Reviewerly/exHarmony huggingface dataset:
Layerwise learning for quantum neural networks

With the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of parametrized quantum circuits, strategies for effective training are becoming increasingly important. In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits. The circuit depth is incrementally grown during optimization, and only subsets of parameters are updated in each training step. We show that when considering sampling noise, this strategy can help avoid the problem of barren plateaus of the error surface due to the low depth of circuits, low number of parameters trained in one step, and larger magnitude of gradients compared to training the full circuit. These properties make our algorithm preferable for execution on noisy intermediate-scale quantum devices. We demonstrate our approach on an image-classification task on handwritten digits, and show that layerwise learning attains an 8% lower generalization error on average in comparison to standard learning schemes for training quantum circuits of the same size. Additionally, the percentage of runs that reach lower test errors is up to 40% larger compared to training the full circuit, which is susceptible to creeping onto a plateau during training.
Guest Editors' Foreword

Special issue of selected papers from the 26th international symposium on graph drawing and network visualization (GD 2018) : guest editors' foreword
Ubiquitous cousins and parental slides

In a previous article [1] we examined the concept of blood relations in a family tree and we used mathematics to highlight and simplify the descriptions involved, such as child, parent, great-uncle, second cousin, third cousin once removed.
Machine Reading Comprehension with Enhanced Linguistic Verifiers

We propose two linguistic verifiers for span-extraction style machine reading comprehension to respectively tackle two challenges: how to evaluate the syntactic completeness of predicted answers and how to utilize the rich context of long documents. Our first verifier rewrites a question through replacing its interrogatives by the predicted answer phrases and then builds a cross-attention scorer between the rewritten question and the segment, so that the answer candidates are scored in a \emph{position-sensitive} context. Our second verifier builds a hierarchical attention network to represent segments in a passage where neighbour segments in long passages are \emph{recurrently connected} and can contribute to current segment-question pair's inference for answerablility classification and boundary determination. We then combine these two verifiers together into a pipeline and apply it to SQuAD2.0, NewsQA and TriviaQA benchmark sets. Our pipeline achieves significantly better improvements of both exact matching and F1 scores than state-of-the-art baselines.
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling

Energy-based models (EBMs) for generative modeling parametrize a single net and can be directly trained by maximum likelihood estimation. Despite the simplicity and tractability, current approaches are either unstable to learn or difficult to synthesize diverse and high-fidelity images. We propose to train EBM via a multistage coarse-to-fine expanding and sampling strategy, namely CF-EBM. To improve the learning procedure, we propose an effective net architecture and advocate applying smooth activations. The resulting approach is computationally efficient and achieves the best performance on image generation amongst EBMs and the spectral normalization GAN. Furthermore, we provide a recipe for being the first successful EBM to synthesize 512×512-pixel images and also improve out-of-distribution detection. In the end, we effortlessly generalize CF-EBM to the one-sided unsupervised image-to-image translation and beat baseline methods with the model size reduced by 1000× and the training budget by 9×. In parallel, we present a gradient-based discriminative saliency method to interpret the translation dynamics which align with human behavior explicitly.
SISTEM KOMPUTERISASI ABSENSI PEGAWAI PT MITRA INFORMATIKA DENGAN MENGGUNAKAN FASILITAS BARCODE DAN PENERIMAAN GAJI INSENTIF BAGI PEGAWAI BERDASARKAN ABSENSI

Abstract The development of science and technology (science and technology) is currently experiencing very rapid progress, especially in the computer world. The application of Information Technology (IT) has now spread almost in all fields. The use of increasingly advanced technological tools today will greatly help entrepreneurs to develop their businesses. With proper data processing, accurate and fast information will be obtained to make the company's performance more efficient. Computers with adequate hardware and software will be very helpful in carrying out company activities. Software that is easily understood by users will greatly assist in data processing which will then become the information needed to improve work efficiency and assist in making important decisions in the company. Staffing Division of PT. Mitra Informatics is a part whose performance is quite heavy, because this section regulates employee discipline regarding attendance which is very influential on the progress of the company. In the personnel division of PT. Mitra Informatics, there is a need for an information system that helps the data processing operational activities needed, so that with a computerized employee attendance system will be very influential for the smooth performance of employees which ultimately will also be very influential for the progress of the company.
전남어의 의문문 억양 경계톤에 대한 강세구초 자음의 원거리 효과

