Reviewerly/exHarmony huggingface dataset:
Few Shot Text-Independent speaker verification using 3D-CNN.

Facial recognition system is one of the major successes of Artificial intelligence and has been used a lot over the last years. But, images are not the only biometric present: audio is another possible biometric that can be used as an alternative to the existing recognition systems. However, the text-independent audio data is not always available for tasks like speaker verification and also no work has been done in the past for text-independent speaker verification assuming very little training data. Therefore, In this paper, we have proposed a novel method to verify the identity of the claimed speaker using very few training data. To achieve this we are using a Siamese neural network with center loss and speaker bias loss. Experiments conducted on the VoxCeleb1 dataset show that the proposed model accuracy even on training with very few data is near to the state of the art model on text-independent speaker verification
CausalFlow: Visual Analytics of Causality in Event Sequences

Understanding the relation of events plays an important role in different domains, such as identifying the reasons for users' certain actions from application logs as well as explaining sports players' behaviors according to historical records. Co-occurrence has been widely used to characterize the relation of events. However, insights provided by the co-occurrence relation are vague, which leads to difficulties in addressing domain problems. In this paper, we use causation to model the relation of events and present a visualization approach for conducting the causation analysis of event sequences. We integrate automatic causal discovery methods into the approach and propose a model for detecting event causalities. Considering the interpretability, we design a novel visualization named causal flow to integrate the detected causality into timeline-based event sequence visualizations. With this design, users can understand the occurrence of certain events and identify the causal pathways. We further implement an interactive system to help users comprehensively analyze event sequences. Two case studies are provided to evaluate the usability of the approach.
Privacy-preserving photo sharing based on blockchain

Sharing photos online has become an extremely popular activity, raising a wide concern on privacy issues related to the shared content. ProShare, a photo-sharing solution developed by Multimedia Signal Processing Group of EPFL addresses some of these privacy issues by relying on a trusted third party. Recently, distributed ledger technologies in general and blockchain, in particular, have been used to provide trust and censorship resistance in a decentralized manner. This paper proposes the use of blockchain technology to remove the need for a trusted third-party in the sharing process introduced by ProShare.
Analisis Efektivitas Biaya Amitriptilin dan Gabapentin pada Pasien Stroke dengan Nyeri Neuropati di Instalasi Rawat Inap RSUD Provinsi NTB Tahun 2017

Neuropathic pain which is suffered by stroke patients is a pain that occurred because of the damage of the somato sensory system. The first-line therapy of neuropathic pain can be done by using amitriptyline and gabapentin. Both amitriptyline and gabapentin have significant cost-effectiveness differences. This research aims to investigate the cost effectiveness of both amitriptyline and gabapentin through stroke patients who suffered neuropathic pain. The techniques of data collection applied to a total sampling which are 17 samples were collected. The data were retrospective by the year of 2017, meanwhile, the research was conducted from April to July 2019. Clinical evaluation of neuropathic pain is conducted by applying the Wong Baker Pain Rating Scale. Amitriptyline and gabapentin cost effectiveness analysis showed Average Cost-Effectiveness Ratio (ACER) are Rp 45.443,08 and Rp 52.241,52 and Incremental Cost-Effectiveness Ratio (ICER) Rp 72.636,87. Based on the results, Incremental Net Benefit (INB) showed that gabapentin is the most cost-effective than amitrityline with Rp + 59.078,37 score. &#x0D; &#x0D; Keywords: Stroke, neuropathic pain, amitriptyline, gabapentin, cost-effectiveness analysis
Tomography-based Quantum Key Distribution

It has previously been shown that quantum state tomography can be used to increase the key rate of quantum key distribution (QKD) for the special case of qubits (i.e.,d=2). Here, we consider the case of higher dimensions, i.e., qudits, and show that, for a prime number d&gt;2, quantum state tomography can also improve the key rate of d-dimensional qudit-based QKD schemes, compared to the standard (d+1)-basis protocol. We apply our results to previous QKD experiments based on OAM (orbital angular momentum) encoding and demonstrate the advantage of tomography-based QKD protocols. Moreover, we compare the key rate of tomography-based QKD protocol with reference-frame-independent QKD protocol. We show that, for a rotation channel, the two protocols give the same key rate. However, for any other channels, tomography-based QKD protocol always gives a higher key rate than reference-frame-independent QKD protocol.
Investigating Secure Agile Requirements Engineering Practices in Software Development

