Reviewerly/exHarmony huggingface dataset:
A Rigorous Review on an Ensemble Based Data Stream Drift Classification Methods

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Marie A Statistical Approach to Build a Machine Translation System for English Assamese Language Pair

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Research on the Path Tracking of Emergency Avoidance Based on the Change of Road Curvature

This paper presents a path tracking method based on road curvature variation for emergency collision avoidance. In order to control the actual vehicle driving along the ideal predefined path curvature, the second order active disturbance rejection (ADRC) path tracking controller is built based on the two degrees of freedom vehicle dynamic model. In addition, the feedforward controller is combined with the feedback controller to control the path curvature. Traditional evasive collision avoidance process suffers serious problems such as high lateral acceleration and discontinuous curvature, the cubic B spline is used to solve curvature discontinuity. In order to verify the performance of the proposed path tracking controller, this paper adopts co-simulation method using Carsim and Simulink. The simulation results show that the proposed controller exhibits higher effectiveness and better robustness compared with LQR method when suffer external disturbances under the operation conditions of opposite road.
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry.By contrast, human learning is significantly faster because prior and general knowledge and multiple information resources are utilized.In this paper, we propose a Planner-Actor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, and also integrates the Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback.This work is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent.Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.
Review Article : Investigasi Forensik Email dengan Berbagai Pendekatan dan Tools

Computer forensic science is a relatively new science and not even widely known among the public. Unlike the real world, crime in the world of computers and the internet has so many variations, one of which is forgery or spam email, where spam e-mail can be a means of transporting malicious content on a network. The problem that arises at this time is that very little research is conducted in the case of forensic investigations in the face of crime in the cyber world, especially in the spam e-mail. The method used is observation of literature, in this case in the form of original articles that analyze about crimes that use email, including the flow and tools used. The results obtained in this article review are that each investigation and tools approach has advantages and disadvantages of each, so that users can adjust to their needs.
A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.
Quantum Services Architecture in Softwarized Infrastructures

Quantum computing is posing new threats on our security infrastructure. This has triggered a new research field on quantum-safe methods, and those that rely on the application of quantum principles are commonly referred as quantum cryptography. The most mature development in the field of quantum cryptography is called Quantum Key Distribution (QKD). QKD is a key exchange primitive that can replace existing mechanisms that can become obsolete in the near future. Although QKD has reached a high level of maturity, there is still a long path for a mass market implementation. QKD shall overcome issues such as miniaturization, network integration and the reduction of production costs to make the technology affordable. In this direction, we foresee that QKD systems will evolve following the same path as other networking technologies, where systems will run on specific network cards, integrable in commodity chassis. This work describes part of our activity in the EU H2020 project CiViQ in which quantum technologies, as QKD systems or quantum random number generators (QRNG), will become a single network element that we define as Quantum Switch. This allows for quantum resources (keys or random numbers) to be provided as a service, while the different components are integrated to cooperate for providing the most random and secure bit streams. Furthermore, with the purpose of making our proposal closer to current networking technology, this work also proposes an abstraction logic for making our Quantum Switch suitable to become part of software-defined networking (SDN) architectures. The model fits in the architecture of the SDN quantum node architecture, that is being under standardization by the European Telecommunications Standards Institute. It permits to operate an entire quantum network using a logically centralized SDN controller, and quantum switches to generate and to forward key material and random numbers across the entire network. This scheme, demonstrated for the first time at the Madrid Quantum Network, will allow for a faster and seamless integration of quantum technologies in the telecommunications infrastructure.
Pengaruh Model Pembelajaran Paired story telling Terhadap Keterampilan Menulis Karangan Narasi Pada Muatan Bahasa Indonesia Siswa Kelas V SD Negeri Kompleks IKIP I Kota Makassar

The problem in this study was the low narrative essay writing skills of the fifth grade students of the Complex Elementary School of IKIP I Makassar City. The research objective was to find out: (1) the description of the paired story telling learning model in class V of IKIP I Elementary School, (2) description of students 'narrative essay writing skills on Indonesian language class V content of SD Negeri IKIP I Elementary School, and (3) whether the paired story telling learning model influences students' narrative essay writing skills on the fifth grade Indonesian language lesson at IKIP I Elementary School The approach taken is quantitative research type quasy experiment It is in the form of non-equivalent control group design. The independent variable in this study is the paired story telling learning model, while the dependent variable is narrative essay writing skills. The total population of 93 students namely the Va class was 46 students and the Vb class was 47 students. The research sample was 75 students conducted by drawing using the Simple Random Sampling technique with Slovin formula whose error rate was 5%. Data collection techniques using observation, tests and documentation. The data analysis technique used is descriptive analysis and inferential statistical analysis using the Statistical Package for Social Scenario (SPSS) system Version 25.0. The results of the study can be concluded that, the description of the paired story telling learning model takes place well because the percentage category for each meeting increases. So that it can be concluded that the paired story telling learning model influences the students' narrative essay writing skills in the fifth grade Indonesian language learning content at the IKIP I Makassar Complex State Elementary School.
At what stage of the visual processing hierarchy is visual search relational and context-dependent vs. feature-specific?