This study explores the factors which affect the shapes of boundary tones in Korean interrogatives. 26 Chonnam speakers produced three types of interrogatives in Korean, (i.e., simple polar questions, wh-questions, and indefinite pronoun questions) under variation in verb-initial consonants and question endings. A phonetic analysis of their production reveals three results. First, the initial segment of the Intonation Phrase (IP)-final Accentual Phrase (AP) plays a significant role in determining the shapes of boundary tones. Second, the distribution of various boundary tones is different across the three types of interrogatives in Korean. Third, the lexical ending forms also condition the shapes of boundary tones. I propose that the AP-initial consonant effect weighs more than the constraints for different types of interrogatives, which are in turn more weighted than the effect of the ending form. I test the validity of the proposed weighted constraints by the learned grammar in the Maximum Entropy Model based on the frequency of each boundary tone. This study shows that the differently weighted factors interact to produce various boundary tones in Korean interrogatives.
Ethics and Smart Cities

chapter Share on Ethics and Smart Cities Author: David McMenemy Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 235–236https://doi.org/10.1145/3447404.3447417Online:23 February 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Dynamic Matching of Local Features for Re-Identification of Pedestrians

This paper presents a person re-identification (reid) method based on a convolutional neural network (CNN). There are many CNNs for the re-id problems, where few of them considered the image properties in real-world situations that the images could be deformed due to the perspective view of surveillance cameras and that pedestrian detectors do not give perfect bounding box. In this paper, we address the problem of perspective view and incomplete bounding box by proposing a new network architecture and metric learning method. Specifically, we compensate for the vertical and horizontal misalignment due to the incomplete bounding boxes of the pedestrian detector, and also horizontal squeezing that was not considered in the existing algorithms. For this, we partition the bounding box of pedestrian detection results into M horizontal region and N vertical regions. Then, we apply a dynamic matching technique in both horizontal and vertical directions to compensate for the effects of unfit bounding boxes and squeezed appearance due to the perspective views. The partitioning and dynamically matched distance are also considered for the metric training of CNN. We compare our method with state-of-the-art ones and validate the improved performance.
Quantized Turbo Equalization for Non-binary LDPC Coded Partial-Response Channels

In this paper, we consider iterative detection and decoding (i.e., turbo equalization) for nonbinary low-density parity-check (LDPC) coded partial-response channels, where a quantizer is present to discretize the continuous received signal. We propose a turbo equalizer that uses the pre-computed quantized channel transition probabilities in the symbol-level BCJR channel detection algorithm, which significantly reduces the computational complexity by avoiding real-time floating-point multiplications. The proposed approach is further extended to nonbinary LDPC coded bit-patterned media recording (BPMR) channels. Simulation results show that with a small number of quantization bits, the proposed receiver approaches closely the performance of the conventional turbo equalizer operating on unquantized signals.
Mathematical modelling on topological space by networking

The objective of this presentation is to discover a new concept of One-way Function which is apply on computer network by Mathematical modelling on topological space that easy to analyse a result without facing error.
Fast Inter-frame Prediction Based Array Processor for Depth Maps in 3D-HEVC

For the characteristics of the depth map with a large smooth area, it is not necessary in the 3D-High-Efficiency Video Coding (3D-HEVC) to use TZ search for inter-frame prediction. According to the parallelism of depth map interframe algorithms, analyzed the parallelism of full search, threestep search and diamond search in the array structure, this paper proposes a parallel implementation method of 3D-HEVC depth map inter-frame prediction based on array processor. Considering only the depth map encoding, the experimental results show that the synthesized frequency under the BEE4 FPGA chip is as high as 100.261MHZ. Compared with the software 3D-HEVC Test Model (HTM) version 16.1, without affecting the video quality, the speedup ratio of the full search mode can reach 35, the speedup ratio of the three-step search mode can reach 160, and the speedup ratio of the diamond search can reach 233.
The Nexus of Training, Employee Commitment and Employee Performance in Nepalese Commercial Banking Sector

