Reviewerly/exHarmony huggingface dataset:
Smart Surveillance System Using Computer Vision and Internet of Things

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.
KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.
Use of Signed Permutations in Cryptography

In this paper we consider cryptographic applications of the arithmetic on the hyperoctahedral group. On an appropriate subgroup of the latter, we particularly propose to construct public key cryptosystems based on the discrete logarithm. The fact that the group of signed permutations has rich properties provides fast and easy implementation and makes these systems resistant to attacks like the Pohlig-Hellman algorithm. The only negative point is that storing and transmitting permutations need large memory. Using together the hyperoctahedral enumeration system and what is called subexceedant functions, we define a one-to-one correspondence between natural numbers and signed permutations with which we label the message units.
KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.
Validation of Automated Scoring Systems

In this chapter we discuss the considerations and processes of validating automated scoring systems including validity, validation, and evaluation. We start with a high-level overview of the considerations and the aspects of validations and then describe the processes of validation of automated scoring systems. This spans the system architecture, linguistic features that are typically extracted using an automated text or speech scoring system, sampling for the training and evaluation samples, scoring engine models, scoring engine evaluations, evaluation criteria, population subgroup evaluations, external criteria prediction, and gaming. In addition, we also discuss evaluation examples of automated scoring systems such as essay scoring, speech scoring, and intelligent tutoring systems. Finally, we conclude with discussions on the readiness of automated scoring systems and implications for the field.
Pre-trained and Shared Encoder in Cycle-Consistent Adversarial Networks to Improve Image Quality

Images generated from Cycle-Consistent Adversarial Network (CycleGAN) become blurry especially in areas with complex edges because of loss of edge information in downsampling of encoders. To solve this problem, we design a new model called ED-CycleGAN based on original CycleGAN. The key idea is using a pre-trained encoder: training an Encoder-Decoder Block (ED-Block) at first in order to get a difference map, which we call an edge map and is produced by the subtraction of input and output of the block. Then, the encoder part of a generator in CycleGAN share the parameters with the trained encoder of ED-Block and they will be frozen during training. Finally, by adding the output from a generator to the edge map, higher quality images can be produced. This structure performs excellently on “Apple2Orange”, “Summer2Winter” and “blond-hair2brown-hair” datasets. We use SSIM and PSNR to evaluate resolution of results and our method achieved the highest evaluation scores among CycleGAN, Unit and DiscoGAN.
How Not to Use a Privacy-Preserving Computation Platform: Case Study of a Voting Application

We present an analysis of a recent proposal by Dang-awan et al. who develop a remote electronic voting protocol based on secure multi-party computation framework Sharemind. Even though Sharemind comes with provable security guarantees and an application development framework, the proposed protocol and its implementation contain a number of flaws making the result insecure. We hope this case study serves as a good educational material for future secure computation application and voting protocol developers.
Failing & !Falling (F&!F): Learning to Classify Accidents and Incidents in Aircraft Data

Journey by aircraft is the only option for long distance transportation and also one of the frequently used modes of transportation of passengers. As a result, safety of passengers and efficiency of the aircraft depend on maintaining efficient running conditions. Although many safety standards are followed in the design of the aircraft and thus there are fewer accidents, it is necessary to perform a thorough analysis to avoid risks that may occur during flight time. In the present work, we propose a maintenance strategy, Failing And Not Falling (F&!F), based on the Federal Aviation Administration (FAA) data in the United States. We work with the dataset of Boeing 737. The data consists of 72 features with 137,236 records which describe an aircraft accident or incident. These features are used to predict whether an incident will be identified during aircraft maintenance or during aircraft operation and what specific type of incident will occur. The prediction method is based on the integration of a decision tree and a unique neural network at each node of the decision tree. The results obtained using different architectures show how deep the neural networks should be, how to identify the relevant features, and the success of combining decision trees and neural networks. Moreover the neural networks and the decision tree approach also successfully identified the important features of maintenance. This method can be used for the maintenance of any data in multiple domains.
Side Scan Sonar Images Based Ocean Bottom Sediment Classification

This paper describes a method for classifying different textures of Side Scan Sonar (SSS) images. The many applications in the field of oceanography requires a high-resolution image. In order to enhance the texture feature of the SSS images Super Resolution (SR) techniques are applied. Then the texture features of the SR images are extracted using Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Ternary Pattern (LTD). The classification is carried for the SSS image database by using Support Vector Machine (SVM). The LDP with SR shows higher accuracy rate of 83.4 % compared to other methods.
Quantum stabilizer codes based on a new construction of self-orthogonal trace-inner product codes over GF(4)

