Reviewerly/exHarmony huggingface dataset:
Telepathology overview in brief

Telecommunications technology refers to distance communications such as radio, internet, television, cable, microwave, computer networking, and data transmission. It's necessary for the conduct of most elections in the 21st century. Telecommunication is the transmission of information over cable, radio, optical or other electromagnetic networks by different types of technologies.
Hidden Costs and Opportunities of Kotlin versus Java on Android Runtime

In mid-2019, Kotlin became Google’s preferred language for Android application development instead of Java. Therefore, it is in entities’ interest to migrate from Java to Kotlin for their Android p ...
Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020

This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.
Preparing to Build

Building custom SSIS tasks is neither trivial nor easy. You are building components that will be used to construct software. Perhaps you have done this before. Many have not.
A SECURE METHOD FOR MANAGING DATA IN CLOUD STORAGE USING DEDUPLICATION AND ENHANCED FUZZY BASED INTRUSION DETECTION FRAMEWORK

Cloud services increase data availability so as to offer flawless service to the client. Because of increasing data availability, more redundancies and more memory space are required to store such data. Cloud computing requires essential storage and efficient protection for all types of data. With the amount of data produced seeing an exponential increase with time, storing the replicated data contents is inevitable. Hence, using storage optimization approaches becomes an important pre-requisite for enormous storage domains like cloud storage. Data deduplication is the technique which compresses the data by eliminating the replicated copies of similar data and it is widely utilized in cloud storage to conserve bandwidth and minimize the storage space. Despite the data deduplication eliminates data redundancy and data replication, it likewise presents significant data privacy and security problems for the end-user. Considering this, in this work, a novel security-based deduplication model is proposed to reduce a hash value of a given file size and provide additional security for cloud storage. In proposed method the hash value of a given file is reduced employing Distributed Storage Hash Algorithm (DSHA) and to provide security the file is encrypted by using an Improved Blowfish Encryption Algorithm (IBEA). This framework also proposes the enhanced fuzzy based intrusion detection system (EFIDS) by defining rules for the major attacks, thereby alert the system automatically. Finally the combination of data exclusion and security encryption technique allows cloud users to effectively manage their cloud storage by avoiding repeated data encroachment. It also saves bandwidth and alerts the system from attackers. The results of experiments reveal that the discussed algorithm yields improved throughput and bytes saved per second in comparison with other chunking algorithms.
Design Principles of a Chatbot

The overall design of your bot is the most important factor for your chatbot’s success. Your aim as a developer is to build chatbots, which will be used by users. Therefore, you will need to think about how you could add benefits for users when using your chatbot instead of any other web application or other solutions. To make your users actually use your chatbot, the bot needs to solve the user’s needs in the quickest and most importantly easiest way possible, compared to any other solution. An example would be the use case for booking a table in a restaurant. The “classic” approach would be as follows:
Corruption and Access to Socio-Economic Services in Africa

Corruption is one of the world’s most widespread political problems. It can be found on international, national and sub-national level. Access to education and other socio-economic services is of utmost importance for all humans. It is still not exclusively based on merit, but often rather unfairly distributed and allocated depending on corrupt local bureaucrats. We utilize a micro level measure of corruption based on the personal experiences of individuals, which realistically represents the linkage between individuals and public office holders. For the empirical analysis, we utilized the Afrobarometer survey of 36 African countries that contains information of more than 50,000 citizens. Corruption is found being negatively correlated with the access to water, education, health and paved roads, while positively associated with access to sewage system and having no significant association with access to electricity grid. The findings reveal that in order to expand the access to basic socioeconomic services, governments need to control corruption in public offices on a daily basis.
Practical generation of common random strings for secure multiparty computations over the Internet

It is known that most of the interesting multiparty cryptographic tasks cannot be implemented securely without trusted setup in a general concurrent network environment like the Internet. We need an appropriate trusted third party to solve this problem. An important trusted setup is a public random string shared by the parties. We present a practical n-bit coin toss protocol for provably secure implementation of such setup. Our idea is inviting external peers into the execution of the protocol to establish an honest majority among the parties. We guarantee security in the presence of an unconditional, static, malicious adversary. Additionally, we present an original practical idea of using live public radio broadcast channels for the generation of common physical random source.
IMPLEMENTATION OF “KAMPUNG LITERASI” PROGRAM A CASE STUDY REGARDING TO FOSTER A READING CULTURE IN CINAMBO BANDUNG, WEST JAVA