The aim of this research study was to assess Agile RE practices in the South African software development industry and investigate secure Agile RE initiatives towards developing secure products. This qualitative research study was contextualized in seventeen South African software development companies. The researchers used structured interviews and document reviews as the primary data collection instruments. Qualitative data was analyzed inductively using content analysis. Emanating from the research were recommendations to guide a regular software developer on good Agile RE practices. The study concluded that although Agile Software Development is practiced in the South African software industry, there needs to be stricter adherences to the Agile Manifesto and Agile Security Manifesto in requirements engineering.
Understanding Object-Oriented Programming

This Exploration takes a break from C++ programming to turn to the topic of object-oriented programming (OOP). You may already be familiar with this topic, but I urge you to continue reading. You may learn something new. To everyone else, this Exploration introduces some of the foundations of OOP in general terms. Later Explorations will show how C++ implements OOP principles.
Obliterarsi

The paper analyses the first three works published by Franca Mancinelli, outlining their thematic continuities as well as their foundational unity. By studying the chronological progress of specific themes, the article sheds a new light on the intertextual connections that can be found both within a single book as well as across the three works. The study of these links has the purpose of showing the singular structure as well as the formational dynamics at the basis of a poetic work by Mancinelli. With particular attention towards the declarations of a poetics by the author as well as the variants within the microtexts, the objective of the paper is to define the intentions behind the production of new poetic and prose versions of the author’s texts. The process of rewriting finds its explanation through a thematic, stylistic and structural perspective, ultimately identifying a distinctive feature of Mancinelli’s style.
LibGuides: Resources for Remote Teaching & Learning: Newspapers

Library and Digital Learning remote teaching and learning resources for students and faculty
Developing Extended Reality Projects in Support of Design, Fabrication and Procedure

The goal of this internship was to improve and create virtual reality simulations and visualizations for use in parallel with the design, fabrication, and analysis of flight ready hardware for areas like the Environmental Control and Life Support Systems (ECLSS) and also Space Systems. Specifically, my work was done in the XRSpace lab at Marshall Space Flight Center (MSFC) with assistance directly and indirectly from workers at KSC, JSC and LaRC. Led by David Reynolds, the XRSpace lab develops products for various entities at NASA. The work done in the XRSpace lab focuses on Extended Reality (XR) solutions for both simulations and visualization capabilities. The goal of the lab is to support the larger systems of NASA and to help find ways that XR technologies can streamline and optimize the design process. Extended Reality is an umbrella term that encompasses Mixed Reality, Augmented Reality, and Virtual Reality. In this capacity, I was able to complete several elements in the design, building, testing, and deployment for a variety of immersive experiences, including a VR procedure simulation, visualization aids, and a 360-image capture tool.
Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering

To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a different graph Fourier transform. Therefore to analyze the graph-frequency domain properties of a graph signal, the graph Fourier modes and graph frequencies must be computed for the graph under study. Although to find these graph frequencies and modes, a computationally expensive, or even prohibitive, eigendecomposition of the graph is required, there exist families of graphs that have properties that could be exploited for an approximate fast graph spectrum computation. In this work, we aim to identify these families and to provide a divide-and-conquer approach for computing an approximate spectral decomposition of the graph. Using the same decomposition, results on reducing the complexity of graph filtering are derived. These results provide an attempt to leverage the underlying topological properties of graphs in order to devise general computational models for graph signal processing.
Morph Analyzer of Verbs in Nepali Language

Morph analyzer is a tool which deals with forming of words in any natural language. It also highlights the relationship of any word to other words in a language. Relationship among words in inflection and derivation is common in Nepali. In this paper, a study on various works on morph analysis from the literature is presented. The main objective is to focus on the variation aspect of verb morphology and the analysis of verb stems from a given sentence/speech.
Research on modeling of electric power transaction process based on block chain technology

Block chain technology is one of the optional technologies for the realization of intelligent power transactions on the residential side, and the specific transaction process is lacking research. Based on the composition of the block chain and its technical characteristics, this paper proposes a typical transaction flow for market participants in the electricity transaction under the environment of the residential side open electricity market based on block chain technology. According to the needs of power trading, the power trading using block chain technology is divided into 7 steps and abstracted separately. Finally, the power transaction based on block chain technology is analyzed, and the transaction process is further explained in combination with the case, which provides a reference for the introduction of new technologies for the design of ubiquitous power internet of things power transactions.
Meta Learning Approach to Collaborative Filtering for Tackling Learning from Sparse Data and Handling Uncertainty

