Reviewerly/exHarmony huggingface dataset:
Multi-Faceted Aspects of Reconstruction in Korean Scrambling

This paper investigates reconstruction in Korean long-distance scrambling.Saito (1989) argues that in long-distance scrambling, a wh-phrase is undone or radically reconstructed to its underlying position.In this paper, adapting Keine and Poole's (2018) analysis for the reconstruction of long-distance scrambled universal noun phrases (NPs) and indefinite NPs in Hindi-Urdu, we use Binding Condition (C) as a probe to test the validity of Saito's (ibid.)thesis regarding the reconstruction of such NPs as well as wh-phrases in Korean.We show that both universal quantificational phrases and wh-phrases in Korean have their quantificational and wh-licensing element/features reconstructed, with the rest of the parts staying in their surface positions and thus being outside the scope of Condition (C).On the contrary, all the indefinite NPs in the language at issue can optionally undergo radical/total reconstruction, thus interacting with Condition (C).
Using Rényi-divergence and Arimoto-Rényi Information to Quantify Membership Information Leakage

Membership inference attacks, i.e., adversarial attacks inferring whether a data record is used for training a machine learning model, has been recently shown to pose a legitimate privacy risk in machine learning literature. In this paper, we propose two measures of information leakage for investigating membership inference attacks backed by results on binary hypothesis testing in information theory literature. The first measure of information leakage is defined using Rényi α-divergence of the distribution of output of a machine learning model for data records that are in and out of the training dataset. The second measure of information leakage is based on Arimoto-Rényi α-information between the membership random variable (whether the data record is in or out of the training dataset) and the output of the machine learning model. These measures of leakage are shown to be related to each other. We compare the proposed measures of information leakage with α-leakage from the information-theoretic privacy literature to establish some useful properties. We establish an upper bound for α-divergence information leakage as a function of the privacy budget for differentially-private machine learning models.
Overcoming plastic waste problem in Indonesia: Case study in the art history class

Abstract Nowadays, more than ever, plastic waste becomes a major problem for almost every nation in the world, including Indonesia. Creative solutions are needed to be the answer to overcome this urgent issue. Although there have been many attempts to create better waste management, these ongoing efforts are still far from the expectation. Therefore, every sector needs to collaborate to be part of the solution, including the education sector. The young generation is fertile ground for the birth of innovative ways to reduce plastic waste. This research aims to show that everything, even things as old as historical artifacts, can be a source of inspiration to overcome the plastic waste problem in Indonesia. However, an appropriate skill set is needed to extract this local content and make it suitable for the current digital society. This qualitative research is conducted through a case study using lateral thinking mode to prove that all things can be the source of inspiration to generate ideas. We can conclude that ideas can come from anywhere in the form of anything. Even though historical artifacts come from a long time ago, and even some have been around for hundreds of years, they possess unique local ingenious capacities.
The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing

Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner.
Biorthogonal Boundary Multiwavelets

The discrete wavelet transform is defined for functions on the entire real line. One way to implement the transform on a finite interval is by using special boundary functions. For orthogonal multiwavelets, this has been studied in previous papers. We describe the generalization of some of these results to biorthogonal multiwavelets.
Measuring The “Impact” of People, Films, And TV using the IMDb Graph Database

In this paper, we use Neo4j, a popular graph database, to store a version of the Internet Movie Database (IMDB) dataset, which is a real-world dataset. The graph database provides persistent availability for users to retrieve data using Neo4j Graph Query Language called cypher, for many applications. Users can use Neo4j clients such as Python and R together with cypher and server plugins such as APOC and graph algorithm library to explore the dataset. We explain the basic concepts and applications of cypher graph pattern language to measure the impact of people, title, and TV episodes.
Probing the Underlying Implementation Mechanisms of SW26010

SW26010 is a heterogeneous many-core CPU equipped in the Sunway TaihuLight Supercomputer, which ranks fourth in June 2020 Top500 list. Large varieties of applications have been tuned on SW26010, but only a few researches focus on system-level modeling, benchmarking. In this paper, we evaluated the threading performance on SW26010, concentrating on the underlying implementations of systematic threading and data transmission operations inside a thread, which are the two main overheads deteriorating the overall performance of heterogeneous computing. We design several benchmarks to test and verify the underlying implementation mechanisms, and present thorough analysis of the experimental results. We find that threading overhead is related to the active slave core number and is nontrivial when the data volume and flops/byte ratio are relatively low; data transmission requests are serially handled, while data is transferred in parallel with four buses, each of which is shared by two rows of 16 slave cores. In addition, given that not all slave cores are used, the interleave mode performs better than the continuous mode in both request response and data transmission. At last, several programming methods and optimization principles for SW26010 are proposed.
XR-Immersive Labs Improve Student Motivation to Learn Kinesiology

