Reviewerly/exHarmony huggingface dataset:
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks

Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a central challenge to the field. Learning to reach such goals is particularly hard without any offline data, expert demonstrations, and reward shaping. In this paper, we propose an algorithm to solve the distant goal-reaching task by using search at training time to automatically generate a curriculum of intermediate states. Our algorithm, Classifier-Planning (C-Planning), frames the learning of the goal-conditioned policies as expectation maximization: the E-step corresponds to planning an optimal sequence of waypoints using graph search, while the M-step aims to learn a goal-conditioned policy to reach those waypoints. Unlike prior methods that combine goal-conditioned RL with graph search, ours performs search only during training and not testing, significantly decreasing the compute costs of deploying the learned policy. Empirically, we demonstrate that our method is more sample efficient than prior methods. Moreover, it is able to solve very long horizons manipulation and navigation tasks, tasks that prior goal-conditioned methods and methods based on graph search fail to solve.
Named Entity Recognition for Distant Reading in ELTeC

The Distant Reading for European Literary History COST Action, which started in 2017, has among its main objectives the creation of an open source, multilingual European Literary Text Collection (ELTeC). In this paper we present the work carried out to manually annotate a selection of the ELTeC collection for Named Entities, as well as to evaluate existing NER tools as to their capacity to reproduce such annotation. In the final paragraph, points of contact between this initiative and CLARIN are discussed.
Manufacturing Variation in Folsom Points and Fluted Peforms

This chapter discusses the archaeological data on manufacturing variation observed in Folsom points and preforms that may help guide the design and evaluation of experimental replication studies and lead to better understanding of Folsom stone tool design constraints, variations, and possible causes. Recognition and interpretation of variability in the archaeological record is essential for understanding the factors that may have conditioned past human behavior. The manufacture of chipped-stone tools involves a large number of potential sources of variation, including raw material, blank form, and skill level. In addition, variation in manufacturing strategies of hunter-gatherers is often contingent upon situational factors caused by group mobility. The foundation of my research design was inspired by the initial efforts of Jack Hofman to understand Folsom technological variation. Technological data on Folsom preforms and channel flakes can also provide useful observations about the range of strategies used in producing Folsom projectile points.
Orthus: A Blockchain Platform for Smart Cities

Blockchain has been widely used to store decentralized and secure transactions involving cryptocurrency (for instance, Bitcoin and Ethereum solutions). On the other hand, Smart City applications are concerned about how data and services can be safely stored and shared. In this regard, this research investigates the use of Blockchain in the Smart Cities scenario. Based on that, a platform named Orthus was proposed to support Blockchain in Smart City initiatives focused on scalability. This work presents a case study about how to use the proposed platform in the Natal Smart City Initiative in Brazil to handle land registration. Moreover, it also compares this platform with other implementations that use Blockchain in different domains. Finally, this research confirms that the use of blockchain technology has much to contribute to Smart City solutions once it enables the creation of solutions in distributed networks to meet the demand of the entire population.
How and When Adversarial Robustness Transfers in Knowledge Distillation?

Knowledge distillation (KD) has been widely used in teacher-student training, with applications to model compression in resource-constrained deep learning. Current works mainly focus on preserving the accuracy of the teacher model. However, other important model properties, such as adversarial robustness, can be lost during distillation. This paper studies how and when the adversarial robustness can be transferred from a teacher model to a student model in KD. We show that standard KD training fails to preserve adversarial robustness, and we propose KD with input gradient alignment (KDIGA) for remedy. Under certain assumptions, we prove that the student model using our proposed KDIGA can achieve at least the same certified robustness as the teacher model. Our experiments of KD contain a diverse set of teacher and student models with varying network architectures and sizes evaluated on ImageNet and CIFAR-10 datasets, including residual neural networks (ResNets) and vision transformers (ViTs). Our comprehensive analysis shows several novel insights that (1) With KDIGA, students can preserve or even exceed the adversarial robustness of the teacher model, even when their models have fundamentally different architectures; (2) KDIGA enables robustness to transfer to pre-trained students, such as KD from an adversarially trained ResNet to a pre-trained ViT, without loss of clean accuracy; and (3) Our derived local linearity bounds for characterizing adversarial robustness in KD are consistent with the empirical results.
Izgradnja knjižnične zbirke v knjižnici na Glasbeni šoli Jesenice