Latent factor models such as Matrix Factorization have become the default choice for recommender systems due to their performance and scalability. However, such algorithms have two disadvantages. First, these models suffer from data sparsity. Second, they fail to account for model uncertainty. In this paper, we exploit a meta learning strategy to address these problems. The key idea behind our method is to learn predictive distributions conditioned on context sets of arbitrary size of user/item interaction information. Our proposed framework has the advantages of being easy to implement and applicable to any existing latent factor models, providing uncertainty capabilities. We demonstrate the significant superior performance of our model over previous state-of-the-art methods, especially for sparse data in the top-N recommendation task.
Based on Numerical Control Fold the Speed of Curved Robot System from Meet Speed with Swing to Study

Abstract In order to solve the current domestic sheet metal enterprise users has increased rapidly, but there are still most bending machine or manual operation problem, involving the employment difficult problems, according to labor shortage problem, must promote intelligent enterprise transformation, realize the exchange Labour “machine”, the development and application of nc bending robot system is the inevitable developing trend of sheet metal industry, speed adaptive speed and oscillation is important in the development and application of nc bending robot system. This paper describes the system model of the speed adaptive speed and swing of the CNC bending robot system, and analyzes the performance of the speed adaptive speed and swing system comprehensively. The results of computer simulation show the consistency between simulation and theory.
Scaling Up Deep Neural Network Optimization for Edge Inference

Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference directly on edge devices (a.k.a. edge inference) with a satisfactory performance, optimizing the DNN design (e.g., network architecture and quantization policy) is crucial. While state-of-the-art DNN designs have leveraged performance predictors to speed up the optimization process, they are device-specific (i.e., each predictor for only one target device) and hence cannot scale well in the presence of extremely diverse edge devices. Moreover, even with performance predictors, the optimizer (e.g., search-based optimization) can still be time-consuming when optimizing DNNs for many different devices. In this work, we propose two approaches to scaling up DNN optimization. In the first approach, we reuse the performance predictors built on a proxy device, and leverage the performance monotonicity to scale up the DNN optimization without re-building performance predictors for each different device. In the second approach, we build scalable performance predictors that can estimate the resulting performance (e.g., inference accuracy/latency/energy) given a DNN-device pair, and use a neural network-based automated optimizer that takes both device features and optimization parameters as input and then directly outputs the optimal DNN design without going through a lengthy optimization process for each individual device.
The Effect of Using Vocabulary Journal Strategy on Students’ Reading Comprehension at State Junior High School 1 Cerenti Kuantan Regency

This research was urged by the students’ problem in reading narrative texts at State Junior High School 1 Cerenti Kuantan Singingi Regency. For example, some students still had the difficulties in finding out the main idea and analyzing the reading text. Thus, the writer was interested in carrying out this research based the problems above. The objective of this research was to find out whether there was or nor significant effect of using Vocabulary Journal strategy on students’ reading comprehension at State Junior High School 1 Cerenti Kuantan Singingi Regency.The design of this research was a quasi experimental design. The population of this research was the eighth grade students of Junior High School 1 Cerenti Kuantan Singingi Regency in 2015-2016 academic years. The sample was taken by using cluster random sampling. The sample was VIII 1 as an experimental class, and VIII 2 as control class. Thus, the number of sample from two classes was 54 students. To analyze the data, the writer adopted independent sample T-test formula by using SPSS 16.0 version. After analyzing the data, the writer found the result of t-observe was 2.03 &lt; 5.386 &gt; 2.72. Thus, the writer could conclude that there is a significant difference between the students taught by using and taught without using Vocabulary Journal strategy on students’ reading comprehension in narrative text at State Junior High School 1 Cerenti Kuantan Singingi Regency.
On collisions related to an ideal class of order 3 in CSIDH.

CSIDH is an isogeny-based key exchange, which is a candidate for post quantum cryptography. It uses the action of an ideal class group on \(\mathbb {F}_{p}\)-isomorphism classes of supersingular elliptic curves. In CSIDH, the ideal classes are represented by vectors with integer coefficients. The number of ideal classes represented by these vectors determines the security level of CSIDH. Therefore, it is important to investigate the correspondence between the vectors and the ideal classes. Heuristics show that integer vectors in a certain range represent “almost” uniformly all of the ideal classes. However, the precise correspondence between the integer vectors and the ideal classes is still unclear. In this paper, we investigate the correspondence between the ideal classes and the integer vectors and show that the vector \((1, \dots , 1)\) corresponds to an ideal class of order 3. Consequently, the integer vectors in CSIDH have collisions related to this ideal class. Here, we use the word “collision” in the sense of distinct vectors belonging to the same ideal class, i.e., distinct secret keys that correspond to the same public key in CSIDH. We further propose a new ideal representation in CSIDH that does not include these collisions and give formulae for efficiently computing the action of the new representation.
Towards Securing Cloud Computing from DDOS Attacks

