Reviewerly/exHarmony huggingface dataset:
Adversarial subsequences for unconditional text generation

Generative Adversarial Nets (GAN) has been successfully introduced to unconditional generating text to alleviate exposure bias. However, the discriminator in this model only evaluates the entire sequence, which causes feedback sparsity and mode collapse. To tackle these problems, we propose a novel mechanism. The mechanism first segments the entire sequence into several subsequences. Then, these subsequences, together with the entire sequence, are evaluated individually by the discriminator. Finally, these feedback signals are all used to guide the learning of GAN. This mechanism learns the generation of both the entire sequence and the subsequences simultaneously. Learning to generate subsequences is easy and is helpful in generating an entire sequence. It is easy to improve the existing GAN-based models with this mechanism. Although Li et al. (2017) segments the generated responses in a conditional text generation task, i.e., a dialogue system, they conclude it is weaker than the Monte Carlo search. However, for unconditional text generation, we observe that adversarial learning on subsequences works well. We rebuild three previous models with our mechanism, and the experimental results on two benchmark datasets show these models are improved greatly and outperform the state-of-the-art model.
Behavior-Driven Development

Behavior-Driven Development (BDD) was created by Dan North. His goal was to improve communication between business and technical teams to aid in the creation of software with business value. Miscommunication between business and technical teams is often the biggest bottleneck in the delivery of software projects, and developers often misunderstand the business goals, and business teams fail to grasp the capabilities of the technical team.
Detecting DNS Tunnel Based on Multidimensional Analysis

DNS (Domain Name System) is an important basic application of the Internet. The network management strategy has less restrictions on DNS protocol, which makes DNS become one of the means for attackers to establish covert channel for malicious activities. Taking the DNS tunnel as research object, through multidimensional analysis, this paper mined the characteristics of DNS tunnel in domain name, packet and traffic dimension, and proposed a DNS tunnel identification method which combined machine learning classification and anomaly detection. The experiments proved that this method has high accuracy and low false rate and it can effectively detect DNS tunnels.
Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.
Tweaking Deep Neural Networks

Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data.Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes of interest that may be valuable to some users or applications.To address this issue, we propose the synaptic join method to tweak neural networks by adding certain additional synapses from the intermediate hidden layers to the output layer across layers and additionally training only these synapses, if necessary.To select the most effective synapses, the synaptic join method evaluates the performance of all the possible candidate synapses between the hidden neurons and output neurons based on the distribution of all the possible proper weights.The experimental results show that the proposed method can effectively improve the accuracies of specific classes in a controllable way.
An Additive Rotational Perturbation Technique for Privacy Preserving Data Mining

As the usage of internet and web applications emerges faster, security and privacy of the data is the most challenging issue which we are facing, leading to the possibility of being easily damaged. The privacy preservation techniques like condensation, randomization and tree structure etc., are having limitations, they are not able to maintain proper balance between the data utility and privacy and it may have the problem with privacy violations. This paper presents an Additive Rotation Perturbation approach for Privacy Preserving Data Mining (PPDM). In this proposed work, various dataset from UCI Machine Learning Repository was collected and it is protected with a New Additive Rotational Perturbation Technique under Privacy Preserving Data Mining. Experimental result shows that the proposed algorithm’s strength is high for all the datasets and it is estimated using the DoV (Difference of Variance) method.
Application of Computer Assisted Audit Techniques in Public Accounting Firm

Facing the industrial revolution 4.0, every profession in the world must improve, every professional workforce involved in it, must begin to add one skill that is computer literacy. This phenomenon certainly occurs also for the financial auditor profession. Auditors are required to no longer only carry out the audit process manually or traditionally but to use computerization. In auditing this is referred to as computer assisted audit techniques. Our research is a qualitative study, we use literature studies as secondary data and primary data collection techniques. Primary data collection is done by observation to public accounting firms and conducting interviews with auditors to find out how the application of audit software in their environment. Our interviews include: understanding, constraints faced, and the auditor's views regarding the audit software itself. In this study we obtained results regarding the advantages and disadvantages of the computer assisted audit technique and a review of its application in public accounting firms in Indonesia both the big four and the non big four. Our research can be a reference or reference for subsequent research in the field of audit and software.
Semantic similarity metrics for learned image registration

We propose a semantic similarity metric for image registration. Existing metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach using an auto-encoder, and a semi-supervised approach using supplemental segmentation data to extract semantic features for image registration. Comparing to existing methods across multiple image modalities and applications, we achieve consistently high registration accuracy. A learned invariance to noise gives smoother transformations on low-quality images.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-paths for a given corpus to further improve the performance of our proposed method. To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods. Empirical results and analysis show that our approach outperforms the state-of- the-art methods in both entity and trigger extraction.
IMAGE COMPRESSION USING DISCRETE TCHEBICHEF TRANSFORM AND SINGULAR VALUE DECOMPOSITION (DTT-SVD)