In this paper, we propose quantum stabilizer codes based on a new construction of self-orthogonal trace-inner product codes over the Galois field with 4 elements (GF(4)). First, from any two binary vectors, we construct a generator matrix of linear codes whose components are over GF(4). We prove that the proposed linear codes comply with the self-orthogonal, trace-inner product. Then, we propose mapping tables to construct new quantum stabilizer codes by using linear codes. Comparison results show that our proposed quantum codes have various dimensions for any code length with the capacity for better errors correction relative to the referenced quantum codes.
MANIFESTATION OF INNOVATIVE LEADERSHIP IN CULTURAL ORGANIZATIONS: WHAT IS IT LIKE?

In the context of the epoch change and the fourth industrial revolution, it becomes particularly important to outline new directions for organizations to seek advancement and competitive advantage in today's globalized world. These complex social, cultural and economic processes are driven by innovative leadership, which is treated as one of the key factors promoting the development of future organizations. It is acknowledged that the ability to create innovations and the organizational culture that supports innovations are one of the most important sources of competitive advantage for every organization. Relevance of the topic of innovative leadership is widely discussed in the context of business organizations' activities, but it should be noted that cultural organizations are also looking for ways to operate and develop their activities in the context of change of the 21 st century. The need for innovative leadership is also particularly significant in them, as the field of cultural organizations' activities is undergoing cardinal transformations. This article aims to analyse the manifestation of innovative leadership and its need in cultural organizations.
TurboPy: A lightweight python framework for computational physics

Computational physics problems often have a common set of aspects to them that any particular numerical code will have to address. Because these aspects are common to many problems, having a framework already designed and ready to use will not only speed the development of new codes, but also enhance compatibility between codes. Some of the most common aspects of computational physics problems are: a grid, a clock which tracks the flow of the simulation, and a set of models describing the dynamics of various quantities on the grid. Having a framework that could deal with these basic aspects of the simulation in a common way could provide great value to computational scientists by solving various numerical and class design issues that routinely arise. This paper describes the newly developed computational framework that we have built for rapidly prototyping new physics codes. This framework, called turboPy, is a lightweight physics modeling framework based on the design of the particle-in-cell code turboWAVE. It implements a class (called Simulation) which drives the simulation and manages communication between physics modules, a class (called PhysicsModule) which handles the details of the dynamics of the various parts of the problem, and some additional classes such as a Grid class and a Diagnostic class to handle various ancillary issues that commonly arise.
Sistem informasi manajemen pendamping minum obat pasien tuberkulosis paru

Android and web-based application for monitoring patients with tuberculosis for puskesmas level program Multi-drug resistant tuberculosis (MDR TB) is a challenge in every country with a high prevalence of TB, including Indonesia. MDR TB itself is influenced by many factors including the lack of discipline in taking medication due to forgetfulness or rejection from the patient. This study aims to design a prototype of the drug-taking companion management information system (PMO) of pulmonary tuberculosis patients. The stages of this design are system requirements analysis, logical design, physical design, and interface. The results of the prototype design will be tested with officers responsible for TB and supervisors taking medication. This android platform-based application helps supervisors to remind patients to take their medicine regularly and the dose. This application continues to be developed by fixing bugs and creating a channel for user complaints
Detecting fingerprints of audio steganography software

Steganography has experienced rapidly growth with spreading access to the Internet in recent decades. Such low cost, simple process and easy promotion steganographic softwares pose serious and growing threats and challenges to network security. The fingerprint of steganography has proven itself an effective solution. However, large gaps exist between experiments results and practical applications. Few attention has been paid on the fingerprint of steganography. In this paper, we provide highly effective solutions to identify the fingerprints of audio steganography. Based on the analysis of audio stego produced by three kinds of audio steganographic softwares, including Xiao Steganography, Invisible Secrets, Deep Sound, we successfully detect audio stego across WAV files. Besides, we summarize a general approach of fingerprint extraction, and its effectiveness has been confirmed through experiments. The results solve an urgent need for further research on reverse engineering steganography softwares and detecting audio stego.
Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames

