Reviewerly/exHarmony huggingface dataset:
Semi-automatic Acquisition of Datasets for Retail Recognition

The acquisition of datasets is typically a laborious task. It is challenging, especially if the required annotations in every image in the dataset are vast. It is even more challenging if the inter-class variance, the visual difference between two distinct classes, is low. Retail product recognition constitutes an example of both issues. Products are densely packed on shelves, resulting in many objects within an image. Products share visual similarities, which makes them hard to distinguish. In this work, we propose Annotron, a tool tackling the acquisition problem in this domain. Exploiting dataset struc- tures, such as being organized in consecutive frames, we detect real-world objects through pre-trained detectors and reproject detections to generate candidate traces over time. Further, we aid labelers by computing poten- tial matches of real-world objects and reference images based on their visual similarity: We cluster consecutive detections based on a large set of reference images using embeddings acquired from pre-trained networks. Using the proposed tool reduces manual efforts drastically by diminishing the time spent on repetitive, error-prone tasks. We evaluate Annotron in the retail recognition domain. The domain is commonly considered fine-grained, which means that instance-level annotations are costly due to the described problems. We refine the given dataset, surpass the number of previously found stock-keeping units, and label over 446.500 individual bounding boxes.
Chatbot Explorer: Towards an understanding of knowledge bases of chatbot systems

A chatbot can automatically process a user’s request, e.g. to provide a requested information. In doing so, the user starts a conversation with the chatbot and can specify the request by further inquiry. Due to the developments in the field of NLP in recent years, algorithmic text comprehension has been significantly improved. As a result, chatbots are increasingly used by companies and other institutions for various tasks such as order processes or service requests. Knowledge bases are often used to answer users queries, but these are usually curated manually in various text files, prone to errors. Visual methods can help the expert to identify common problems in the knowledge base and can provide an overview of the chatbot system. In this paper, we present Chatbot Explorer, a system to visually assist the expert to understand, explore, and manage a knowledge base of different chatbot systems. For this purpose, we provide a tree-based visualization of the knowledge base as an overview. For a detailed analysis, the expert can use appropriate visualizations to drill down the analysis to the level of individual elements of a specific story to identify problems within the knowledge base. We support the expert with automatic detection of possible problems, which can be visually highlighted. Additionally, the expert can also change the order of the queries to optimize the conversation lengths and it is possible to add new content. To develop our solution, we have conducted an iterative design process with domain experts and performed two user evaluations. The evaluations and the feedback from our domain experts have shown that our solution can significantly improve the maintainability of chatbot knowledge bases.
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.
Hierarchical Bayesian data selection

There are many issues that can cause problems when attempting to infer model parameters from data. Data and models are both imperfect, and as such there are multiple scenarios in which standard methods of inference will lead to misleading conclusions; corrupted data, models which are only representative of subsets of the data, or multiple regions in which the model is best fit using different parameters. Methods exist for the exclusion of some anomalous types of data, but in practice, data cleaning is often undertaken by hand before attempting to fit models to data. In this work, we will introduce the concept of Bayesian data selection; the simultaneous inference of both model parameters, and parameters which represent our belief that each observation within the data should be included in the inference. The aim, within a Bayesian setting, is to find the regions of observation space for which the model can well-represent the data, and to find the corresponding model parameters for those regions. A number of approaches will be explored, and applied to test problems in linear regression, and to the problem of fitting an ODE model, approximated by a finite difference method, to data. The approaches are extremely simple to implement, can aid mixing of Markov chains designed to sample from the arising densities, and are very broadly applicable to the majority of inferential problems. As such this approach has the potential to change the way that we conduct and interpret the fitting of models to data.
Evaluation of methods to represent the structure in tomographic images of freeze-dried porous media