Nowadays, images are sent through the internet in very large scale. More memory space and transmission bandwidth are needed for an uncompressed image. So, it increases the size of the image database. By greatly reducing the need for bit-rate by using the redundancies that are usually present in video and image signals, compression can be accomplished. The device that achieves a high degree of compression while retaining information relevant to the sensitive pictures is necessary. The DTT will replace the DCT. The new high-energy compaction and lower transformation of computational benefit used for compression are DTT. A given matrix is divided into a product of orthonormal matrices and a diagonal matrix by the SVD technique. Combining the DTT and SVD strategies speeds up the transition of the image into a non-visual format intended to minimise inter-pixel redundancy, and without compromising efficiency, the SVD will delete enormous portions of our matrix. DTT-SVD compression has been shown to result in higher CR, PSNR values ranging from 28 to 32db, and lower MSE values compared to the DCT and SVD form combine (DCT-SVD).
Portfolio solver for verifying Binarized Neural Networks

Although deep learning is a very successful AI technology, many concerns have been raised about to what extent the decisions making process of deep neural networks can be trusted.Verifying of properties of neural networks such as adversarial robustness and network equivalence sheds light on the trustiness of such systems.We focus on an important family of deep neural networks, the Binarized Neural Networks (BNNs) that are useful in resourceconstrained environments, like embedded devices.We introduce our portfolio solver that is able to encode BNN properties for SAT, SMT, and MIP solvers and run them in parallel, in a portfolio setting.In the paper we propose all the corresponding encodings of different types of BNN layers as well as BNN properties into SAT, SMT, cardinality constrains, and pseudo-Boolean constraints.Our experimental results demonstrate that our solver is capable of verifying adversarial robustness of medium-sized BNNs in reasonable time and seems to scale for larger BNNs.We also report on experiments on network equivalence with promising results.
THE ROLE OF BLOCKCHAIN TECHNOLOGY IN FACING REVOLUTION EDUCATION 4.0

Although several concepts for implementing education exaggerated using blockchain technology, t agreements which are allocated ledgers carefully and thoroughly. Blockchain technology, unlike databases that are processed by certain forces that provide policies in correctness of data through real-time implementation, Blockchain is digital data storage system containing records that are connected via cryptography. Blockchain technology has now been used by various sectors, one of which is for transactions in cryptocurrencies such as Bitcoin. The benefit of the blockchain technology approach in education is that people who do not have knowledge of blockchain technology can benefit from blockchain technology, because lot of the data generated by this sector is continuous from education to Bachelor. Blockchain, can be used as, a machine to create high-level security and reliable security, the high level of security and efficiency of blockchain as storage place for financial transactions and data has opened up opportunities for innovation in education as storage for educators and students' data.
A Study on Prediction of Wind Power Based on Deep-Learning Using Weather Data

Although the proportion of wind energy sources in the system is increasing, the variability and stochastic characteristics of wind power have a negative effect on system stability and on the rescheduling of the system’s power generation. Therefore, an accurate prediction of wind power generation is necessary for stable power distribution in the power system. High-precision wind power forecasting provides a reliable basis for dispatching of power system. Since wind power data is time series data with uncertainty, a suitable model, the LSTM algorithm of Keras, was proposed to predict a short-term wind power generation. The LSTM has memory to store past states, so it is suitable for the problem of predicting time series data. This paper proposes a forecasting procedure using an LSTM neural network to forecast wind power. First, Pearson correlation coefficient method is utilized to determine the parameters of LSTM forecasting model. Then a case study was performed using real data collected from a wind farm in Yeongheung to confirm that the LSTM model was suitable for wind power prediction.
Edge-Aware Monocular Dense Depth Estimation with Morphology

Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.
Encrypting Image based on Chaotic Map algorithm

Due to the network and multimedia application developments, information security become important since information can be attacked over the transmission channels, and the combination of cryptography and chaotic become an important field of information security, where many encryption algorithms are based on chaotic mapping due to the inherent features of Image like high redundancy and bulk data capacity.In this paper, three chaotic maps are used to achieve a good diffused Image by setting the initial conditions to generate the shuffling sequence randomly by 2D Zaslevskii map and employ them in 2D cat map for shuffling the image pixels positions; also baker map is used to decomposed Image into four rectangles and shuffle them.For increasing the levels of security related to the encryption algorithm, the Gaffe random key generator is used to generate 128 bits keys and employ it with an exclusive-OR equation to the diffused Image.The results indicated that the combination of chaotic and cryptography proved to be good for the high-security level.The experimental results have shown that the suggested algorithm is secure due to the large key space and the high sensitivity to the secret key.This paper applies a chaotic map algorithm by Mat lab a program.
Cryptoassets—nature, valuation and disclosures in accounting