Purpose: The construction of a library collection at the Music School Jesenice is being described with some emphasis how the library collection should develop in future.Methodology/approach: The library collection was presented according to the activities that form a planned and systematic process of its development.Results: The acquisition of the material must be in accordance with the number of pupils that attend classes at each individual instrument. The goal is that the turn of the material at Music School Jesenice should achieve value 1.Research limitations: We do not know how the library collection at Music School Jesenice developed before 2003. The data how the material was obtained nor about the value for individual unit of library material included in the library collection before 2003 are not known.Originality/practical implications: The researched helped to understand the theoretical starting points related to the construction of library collections. The theoretical background was compared to the actual situation at Music School Jesenice library. The data will be used for future changes and improvements to increase the relevance of Music School Jesenice library collection and to meet the needs of teachers and students of Music School Jesenice.
VRAIN-UPV MLLP's system for the Blizzard Challenge 2021

This paper presents the VRAIN-UPV MLLP's speech synthesis system for the SH1 task of the Blizzard Challenge 2021. The SH1 task consisted in building a Spanish text-to-speech system trained on (but not limited to) the corpus released by the Blizzard Challenge 2021 organization. It included 5 hours of studio-quality recordings from a native Spanish female speaker. In our case, this dataset was solely used to build a two-stage neural text-to-speech pipeline composed of a non-autoregressive acoustic model with explicit duration modeling and a HiFi-GAN neural vocoder. Our team is identified as J in the evaluation results. Our system obtained very good results in the subjective evaluation tests. Only one system among other 11 participants achieved better naturalness than ours. Concretely, it achieved a naturalness MOS of 3.61 compared to 4.21 for real samples.
Towards better data discovery and collection with flow-based programming.

Despite huge successes reported by the field of machine learning, such as voice assistants or self-driving cars, businesses still observe very high failure rate when it comes to deployment of ML in production. We argue that part of the reason is infrastructure that was not designed for data-oriented activities. This paper explores the potential of flow-based programming (FBP) for simplifying data discovery and collection in software systems. We compare FBP with the currently prevalent service-oriented paradigm to assess characteristics of each paradigm in the context of ML deployment. We develop a data processing application, formulate a subsequent ML deployment task, and measure the impact of the task implementation within both programming paradigms. Our main conclusion is that FBP shows great potential for providing data-centric infrastructural benefits for deployment of ML. Additionally, we provide an insight into the current trend that prioritizes model development over data quality management.
Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units.

Language models (LMs) for text data have been studied extensively for their usefulness in language generation and other downstream tasks. However, language modelling purely in the speech domain is still a relatively unexplored topic, with traditional speech LMs often depending on auxiliary text LMs for learning distributional aspects of the language. For the English language, these LMs treat words as atomic units, which presents inherent challenges to language modelling in the speech domain. In this paper, we propose a novel LSTM-based generative speech LM that is inspired by the CBOW model and built on linguistic units including syllables and phonemes. This offers better acoustic consistency across utterances in the dataset, as opposed to single melspectrogram frames, or whole words. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features. Through our experiments, we also highlight some well known, but poorly documented challenges in training generative speech LMs, including the mismatch between the supervised learning objective with which these models are trained such as Mean Squared Error (MSE), and the true objective, which is speech quality. Our experiments provide an early indication that while validation loss and Mel Cepstral Distortion (MCD) are not strongly correlated with generated speech quality, traditional text language modelling metrics like perplexity and next-token-prediction accuracy might be.
SISTEM INFORMASI PENGOLAHAN DATA HASIL PRODUKSI KREASI ULOS PADA UKM OLIVIA HANDMADE

Small and Medium Enterprises (UKM) Olivia Handmade is engaged in handicrafts or handicrafts, namely the manufacture of ethnic souvenirs made from ulos cloth, songket and North Sumatran batik. In the era of digitalization, information systems play an important role, especially in companies as a support for progress and development. In designing and building an information system, of course, it must be based on the level of user needs. Likewise with information which is the main source of an information system. Over time, information systems based on computer technology are needed to make an innovation, either in hardware or software. Olivia Handmade UKM in data processing still uses manual system, namely using ordinary writing tools. With current technological advances, desktop-based production information systems have been created with supporting applications such as Visual Basic.net and connected by the Sql Server database, these supporting applications are applications that run on a computer and are able to operate without being connected to an internet connection (offline). So that it can produce information quickly to help the performance of UKM Olivia Handmade
MODEL PREDIKTIF KEBAKARAN HUTAN DENGAN SUPPORT VECTOR REGRESSION