Morphological image processing is used to translate the structural information from X-ray microtomography scans of porous freeze dried maltodextrin into a 3-dimensional (3D) pore network.The purpose is to conduct pore network simulations on realistic porous structures.The success of this translation decisively depends on the binarization of the primary graylevel image.This is, however, based on thresholding of the image data, the result of which is strongly affected by the user and the method as well.In this paper, we present and discuss a comparison of three thresholding methods and their (dis)agreement with the underlying real structure properties.
PROTOCOL –Availability of Open Access Metadata from Open Journals – A case study in DOAJ and Crossref v5

This protocol is for the research about the availability of Metadata from Open Journals in the DOAJ in Crossref. The goal is to find out how many papers from DOAJ journals are available on Crossref, whether their metadata is available and the origin of their references' DOIs. This will provide us a clearer picture about the state of Open Access research. Our research involves the articles from Open Access journals in DOAJ and their data on Crossref. We scouted the availability of these articles’ reference lists, the presence of IDs such as DOIs and the entities responsible for their specification. This analysis was carried using DOAJ article metadata dump, then modified and enriched thought Crossref’s APIs DOI requests. The data will be further analysed to verify the distribution of the provenance of the articles' DOIs.
DConvLSTM: An Autotuning Distributed CNN Model in Cloud Environment for Optimized Accuracy, Scaling, and Execution Time

Abstract Artificial Intelligence (AI) and deep learning have altered the digital landscape. Data grows in lockstep with technological advancements. The training procedure for Deep Learning algorithms is becoming increasingly complex as the amount of data available grows. In the deep learning process, having superior technology to speed up the training and testing time is critical. CNN (Convolutional Neural Networks) is a deep learning algorithm for image processing that is widely utilized. In this paper, a novel distributed CNN with LSTM is proposed to optimize accuracy, execution time, and scalability. Different methodologies and tactics for improving CNN training time through distributed models are studied. To comprehend the neural network structure of parallelizing and spreading the execution, each layer of the CNN is investigated and analyzed. Computations on the CPU, GPU, and TPU are also investigated, as well as settings like Google Colab, AWS SageMaker, and others. According to the analysis, the use of an L2 regularization, dropout layer, and a ConvLSTM for autotuning of hyperparameters improves speed in all settings, The final CNN, with these additional layers for performance acceleration, is subsequently deployed in Google Cloud Platform Virtual Machine (VM) instances. This makes it easier to investigate the performance of the proposed distributed deep learning model at scale. Due to the communication between multiple nodes involved in distributing a deep learning model, the model's accuracy usually suffers. But in this proposed work utilizing an autotuning LSTM and Distributed CNN algorithm that distributes optimally and balances accuracy and speed-up is proposed in this proposed work. The proposed models achieved a 2.206 percent boost in speed with respect to data parallel DConvLSTM and a 1.4 percent improvement in model parallel training time of DConvLSTM. The maximum accuracy achieved was 93.3825 and 89.59 percent in data-parallel and model-parallel executions respectively.
A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple Agents

Abstract Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target’s escaping success by 13% and in some individual cases by 37%.
DEEP, a methodology for entity extraction using organizational patterns: Application to job offers

Plain texts written in natural language may have several specific features, such as organizational patterns and an ambiguous and evolving vocabulary. From the literature, entity extraction approaches are not sufficient to consider these specific features jointly. To address this issue, we propose DEEP, a methodology that improves the quality of entity extraction by using organizational patterns through a sequence labelling technique. To this end, DEEP creates a high-quality corpus and relies on an appropriate learning algorithm. DEEP is validated on a real corpus of job offers. Experiments show that (1) considering organizational patterns improves the quality of entity extraction, (2) vocabulary evolution is taken into consideration and ambiguity in vocabulary is reduced, (3) DEEP provides clear guidelines for the creation of a high-quality corpus for entity extraction, (4) the Bidirectional Long Short-Term Memory + Conditional Random Field architecture for sequence labelling is the one that takes the most advantage of the organizational patterns.
Evaluation of Sedimentary Basins For Hydrocarbon Exploration Using Aeromagnetic Data, Northwestern Sinai Peninsula, Egypt

