Reviewerly/exHarmony huggingface dataset:
Representation Learning for Efficient and Effective Similarity Search and Recommendation

How data is represented and operationalized is critical for building computational solutions that are both effective and efficient. A common approach is to represent data objects as binary vectors, denoted \textit{hash codes}, which require little storage and enable efficient similarity search through direct indexing into a hash table or through similarity computations in an appropriate space. Due to the limited expressibility of hash codes, compared to real-valued representations, a core open challenge is how to generate hash codes that well capture semantic content or latent properties using a small number of bits, while ensuring that the hash codes are distributed in a way that does not reduce their search efficiency. State of the art methods use representation learning for generating such hash codes, focusing on neural autoencoder architectures where semantics are encoded into the hash codes by learning to reconstruct the original inputs of the hash codes. This thesis addresses the above challenge and makes a number of contributions to representation learning that (i) improve effectiveness of hash codes through more expressive representations and a more effective similarity measure than the current state of the art, namely the Hamming distance, and (ii) improve efficiency of hash codes by learning representations that are especially suited to the choice of search method. The contributions are empirically validated on several tasks related to similarity search and recommendation.
Quantum-enhanced neural networks in the neural tangent kernel framework

Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cNN), but their practical and theoretically-guaranteed performance is still to be investigated. In contrast, cNNs and especially deep cNNs, have acquired several solid theoretical basis; one of those basis is the neural tangent kernel (NTK) theory, which can successfully explain the mechanism of various desirable properties of cNNs, particularly the global convergence in the training process. In this paper, we study a class of qcNN composed of a quantum data-encoder followed by a cNN. The quantum part is randomly initialized according to unitary 2-designs, which is an effective feature extraction process for quantum states, and the classical part is also randomly initialized according to Gaussian distributions; then, in the NTK regime where the number of nodes of the cNN becomes infinitely large, the output of the entire qcNN becomes a nonlinear function of the so-called projected quantum kernel. That is, the NTK theory is used to construct an effective quantum kernel, which is in general nontrivial to design. Moreover, NTK defined for the qcNN is identical to the covariance matrix of a Gaussian process, which allows us to analytically study the learning process. These properties are investigated in thorough numerical experiments; particularly, we demonstrate that the qcNN shows a clear advantage over fully classical NNs and qNNs for the problem of learning the quantum data-generating process.
Improving Automation for Higher-Order Proof Steps

We have extended the TLA+ proof system TLAPS with a new backend to improve the automation of proof steps that involve higher-order reasoning. The current support for such steps is poor, requiring the user to break down proofs into unnecessarily small steps. We defined a translation from TLA+ to THF, the TPTP dialect for higher-order logic, and evaluated several higher-order solvers on proof obligations generated from the standard library of TLA+. Our results demonstrate that the solvers are able to handle much coarser proof steps than the other strategies provided by TLAPS, reducing the amount of necessary user interactions by a significant margin.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequential recommender models consider the next item prediction task as the training signal. Unfortunately, there are two essential challenges for these methods: (1) the long-term preference is difficult to capture, and (2) the supervision signal is too sparse to effectively train a model. In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec. The basic contrastive predictive coding (CPC) serves as encoders of sequences and items. The memory module is designed to augment the auto-regressive prediction in CPC to enable a flexible and general representation of the encoded preference, which can improve the ability to capture the long-term preference. For effective training of the MMInfoRec model, a novel multi-instance noise contrastive estimation (MINCE) loss is proposed, using multiple positive samples, which offers effective exploitation of samples inside a mini-batch. The proposed MMInfoRec framework falls into the contrastive learning style, within which, however, a further finetuning step is not required given that its contrastive training task is well aligned with the target recommendation task. With extensive experiments on four benchmark datasets, MMInfoRec can outperform the state-of-the-art baselines.
Countering Online Hate Speech: An NLP Perspective

Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for online hateful behavior, manifests itself in forms such as online hate speech. Hate speech is a deliberate attack directed towards an individual or a group motivated by the targeted entity's identity or opinions. The rising mass communication through social media further exacerbates the harmful consequences of online hate speech. While there has been significant research on hate-speech identification using Natural Language Processing (NLP), the work on utilizing NLP for prevention and intervention of online hate speech lacks relatively. This paper presents a holistic conceptual framework on hate-speech NLP countering methods along with a thorough survey on the current progress of NLP for countering online hate speech. It classifies the countering techniques based on their time of action, and identifies potential future research areas on this topic.
An Investigation on Curriculum Affecting Students’ Choice of Physics at Advanced Level in Kabale District

The study investigated on curriculum affecting students’ choice of Physics at Advanced Level in Kabale District. The study objective include; influence of curriculum on students’ choice of Physics at Advanced level Secondary school in Kabale District. The study adopted a cross-sectional survey and descriptive research design. A total number of 133 students from 9 (3 private and 6 government) Advanced level secondary school students in Kabale District were used. Purposive and stratified sampling approaches were used in particular for the schools to participate in the study. Instruments for the study used were questionnaires and interview guide for S.5 Arts and Science students. Findings of the study indicated that curriculum variables did not significantly influence students’ choice of Physics at Advanced level. It was recommended Physics curriculum should be made relevant to the learners and appealing by teachers using daily life experiences in the environment.
ISyNet: Convolutional Neural Networks design for AI accelerator

In recent years Deep Learning reached significant results in many practical problems, such as computer vision, natural language processing, speech recognition and many others. For many years the main goal of the research was to improve the quality of models, even if the complexity was impractically high. However, for the production solutions, which often require real-time work, the latency of the model plays a very important role. Current state-of-the-art architectures are found with neural architecture search (NAS) taking model complexity into account. However, designing of the search space suitable for specific hardware is still a challenging task. To address this problem we propose a measure of hardware efficiency of neural architecture search space - matrix efficiency measure (MEM); a search space comprising of hardware-efficient operations; a latency-aware scaling method; and ISyNet - a set of architectures designed to be fast on the specialized neural processing unit (NPU) hardware and accurate at the same time. We show the advantage of the designed architectures for the NPU devices on ImageNet and the generalization ability for the downstream classification and detection tasks.
A Comprehensive Study of Performance Appraisal of the Business Entity

Purpose: Performance appraisal is described as the measurement of outcome to generate reliable data on its effectiveness and efficiency. Performance appraisal measures the business activities and implements a strategy to bring them back to the desired position. This paper is useful in presenting a systematic understanding of the performance appraisal concept of the business entity.Research Methods: The Study of performance appraisal of a business entity has been performed using the literature review and case study analysis, interviews, and personal observation as the secondary source of data.Findings: It could be interpreted that the selection of methodology for performance appraisal should be based on the objective, strategy, mission, and vision of the company. The performance indicators could be financial and nonfinancial. Therefore, for a precise performance appraisal, the purpose and subjective methods needed to be combined based on the organisation's requirement. Conclusion: The performance appraisal's primary objective is to measure the business working by aligning the long term and short-term strategy. Long-term performance reflects the vision, while short term business performance is related to operations. The performance appraisal method should create an alignment between the measurement of procedures and strategies.
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud

Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs. Elastic training is a service embedded in cloud training platforms that dynamically scales up or down the resources allocated to a job. The core technique of an elastic training system is to best allocate limited resources among heterogeneous jobs in terms of shorter queueing delay and higher training efficiency. This paper presents an optimal resource allocator for elastic training system that leverages a mixed-integer programming (MIP) model to maximize the training progress of deep learning jobs. We take advantage of the real-world job data obtained from ModelArts, the deep learning training platform of Huawei Cloud and conduct simulation experiments to compare the optimal resource allocator with a greedy one as benchmark. Numerical results show that the proposed allocator can reduce queuing time by up to 32% and accelerate training efficiency by up to 24% relative to the greedy resource allocator, thereby greatly improving user experience with Huawei ModelArts and potentially enabling the realization of higher profits for the product. Also, the optimal resource allocator is fast in decision-making, taking merely 0.4 seconds on average.
Digital score of Swing five - piece for saxophone quartet

Musical score of 'Swing five - piece for saxophone quartet'. Title missing from item. Title taken from file name. Quantity: 1 Electronic document(s).
How to Do Things with Memes