“Kampung Literasi” is a program from Indonesian Ministry of education and culture to improveIndonesian reading culture. Sukamulya Cinambo Village is one of the areas designated to implement theprogram. The aim of this study is to investigate further about the background of Cinambo Sukamulya beingchosen as “Kampung Literasi”, to understand about planning process of “Kampung Literasi” Program inSukamulya Cinambo, and to identify implementation of “Kampung Literasi” Program in Sukamulya Cinambo.Qualitative method with study case approach is applied in this study. Data collection techniques are in-depthinterview, observation and literature study. The result revealed that Sukamulya Cinambo is pointed as“Kampung Literasi” because TBM and PKBM there have already active, so it is expected to be a pilot projectarea. Planning program is using bottom-up approach to determine activity based on community needs. Severalprograms produced are seminar, training, GEMMA, GEMES, GEMATIK and book publishing.Implementations mechanism for these programs is “mangutub polled” which involve many parties with specifictarget group, but each party is not dependent on delivery mechanism program that have been made
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system.

Question-answering systems and voice assistants are becoming major part of client service departments of many organizations, helping them to reduce the labor costs of staff. In many such systems, there is always natural language understanding module that solves intent classification task. This task is complicated because of its case-dependency - every subject area has its own semantic kernel. The state of art approaches for intent classification are different machine learning and deep learning methods that use text vector representations as input. The basic vector representation models such as Bag of words and TF-IDF generate sparse matrixes, which are becoming very big as the amount of input data grows. Modern methods such as word2vec and FastText use neural networks to evaluate word embeddings with fixed dimension size. As we are developing a question-answering system for students and enrollees of the Perm National Research Polytechnic University, we have faced the problem of user's intent detection. The subject area of our system is very specific, that is why there is a lack of training data. This aspect makes intent classification task more challenging for using state of the art deep learning methods. In this paper, we propose an approach of the questions embeddings representation based on calculation of Shannon entropy.The goal of the approach is to produce low dimensional question vectors as neural approaches do and to outperform related methods, described above in condition of small dataset. We evaluate and compare our model with existing ones using logistic regression and dataset that contains questions asked by students and enrollees. The data is labeled into six classes. Experimental comparison of proposed approach and other models revealed that proposed model performed better in the given task.
Identity and Education in Informational Society

Starting from a theoretical recognition of the classical positions on identity, we propose an integrated concept of human identity. It assumes the relationship between man and technology as an unavoidable feature. This hypothesis – based on the paradigm of the informational society – focuses on the consequences of the constant interaction between human beings and information and communication technologies (ICT), showing several effects on habits and on formation of human identity determined by digital technologies. According to an axiological point of view, we intend to identify potentialities and risks of this interaction, recovering a humanistic orientation at the basis of education.
Quantum Key Secure Communication Protocol via Enhanced Superdense Coding

Abstract In the last decades, Quantum Cryptography has become one of the most important branches of Quantum Communications with a particular projection over the future Quantum Internet. It is precisely in Quantum Cryptography where two techniques stand out above all others: Quantum key Distribution (QKD), and Quantum Secure Direct Communication (QSDC). The first one has four loopholes relating to the exposure of the key at all points in the communication system, while the second has clear practical implementation problems in all its variants currently in use. Here, we present an alternative to QKD and QSDC techniques called quantum key secure communication (QKSC) protocol with a successful implementation on two free access quantum platforms.
3D Skeletal Movement enhanced Emotion Recognition Network

Automatic emotion recognition has become an important trend in the field of human-computer natural interaction and artificial intelligence. Although gesture is one of the most important components of nonverbal communication, which has a considerable impact on emotion recognition, motion modalities are rarely considered in the study of affective computing. An important reason is the lack of large open emotion databases containing skeletal movement data. In this paper, we extract 3D skeleton information from video, and apply the method to IEMOCAP database to add a new modality. We propose an attention based convolutional neural network which takes the extracted data as input to predict the speaker’s emotion state. We also combine our model with models using other modalities to provide complementary information in the emotion classification task. The combined model utilizes audio signals, text information and skeletal data simultaneously. The performance of the model significantly outperforms the bimodal model, proving the effectiveness of the method.
The concept of vitagen education and the holographic approach to education