Abstract The Sinai Peninsula's northwestern area has many structure patterns with different directions and strong variations in basement depth. This work aims to deduce the basement surface’s depth and sedimentary basin’s locations and evaluate the subsurface structural framework to determine the new concession for hydrocarbon exploration in the research area.Geophysical data used in this study compiled between total aeromagnetic intensity (TM) data and wells data as locations and depths were used in 2D magnetic profiles in basement depth determination. The technique used for magnetic data interpretation to determine basement depth and structures include; Reducing to the pole (RTP), Power spectrum (low and high pass filters to determine deep and shallow sources), and Centre for Exploration Targeting (CET) Grid analysis.Therefore, according to these results, the basement depths range from 1500 to 5000 meters and the area has two principal structural trends: NW-SE trends related to extensions that happened in the Delta belt (Hing line) north Pelusium line and NE-SW trends associated with the Syrian arch system compressional force south Pelusium line. The new concession for hydrocarbon exploration is represented in the northwestern part of the study area.
Formation of the professional status of teachers on the basis of a synergetic approach to the organisation of methodological work

The purpose of this research is to receive teacher opinions on the formation of the professional status of teachers on the basis of a synergistic approach to the organisation of methodological work. This research was created in accordance with the qualitative research method. The study group of the research consisted of 40 primary schoolteachers working in various primary schools in Almaty, Kazakhstan, in the 2021–2022 academic year. The data of the research were collected with a semi-structured interview form developed by the researchers. As a result of the research, the majority of primary schoolteachers participating in the research stated that teachers’ behaviour in accordance with the ethical principles of the profession, providing a qualified teaching education, transforming them into a profession with high economic returns and improving their personal rights are factors that positively affect the status of the teaching profession. The majority of primary schoolteachers participating in the research evaluated the low economic return of the profession, the insufficient personal rights of the profession, the lack of appreciation of the teacher in the education system and the high expectation from the teacher as factors that negatively affect the status of the teaching profession. The majority of primary schoolteachers participating in the research determined that developing the reorganisation of teacher training policies, providing professional motivation in-service trainings for teachers, creating a sharing platform for teachers and working to improve the perception of society in a positive way are suggestions for the formation of the professional status of teachers on the basis of a synergetic approach to the organisation of methodological work.&#x0D; &#x0D; Keywords: Occupational status, teaching profession, teacher opinions;
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels

In supervised learning, obtaining a large set of fully-labeled training data is expensive. We show that we do not always need full label information on every single training example to train a competent classifier. Specifically, inspired by the principle of sufficiency in statistics, we present a statistic (a summary) of the fully-labeled training set that captures almost all the relevant information for classification but at the same time is easier to obtain directly. We call this statistic "sufficiently-labeled data" and prove its sufficiency and efficiency for finding the optimal hidden representations, on which competent classifier heads can be trained using as few as a single randomly-chosen fully-labeled example per class. Sufficiently-labeled data can be obtained from annotators directly without collecting the fully-labeled data first. And we prove that it is easier to directly obtain sufficiently-labeled data than obtaining fully-labeled data. Furthermore, sufficiently-labeled data is naturally more secure since it stores relative, instead of absolute, information. Extensive experimental results are provided to support our theory.
A critical Schrödinger cat qubit