A wargame is defined as: "A simulation, by whatever means, of a military operation involving two or more opposing forces using rules, data, and procedures designed to depict an actual or assumed real life situation" (Gortney 2016, p503).Wargaming is used for several purposes, such as teaching strategic planning, practising the tasks associated with war, and analytic purposes (Burns et al., 2015).While wargaming is predominantly a human centric analytical activity, it is an area where artificial intelligence (AI) may play a useful role because of a computer's ability to play through a wider range of possible strategies.However, creating simulated AI participants for wargame scenarios is challenging because of the complexity and uncertainty in the environments in which they exist.The recent rise of automated behaviour discovery for agents in a variety of games traditionally dominated by humans offers a possible solution.Commercial real-time strategy (RTS) games provide an abstract simulation of a world where players aim to dominate and defeat other players by acquiring, using and managing resources, often including a mix of military, political, scientific and economic factors.As there is considerable overlap between the objectives found in RTS games to those that exist in military style wargames, RTS games are ideal platforms to conduct research and development in support of our AI-enabled wargaming research objectives.In this paper, we document our work on the use of evolutionary algorithms for automated behaviour discovery, where evolved behaviour trees are used as controllers for the blue team entities in the wargaming simulation.As behaviour trees are constructs formulated as a tree-type graph data structure, genetic programming (Koza, 1992), a technique developed specifically for the evolution of such structures was employed.In order to systematically evaluate the approach in terms of novel behaviour discovery, two test scenarios were designed to isolate particular features of land-based combat, inspired by terrain design patterns from computer games.A more complex scenario, involving multiple terrain constructs was also evaluated.A set of experiments were run on the developed scenarios to evolve a blue team against a static red team opponent.The red entities employed a reactive AI, with the simplicity of the red AI balanced by having a much greater number of red units in the scenario.Results from the experiments indicate that evolved behaviour tree controllers in a multi-agent scenario can be useful to identify a set of behaviours that exploit the properties of the scenario and lead to victory.In particular, we observed the 'expected' behaviour in the control scenarios:• Units learned to work together as a team, for example, armor units shielding artillery units.• Units in a relatively weak blue side learned to exploit a chokepoint in the terrain to defeat a superior red side.• Units learned to avoid dangerous sections of terrain, such as those protected by enemy snipers.
A hybrid representation-based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models.
Activated Carbon Fiber Fabrics in Filtration and Clean Water Resources

Activated carbon fiber has excellent structural characteristics and adsorption performance. It can effectively adsorb various inorganic and organic compounds and has good regeneration performance. Therefore, it is widely used in the market. To study the advantages of activated carbon fiber in purifying water resources, this research use application of activated carbon fiber material in purification and filtration of water filter, and the test of adsorption and filtration effect of different host materials are described. The main body and influencing factors of filtering effect are obtained. In this research, the viscose-based activated carbon fiber mats and acrylic-based activated carbon fabrics are the main research subjects, and other materials were added for coating composite research. Through a comparative study of the two materials, it has been found that carbon fiber has many advantages, such as is more economical, practical, convenient, low cost, and cost-effective, and is worthy of promotion in industry and daily life using.
A Demodulation Method of Spatial Domain for Low-Coherence Interferometry With High Accuracy and Adaptability

A demodulation method of spatial domain based on low-coherence interferometry is proposed. The original signal of spatial domain acquired by linear CCD is filtered with all-phase filter to obtain the fringe pattern, and the phase signal of the spatial frequency domain is retained after filtering. The fringe pattern analysis is employed to avoid phase ambiguity and realize large measurement range in demodulation. Afterwards, using the centroid position of the fringe, the peak position of a single fringe pattern can be obtained, and finally the accurate peak position is calculated with seven-step phase-shifting interferometry. The demodulation method is demonstrated experimentally with Fabry-Perot sensing system. At room temperature, it is proved that the full-scale measurement error is less than 0.019% in the range of 11 kPa to 290 kPa. From -20 °C to 70 °C, the accuracy is stable and the maximum error is less than 0.054 kPa. The demodulation method has the potential to enhance the measurement accuracy and adaptability in actual environment for universal applications.
A method of dividing clinical data set for medical image AI training

In the AI training, the data set is always divided into training set and test set at random, but the clinical image data from hospitals is different from the public data set. The division of public data set is reasonably divided and evenly distributed after many experiments. Accurate understanding of the data distribution directly affects the training model quality. So we proposed a new method of dividing clinical data set based on distance metric learning of the Gaussian mixture model to obtain more reasonable data set divisions. The distance metric learning based on deep neural network, first embeds data into a new metric space, then in the metric space uses in-depth mining based on data characteristics, calculates the distance between samples, finally compares the differences. The method can accurately know the data distribution characteristics to a certain extent. Under the condition of understanding the data distribution characteristics, more reasonable divisions can be obtained. That can greatly affect the accuracy and generalization performance of the models we trained.