Kinesiology is an inherently spatial discipline, both physically and visually. The use of extended reality-immersive lab activities may enhance students’ motivation to learn by enabling students to interact with visual content and illustrate and demonstrate kinesiology content and concepts. Using an instrumental case study method, this article assesses the use of extended reality immersion across three semesters of an upper division kinesiology course focused on motor control. This is a unique approach because it blends established physical motor control and biomechanical data collection techniques with emerging virtual reality technology to enhance—rather than replace—the lab experience. The effectiveness is measured via an experimental design to contribute to the small, but growing, body of knowledge on the efficacy of immersive learning.
Optimal Defense Policies for Cost-Constrained Cyber Physical System

Cyber-Physical System (CPS) usually consists of vast amounts of heterogeneous nodes, and several types of nodes may not be upgraded or installed with new programs during their life spans. How to choose appropriate nodes to deploy security defense mechanisms to ensure system security is a vital and challenging issue. This paper considers the selection of optimal defense policies under a given cost constraint. First, we use the Attack Defense Tree (ADTree) to model attacks and countermeasures for CPS. Then, each defense node in ADTree is given a defense cost attribute and a defense success probability attribute as security evaluation indicators. After that, a probability tree model is proposed for evaluating the final defense success probability. Finally, based on the Minimum Cut Set (MCS) theory, the nodes that can maximize the defense probability with acceptable cost are selected as the optimal defense nodes. An open-source calculation tool is implemented, and the evaluation is performed through a denial of service attack against CPS. The results show that the proposed method is accurate and efficient, which can guide the formulation of CPS security protection.
Deducibility and independence in Beklemishev's autonomous provability calculus

Beklemishev introduced an ordinal notation system for the Feferman-Schütte ordinal Γ0 based on the autonomous expansion of provability algebras. In this paper we present the logic BC (for Bracket Calculus). The language of BC extends said ordinal notation system to a strictly positive modal language. Thus, unlike other provability logics, BC is based on a self-contained signature that gives rise to an ordinal notation system instead of modalities indexed by some ordinal given a priori. The presented logic is proven to be equivalent to RCΓ0, that is, to the strictly positive fragment of GLPΓ0. We then define a combinatorial statement based on BC and show it to be independent of the theory ATR0 of Arithmetical Transfinite Recursion, a theory of second order arithmetic far more powerful than Peano Arithmetic.
Fading Artificial Intelligence Theory

Because of relentless ascent of Artificial Knowledge, there has been an astounding spotlight on the topic of Will shrewd frameworks be ok for mankind of things to come? Therefore, numerous scientists have begun to coordinate their takes a shot at managing issues that may cause issues on empowering Artificial Intelligence frameworks to carry on wild or on the other hand take positions risky for people. Such research works are as of now included under the writing of Artificial Intelligence Wellbeing and/or Future of Artificial Intelligence. With regards to the clarifications, this exploration paper proposes a hypothesis on accomplishing safe keen frameworks by considering life-time of an Man-made consciousness based framework as indicated by a few operational factors and wipe out – end a canny framework, which is 'mature enough' to work for offering opportunity to new ages of frameworks, which appear to be more secure. The paper makes a brief prologue to the hypothesis and opens entryways broadly for further research on it.
Leveraging Natural Language Processing to Analyze Scientific Content: Proposal of an NLP Pipeline for the Field of Computer Vision

In this paper we elaborate the opportunity of using natural language processing to analyze scientific content both, from a practical as well as a theoretical point of view. Firstly, we conducted a literature review to summarize the status quo of using natural language processing for analyzing scientific content. We could identify different approaches, e.g., with the aim of clustering and tagging publications or to summarize scientific papers. Secondly, we conducted a case study where we used our proposed natural language processing pipeline to analyze scientific content about computer vision available at the database IEEE. Our method helped us to identify emerging trends in the recent years and give an overview of the field of research.
Illumination Invariant Face Recognition By Expected Patch Log Likelihood