This research attempts to measure the nexus of training, employee commitment, and employee performance in the Nepalese commercial banking sector. Descriptive and causal research design is used to achieve the objectives of the research. Structured questionnaires were made, and the survey was done to collect primary cross-sectional data. Employees of the commercial banks in Kathmandu Valley who have been involved in the training program at least one time are included in the survey. Judgmental sampling was taken. This research confirmed that the training need assessment, mode of training, and training feedback significantly influence employee commitment, and employee commitment influences employee performance in commercial banks. So, bank managers must accept the nexus of training in employee commitment and employee performance.
Rapid Neural Pruning for Novel Datasets with Set-based Task-Adaptive Meta-Pruning

As deep neural networks are growing in size and being increasingly deployed to more resource-limited devices, there has been a recent surge of interest in network pruning methods, which aim to remove less important weights or activations of a given network. A common limitation of most existing pruning techniques, is that they require pre-training of the networks at least once before pruning, and thus we can benefit from reduction in memory and computation only at the inference time. However, reducing the training cost of neural networks with rapid structural pruning may be beneficial either to minimize monetary cost with cloud computing or to enable on-device learning on a resource-limited device. Recently introduced random-weight pruning approaches can eliminate the needs of pretraining, but they often obtain suboptimal performance over conventional pruning techniques and also does not allow for faster training since they perform unstructured pruning. To overcome their limitations, we propose Set-based Task-Adaptive Meta Pruning (STAMP), which task-adaptively prunes a network pretrained on a large reference dataset by generating a pruning mask on it as a function of the target dataset. To ensure maximum performance improvements on the target task, we meta-learn the mask generator over different subsets of the reference dataset, such that it can generalize well to any unseen datasets within a few gradient steps of training. We validate STAMP against recent advanced pruning methods on benchmark datasets, on which it not only obtains significantly improved compression rates over the baselines at similar accuracy, but also orders of magnitude faster training speed.
Speech Recognition Fundamentals and Features

Speech communication is an essential part of humans. It is natural in humans but artificial in machines. Speech waveform defines the message communicated along with various parameters regarding the speaker. It may be about his/ her age group, gender, and emotions. Speech processing has different areas according to its applications, such as in communication, reproduction of speech signal, and its transmission and recognition of speaker or speech. The speech recognition system means letting a machine to understand voice. In this chapter, the speech recognition fundamentals, features, and difficulties faced are explored.
Pembuatan Video Dokumenter Kegiatan Pengabdian Masyarakat

Community service activities of the Batam State Polytechnic in 2018 are held together by all departments and taken place in Rempang Cate Village, Galang District, Batam City. A good documentation that can be seen more real is needed so that this activity can be better known by the outside community. One documentation that fulfills this expectation is a documentary video. This video production consists of 3 stages, namely pre-production, production, and post-production. The activity not only ends when the documentary video is finished, but continues with an evaluation to find out whether the video in presenting service activities is better when compared to ordinary documentation (by photo). Then, the video is published in the mass media, both print and electronic media. The results of the devotion to the making of this documentary video is the documentation of the community service of the Batam State Polytechnic in 2018 well and real, as well as the recognition of the Batam State Polytechnic and its social services in the wider community.
Winter sunshine on tap Wednesday with the return of rain Thursday into the weekend

We're finally turning the corner and warming up this week!  By the end of the weekend, temperatures will land near 50 with overnights in the upper 30s to low 40s, which is slightly above normal.
FORMATION OF MATEMATIKAL COMPETENCE IN THE PROCESS OF LEARNING FICTION AT SCHOOL

