Reviewerly/exHarmony huggingface dataset:
Case Study of Practical Education on Intellectual Property in the Technical College Based on Its Activity Experience in the Corporation

In this paper, we examined practical education of intellectual property using a simple patent search based on the authors′ corporate experience in the department of chemical and biological engineering of the technical college. We took up the subjects of “the basic PBL exercise” and “the graduation research” as a case study. In the basic PBL exercise classes in the 4th year of this department, students could utilize the knowledge gained from intellectual property education at the partner university; they adopted an “intellectual property perspective” when giving presentations of their ideas. Furthermore, in the graduation research of the 4th and 5th years of the department, they obtained research guidelines by searching and understanding patent documents written in Japanese and giving presentations. Some of the students finally achieved good results for presentations at academic conferences. In the future, it is desirable to increase the number of faculty members who can provide intellectual property education through inter-departmental collaboration in the college and incorporate the intellectual property into the subjects on generic skills for lower grade students.
Background Subtraction in Surveillance Systems Using Local Spectral Histograms and Linear Regression

Background subtraction is a fundamental and crucial task for computer vision-based automatic video analysis due to various challenging situations that occur in real-world scenarios.This paper presents a novel background subtraction method by estimating the background model using linear regression and local spectral histogram which captures combined spectral and texture features.Different linear filters are applied on the image window centered at each pixel location and the features are captured via these filter responses.Each feature has been approximated by a linear combination of two representative features, each of which corresponds to either a background or a foreground pixel.These representative features have been identified using K-means clustering, which is used in background modeling using a least square method.Constraints have been introduced in the least square solution to make it robust in noisy environments.Experiments on existing datasets show that our proposed method outperforms the methods in the literature, with an overall accuracy of 92%.
Trace-to-Trace Translation for SCA

Neural Networks (NN) have been built to solve universal function approximation problems. Some architectures as Convolutional Neural Networks (CNN) are dedicated to classification in the context of image distortion. They have naturally been considered in the community to perform side-channel attacks showing good results on traces exposing time misalignment. However, even where these timing distortions are not present, NN have produced better results than legacy attacks. Recently in TCHES 2020, auto-encoders have been used as preprocessing for noise reduction. The main idea is to train an auto-encoder using as inputs noisy traces and less noisy traces so that the auto-encoder is able to remove part of the noise in the attack dataset. We propose to extend this idea of using NN for pre-processing by not only considering the noise-reduction but to translate data between two side-channel domains. In a nutshell, clean (or less noisy) traces may not be available to an attacker, but similar traces that are easier to attack may be obtainable. Availability of such traces can be leveraged to learn how to translate difficult traces to easy ones to increase attackability.
A Multiple Positives Enhanced NCE Loss for Image-Text Retrieval

Image-Text Retrieval (ITR) enables users to retrieve relevant contents from different modalities and has attracted considerable attention. Existing approaches typically utilize contrastive loss functions to conduct contrastive learning in the common embedding space, where they aim at pulling semantically related pairs closer while pushing away unrelated pairs. However, we argue that this behaviour is too strict: these approaches neglect to address the inherent misalignments from potential semantically related samples. For example, it commonly exists more than one positive samples in the current batch for a given query and previous methods enforce them apart even if they are semantically related, which leads to a sub-optimal and contradictory optimization direction and then decreases the retrieval performance. In this paper, a Multiple Positives Enhanced Noise Contrastive Estimation learning objective is proposed to alleviate the diversion noise by leveraging and optimizing multiple positive pairs overall for each sample in a mini-batch. We demonstrate the effectiveness of our approach on MS-COCO and Flickr30K datasets for image-to-text and text-to-image retrieval.
Sentiment Review Analysis and Text Summarization Using Supervised Machine Learning Algorithms

Online opinions have turn out to be a critical supply of data for customers earlier than making a knowledgeable buy rule. Rapid product opinions have a tendency to keep an excessive effect on the following product sales. We take the drive to examine the conduct traits of the early analysis via they have published opinions on universally huge e-trade tenets, namely Amazon and Yelp. In our project, we had selected to paintings on reading opinions of the diverse online product that has been reviewed in the shape of texts and the feature additionally been given a score on a scale from 1 to 5. We had received these statistics have set which had 2 statistics to be set: educate and check (break up as 75–25%). We had broken up the range score for the product into instructions in general: positive, negative, thereby producing the accuracy of the graph among the three algorithms of machine learning based on the reviews of the product given by the customer for the online products.
Recursion

