Reviewerly/exHarmony huggingface dataset:
A SERP-Mining Approach for Classification of DNS Requests

DNS request classification is an area that has received a lot of attention, mostly as part of network security process, in order to classify requests into malicious and non-malicious. However, there exist several categories of web pages that even though not malicious, they belong to “borderline” categories and need to be monitored. For instance, websites selling illegal substances or weapons might be of interest for any public or private organization to monitor as outgoing traffic. In this work, we treat this as a topic classification problem. We present and evaluate a machine learning framework that takes as input a domain name (based on the respective DNS request) and outputs the content category it belongs to. We evaluate several options for feature engineering and classification to find the most appropriate setup for the specific problem domain. We also address the problem of data collection and preprocessing. While there exist several labelled datasets with malicious/non-malicious requests, a similar labelled dataset does not exist for general web content categories. We therefore propose a SERP (Search Engine Response Pages)-mining approach to collect and label an appropriate dataset. Our experimental evaluation uncovers several interesting insights and forms the basis for further work into this interesting domain.
Peningkatan kemampuan kognitif pada remaja melalui layanan informasi

Individual cognitive abilities become a benchmark in making decisions to act. High cognitive abilities can provide the potential for success in individuals in various ways, including in their teens. Various kinds of methods are carried out on adolescents so that they are able to maximize their potential so that they develop properly. Information counseling services provide a number of information to clients so that clients are able to process the information and use it as needed. In information counseling services, a number of cognitive activities are carried out so that in addition to adding a number of knowledge and insights, they also develop adolescent cognitive abilities. Based on these assumptions, we conduct research aimed at observing the improvement of adolescent cognitive abilities through information counseling service activities. We conducted an experiment involving 35 teenagers. Based on the results of data analysis, a significant increase in the cognitive abilities of adolescents is presented before and after information counseling services are provided. With an average difference of 20,029. Thus, counselors can provide information counseling services to improve the cognitive abilities of adolescents both at school and outside of school
Hubungan Persepsi Peserta Didik Tentang Pola Asuh Orang Tua, Kebiasaan Belajar dengan Hasil Belajar

The purpose of this research was to find out the significant and positive correlation between student perception about parenting parents and learning achievement , learning habits and learning achievement , and student perception about parenting parents and learning habits together with learning achievement thematic of fourth grade students SD Muhammadiyah Metro Central. The type of research was ex-postfacto correlation. Population was 230 and sample was 70 students. Data collection techniques used observation, questionnaire and documentation studies. Data analysis technique used was product moment correlation and multiple correlation . The results showed that there was a significant and positive correlation between student perception about parenting parents and learning achievement indicated at low level , there was a significant and positive correlation between learning habits and learning achievement indicated at low level , and there was a significant and positive correlation between student perception about parenting parents and learning habits together with learning achievement indicated at “ moderate” level. Keywords: Learning achievement , study habits, parenting parents DOI: http://dx.doi.org/10.23960/jiip.v1i1.18021
UnderTracker: Binary Hardening Through Execution Flow Verification

Programs are developed in a manner so that they execute and fulfill their intended purpose. In doing so, programmers trust the language to help them achieve their goals. Binary hardening is one such concept, which prevents program behavior deviation and conveys the intention of the programmer. Therefore, to maintain the integrity of the program, measures need to be taken to prevent code-tampering. The proposed approach enforces code verification from instruction-to-instruction by using the programmer’s intended control flow. UnderTracker enforces execution flow at the instruction cache by utilizing the read-only data-cache available in the program. The key idea is to place a control transfer code in data-cache and to call it from instruction cache via labels. UnderTracker injects labels into the binary without affecting the semantics of the program. After the code execution starts, it verifies every control point’s legality before passing the control to the next instruction, by passively monitoring the execution flow. This paper proposes an efficient technique, called UnderTracker, to strengthen the binary integrity of an I/O intensive running program, with the nominal overhead of only 5–6% on top of the normal execution.
Prototype Analysis of Basic Words in Social Media in Indonesia