Previous studies showed that attention, eye movements and visual short-term memory operate on (partly) context-dependent representations of stimuli. Specifically: When observers have to search for a target with particular features (e.g., medium orange), attention is usually tuned to the relative size and colour (e.g., largest, reddest; ‘relational search’) rather than the physical features (e.g., medium, orange). Attention can also be tuned to the specific features of the target, but feature-specific search is more effortful and slower. Importantly, it is currently unknown whether information about relative features is derived from lower-level neurons that respond to specific features, or whether visual inputs are first encoded relationally, with feature-specific codes extracted later. The present study addressed this question using functional magnetic resonance imaging (fMRI) in a colour search task in which we enforced relational vs. feature-specific search. Our current findings support the first possibility, with inputs being first processed in a feature-specific manner, and later relationally: In V1, repetition suppression was most pronounced in the feature-specific condition, indicating that these neurons respond to specific feature values. In V2, repetition suppression was equally strong for both conditions, but in later areas (V3, parietal and frontal areas), the result reversed, with stronger repetition suppression for relational search. Surprisingly, these results were obtained even when both the target and nontarget colours changed on a trial-by-trial-basis in relational search, and only the nontarget colour in feature-specific search. These findings show that repetition suppression is not always tightly linked to repetitions of the stimulus input, but can depend on top-down search goals, especially during later processing stages. Moreover, while V1 seems to respond to specific features, relational information is apparently derived as early as V3, and dominates throughout the visual processing hierarchy. This dominance may explain why relational search is more efficient and generally preferred to feature-specific search.
Research on Association Rules Algorithm Based on Bit Storage and Deep Pruning

Association rule mining is an important part of data mining and is widely used in many fields. Mining frequent itemsets is the most important step and technology. In this paper, the Eclat algorithm in the frequent itemset mining algorithm is taken as the research point. In order to solve the problem that the Eclat algorithm increases the size of the itemset, causing the vertical list to store transaction records and intersection operations, etc, consumes a lot of time and memory. From the algorithm itself and storage mechanism considerations, a DBEclat (Deep Bit Eclat) algorithm based on bit storage mechanism and deep pruning strategy is proposed. The algorithm stores the vertical data list in a binary form that reduces memory consumption, reduces the time consumption of the intersection operation by using the AND operation of the binary data, and combines the multi-angle deep pruning strategy to compress the size of the candidate set. The experimental results show that the proposed algorithm has higher time efficiency and lower memory consumption than the original algorithm.
PENGGUNAAN MEDIA KARTU BERGAMBAR UNTUK MENINGKATKAN KEMAMPUAN MEMBACA SISWA KELAS II SDN 067 PEKANBARU

This research is motivated by the low reading ability of 2nd grade students at SDN 067 Pekanbaru. The low reading ability of students is caused by the teacher not using learning media in learning so that the learning process becomes monotonous and students' abilities are different. One of the media that can be used to improve reading skills is the flashcard media. Media flashcard is a method of reading by using a card that contains pictures that are equipped with words. This study aims to improve the reading skills of 2nd grade students at SDN 067 Pekanbaru the use of flashcard. The research conducted was classroom action research with research subjects namely students in class 2 SDN 067 Pekanbaru totaling 24 people. The results of research on teacher activity increased at the first meeting of the cycle I with a presentation of 70.83%, at the second meeting increased to 75%. Then in the cycle II increased again at the first meeting 83.33%, and the second meeting increased to 87.5%. Meanwhile student activity also increased, namely at the first meeting of the cycle I having a percentage of 70.83% at the second meeting to 79.17%. Then at the first meeting the cycle I increased to 87.5%, at the second meeting increased again to 91.67%. The results of increasing the reading ability of 2nd grade students at SDN 067 Pekanbaru, from the base score of 63.75, increased in the cycle I to 73.88 with a percentage of 15.89%, then in the cycle I again increased to 83.96 with a percentage of 31.70%
Development of Inventory Information System Using Enterprise Architecture Planning Method