The purpose of the article is to align the notion of "mathematical competence" with the peculiarities of the academic subject "Ukrainian literature". Using methods of specification, theoretical analysis and modeling, a hypothetical construct of "mathematical competence in the field of literary education, the process of its formation by the means of the art of words" has been created. This represents the results and scientific novelty of the research. Mathematical competence is a synthetic formation. These include: procedural competence, logical competence, technological competence, research competence, and methodological competence. Ukrainian literature provides its opportunities to conceptualize these concepts. Procedural competence concerns the implementation of a well-established order of literary analysis and literary creation, which have an algorithmically predetermined sequence, focus on a particular result, and consist of a system of normalized actions. Logical competence addresses the application of induction and deduction methods.Schoolchildren use literary concepts as definite general terms, understanding their scientific content and systemic connection, performing logically motivated literary research and literary creative operations. Technological competence focuses on the readiness of students to achieve a programmed literary result, the ability to consciously implement conceptual principles, special approaches, methods, methods, procedures, stages, cycles of operations. Literary research refers to the possession of accurate methods of observation and analysis, which include the isolation, evaluation, systematization of artistic elements of thework, establishing links between them. Literary and creative competence represents the ability of students to write their own texts of feasible artistic genres, providing all the necessary technological (mathematically accurate and logically determined) process, which is to create the requirements of the genre of content and formal artistic elements. Methodological competence deals with understanding the role and advantages of each of the ways of analyzing a literary work or historical and literary materials, determining the productivity of each, consciously applying them.
Library guides / Biblioteekgidse: Makerspace: 3D Printing

Provide a collaborative technologically focused environment for SU students and staff to invent, create and learn while sharing. Guide on how to use 3d printing
Control-Aware Representations for Model-based Reinforcement Learning

A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the latent dynamics, and utilizing it to perform control in the latent space. Two important questions in this area are how to learn a representation that is amenable to the control problem at hand, and how to achieve an end-to-end framework for representation learning and control. In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space. We call this model control-aware representation learning (CARL). We derive a loss function and three implementations for CARL. In the offline implementation, we replace the locally-linear control algorithm (e.g., iLQR) used by the existing LCE methods with a RL algorithm, namely model-based soft actor-critic, and show that it results in significant improvement. In online CARL, we interleave representation learning and control, and demonstrate further gain in performance. Finally, we propose value-guided CARL, a variation in which we optimize a weighted version of the CARL loss function, where the weights depend on the TD-error of the current policy. We evaluate the proposed algorithms by extensive experiments on benchmark tasks and compare them with several LCE baselines.
Research Guides: Authority: Evaluation

This guide will help you better understand authority in the context of research and information and provide you with methods of evaluating an information source to determine its authority.
Socio-Psychological Characteristics Of A Modern School Leader And A Program Of Continuous Professional Development

In today’s rapidly changing and developing information and communication technologies, competition is intensifying, and the level of competence of leaders and professionals is becoming a key factor in science, technology and industry. In order to bring up a young generation with independent thinking, intellectual potential, deep knowledge and a modern outlook, able to take responsibility for the fate and future of the Motherland, it is necessary to professional development in the continuous professional development of the school leader is at the same time important and relevant
Towards Powerful Graph Neural Networks: Diversity Matters

Graph neural networks (GNNs) offer us an effective framework for graph representation learning via layer-wise neighborhood aggregation. Their success is attributed to their expressive power at learning representation of nodes and graphs. To achieve GNNs with high expressive power, existing methods mainly resort to complex neighborhood aggregation functions, e.g., designing injective aggregation function or using multiple aggregation functions. Consequently, their expressive power is limited by the capability of aggregation function, which is tricky to determine in practice. To combat this problem, we propose a novel framework, namely diverse sampling, to improve the expressive power of GNNs. For a target node, diverse sampling offers it diverse neighborhoods, i.e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model. High expressive power is guaranteed by the diversity of different neighborhoods. We use classical GNNs (i.e., GCN and GAT) as base models to evaluate the effectiveness of the proposed framework. Experiments are conducted at multi-class node classification task on three benchmark datasets and multi-label node classification task on a dataset collected in this paper. Extensive experiments demonstrate the proposed method consistently improve the performance of base GNN models. The proposed framework is applicable to any GNN models and thus is general for improving the expressive power of GNNs.
LibGuides: Current Library Services: Borrowing print items

Find out what services are currently available from the Library and how they are being delivered.
A Mixture of Variational Autoencoders for Deep Clustering

In this study, we propose a deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with a multi encoder-decoder neural architecture. This setup enforces a complementary structure that guides the learned latent representations towards a more meaningful space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to outperform state-of-the-art deep clustering methods significantly.