In nature and mathematics, you can find the topic self-similarity or recurring structures, such as snowflakes, fractals, and Julia sets, which are interesting graphical formations. In this context, one speaks of recursion, meaning that things repeat or resemble each other. Related to methods, this means that they call themselves. Important therefore is a termination condition in the form of special input values, which leads to the end of the self calls.
YouTube Transcript Summarizer using Natural Language Processing

We spend a noticeable amount of our weekly time watching YouTube videos, be it for entertainment, education, or exploring our interests. In most cases, the overall intent is to obtain some form of information from the video. We were seeking a solution to increase the efficiency of this "information extraction" process as YouTube's speed adjustment option is the only relevant tool. The summarizer is a Chrome extension that works with YouTube to extract the key points of a video and make them accessible to the user. The summary is customizable per user's request, allowing varying extents of summarization. Key points from the summarization process, together with corresponding time-stamps, are then presented to the user through a small UI next to the video feed. This allows the user to navigate to more important sections of the video, to get to the key points more efficiently. The main idea behind it is to be able to find a short subset of the most essential information from the entire set and present it in a human-readable format. As online Textual data grows, automatic Summarization of text methods has the potential to become very helpful because more useful information can be read in a short time.
Sum-of-Products with Default Values: Algorithms and Complexity Results

Weighted Counting for Constraint Satisfaction with Default Values (#CSPD) is a powerful special case of the sum-of-products problem that admits succinct encodings of #CSP, #SAT, and inference in probabilistic graphical models. We investigate #CSPD under the fundamental parameter of incidence treewidth (i.e., the treewidth of the incidence graph of the constraint hypergraph). We show that if the incidence treewidth is bounded, #CSPD can be solved in polynomial time. More specifically, we show that the problem is fixed-parameter tractable for the combined parameter incidence treewidth, domain size, and support size (the maximum number of non-default tuples in a constraint). This generalizes known results on the fixed-parameter tractability of #CSPD under the combined parameter primal treewidth and domain size. We further prove that the problem is not fixed-parameter tractable if any of the three components is dropped from the parameterization.
Symbolic Predictive Cache Analysis for Out-of-Order Execution

Abstract We propose a trace-based symbolic method for analyzing cache side channels of a program under a CPU-level optimization called out-of-order execution (OOE). The method is predictive in that it takes the in-order execution trace as input and then analyzes all possible out-of-order executions of the same set of instructions to check if any of them leaks sensitive information of the program. The method has two important properties. The first one is accurately analyzing cache behaviors of the program execution under OOE, which is largely overlooked by existing methods for side-channel verification. The second one is efficiently analyzing the cache behaviors using an SMT solver based symbolic technique, to avoid explicitly enumerating a large number of out-of-order executions. Our experimental evaluation on C programs that implement cryptographic algorithms shows that the symbolic method is effective in detecting OOE-related leaks and, at the same time, is significantly more scalable than explicit enumeration.
Dilated Convolutions Based 3D U-Net for Multi-modal Brain Image Segmentation

Several deep learning based medical image segmentation methods use U-Net architecture and its variants as a baseline model. This is because U-Net has been successfully applied to many other tasks. It was noticed that the U-Net-based models are unable to extract features for segmenting small masks or fine edges. To overcome this issue, we propose a new 3D U-Net-based model, baptized Y- Net. In this model, we make use of dilated convolution which has shown its effectiveness in grasping different features at different scales. This allows us to capture more information from small anatomical parts. Our model is assessed on MRbrains13 dataset for brain tissue segmentation task. Compared to the traditional UNet 3D, the obtained results show that the proposed model performs better in segmenting cerebrospinal fluid, white matter and gray matter tissues.
Dilated Continuous Random Field for Semantic Segmentation

Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation. In this paper, we propose to relax the hard constraint of mean field approximation - minimizing the energy term of each node from probabilistic graphical model, by a global optimization with the proposed dilated sparse convolution module (DSConv). In addition, adaptive global average-pooling and adaptive global max-pooling are implemented as replacements of fully connected layers. In order to integrate DSConv, we design an end-to-end, time-efficient DilatedCRF pipeline. The unary energy term is derived either from pre-softmax and post-softmax features, or the predicted affordance map using a conventional classifier, making it easier to implement DilatedCRF for varieties of classifiers. We also present superior experimental results of proposed approach on the suction dataset comparing to other CRF-based approaches.
THE INVESTIGATION ON APPLICATION OF GENRE BASED APPROACH (GBA) TO IMPROVE STUDENTS' WRITING ABILITY ON PROCEDURAL TEXT IN EFL CLASSROOM

This study investigated the application of Genre-Based Approach (GBA) to improve students` writing ability on procedural text in EFL classroom and to investigate the students` perception of using Genre-Based Approach in improving their writing ability in MA Safinatunnaja NW Repok Oak. The researchers utilized Classroom Action Research (CAR) that was applied in two cycles. Cycle 1 was done in three meetings and cycle 2 was in two meetings. Each cycle consisted of some phases such as planning, action, observation, and reflection. The instruments used by the researchers were observation sheet, questionnaire and writing test. This research took 24 students as the sample. The data obtained were analyzed by using descriptive statistic and inferential statistic through SPSS software version 24. The research resulted that Genre Based Approach gave greater contribution to the students’ writing procedural text. There was an improvement of students` perception in learning process. It was proven by the result of inferential statistical analysis either in cycle 1 or cycle 2. There was a significant improvement between the result of cycle 1 and cycle 2, where in cycle 2 showed significantly greater improvement (M = 82,08) than the cycle 1 (M = 73,33). The sig. 2 tailed between both cycle 1 and cycle 2 was 0,000. This led to the conclusion that the application of Genre Based Approach improved the students’ writing skill. Genre Based Approach was positively improving students` writing ability.
Are We a Bigger Problem Than We Realize?

Abstract The impact of social media — in particular Twitter — on the field of international criminal justice cannot be overstated. Thanks to an active community of online commentators made up primarily of academics, researchers, staff of non-governmental organizations and journalists, Twitter is a vibrant place to monitor the pulse of international criminal law generally and the International Criminal Court specifically. Twitter, thus, uniquely elevates a comparatively small commentator class and amplifies their narratives about the field of international criminal justice. Yet with an apparent lack of voices from across the diverse community of practitioners, or the input of diplomats and technocrats who make the field possible, commentators may exert a disproportionate influence over public perceptions of international criminal justice. Cognisant of the stark interplay between politics and law in international criminal justice — often including powerful forces with nefarious agendas — and an abundance of individuals motivated by a desire to be seen and heard, this community may at times exhibit problematic behaviours (unwittingly or otherwise). This article dissects these issues of online communication, and argues that greater care with commentary may be the best salve to these cultural flaws in the formation of public perception.
Analysis of the 2017 Valparaiso Earthquake Sequence

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Analysis of the 2017 Valparaiso Earthquake SequenceAuthorsLucMoutoteiDOlivierLenglinéiDZacharieDuputeliDSee all authors Luc MoutoteiDCorresponding Author• Submitting AuthorInstitut Terre et Environnement de Strasbourg, UMR7063, Université de Strasbourg/EOST, CNRSiDhttps://orcid.org/0000-0002-1227-1524view email addressThe email was not providedcopy email addressOlivier LenglinéiDInstitut Terre et Environnement de Strasbourg, UMR7063, Université de Strasbourg/EOST, CNRSiDhttps://orcid.org/0000-0003-0678-2587view email addressThe email was not providedcopy email addressZacharie DuputeliDObservatoire Volcanologique du Piton de la Fournaise, Institut de Physique du Globe de Paris, Université de Paris, CNRSiDhttps://orcid.org/0000-0002-8809-451Xview email addressThe email was not providedcopy email address
Advancing communication strategies to support future HIV vaccine use (NIMH/NIDA)

Federal Grants & ContractsVolume 46, Issue 5 p. 7-7 Grants alerts Advancing communication strategies to support future HIV vaccine use (NIMH/NIDA) First published: 29 January 2022 https://doi.org/10.1002/fgc.32204Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume46, Issue53 February 2022Pages 7-7 RelatedInformation
Pendidikan Orang Tua terhadap Kemampuan Literasi Membaca pada Anak Usia Dini di Masa Pandemi