Within cryptoassets we can find cryptocurrencies (e.g. Bitcoin, Ethereum) and digital tokens which are specific right or value representatives. One of areas that there are a lot of doubts regarding these new technological solutions is accounting. It is not clear how we can classify particular groups of cryptoassets and how to value them in financial statement. The aim of the chapter is to present the essence, use and valuation issues of cryptoassets and also to review the definitions of selected asset groups in the currently applicable accounting regulations to identify those asset groups to which cryptocurrencies can be classified. The chapter also discusses available accounting models under IFRS, with a major focus being put on the recognition and presentation in balance sheet. The assessment of models is based on distinguishing different types of tokens (payment, security, utility) as well as on differentiating between holders’ and issuers’ perspective.
Analisis Faktor Penyebab Kesulitan Membaca Permulaan Pada Siswa Kelas I SDN Argopeni Tahun Ajaran 2019/2020

&lt;p&gt;&lt;em&gt;The study aimed to determine the early reading difficulties to first grade students of SDN Argopeni in academic year of 2019/2020, to determine the causal factors of early reading difficulties, and to describe the solutions to overcome early reading difficulties to first grade students of SDN Argopeni in academic year of 2019/2020. It was qualitative research. The subjects were four students of first grade. The results showed that: (1) the early reading difficulties of first grade students were: (a) not able to analyze words into letters, (b) not able to understand letter-sound, (c) not able to arrange words and letters and read word for word; (2) the causal factors of early reading difficulties were: (a) internal factors, (b) external factors; (3) alternative solutions applied by the teachers to overcome the early reading difficulties were: (a) having additional classes, (b) applying interesting media, (c) using the SAS method. It concludes that there are difficulties of early reading, the causal factors, and the teachers have applied the solution to overcome the early reading difficulties.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Optimizing Depok City Government Website in Realizing an Electronic-Based Government System

The Electronic Based Government System (SPBE) is implemented in accordance with Presidential Regulation Number 95 of 2018 concerning SPBE. Ministry. Institutions and agencies must create clean, effective, transparent and accountable governance. One of the studies in SPBE is transparency of information through the City Government website. City Government is serious about building city government web to provide complete information needed by all people. Web optimization is needed to support the performance and achievements of the City Government in general. This study aims to determine how much influence the variables of usability, information quality, quality of service interactions have on user satisfaction of city government websites partially and simultaneously. The subjects in this study were 100 people who were the visitors of the website depok.go, id. In this study, the quality of the depok.go.id website was measured using the WebQual 4.0 method. The sampling technique was using purposive sampling technique. The data analysis used in this research is multiple analysis. The results showed that the variables of use and quality of information were partially influential with the effect of 67.5% and 37.5%, while the quality of service interactions had no effect. Simultaneously, the three variables have an effect on website user satisfaction with a large effect of 70.35. This means that the website of the City Government of Depok has gained the public's trust as a source of quality and satisfying information for the Depok community, including those related to SPBE.
Perancangan Aplikasi Helpdesk Menggunakan Pendekatan Knowledge Management System pada Jurusan Sistem Informasi UIN Syarif Hidayatullah Jakarta

Syarif Hidayatullah State Islamic University of Jakartais an educational institution that is tasked with providing quality knowledge and education to students in educating the nation's children. In the current era of globalization, there is knowledge that must be managed for agencies. To achieve this, it is necessary to use existing information technology, namely the online-based Knowledge Management System application. In the world of education, a helpdesk application that helps in improving existing performance and competence. The helpdesk application is widely used by companies and agencies, this application helps facilitate the work of the university technician section. In addition, this helpdesk application makes it easy for students to report damaged campus facilities, so that the technician will make repairs quickly. By looking at this situation, we need a web-based helpdesk application that can coordinate information between technicians and students using the PHP programming languages and My SQL as database design.
Generalized Information: A Straightforward Method for Judging Machine Learning Models

Generalized Information (GI) is a measurement of the degree to which a program can be said to generalize a dataset. It is calculated by creating a program to model the data set, measuring the Active Information in the model, and subtracting out the size of the model. Active Information allows GI to be usable with both exact and inexact models.