Reviewerly/exHarmony huggingface dataset:
CNN Based Approach for Copy-Move Video Forgery Detection

The utilization of videos as a means to exchange information has increased on a vast scale. The advancement and the revolutionary changes taking place in the video editing tools and technologies have made it of greater importance to assure the authenticity of video content and to ensure thatthe right information has been circulated across the globe. Video as an entity is used in surveillance, medical, forensics, and various other fields. To use this as a proof for any crime scene its authenticity must be scrutinized, which demands AdvancedForgery Detection techniques. This paper presents CNN (Convolutional Neural Network) to find whether the video is a real one ornot. CNN is used here to overcome the shortcomings of the traditional methods such as time and computational complexity. The traditional method uses handcraft feature analysis which has high resource consumption and yields less accurate results. The proposed deep learning approach accurately detects forged and original videos with 97% accuracy.The method is compared with existing deep learning methodsand perform superior for copy-move forgery detection.
Experimental Study on the Effect of Loss Function on Object Detection

Abstract. As a classic task in the field of computer vision, the purpose of Object Detection is to find out all the objects of interest in the image and determine their location and size. With the development of neural network technology, Object Detection has entered the era of deep learning. At present, the object detector is divided into two categories: Two-Step detector and One-step detector. The loss function is an important part of the object detector. This paper introduces six loss functions: Smooth L1 Loss, Balanced L1 Loss, IoU Loss, GIoU Loss, DIoU Loss and CIoU Loss. Among these six loss functions, we select the three loss functions of Smooth L1 Loss, Balanced L1 Loss and IoU Loss to experiment. The main purpose of this experiment is to explore and compare the effect of loss function on the performance of Object Detection algorithm. In the experiment, Faster-RCNN and Retinanet represent two different kinds of detectors. We introduce the loss function into the detector in turn and evaluate the performance of the detector using the Pascal VOC0712 dataset. In this experiment, we used the object detection toolbox mmdetection. Meanwhile, the evaluation metric used to evaluate the detector are Recall AP and mAP. The experimental results show that some loss functions have the opposite effect on two different detectors.
Intelligent Information Processing Technology in Teaching Reform of Computer Specialty

With the development of information technology, computer education is paid more and more attention. However, in the face of the same new development of computer technology, there are still many problems in the teaching of computer major. Intelligent information processing technology is a kind of modern technology, which can realize the collection and arrangement of data information and make the data clear. This paper studies the application of intelligent information processing technology in the teaching reform of computer specialty. In the research, we use the principal component analysis method of intelligent information processing technology, through questionnaire survey and literature analysis, to study the teaching and reform of computer major, sort out the key problems, and put forward some reform suggestions. This study found that there are many problems in the teaching of computer science, among which the most prominent problems are backward teaching methods and low teaching efficiency, accounting for 31%. In addition, there are some other problems that need to be paid attention to. Thus, we can see that the application of intelligent information processing technology in the teaching reform of computer specialty can make the data become clear and clear, which is conducive to us to find the key of problems and provide solutions for better solving problems.
SALIENCY BASED ILLUMINATION CONTROL FOR GUIDING USER ATTENTION IN 3D SCENES

Visual attention has a major impact on how we perceive 3D environments and saliency is a component of visual attention expressing how likely a scene or item is to capture our attention due to its apparent features. Saliency relies upon shape, shading, brightness, and other visual attributes of items. The saliency distribution of a visual field is influenced by the illumination of a scene, which has a significant impact on those visual properties. This work aims to control the saliency by manipulating the illumination parameters in a 3D scene. For this reason, given a sensible 3D scene, the light parameters that provide maximum saliency for the point of interest objects are investigated. In other words, we propose a method for task-aware automatic lighting setup. In this paper, 2D renderings of a 3D scene from various perspectives are considered, and the effects are analyzed in terms of saliency distribution under various lighting conditions. Also, for this process, different saliency estimation methods and calculations are investigated and eye tracker based user experiments are conducted to verify the results.
Smart Portable Musical Simulation System Based on Unified Temperament

This study builds a digital system of a portable musical instrument based on Unified Temperament. The system utilizes Equal-temperament, which integrates different modes of playing on the Musical Pad. By using the visualized and digitalized system, people without musical training will be able to give a musical performance. The Musical Pad simulates different musical instruments, including keyboard, woodwind, string, and other orchestral instruments. Therefore, music lovers can cooperate to play a variety of parts in polyphonic music. The system is suitable for general music education for non-artistic students in primary and middle schools. In the new form for music teaching and appreciation, students can participate more actively.
Integrating Topic Model and Network Embedding for Thread Recommendation