Illumination is an important factor that impairs face recognition. Many algorithms have been proposed to solve the illumination problem. Most algorithms focus on one image information and only use local illumination change, to improve the effects of removing facial illumination. In this paper, we apply the Expected Patch Log Likelihood (EPLL) algorithm to extract illumination weight and we combine it with the Neighboring Radiance Ratio algorithm (NRR) to optimize the initial vector of the Gaussian mixture model, which makes full use of the redundant information in images. The experimental results on the extended Yale B and CMU PIE face databases show that the proposed algorithm can effectively eliminate the influence of illumination on face images and has a high robustness.
An Intelligent Fitness-Scaling Chaotic Genetic Ant Colony Algorithm Based on Task-Scheduling in Cloud Computing Environments

In a cloud platform, task scheduling of IoT applications is tedious because of the non-homogenous features of the IoT. An effective scheduling algorithm for IoT applications is essential for minimizing computation time. This chapter introduces an optimization algorithm for the identification of an optimum solution of task scheduling for IoT applications in a different multiprocessor cloud platform. The task-scheduling algorithm based on the fitness-scaling chaotic genetic ant colony (FSCGAC) algorithm is utilized in this chapter. During the simulation, a series of experiments occur and show competitive performance over the other methods in a significant way.
An Improved Firefly Algorithm with Adjustable Step and Double-bottom Map

Firefly algorithm has been widely used in many optimization problems since it was proposed because of its good searching ability. However, in the searching process, the standard firefly algorithm can easily fall into local optimal because a fixed step value is used. To solve this problem, an improved firefly algorithm is proposed in this paper by introducing an adjustable strategy for step size setting to improve the optimization accuracy and a double-bottom map to accelerate the convergence. In order to test the performance of the algorithm, twelve benchmark functions are used. Compared to other algorithms, the experimental results show that the modified firefly algorithm has better convergence capabilities and higher accuracy.
Online Training Refinement Network and Architecture Design for Stereo Matching

Sending local data to cloud servers is vulnerable to user privacy, and its long update latency. Meanwhile, the state-of- the-art stereo matching method is still computation demanding, fine-tuning the whole model on-device is not a practicable solution because of the limited power budget and computation ability on edge devices. In this study, we propose a two-stage online stereo matching refinement system, using an additional light-weight network to learn the domain gap between local data and cloud training data. We define a load-gain ratio to evaluate computer efficiency. This refinement system has a much better load-gain ratio than fine-tune. (0.2 v.s. 35.7 operation overhead/accuracy gain) Nevertheless, we only disburse 0.2% of additional parameters and 0.7% additional computation as set by inference the stereo matching model. Thus, it would be a suitable choice for an online training scenario. With re-scheduling the training pipeline, we use a patch-based layer fusion technique and reduce the off-chip memory bandwidth by 97%.
Research on Motion Blur Image Restoration Algorithm Based on Improved Wiener Filter

The image is often distorted during the shooting process, for example, the train is blurred due to motion and accompanied by some Gaussian noise, salt and pepper noise, etc. In this paper, an improved Wiener filter algorithm is proposed for the blur phenomenon caused by the camera in the movement process. First, the optimal k value is obtained through the quadratic wiener filter, and then the problem of boundary ringing in the image restoration process is proposed. The continuation sigmoid window and ratio method are used to suppress boundary ringing. Finally, simulations and experiments verify the effectiveness of the proposed algorithm.
Type analysis of laboratory seismic events by convolutional neural network

In this work, we successfully identified seismic events (observations of earthquakes) in seismograms using a Convolutional Neural Network (CNN). In accordance with past (analogue) seismogram interpretations, we did not treat digital seismograms as a time series, as per the general method, but, rather, converted them into time snaps of continuous data flow. Multichannel seismograms were represented with a time frequency domain in the form of multilayer images, with each signal channel forming one image layer. Images were then exposed to CNN (composed of three convolutional blocks whose architecture design was justified using Bayesian optimization). To improve reliability, we evaluated the posterior type function (PTP) as a combination of the probabilities of all of the considered classes of signal types (five in our case) which increased robustness of the identification. For data, we used records of acoustic emission (AE) events. The events were generated during laboratory loading experiments originally performed to study material/rock properties. As known, AE events may be studied in the same manner as natural earthquakes and treated in other ways as laboratory earthquake models. AE events are less complex compared to natural earthquakes where many of the physical parameters are known or may be controlled. Based on our results, we concluded that the successful identification of AE events is a necessary step prior to applying the proposed methodology for identifying natural earthquakes in seismograms.
ISDB: An Effective Ciphertext Retrieval Method for Electronic Health Records Based on SDB