Data Mining, Kebakaran Hutan, Support Vector Regression, Mean Abseolute Error (MAE), Root Mean Square Error (RMSE).
Software Engineering Meets Systems Engineering: Conceptual Modeling Applied to Engineering Operations

Models are fundamentally crucial to many scientific fields, including software engineering, systems engineering, enterprise modeling, and business modeling. This paper focuses on diagrammatic conceptual modeling, as opposed to mathematical or computational models, wherein a conceptual model is a translation of reality processes into an abstract mechanism that has similar structure and parallel events of the external processes. Although various modeling approaches exist, including UML (Unified Modeling Language) in software engineering and its dialect, SysML (System Modeling Language), in systems engineering, several difficulties arise in such models, including the problem of model multiplicity that is related to the lack an integrated view of structure and behavior. This paper generalizes conceptual modeling to be applied in organizations at large. According to authorities, the so-called organization theory portrays organizations as machine-like systems. As a machine, an organization coordinates its parts to transform inputs into outputs. Therefore, we synthesize the notion of an organization as a machine and apply a new modeling methodology called thinging machine (TM) to real engineering operations. The results show the viability of the TM methodology serving as a foundation for high-level modelling of systems.
Robustness and Discrimination Oriented Image Hashing with CVA and Texture Structure Descriptor

Image hashing is an essential technology for the community of multimedia security, and has been extensively applied in image authentication and content protection. This paper designs a new image hashing scheme with CVA and texture structure descriptor. A valuable contribution is the color DWT (Discrete Wavelet Transform) representation by incorporating CVA (color vector angle) matrix into LL sub-band. Since CVA can well retain the color information, desirable discrimination of our hashing is achieved. In addition, as LBP (local binary pattern) is texture structure descriptor, feature extraction with LBP from the color DWT representation ensures good classification performance between robustness and discrimination. The effectiveness of our hashing scheme is validated by various experiments with open databases. Experimental results demonstrate that the proposed image hashing scheme is superior to the state-of-the-art schemes in terms of classification performance between perceptual robustness and discriminative capability.
LibGuides: Teaching & Learning: COLA 1500

Overview of services provided by the Teaching & Learning department with recommended resources and more.
A Critical Discourse Analysis of the Language of Evaluation and Stance : Chinese and Korean Newspapers' Headlines on THAAD

In the contrasting reports of the THAAD deployment in South Korea, the conflicting national media stances are reflected in the discursive patterns of the Chinese and Korean newspapers' headlines. Grounded upon van Dijk's socio-cognitive perspective in the critical discourse analysis, these patterns are analyzed in terms of the Attitude systems of the Appraisal Theory. The data of headlines are collected from online articles in Chinese and Korean major daily newspapers. The findings show that the newspapers' contrasting stances on the THAAD issues are discursively differentiated into their attitudinal representations of the headlines. These results also demonstrate the discursive strategy of positive self and negative other presentation to establish each nation's solidarity involved in the THAAD issues.
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching

Learning meaningful behaviors in the absence of reward is a difficult problem in reinforcement learning. A desirable and challenging unsupervised objective is to learn a set of diverse skills that provide a thorough coverage of the state space while being directed, i.e., reliably reaching distinct regions of the environment. In this paper, we build on the mutual information framework for skill discovery and introduce UPSIDE, which addresses the coverage-directedness trade-off in the following ways: 1) We design policies with a decoupled structure of a directed skill, trained to reach a specific region, followed by a diffusing part that induces a local coverage. 2) We optimize policies by maximizing their number under the constraint that each of them reaches distinct regions of the environment (i.e., they are sufficiently discriminable) and prove that this serves as a lower bound to the original mutual information objective. 3) Finally, we compose the learned directed skills into a growing tree that adaptively covers the environment. We illustrate in several navigation and control environments how the skills learned by UPSIDE solve sparse-reward downstream tasks better than existing baselines.
On Success and Simplicity: A Second Look at Transferable Targeted  Attacks