In the 1950s and 1960s, J. L. Austin changed the face of pragmatic linguistic analysis with his work How to Do Things with Words. Famously claiming that language does not simply describe the world but also changes it, Austin (1962) established the theory of speech acts. This paper builds on Austin’s research by investigating internet memes as a kind of speech act; hence the title “How to do things with memes.” Focusing in particular on a specific genre of memes that incorporates images and discourse from the American space epic media franchise Star Wars, the paper explores the following question: What are the sociopragmatic functions of memes used on the internet today by younger generations of internet users? The conversation analytic concept of adjacency pairs is used to understand the redistribution, recontextualization, and remediation of original media sources into memes, together with linguistic anthropological research that interrogates recontextualization as a kind of performance (Baumann and Briggs 1990). An investigation of the illocutionary forces behind select Star Wars memes exposes what exactly these memes are “doing” in their respective internet spheres. Specifically, the paper outlines how these memes function to build community, as illustrated by sociocultural linguistic work on identity (Bucholtz and Hall 2004, 2005) and social semiotic concepts such as dual indexicality (Hill 1995). Through processes of memetic participation, digital users build community and draw closer together by expanding upon their existing communicative repertoires (Rymes 2012, 2014).
Analyzing XOR-Forrelation through stochastic calculus

In this note we present a simplified analysis of the quantum and classical complexity of the $k$-XOR Forrelation problem (introduced in the paper of Girish, Raz and Zhan) by a stochastic interpretation of the Forrelation distribution.
Legal Validity of Virtual Reality Tourism in Indonesian Society 5.0

Purpose: This study focuses on the legality of paid-virtual tour that crosses the typical tourism guide boundary into a non-physical tour choice due to the ease of technology. The aim of the study is to find legal framework to regulate this new practice of tourism in Indonesia. Methodology: The study methodology uses doctrinal research. Statute approach and conceptual approach are applied in this study. The data collection process is conducted through doctrinal method through statutes, legal literatures, along with news.Findings: The result showed that there hasn’t been any legal requirements that regulates about virtual tour such as Wisata Kreatif Jakarta and Atourin in Indonesia, therefore there needs to be district regulation about the license.Limitation: This study is limited to specific type of tourism license, namely virtual tour. Contribution: This contribute to the area of law, especially tourism law, and economic, especially in micro economy of tourism.
Software Bugs Identification and Prediction Approaches and their Interrelationships with Levels of Inheritance: An Empirical Analysis

The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process and prevent rework. The researchers have successfully attributed metrics (product, process and project) as the dominant causes of the bugs. Through this work, the researchers aspire to spread awareness among the developers regarding prominent causes of the bugs while facilitating a smooth software development process. Additionally, the researchers have correlated the effectiveness in the processes related to prediction of severities of the bugs corresponding to the levels and the levels of inheritance. The software quality is predominantly and adversely affected by product metricsoriented software bugs with significant correlation. Different levels of inheritance are associated with the severities of the bugs with a correlation that the severities are bound to rise with an increase in the levels of inheritance. The outcomes of this research work are of particular significance to software developers, software quality expertsandresearchers.The outcomes of this research work while promoting a set of practices to rationalize the inheritance suggest a lookout on severities of the bugs to roll out an acceptable quality of software applications. The outcomes of this research work also suggest rationalizing the efforts to identify and predictthe bugs. Keywords: Software Bugs, Bug Prediction, Reasons of Bugs, Neural Networks, Levels of Inheritance.
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning

Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that can randomize among multiple actions. To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy. However, these approaches require storing a large set of policies, which is not policy efficient, and may incur prohibitive memory costs in constrained deep RL. To address this problem, we propose an alternative approach. Our approach first reformulates the CRL to an equivalent distance optimization problem. With a specially designed linear optimization oracle, we derive a meta-algorithm that solves it using any off-the-shelf RL algorithm and any conditional gradient (CG) type algorithm as subroutines. We then propose a new variant of the CG-type algorithm, which generalizes the minimum norm point (MNP) method. The proposed method matches the convergence rate of the existing game-theoretic approaches and achieves the worst-case optimal policy efficiency. The experiments on a navigation task show that our method reduces the memory costs by an order of magnitude, and meanwhile achieves better performance, demonstrating both its effectiveness and efficiency.
Mastery of Bugis Language Vocabulary in the Maqgalaceng Game Students of SDN 257 Akkalibatue, Soppeng Regency