Aims: The use of social media in generation Y and generation Z as a forum for communication is commonly used in everyday life. The use of language as a means of communication in social media also attracts attention, especially the use of Indonesian, as a national language and as a unifying language of the nation, Indonesian must of course be maintained. Especially in writing and using it. The frequency of writing words on social media often becomes a problem in Indonesian as a national language. Both consciously and unconsciously, Indonesian writing is often found that is not in accordance with the structure of grammar and vocabulary in social media, causing concerns about the use of Indonesian in the future that has strayed far from the general guidelines of Indonesian spelling. Researchers conduct research related to the use of basic words in social media.&#x0D; Study Design: The analysis process is carried out using an approach that starts from data collection, stemming and grouping data from random social media (WhatsApp, Instagram and Facebook).&#x0D; Place and Duration of Study: Data from the top 3 (three) social media (WhatsApp, Instagram and Facebook) with the keyword "belajar" between December 2019 and January 2020.&#x0D; Methodology: From the data collected there were 40492 basic words that were successfully obtained. Then the data obtained is done by sorting and deleting repeated words, so as to get the base word data of 5243 words.&#x0D; Results: From the results of the study obtained information that the word "ajar" is the most frequent word with 2502 times repeated. The words "yang" and the word "ga" are non-standard words most often appear with the appearance 759 and 530 times. Mistakes most often occur because of abbreviated writing and hypercorrection.&#x0D; Conclusion: The use of words that are not standard and not in accordance with the general guidelines of Indonesian spelling are words that are dominated by abbreviated words. Abbreviations like "yg" should "yang" rank first, while the word "ga" who should "tidak" rank second. The writing of words caused by hypercorrection is also one of the causes of errors in writing words. Hypercorrection words that appear include "zenius", "coba" and "smoga".
Parametric Majorization for Data-Driven Energy Minimization Methods

Energy minimization methods are a classical tool in a multitude of computer vision applications. While they are interpretable and well-studied, their regularity assumptions are difficult to design by hand. Deep learning techniques on the other hand are purely data-driven, often provide excellent results, but are very difficult to constrain to predefined physical or safety-critical models. A possible combination between the two approaches is to design a parametric energy and train the free parameters in such a way that minimizers of the energy correspond to desired solution on a set of training examples. Unfortunately, such formulations typically lead to bi-level optimization problems, on which common optimization algorithms are difficult to scale to modern requirements in data processing and efficiency. In this work, we present a new strategy to optimize these bi-level problems. We investigate surrogate single-level problems that majorize the target problems and can be implemented with existing tools, leading to efficient algorithms without collapse of the energy function. This framework of strategies enables new avenues to the training of parameterized energy minimization models from large data.
Image Scene Conversion Algorithm Based on Generative Adversarial Networks

This paper presents an image scene conversion algorithm based on generative adversarial networks (GANs). First, the generator uses the generator network with cross-layer connection structure to realize the sharing of image structure information, so that the structure and edge of the generated image are consistent with the input image as far as possible. Secondly, the multi-scale global convolution network discriminator is used to determine different scales of image. Then, the combinational loss functions including GAN, L1, VGG and feature matching (FM) are designed. The network structure of the generator, the number of multi-scale discriminator and the weighted combination of multiple loss functions are evaluated and analyzed, and the optimized algorithm structure is given. Finally, through image fogging and day-to-night conversion experiment, the results show that the details of the converted image are more complete and the generated image is more realistic.
Preservation of nonmaterial cultural heritage through the implementation of additional educational programs in the field of folk crafts and handicrafts

We substantiate the importance of preserving the nonmaterial cultural heritage, folk culture and the development of traditional handicrafts, the need to integrate culture and education. We prove the effectiveness of measures to preserve the nonmaterial cultural heritage through the development of children’s traditions and technologies of folk art crafts and handicrafts in additional education. We analyze the implementation of additional general educational programs in the field of decorative and applied arts and activities to preserve folk art crafts and handicrafts are being conducted in the regions of the Russian Federation. We make proposals for the development of educational programs in the field of folk crafts.
PELATIHAN E-LEARNING MENGGUNAKAN PLATFORM MOODLE SEBAGAI UPAYA MENINGKATKAN PROFESIONALISME GURU