Universitas Bina Darma is a university that has a vision to become an international standard university driven by innovations in Information and Communication Technology (ICT). The aim of this research is to support the vision of Universitas Bina Darma by creating an inventory information system for the university-wide implementation. The research uses Enterprise Architecture Planning (EAP) which defines the data architecture, application architecture, technology architecture, and its implementation. EAP consists of four levels, where by the top level is planning initiation, the second level consists of two stages namely business modeling, and current systems and technology, the third level consists of three stages of data architecture, application architecture and technology architecture. Lastly, the bottom level is implementation or migration plans. The results of this research is in the form of the Inventory Information Systems that has several menus, among others: procurement menu, maintenance menu, move transfer menu, space menu, loan space menu, asset menu and request menu. This Inventory Information System facilitates the faster processing of inventory data so as to produce quality report Â that is timely available when needed by the leadership, as well as facilitates the supervision of existing inventory.
R3D3: A Doubly Opportunistic Data Structure for Compressing and Indexing Massive Data

Opportunistic data structures are used extensively in big data practice to break down the massive storage space requirements of processing large volumes of information. A data structure is called (singly) opportunistic if it takes advantage of the redundancy in the input in order to store it in iformationtheoretically minimum space. Yet, efficient data processing requires a separate index alongside the data, whose size often substantially exceeds that of the compressed information. In this paper, we introduce doubly opportunistic data structures to not only attain best possible compression on the input data but also on the index. We present R3D3 that encodes a bitvector of length n and Shannon entropy H0 to nH0 bits and the accompanying index to nH0(1/2 + O(log C/C)) bits, thus attaining provably minimum space (up to small error terms) on both the data and the index, and supports a rich set of queries to arbitrary position in the compressed bitvector in O(C) time when C = o(log n). Our R3D3 prototype attains several times space reduction beyond known compression techniques on a wide range of synthetic and real data sets, while it supports operations on the compressed data at comparable speed.
Multimodal object detection using unsupervised transfer learning and adaptation techniques

Deep neural networks achieve state-of-the-art performance on object detection tasks with RGB data. However, there are many advantages of detection using multi-modal imagery for defence and security operations. For example, the IR modality offers persistent surveillance and is essential in poor lighting conditions and 24hr operation. It is, therefore, crucial to create an object detection system which can use IR imagery. Collecting and labelling large volumes of thermal imagery is incredibly expensive and time-consuming. Consequently, we propose to mobilise labelled RGB data to achieve detection in the IR modality. In this paper, we present a method for multi-modal object detection using unsupervised transfer learning and adaptation techniques. We train faster RCNN on RGB imagery and test with a thermal imager. The images contain object classes; people and land vehicles and represent real-life scenes which include clutter and occlusions. We improve the baseline F1-score by up to 20% through training with an additional loss function, which reduces the difference between RGB and IR feature maps. This work shows that unsupervised modality adaptation is possible, and we have the opportunity to maximise the use of labelled RGB imagery for detection in multiple modalities. The novelty of this work includes; the use of the IR imagery, modality adaption from RGB to IR for object detection and the ability to use real-life imagery in uncontrolled environments. The practical impact of this work to the defence and security community is an increase in performance and the saving of time and money in data collection and annotation.
Data anonymization with imprecise rules and its performance evaluations

Privacy protection is absolutely imperative for data releases when the utilization of public data and big data is getting popular. In this paper, data anonymization methods using rough set-based rule induction are investigated. It has been shown that many rules with imprecise conclusions can improve the classification accuracy of the rule-based classifier. Data anonymization methods utilizing rules with imprecise conclusions are proposed. The data tables anonymized by one of the proposed methods can preserve the classification accuracy of the rules induced from them. The proposed methods as well as conventional data anonymization methods are compared from two viewpoints: the classification accuracy of rules induced from the anonymized data table and the preservation of data anonymity. The results show the usefulness of the proposed methods.
AWS Directory Service

In this chapter, we will cover the AWS Directory Service, which is a highly scalable and managed multi-directory store; it can be used for authentication and Single Sign-On services. As mentioned, there are multiple directories supported by AWS Directory Service, each one helping solve a unique business problem, including authentication services for traditional applications, support for SaaS application developers, and cloud applications with complex authentication relationships. The directories available include Amazon Cloud Directory, AD Connector, Amazon Cognito, Simple AD, and Microsoft AD. As one can imagine, each of these directories has enough depth, nuance, and complexity to warrant a dedicated chapter in this book. However, in this section we will focus on the services that are most frequently used with Microsoft Workloads and managed with PowerShell; these are Microsoft AD, AD Connector, and Simple AD.
Statistical image analysis for information system

The surface Kirchhoff integral is numerically calculated by the fourth-order Newton – Cotes method with high accuracy. The intensity is normalized. Implemented quantization of the signal with a unit step for different distances of observation is carried out. The cross section of the central topological object intensity and its characteristics are shown. Variation curves and cumulates are constructed for intensity distributions at different observation distances. The basic statistical parameters of distributions are determined, such as, the mode, the mean, the median and the standard deviation.