A thread is the most common information aggregation unit in a health forum, so effective thread recommendation is critical for improving the user experience in an online health community (OHC). This paper proposes an OHC thread recommendation method based on topic model and network embedding, which recommends threads to users by training a classifier and predicting user reply behavior.The proposed model uses the network structure to describe valid information in OHCs and treats a recommendation as the task of predicting links between users and threads in the network. Topic nodes are added to the information network to better represent the features of users and threads. The results of the latent Dirichlet allocation (LDA) model describe thread topics and user interests from the perspectives of consumer health vocabulary in OHCs and social support types. The large-scale information network embedding technology LINE is used to mine the node's contextual information from the network structure to obtain the low-dimensional vectors of nodes. We optimize the representation method and similarity calculation of network nodes and enrich the network structure information contained in the recommended features to improve the recommendation effect.To verify the proposed model, we collected data from the diabetes forum "Sweet Home." The experimental results show that the proposed model can effectively extract user interests in threads from the information network and optimize thread recommendation in OHCs.
IMPACT OF DIGITALIZATION AND AUTOMATIZATION OF THE WORLD ECONOMY ON THE STATE POLICY OF THE RUSSIAN FEDERATION IN THE FIELD OF SHOOL EDUCATION

This article examines the problem of increasing requirements for the competencies of specialists in the world economy and the preventive measures of the Government of the Russian Federation on the issue of basic training of schoolchildren in the field of digital and engineering competencies.
Signal Modulation Identification Based on Deep Learning in FBMC/OQAM Systems

Signal modulation identification (SMI) has always been one of hot issues in filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), which is usually implemented by the machine learning-based feature extraction. However, it is difficult for conventional methods to extract the signal feature, resulting in a limited probability of correct classification (PCC). To tackle this problem, we put forward a novel SMI method based on deep learning to identify FBMC/OQAM signals in this paper. It is noted that the block repetition is employed in the FBMC/OQAM system to achieve the imaginary interference cancelation. In the proposed deep learning-based SMI technique, the in-phase and quadrature samples of FBMC/OQAM signals are trained by the convolutional neural network. Subsequently, the dropout layer is designed to prevent overfilling and improve the identification accuracy. To evaluate the proposed scheme, extensive experiments are conducted by employing datasets with different modulations. The results show that the proposed method can achieve better accuracy than conventional methods.
BCH CODES DECODER BASED ON EUCLID ALGORITHM

In the process of algebraic decoding of BCH codes over the field GF(q) with the word length n = qm-1, correcting t errors, both in the time and frequency domains, it is necessary to find the error locator polynomial ?(x) as the least polynomial for which the key equation. Berlekamp proposed a simple iterative scheme, which was called the Berlekamp-Messi algorithm, and is currently used in most practical applications. Comparative statistical tests of the proposed decoder and decoder using the Berlikamp-Messi algorithm showed that they differ slightly in decoding speed. The proposed algorithm is implemented in the environment in Turbo Pascal and can be used for the entire family of BCH codes by replacing the primitive Galois polynomial.
Swarm Programming Using Multi-verse Optimizer

Swarm programming is the swarm-based automatic programming in which swarm intelligence algorithms are used to evolve computer programs automatically. Automatic programming is a division of machine learning where machines learn how to write the program for themselves. Grammar-based swarm programming is an interesting research topic in recent times. In grammar-based swarm programming, context-free grammar (CFG) is utilized in the generation of computer programs automatically in any arbitrary target computer language. In this paper, grammatical multi-verse optimizer (GMVO) is proposed to generate computer programs automatically. The proposed method is applied to three benchmark problems such as Santa Fe Ant Trail (SFAT), 3-multiplexer, and symbolic regression. The experimental results of the proposed GMVO are compared with that of grammatical fireworks algorithm (GFWA), grammatical bee colony (GBC), and grammatical swarm (GS). The empirical results with analysis demonstrate that the GMVO can be used in automatic generation of computer programs in any arbitrary target computer language.
Persepsi Wartawan mengenai Penggunaan Clickbait pada Judul Berita di Media Siber

