art schemes, the proposed scheme provides better recovered image quality under extensive tampering.