This research is quantitative descriptive. The method used is quantitative descriptive which is non- experimental. The purpose of this study was to describe the mastery of Bugis vocabulary in the aspects of nouns, adjectives, verbs and numbers using maqgalaceng playing media for fifth grade students of SDN 257 Akkalibatue, Soppeng Regency. The population of this study were all students in grade V SD Negeri 257 Akkalibatue Soppeng Regency totaling 16 students. The sample of this research is 16 students if the research subject is less than 100 people, it is better to take all of them so that the technique of determining the sample if all members of the population are used as the sample is called the total sample. The students' mastery of the vocabulary of the Bugis language that has been determined is that from the results of the study with a value range of 75-100, 8 samples were obtained with a percentage of 50% being in the master category while the samples that obtained a value of 0-74 obtained 8 samples with a percentage of 50% being in the non- mastering category. This means that the mastery of Bugis language vocabulary using the maqgalaceng game media for fifth grade students of SD Negeri 257 Akkalibatue, Soppeng Regency is in the under-mastered category because of the 16 samples only 50% have mastered it.
Relational Hypersequent S4 and B are Cut-Free Hypersequent Incomplete

We show that relational hypersequent K4, S4, KB and RB, investigated in Parisi (2020) and Burns and Zach (2020) are cut-free hypersequent incomplete. In addition, the former two are also cut-free sequent and formula incomplete.
Uniform Consistency in Nonparametric Mixture Models

We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression functions are allowed to be nonparametric and the error distributions are assumed to be convolutions of a Gaussian density. We construct uniformly consistent estimators under general conditions while simultaneously highlighting several pain points in extending existing pointwise consistency results to uniform results. The resulting analysis turns out to be nontrivial, and several novel technical tools are developed along the way. In the case of mixed regression, we prove $L^1$ convergence of the regression functions while allowing for the component regression functions to intersect arbitrarily often, which presents additional technical challenges. We also consider generalizations to general (i.e. non-convolutional) nonparametric mixtures.
Novel Method to Reconstruct an Image using Hybrid Decomposition

Tone mapping method is used for mapping single set of colours to other for simulating the presence of scenes with high dynamic range in a medium with a lower dynamic range. Its primary goal is to generate typicalactive range scene from a maximumactive range scene while preserving the visual information.Other tone mapping techniques divide a picture by base as well as detail layer, then process each distinctly. As a result of the lack of suitable imposement on the two layers, the above approaches may suffer from halo artefacts and over-enhancement. To overcome these issues, we suggest a decomposition of coupled L1 and L0 methods in this study.A piecewise constant effect is achieved throughstriking a l1 sparseness term proceeding base layer to simulate its smoothness quality and a l0 sparsity term through detail layer as a physicalpreceding. On the basis of our layer decomposition approach, we also present a multiscale tone mapping method.For subjective and objective estimations, proposed technique delivers aesthetically engaging consequencesby few halo artefacts when compared to existing tone mapping algorithms, and achieves a higher image quality than previous methods.
자국어 보급 기관에서의 교원 운영에 대한 해외 사례 분석 연구

This study aimed to analyze the current teacher management systems at foreign institutes engaged in the dissemination of their national language, and to draw overarching policy implications. The paper considers the cases of the Confucius Institute, the Goethe Institute, the Alliance Francaise, the British Council, and the Japan Foundation. The teacher-training programs organized by the US-based TESOL are also considered. Data were gathered between October and December 2020, and the data collection focused on teacher training, professional development, recruitment, types and roles, qualifications, remuneration, benefits, and evaluation. Data were collected by different methods, such as written interviews, real-time online interviews, document analysis, and other materials obtained through requests to the institutions. The policy implications of this analysis concern language teacher typology, the necessity of training, the systematization of training and professional development programs, and teacher welfare. The results can serve as basic reference materials for teacher management at institutes for the dissemination of the Korean language, such as the Sejong Institute.