Cloud computing (CC) is an advanced technology that provides data sharing and access to computing resources.The cloud deployment model represents the exact type of cloud environment based on ownership, size, and accessibility rights, and also describes the purpose and nature of the cloud.Since all processes today are computerized, consumers need a lot amount of data and cache size.The security of the cloud is ensured in many levels, but the scope of intrusions makes it necessary to understand the factors that affect cloud security.CC-certified users rely on third parties for their other important security issues in third-party computing clouds.A DDoS attack is an attack-type in which it is not necessary to send a large number of packets to the server, which makes it impossible for legitimate users to access them.In this research work, a DDoS attack was launched and a tool for launching a DDoS attack was discussed.In this research, DDoS attacks were rejected using three different SNORT rules.In this research, rules predefined for detecting DDoS attacks on SNORT profiles detect and prevent DDoS attacks, but because they block certain legitimate requests and generate false alarms, this should be the subject of future research.
TINJAUAN KRIMINOLOGI TERHADAP ANAK YANG MELAKUKAN TINDAK PIDANA PENYALAHGUNAAN NARKOTIKA

Narcotics are substances or drugs derived from plants or non-plants, both synthetic and semi-synthetic, which can cause adecline or alteration of consciousness, loss of taste, reduce to pain relief, and can cause dependence, which is differentiatedinto groups as attached to the law. The factors that cause children to misuse narcotics are the factors of individuals, famil ies,economies, associations, social cultures, and others. The consequences of narcotics abuse the impact of drug abuse onphysical. Disorders of the nervous system (neurological) such as: seizures, hallucinations, impaired consciousness, nerveedge damage, disorders of the heart and blood vessels (cardiovascular). Efforts to prevent the misuse of narcotics give orinfuse early on the meaning of healthy living meanings.
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning

A common workflow for many engineering design problems requires the evaluation of the design system to be investigated under a range of conditions. These conditions usually involve a combination of several parameters. To perform a complete evaluation of a single candidate configuration, it may be necessary to perform hundreds to thousands of simulations. This can be computationally very expensive, particularly if several configurations need to be evaluated, as in the case of the mathematical optimization of a design problem. Although the simulations are extremely complex, generally, there is a high degree of redundancy in them, as many of the cases vary only slightly from one another. This redundancy can be exploited by omitting some simulations that are uninformative, thereby reducing the number of simulations required to obtain a reasonable approximation of the complete system. The decision of which simulations are useful is made through the use of machine learning techniques, which allow us to estimate the results of "yet-to-be-performed" simulations from the ones that are already performed. In this study, we present the results of one such technique, namely active learning, to provide an approximate result of an entire offshore riser design simulation portfolio from a subset that is 80% smaller than the original one. These results are expected to facilitate a significant speed-up in the offshore riser design.
A Simple Global Neural Discourse Parser

Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a simple chart-based neural discourse parser that does not require any manually-crafted features and is based on learned span representations only. To overcome the computational challenge, we propose an independence assumption between the label assigned to a node in the tree and the splitting point that separates its children, which results in tractable decoding. We empirically demonstrate that our model achieves the best performance among global parsers, and comparable performance to state-of-art greedy parsers, using only learned span representations.
어린이집 평가제도와 보육서비스 질 간의 비정합성에 대한 연구

(Purpose) This study analyzed whether there were possible institutional disparity between the child care center evaluation system and the quality of child care services in Korea.(Design/methodology/approach) The purpose of this study is to recognize practicable fallacy of inconsistency among policy stakeholders during implementation of child care center evaluation system and suggests available resolution. In this regard, we reviewed the evaluation system of Korea mainly focus on literature research with various grounds.(Findings) According to our analysis, there were two major reason to bring the disparity between the evaluation system and the quality of child care service; ① the discrepancy between the policy characteristics of child care and evaluation ② the divergency among stakeholders in the system and the dissimilarity of accountability among them.(Research implications or Originality) As managerial problems consistently happnes, this study analyzed the critical factors of conducting childcare service in Korea. These findings have practical implications suggested that diagnostic discussion of system should be included in the evaluation process, various policymakers must apprehend the role of evaluation system and its consequences.