ABSTRAK&#x0D; Tujuan penelitian ini yaitu untuk mengetahui atensi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber, untuk mengetahui seleksi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber, untuk mengetahui interpretasi Wartawan Kabupaten Purwakarta dalam penggunaan clickbait pada judul berita di media siber. Penelitian ini menggunakan metode kualitatif deskriptif. Didasarkan pada teori persepsi yaitu atensi, seleksi dan interpretasi. Hasil dari penelitian ini yaitu wartawan anggota PWI Kabupaten Purwakarta memperhatikan clickbait. Dalam membuat judul yang menggunakan clickbait tetap mematuhi aturan seperti relevasi antara judul berita dengan isi berita. Penggunaan clickbait pada judul berita diperbolehkan namun tanpa mengabaikan kode etik dan tetap bersesuaian antara judul dan isi berita.&#x0D; Kata Kunci : Persepsi Wartawan; Clickbait; Media Siber.&#x0D; ABSTRACT&#x0D; The purpose of this study is to determine the attention of journalists in Purwakarta Regency in using clickbait on news headlines in cyber media, to find out the selection of journalists from Purwakarta Regency in using clickbait on news headlines in cyber media, to find out the interpretation of Purwakarta Journalists in using clickbait on news headlines. on cyber media. This research uses descriptive qualitative method. Based on the theory of perception, namely attention, selection and interpretation. . The results of this study are journalists who are members of the PWI Purwakarta Regency pay attention to clickbait. In making titles using clickbait, we must comply with rules such as the relevance of news headlines to news content. The use of clickbait in news headlines is allowed but without ignoring the code of ethics and it is consistent with the title and content of the news. &#x0D; Keywords : Journalists' Perception; Clickbait; Cyber Media.
Janssen Ortho, LLC v. United States

Biotechnology Law ReportVol. 40, No. 5 Original ArticlesJanssen Ortho, LLC v. United StatesUnited States Court of Appeals of the Federal Circuit, 2021995 F.3d 981Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:12 Oct 2021https://doi.org/10.1089/blr.2021.29239.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 5Oct 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Janssen Ortho, LLC v. United States.Biotechnology Law Report.Oct 2021.352-359.http://doi.org/10.1089/blr.2021.29239.cmhPublished in Volume: 40 Issue 5: October 12, 2021Online Ahead of Print:July 1, 2021PDF download
Demonstration of FabHydro: 3D Printing Techniques for Interactive Hydraulic Devices with an Affordable SLA 3D Printer

In this demonstration, we showcase FabHydro [6], a set of rapid and low-cost techniques to prototype interactive hydraulic devices using off-the-shelf SLA 3D printers and flexible photosensitive resin. We introduce two printing processes to seal the transmission fluid: the Submerged Printing process seals liquid resin inside the printed objects without assembly, and the Printing with Plug method which allows using variety of transmission fluid with no modification to the printer. We showcase how a range of relevant 3D printable primitives, including hydraulic generator, transmitter, and actuator can be printed and combined to create interactive examples.
Strobe: An Acceleration Meta-algorithm for Optimizing Robot Paths using Concurrent Interleaved Sub-Epoch Pods

In this paper, we present a meta-algorithm intended to accelerate many existing path optimization algorithms. The central idea of our work is to strategically break up a waypoint path into consecutive groupings called "pods," then optimize over various pods concurrently using parallel processing. Each pod is assigned a color, either blue or red, and the path is divided in such a way that adjacent pods of the same color have an appropriate buffer of the opposite color between them, reducing the risk of interference between concurrent computations. We present a path splitting algorithm to create blue and red pod groupings and detail steps for a meta-algorithm that optimizes over these pods in parallel. We assessed how our method works on a testbed of simulated path optimization scenarios using various optimization tasks and characterize how it scales with additional threads. We also compared our meta-algorithm on these tasks to other parallelization schemes. Our results show that our method more effectively utilizes concurrency compared to the alternatives, both in terms of speed and optimization quality.
Distortion Level Analysis of a 2D Median Filter with a Weighted Central Element

The paper presents the evaluation of the median filters with weighted central elements use for cleaning images from impulse noise. The influence of obtaining undistorted pixels probability and distortion of the image pixel distribution function on the processing is shown. It is demonstrated that median filters with a window <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> allow obtaining a higher quality of image cleaning from noise with a small probability of impulse noise, and median filters with window sizes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times 5$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7 \times 7$</tex> allow to obtain a higher quality of image cleaning from noise with a high probability of impulse noise. The obtained results can be used in medical image processing and adaptive filtering systems for photo and video data processing.
VelocityNet: Motion-Driven Feature Aggregation for 3D Object Detection in Point Cloud Sequences