Mathematics learning is directed at learning that can be done anytime and anywhere. Such learning can be done by conducting learning that is connected to the internet with the e-learning platform. For e-learning teacher use platforms can be used as learning media so that mathematical learning can be done anywhere. Researchers see that teachers especially teachers in junior high schools have difficulty in using e-learning in learning. This has an impact on students' understanding that is not optimal because teachers only rely on learning in the classroom. Service methods used are: 1) receiver theory and practice of e-learning 2) assignment of e-learning applications 3) evaluation of which e-learning has been applied inside outside the classroom. The results of this activity were that the trainees were seen to be motivated by 87% being received motivated, the remaining 13% were less motivated by the existence of this activity, then the response after the activity percentage that gave a positive response was 93% and 7% gave a less positive response. Another result is that most of the 15 teachers at SMPN 3 and 5 have used e-learning using either the google classroom or using moodle.
Quantum legitimacy of reversible gate and a new design of multiplier based on R gate*

Quantum full adders play a key role in the design of quantum computers. The efficiency of a quantum adder directly determines the speed of the quantum computer, and its complexity is closely related to the difficulty and the cost of building a quantum computer. The existed full adder based on R gate is a great design but it is not suitable to construct a quantum multiplier. We show the quantum legitimacy of some common reversible gates, then use R gate to propose a new design of a quantum full adder. We utilize the new designed quantum full adder to optimize the quantum multiplier which is based on R gate. It is shown that the new designed one can be optimized by a local optimization rule so that it will have lower quantum cost than before.
Playing a Strategy Game with Knowledge-Based Reinforcement Learning

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a method that combines a knowledge-based approach and a reinforcement learning (RL) technique into one method for intelligent problem solving. The proposed approach focuses on multi-expert knowledge acquisition, with the reinforcement learning being applied as a conflict resolution strategy aimed at integrating the knowledge of multiple exerts into one knowledge base. The article describes the KB-RL approach in detail and applies the reported method to one of the most challenging problems of current Artificial Intelligence (AI) research, namely playing a strategy game. The results show that the KB-RL system is able to play and complete the full FreeCiv game, and to win against the computer players in various game settings. Moreover, with more games played, the system improves the gameplay by shortening the number of rounds that it takes to win the game. Overall, the reported experiment supports the idea that, based on human knowledge and empowered by reinforcement learning, the KB-RL system can deliver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience.
A Parallel GPU Implementation of SWIFFTX

The SWIFFTX algorithm is one of the candidates of SHA-3 Hash Competition that uses the number theoretic transform (NTT). It has 256-byte input blocks and 65-byte output blocks. In this paper, a parallel implementation of the algorithm and particular techniques to make it faster on GPU are proposed. We target version 6.1 of NVIDIA®CUDA™compute architecture that employs an ISA (Instruction Set Architecture) called Parallel Thread Execution (PTX) which possesses special instrinsics, hence we modify the reference implementation for better results. Experimental results indicate almost 10x improvement in speed and 5 W decrease in power consumption per $$2^{16}$$ hashes.
SEQUENTIAL COLLISION-FREE OPTIMAL MOTION PLANNING ALGORITHMS IN PUNCTURED EUCLIDEAN SPACES

In robotics, a topological theory of motion planning was initiated by M. Farber. We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions between them and avoiding obstacles. Furthermore, we present the multi-tasking version of the algorithms.
Interactive Ontology Matching Based on Evolutionary Algorithm

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.
2DKD: a toolkit for content-based local image search