&lt;p&gt;Children’s learning while at home requires guidance from parents. One of them is in the use of gadgets and parental knowledge as the main factor in developing children’s literacy skills. The ability of children’s literacy is the ability to read. The purpose of this study is to determine the effect of parental education on children’s reading literacy skills during pandemics. The method used in this study is quantitative method. The data were collected by using questionnaire, observation, and interviews. The results of the study are that parental education has an influence on early childhood reading literacy skills in pandemic times. Parental education plays a role in helping children in learning, especially in improving children’s reading skills. Learning can be applied through storytelling activities.&lt;/p&gt;
Vision-Based Machine Learning in Robot Soccer

Robots need to perceive their environment in order to properly interact with it. In the RoboCup Soccer Middle Size League (MSL) this happens primarily through cameras mounted on the robots. Machine Learning can be used to extract relevant features from camera imagery. The real-time analysis of camera data is a challenge for both traditional and Machine Learning algorithms, since all computations in the MSL have to be performed on the robot itself. This contribution shows that it is possible to process camera imagery in real-time using Machine Learning. It does this by presenting the current state of Machine Learning in MSL and providing two examples that won the Scientific and Technical Challenges at RoboCup 2021. Both examples focus on semantic detection of objects and humans in imagery. The Scientific Challenge winner presents how YOLOv5 can be used for object detection in the MSL. The Technical Challenge winner demonstrates how to improve interaction between robots and humans in soccer using OpenPose. This contributes towards the goal of RoboCup to arrive at robots that can beat the human soccer world champion by 2050.
CRYPTOGRAPHY IN E-COMMERCE APPLICATION SECURITY

Internet business/Electronic Business is viewed as a decent option with lower exchange fee and more business way to deal with all clients across the globe. We speak to numerous unbalanced methodologies which are utilized in Web based business to closed electronic exchange and different cryptographic calculations that are primary fascination in setting up of Internet business. Electronic business is needy upon a collection of perils, for instance, unapproved access, misappropriation, change, and devastation of both data and structures. In this seminar report, we put forward a model of E-transaction dependent on Pretty Good Privacy (PGP). It basically tells us how the installments are secured and client request of data will be proficiently dealt with by Pretty Good Privacy (PGP) dependent on computerized signature.
ICT in the Classroom: Speaking and Writing

This chapter introduces specific tools and concepts that can be used in the classroom for developing speaking and writing. The chapter focuses on common ICT tools that can be effectively used for classroom learning. These tools include word processing, multimedia presentation, spreadsheets, email and Blogs. The chapter provides an understanding of these tools and how they can be used within pedagogic frameworks.
Motion Planning for Hexapod Robots in Dynamic Rough Terrain Environments

The hexapod robot is widely used in rough terrain environments due to its flexible and stable locomotion. However, it is hard for the hexapod robot to avoid the dynamic and static obstacles in real time during the motion planning process. To address this problem, in this article, we propose a novel integrated motion planner. The global planner employs the Bidirectional-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> algorithm to find a feasible solution at first, then the TEB algorithm is used in the local planner to optimize the current solution in real time. We conduct a series of simulation experiments in dynamic rough terrain environments, and the results demonstrate that our proposed method can achieve better performance on the motion planning process for the hexapod robot.
An Artificially Intelligent Marine Life Surveillance and Protection System (MLSPS)

Protecting fisheries and the marine environment are a high priority area for UAE Government and regulatory monitoring is very important to detect and prevent illegal activities by the fishermen. Sustainability and developing solutions with technology are an important aspect of this domain. The technology-based solutions will raise the efficiency level and will try to meet the strategic objectives of time optimization and will be helpful to protect and preserve the fisheries and marine environment. In this work, a new Marine Life Surveillance and Protection system is proposed and developed that performs regular monitoring in the fishing area to ensure law compliance. It uses modern IoT-based drone technology and artificial intelligence-based IBM Watson cloud service. The proposed MPSPS has two parts, the cloud-based system and the IoT devices. The system is a web-based implementation using Node-RED programming tool and Watson Visual Recognition nodes in IBM Cloud. The second is the programmable Tello Drone that used the SDK and is connected with the IBM Cloud. Drone flies over the sea and captures the real-time images and videos and sends them to the IBM cloud, where data classification and analysis is performed using Artificial Intelligence techniques. The results show that the new system has improved the performance for time and detection accuracy.