Achieving transferability of targeted attacks is reputed to be remarkably difficult. Currently, state-of-the-art approaches are resource-intensive because they necessitate training model(s) for each target class with additional data. In our investigation, we find, however, that simple transferable attacks which require neither additional data nor model training can achieve surprisingly high targeted transferability. This insight has been overlooked until now, mainly due to the widespread practice of unreasonably restricting attack optimization to a limited number of iterations. In particular, we, for the first time, identify that a simple logit loss can yield competitive results with the state of the arts. Our analysis spans a variety of transfer settings, especially including three new, realistic settings: an ensemble transfer setting with little model similarity, a worse-case setting with low-ranked target classes, and also a real-world attack against the Google Cloud Vision API. Results in these new settings demonstrate that the commonly adopted, easy settings cannot fully reveal the actual properties of different attacks and may cause misleading comparisons. We also show the usefulness of the simple logit loss for generating targeted universal adversarial perturbations in a data-free and training-free manner. Overall, the aim of our analysis is to inspire a more meaningful evaluation on targeted transferability. Code is available at https://github.com/ZhengyuZhao/Targeted-Tansfer
Organization Risk Culture Model for Tourism Performance in UAE

Risk culture is the scenario of controlling, recognizing, and assessing threats that impact the organization's performance. Risk culture attempts and analyzes the potential hazard for future threats that strategize diversification through effectively manage risk. Recent events have reminded the organization's culture to comprehensive view on risk and provide significant control and incentives to overcome it. The main drive of this research was to discuss the relation between organization risk culture factors and tourism performance within the UAE. The major research question was whether organization risk culture factors influence tourism performance in the UAE. This research intends to yield interaction of e-tourism and tourist impact relationship between organization risk culture factors and the tourism performance in the UAE. The research main outcomes focus on developing the conceptual model of organization risk culture towards tourism performance in the UAE.
Mobile Healthcare Design Research: A Special Issue for Information Systems Researchers

Abstract Special Section Editorial - Mobile Decision Support and Analytics for Healthcare: Citizen, Organization, Governmental and Technological Perspectives
Following others' footsteps: A contextual attraction effect induced by biological motion

State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
Improved FRQI on superconducting processors and its restrictions in the NISQ era

In image processing, the amount of data to be processed grows rapidly, in particular when imaging methods yield images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the Flexible Representation of Quantum Images (FRQI). Furthermore, we check experimentally what is the limit in the current noisy intermediate-scale quantum era, i.e. up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates needed, especially of the error-prone controlled-NOT gates, can be reduced. As a consequence, the size of manageable images increases.
Problems of Distributed Computing and Ways to Solve Them

The article considers the problems of distributed computing and possible ways to solve them. With the beginning of the development of computer networks and the advent of the Internet, a large number of remote services have appeared that offer their available computing resources. On the one hand, as a result of such cooperation, it is possible to solve the problem of insufficiency of own hardware resources, but, on the other hand, there are a number of problems, namely: lack of standards and algorithms of development and modeling as the main means of building distributed computing systems; lack of universal and convenient application development tools; technical problems and limitations, which are determined by the currently existing technical solutions for the construction of data processing systems with relatively high performance; small number and narrow focus of tasks that could generate income and require a comprehensive solution. One of the main problems with distributed computing is the slow development of high-speed network equipment that could compete with internal connections. Increasing the data transfer rate using outdated technologies is impossible for a number of technological and physical reasons. Research in the field of development of new data transmission protocols and their optimization is carried out, but the final decision does not yet exist When organizing calculations related to data processing, an important issue is the rational distribution of data in terms of effective problem solving. Ordinary users do not do this, because most often they use ready-made solutions for their tasks. Even when creating new distributed systems by professional programmers, the specifics of application areas are not fully taken into account. That is, the created software products do not provide the ability to adapt to specific tasks of users, although work in this direction has significantly intensified. The solution to the problems of universality of the application of distributed computing lies in the creation of methods for software development and the creation of generalized solutions that would be universal and suitable for use in various fields of research.