The central component of E-Health is electronic health records, however, the number of electronic records that need to be stored is increasing day by day. The electronic records can be stored with the help of DBaaS in the Cloud. Electronic health records contain a lot of private information, so it is unreasonable to store the plaintext records in the cloud. There will be additional computing costs if using traditional encrypted storage to retrieve all the records in the cloud database and retrieve them after decryption. In order to improve the retrieval efficiency of electronic health records as much as possible while ensuring user privacy, ISDB expands on the model of the secure query model SDB, so that the improved model supports the data types of varchar, date and date time in SQL Encryption. In ISDB, the sting-like types of exact matching function is designed, so that the model can be more effectively used for encrypted storage of electronic health records and ciphertext retrieval. By analysis and experiments, the ISDB can meet the security ciphertext retrieval of commonly used data types in electronic health records, and it is a light model and is effective.
Transformer Region Proposal for Object Detection

We present a method for detecting objects in images using Transformer and Region Proposal. Our method discards the traditional convolution neural network, obtain the positional information and category of the detection object by finding the correspondence between the images through the self-attention mechanism. We extract the region proposal by setting anchors, and combine the positional encoding obtained by the image auto-encoder. Then input the features into the transformer encoder and decoder network to obtain the corresponding information of the target object, and obtain the positional information and category of target object. The experimental results on the public dataset proved, our method’s detection speed can reach 26 frames per second, which can meet the needs of real-time detection. In terms of detection accuracy, our method’s average precision can reach the 31.4, and under the large object can reach 55.2, which can reach the detection effect of the current mainstream convolutional neural network detection algorithm.
Detecting Phishing Website Using Machine Learning

A fraud attempt to get sensitive and personal information like password, username, and bank details like credit/debit card details by masking as a reliable organization in electronic communication. The phishing website will appear the same as the legitimate website and directs the user to a page to enter personal details of the user on the fake website. Through machine learning algorithms one can improve the accuracy of the prediction. The proposed method predicts the URL based phishing websites based on features and also gives maximum accuracy. This method uses uniform resource locator (URL) features. We identified features that phishing site URLs contain. The proposed method employs those features for phishing detection. The proposed system predicts the URL based phishing websites with maximum accuracy.
Analysis Of Water Balance Aspect On The Results Of Geo-electric Interpretation With Schlumberger Configuration

The background of the problem is that the need for water for various purposes constantly increases in line with the population growth and changes in water use patterns caused by technological advances, industrial growth, and changes in land use. Moreover, from the existing problems, several problem formulations were obtained as the basis for identifying the presumption of groundwater presence, among others, 1). what is the magnitude of the infiltration rate in the study area in relation to the influence of land use conditions, 2). how the results of geo-electric interpretation using the Schlumberger configuration in estimating aquifer layers based on geo-hydrological aspects, and 3). how the effect of time and cost efficiency. Based on the results of the analysis and calculations, several things can be concluded, including: 1). the volume of the infiltration rate using the Horton method (q) by considering the land cover / land use conditions (C), it is known that the average infiltration capacity on the land in the study location per year is more than 200 cm / year, and has an effective enough infiltration capacity so that it is average. -the annual average can store water up to&gt; 200 cm / year, 2). In that area, an aquifer layer was found at a depth of 30-50 meters at the GL 1 point, and the GL point at a depth of 22-55 meters, with the results of the analysis showing the ability of the land to carry out the infiltration process properly and validated from the results of geo-electric investigations, and 3). the results evaluation analysis is able to streamline the amount of operational costs for groundwater source investigations and the time of implementation
Towards a proof theory for quantifier macros

This paper focuses on globally sound but possibly locally unsound analytic sequent calculi for quantifier macros defined by sequences of quantifiers. It is demonstrated that no locally sound analytic representation based on the usual eigenvariable condition exists. In consequence, representations by globally sound but possibly locally unsound analytic sequent calculi are used. Cut-elimination is shown by translating proofs into LK and retranslating cut-free proofs into the desired format. Finally, criteria are given for sequents to be proved without reference to the extended eigenvariable conditions.