The most successful methods for LiDAR-based 3D object detection use sequences of point clouds in order to exploit the increased data density through temporal aggregation. However, common aggregation methods are rarely able to capture fast-moving objects appropriately. These objects are displaced by large distances between frames and naive approaches are not able to successfully leverage the full amount of information spread across time. Yet, especially in autonomous driving scenarios, fast-moving objects are most crucial to detect as they actively take part in highly dynamic traffic situations. This work presents a novel network architecture called VelocityNet which is explicitly designed to temporally align features according to object motion. Our approach extends traditional 3D convolutions by a motion-driven deformation of the convolution kernels across the temporal dimension. The required motion information can be obtained from various sources, ranging from external computation or complementary sensors to an integrated network branch which is trained jointly with the object detection task. The explicit feature alignment allows the training process to focus on the object detection problem and results in a significant increase in detection performance compared to the popular PointPillars baseline, not only for dynamic but also for static objects. We evaluate our approach on the nuScenes dataset and analyze the main reasons for the observed performance gains.
The Effectiveness of Whatsapp in Teaching Reading in SMP Muhammadiyah Purwareja Klampok

In this global era, technological development gives an impact on every aspect of human life. The growth of technology improves the way people connect. It causes much growth up of social media that allows us to communicate with a much broader group of people in long-distanceOne of the most popular social media which are used by Indonesian people is WhatsApp. WhatsApp is one of the effective applications that used to online interact with other people. It is so simple and easy to use, we can use WhatsApp wherever and whenever just by mobile phones. Besides sending text messages, WhatsApp also allows the users to share various files, such as images, sound, video files, and location, and it can make a group chat. It is so easy to be used as a learning medium in online learning. The aim of this study was to find out about the effectiveness of use WhatsApp by the teacher in teaching reading skills in junior high school. This research was qualitative. The subjects of this research were students of eighth grade A and B at SMP Muhammadiyah Purwareja Klampok. In collecting the data, the researcher was interviewing the teacher about the use of WhatsApp media in teaching reading, and students were given a questionnaire to report their experience about the effectiveness of WhatsApp in developing their reading skills. Findings from interviews result, the teacher stated that 80% of students’ score of tasks and tests are correct, and students’ responses to the questionnaire revealed that WhatsApp media was very effective in developing their reading skills. It can be inferred that using WhatsApp as a medium in teaching reading comprehension in eighth grade at SMP Muhammadiyah Purwareja Klampok is very effective to improve students reading skills because WhatsApp is very easy and affordable for students to use.
ExperienceGen 1.0: A Text Generation Challenge Which Requires Deduction and Induction Ability

This paper introduces a novel commonsense generation task ExperienceGen 1.0, which is used to test whether the current models have deduction and induction capabilities. It includes two subtasks, both are used to generate commonsense knowledge expressed in natural language. The difference is that the first task is to generate commonsense using causal sentences that contain causal relationships, the second is to generate commonsense with the sentence which is the major premise of the syllogism reconstructed from the original causal sentence. ExperienceGen 1.0 is challenging because it essentially requires the model to have 1) abundant commonsense knowledge, 2) the ability of deduction and induction, and 3) relational reasoning with commonsense. We selected webtext 2019 (https://github.com/brightmart/nlp_chinese_corpus) as the data source, filtered causal sentences and got major premise of the syllogism with manual annotations. ExperienceGen 1.0 contains 2000 items which include causal sentence, major premise of the syllogism and their corresponding commonsense. It is worth noting that the ExperienceGen 1.0 is the product of deduction and induction based on commonsense knowledge from people, which is different from existed commonsense knowledge base. Experiments have shown that even the current best-performing generative models still performs poorly. We are currently releasing an initial version which is publicly available at https://github.com/NLUSoCo/ExperienceGen to inspire work in the field along with feedback gathered from the research community.
Path Planning Algorithm of Mobile Robot Based on Dichotomy of Thermal Conduction Topology Optimization

To solve the problem of low efficiency and unstable results of the path planning algorithm based on topology optimization, a dichotomy-based heat conduction topology optimization mobile robot path planning algorithm is proposed. First, considering the analogy between the heat transferring path under steady state and the mobile robot navigation path, the path planning problem is equivalent to the heat conduction topology optimization problem, and a path planning method based on the heat conduction topology optimization is constructed. Secondly, according to the principle of minimizing the thermal compliance across the analysis domain in each growth step, the optimal growth direction of the cooling channel is determined. Third, define the 8 search directions of the mobile robot's motion, construct the cooling channel material library in the corresponding direction, and the dichotomy can be used to solve the problem quickly. Finally, through simulation and comparison experiments in two environments, the results show that the path planning algorithm based on heat conduction topology optimization has strong real-time performance, stable results, and has advantages in complex environments.