The article discusses the issue of cooperation "teacher-student" The author says that this is a reality that arises, however, not by itself, not spontaneously, but according to the strict laws of a pedagogically organized process, taking into account the age and capabilities of students. He comes to the conclusion that cooperation between teacher and student is possible and necessary on a moral and psychological level. However, even at the functional level, regardless of the age of the students, it can "grow flesh" in the framework of the usual pedagogical process Key words: Vitagenic education, life experience, assessment, differentiation, design, holography, hologram, holographic approach, vitagenic projection, constructive projection, vitagenic information
Subjective Quality Driven Image Encoding Method Using Image Completion

This paper presents a still image coding method using deep learning-based image completion. Deep learning-based image completion can restore skipped areas of images in high quality. When we introduce image completion to image coding, it is possible to reduce the coded-bit amount compared with normative codec-based methods by replacing complex areas, such as textures, with simple signal values at the encoder and completing them at the decoder. However, there is no method for automatically detecting the skipped areas at the encoder because we cannot evaluate the quality of completion by objectively comparing the signal difference between the original and completed images. To resolve this issue, we propose an image quality estimation model without referencing original images. Our key idea is to obtain the model by adversarial training with a completion network assuming that original images have higher quality than the completed ones. We also propose a detection algorithm for skipped areas. Our algorithm detects skipped areas by giving priority to complex areas where a large coded-bit amount is required for the normative codec-based method to increase coding efficiency. The proposed method reduced the coded-bit amount by 25% compared with an HEVC-based method while maintaining the subjective quality for particular images.
Introducing the Field Focus Podcast from MSI Data

New podcast series dedicated to improving the productivity of your field workforce through expert insights, trends, and best practices From the ...
An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists

This study investigated the possibility of building an end-to-end deep learning-based model for the prediction of a future breast cancer based on prior negative mammograms. We explored whether the probability of abnormal class membership given by the model was correlated with the gist of the abnormal as perceived by radiologists in negative prior mammograms. To build the model, an end-to-end network, previously developed for breast cancer detection, was fine-tuned for breast cancer prediction by using a dataset containing 650 prior mammograms from women, who were diagnosed with breast cancer in a subsequent screening and 1000 cancer-free women. On a set of 630 test images, the model achieved an AUC of 0.73. For extracting gist responses, 17 experienced radiologists were recruited, viewed mammograms for 500 milliseconds and gave a score showing whether they would categorize the case as normal or abnormal on the scale of 0- 100. The image set contained 40 normal, 40 current cancer images along with 72 prior mammograms from women who would eventually develop a breast cancer. We averaged the scores from 17 readers and produced a single score per image. The network achieved an AUC of 0.75 for differentiating prior images from normal images. For 72 prior mammograms, the output of the network was significantly correlated with the strength of the gist of the abnormal as perceived by experienced radiologists (Spearman’s correlation=0.84, p&lt;0.01). This finding suggested that the network successfully learned the representation of the gist of the abnormal in prior mammograms as perceived by experienced radiologists.
Research on computer forensics based on multiple correlation analysis technology of multi-source logs

Log files are important bases for computer forensics and scenarios reconstruction, on the basis of improving the traditional association algorithm, this paper analyzes the existing and related methods of log forensics, and puts forward a forensics model of multi-source log multi association analysis by using reverse causal association algorithm. The forensics model makes it more suitable for discovering computer crime in time and realizing the reconstruction of computer crime scenarios.
IDEA: Index of Difficulty for Eye Tracking Applications - An Analysis Model for Target Selection Tasks

Fitts' law is a prediction model to measure the difficulty level of target selection for pointing devices. However, emerging devices and interaction techniques require more flexible parameters to adopt the original Fitts' law to new circumstances and case scenarios. We propose Index of Difficulty for Eye tracking Applications (IDEA) which integrates Fitts' law with users' feedback from the NASA TLX to measure the difficulty of target selection. The COVID-19 pandemic has shown the necessity of contact-free interactions on public and shared devices, thus in this work, we aim to propose a model for evaluating contact-free interaction techniques, which can accurately measure the difficulty of eye tracking applications and can be adapted to children, users with disabilities, and elderly without requiring the acquisition of physiological sensory data. We tested the IDEA model using data from a three-part user study with 33 participants that compared two eye tracking selection techniques, dwell-time, and a multi-modal eye tracking technique using voice commands.
Principle of Interval Knowledge in Science Methodology