Direct comparison of 2D images is computationally inefficient due to the need for translation, rotation, and scaling of the images to evaluate their similarity. In many biological applications, such as digital pathology and cryo-EM, often identifying specific local regions of images is of particular interest. Therefore, finding invariant descriptors that can efficiently retrieve local image patches or subimages becomes necessary.We present a software package called Two-Dimensional Krawtchouk Descriptors that allows to perform local subimage search in 2D images. The new toolkit uses only a small number of invariant descriptors per image for efficient local image retrieval. This enables querying an image and comparing similar patterns locally across a potentially large database. We show that these descriptors appear to be useful for searching local patterns or small particles in images and demonstrate some test cases that can be helpful for both assembly software developers and their users.Local image comparison and subimage search can prove cumbersome in both computational complexity and runtime, due to factors such as the rotation, scaling, and translation of the object in question. By using the 2DKD toolkit, relatively few descriptors are developed to describe a given image, and this can be achieved with minimal memory usage.
Feature Matching and Heat Flow in Centro-Affine Geometry

In this paper, we study the differential invariants and the invariant heat flow in centro-affine geometry, proving that the latter is equivalent to the inviscid Burgers' equation. Furthermore, we apply the centro-affine invariants to develop an invariant algorithm to match features of objects appearing in images. We show that the resulting algorithm compares favorably with the widely applied scale-invariant feature transform (SIFT), speeded up robust features (SURF), and affine-SIFT (ASIFT) methods.
Blind Interactive Learning of Modulation Schemes: Multi-Agent Cooperation Without Co-Design

We examine the problem of learning to cooperate in the context of wireless communication. In our setting, two agents must learn modulation schemes that enable them to communicate across a power-constrained additive white Gaussian noise channel. We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed. We employ the “Echo” protocol, a “blind” interactive learning protocol where an agent hears, understands, and repeats (echoes) back the message received from another agent, simultaneously training itself to communicate. To capture the idea of cooperation between “not necessarily co-designed” agents we use two different populations of function approximators - neural networks and polynomials. We also include interactions between learning agents and non-learning agents with fixed modulation protocols such as QPSK and 16QAM. We verify the universality of the Echo learning approach, showing it succeeds independent of the inner workings of the agents. In addition to matching the communication expectations of others, we show that two learning agents can collaboratively invent a successful communication approach from independent random initializations. We complement our simulations with an implementation of the Echo protocol in software-defined radios. To explore the continuum of co-design, we study how learning is impacted by different levels of information sharing between agents, including sharing training symbols, losses, and full gradients. We find that co-design (increased information sharing) accelerates learning. Learning higher order modulation schemes is a more difficult task, and the beneficial effect of co-design becomes more pronounced as the task becomes harder.
THE EFFECTIVENESS OF ICT IN TEACHING THE PREFIX PEN-

This study examines the effectiveness of Information and Communication Technology (ICT) as one of the Formative Assessment tools in the teaching and learning of Malay Language grammar. This study focuses on the level of understanding and understanding of pupils on the lessons learned through the application of Nearpod. The subject involved in this study were 26 students of Primary 4. They are from middle and high progress students. Through Nearpod Application, teachers can monitor and evaluate students' learning-related lessons. Teachers are able to give immediate feedback as students’ responses will be flashed on the projector screen. Peer assessments are also used in this lesson. Through teacher observation, the Nearpod Application was successfully used as one of the Formative Assessment tools. The findings show that 90% of students are able to understand the use of prefix peN- clearly. This application can also arouse students’ interest to learn Malay in a fun environment.
Target Tracking Based on Correlation Filter for Scale Offset

Abstract Existing scale estimation methods mainly use fixed-size tracking box for target tracking. However, when the moving direction of the target changes from near to far or from far to near, the size of the current tracking box cannot adapt to the scale change. In order to overcome the disadvantage of invariant size of tracking box in the scale change, this paper proposes a scale offset estimation method based on correlation filter by combining the state of previous frame and current frame of the target. Select the scale proposal box in the scale layer, and adjust the position and size of the actual tracking box in real time according to the size of the proposal box, target position and scale offset. In this paper, OTB-100 is used as the dataset. The obtained tracking results show that our algorithm Ours has better tracking performance than the existing others tracking algorithm in scale variation, and it has better tracking effect and stronger robustness in drift and occlusion events.
RANDOM FOREST FOR DISSIMILARITY-BASED MULTI-VIEW LEARNING

Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-of-the-art static view combinations.