The article discusses the Principle of Interval Knowledge designed to fill the gap in the methodology of science, associated with the problem of the knowledge boundaries. Over the course of half a century, several versions of the Intervality Principle have been created, different interpretations of its notions have arisen, in­cluding the key concept of “abstraction interval”, but they encountered a number of contradictions. Therefore, the task arises of creating a unified concept and in­troducing precise definitions. The author shows that overcoming contradictions is possible on the way of studying the meaning of introduced notions. This meaning should be correlated with the goal of the intervality concept: to reflect the birth of partial knowledge under the conditions of total restrictions of all components in­volved in the creation of knowledge. The article provides definitions of interval notions, formulation of the Principle and interpretation of the key concept “ab­straction interval”. The author believes that the expansion of the notion of “inter­val” allows accepting it as a generic concept for all types of restrictions. The crite­rion for the meaning of a key concept is the requirement of informational completeness of knowledge representation. Then the concept of “abstraction in­terval” can be understood as a complete informational characteristic of knowledge about a fragment of reality and the conditions for its creation. Two comple­mentary concepts are introduced: “conditions interval” and “content interval”. The author shows that the essence of the Principle consists of asserting condition­ality of the content interval by the conditions interval. The introduced refinements remove the contradictions between different directions and lead to the creation of a unified interval concept.
Mutual Information Measure for Image Segmentation Using Few Labels

Recently several models have been developed to reduce the annotation effort which is required to perform semantic segmentation. Instead of learning from pixel-level annotations, these models learn from cheaper annotations, e.g. image-level labels, scribbles or bounding boxes. However, most of these models cannot easily be adapted to new annotations e.g. new classes since it requires retraining the model. In this paper, we propose a similarity measure between pixels based on a mutual information objective to determine whether these pixels belong to the same class. The mutual information objective is learned in a fully unsupervised manner while the annotations (e.g. points or scribbles) are only used during test time. For a given image, the unlabeled pixels are classified by computing their nearest-neighbors in terms of mutual information from the set of labeled pixels. Experimental results are reported on the Potsdam dataset and Sentinel-2 data is used to provide a real world use case where a large amount of unlabeled satellite images is available but only a few pixels can be labeled. On the Potsdam dataset, our model achieves 70.22% mIoU and 87.17% accuracy outperforming the state-of-the-art weakly-supervised methods.
Linear recurrences over a finite field with exactly two periods

In this paper, we study the periodicity structure of finite field linear recurring sequences whose period is not necessarily maximal and determine necessary and sufficient conditions for the characteristic polynomial f to have exactly two periods in the sense that the period of any sequence generated by f is either one or a unique integer greater than one.
Blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice

The efficiency of homomorphic encryption has always affected its practicality. With the dawn of internet of things, the demand for computation and encryption on lightweight devices is increasing. Complex cryptographic computing is an important burden for lightweight devices, but outsourcing provides great convenience for them. In this paper, based on blockchain, we propose a secure outsourcing scheme for Fully Homomorphic Encryption using Hidden Ideal Lattice (FHEHIL), in which the time-consuming operations (including modular exponentiation and polynomial multiplication) are outsourced. For polynomial multiplication, we propose a secure outsourcing algorithm that reduces the local computation cost to \(O\left(n\right)\). Previous work based on Fast Fourier Transform can only achieve \(O\left(nlog(n)\right)\) for the local cost. Through security analysis, our scheme achieves the goals of privacy protection against passive attackers and cheating detection against active attackers. Experiments also demonstrate our scheme is more efficient in comparison with the non-outsourcing FHEHIL.
LibGuides: Use Library Search: Find books

Learn how to use Library Search to find the different types of resources you require for your research
INCREASING STUDENTS’ ACHIEVEMENT IN WRITING RECOUNT TEXT USING SEQUENCE OF PICTURE AT THE TENTH GRADE OF SMK NEGERI 1 SIBORONGBORONG

This study deals with the increasing  students’ achievement in writing recount text using sequence of pictures. This study was conducted by using action research at the tenth grade of SMK NEGERI 1 SIBORONGBORONG. The sample of this study just one class namely: X TKJ 2. The number of this class was 35 students. This study was coducted in two cyles and every cycle consisted three meetings. To collect the data, the  were used, writing test (quantitative data)  diary notes, interview sheet and observation sheet (qualitative data).  In analyzing data, the mean of  the students’ score for the first competency test was 60,9, for the second competency test was 69.3, and for the third competency test was 80,0 and total percentage of the students who got increasing from the first competecy to the third competency test was 85.72%.