Encoding quantum information onto bosonic systems is a promising route to quantum error correction. In a cat code, this encoding relies on the confinement of the system's dynamics onto the two-dimensional manifold spanned by Schrödinger cats of opposite parity. In dissipative cat qubits, an engineered dissipation scheme combining two-photon drive and loss has been used to autonomously stabilize this manifold, ensuring passive protection against bit-flip errors, regardless of their origin. In Kerr cat qubits, where highly-performing gates can be engineered, two-photon drive and Kerr nonlinearity cooperate to confine the system to a two-fold degenerate ground state manifold spanned by cats of opposite parity. Dissipative, Hamiltonian, and hybrid confinements have been investigated at resonance. Here, we propose a critical cat code, where both two-photon loss and Kerr nonlinearity are present, and the two-photon drive is allowed to be out of resonance. The performance of this code is assessed via the spectral theory of Liouvillians in all configurations, from the purely dissipative to the Kerr limit. We show that large detunings and small, but non-negligible, two-photon loss rates are fundamental to achieve optimal performance. We demonstrate that the competition between nonlinearity and detuning results in a first-order dissipative phase transition, leading to a squeezed vacuum steady state. To achieve the maximal suppression of the logical bit-flip rate requires initializing the system in the metastable state emerging from the first-order transition, and we detail a protocol to do so. Efficiently operating over a broad range of detuning values, the critical cat code is particularly resistant to random frequency shifts characterizing multiple-qubit operations, opening venues for the realization of reliable protocols for scalable and concatenated bosonic qubit architectures.
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets

This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of times one algorithm performs better than another on different data sets. Because of its Bayesian foundations, the Bayesian Bradley Terry model (BBT) has different characteristics than frequentist approaches to comparing multiple algorithms on multiple data sets, such as Demsar (2006) tests on mean rank, and Benavoli et al. (2016) multiple pairwise Wilcoxon tests with p-adjustment procedures. In particular, a Bayesian approach allows for more nuanced statements regarding the algorithms beyond claiming that the difference is or it is not statistically significant. Bayesian approaches also allow to define when two algorithms are equivalent for practical purposes, or the region of practical equivalence (ROPE). Different than a Bayesian signed rank comparison procedure proposed by Benavoli et al. (2017), our approach can define a ROPE for any metric, since it is based on probability statements, and not on differences of that metric. This paper also proposes a local ROPE concept, that evaluates whether a positive difference between a mean measure across some cross validation to the mean of some other algorithms is should be really seen as the first algorithm being better than the second, based on effect sizes. This local ROPE proposal is independent of a Bayesian use, and can be used in frequentist approaches based on ranks. A R package and a Python program that implements the BBT is available.
Pure Data and INScore: Animated notation for new music

New music is made with computers, taking advantage of its graphics displays rather than its audio algorithms. Pure Data can be used to compose them. This essay will show a case study that uses Pure Data, in connection with INScore, for making a new type of score that uses animated notation or dynamic musicography for making music with performers. This sample was made by the author of the text, and it will show a number of notation possibilities that can be done using the combination of software. This will be accompanied by a simple prediction of what a musician could perform with it.
Non-Fungible Tokens in Business and Management -- A Review

Non-Fungible Tokens (NFTs) are a new development in blockchain technology. News around NFTs is surrounded by skepticism because unrealistically high prices are being paid online for these NFTs which are in the form of apparently simple digital arts and photographs. It is not clear if this is a trend, a hype, a bubble, or a legitimate novel way of holding and trading value. A literature review of peer-reviewed scholarly studies, performed in the context of business and management, is presented here. Moreover, we also discuss open issues, and challenges, and present future research directions. Analysis of these studies reveal that schools of thoughts are divided on the validity of this form of digital tokens. On one hand, there is a lot of criticism but on the other hand, we can find novel business models and applications of NFTs especially the feature of smart contracts. It can, therefore, be concluded that NFTs, even if not in their current form, are here to stay and may promise new ways of protecting digital assets in an immutable and easily traceable form.
LinGBM: A Performance Benchmark for Approaches to Build GraphQL Servers (Extended Version)

GraphQL is a popular new approach to build Web APIs that enable clients to retrieve exactly the data they need. Given the growing number of tools and techniques for building GraphQL servers, there is an increasing need for comparing how particular approaches or techniques affect the performance of a GraphQL server. To this end, we present LinGBM, a GraphQL performance benchmark to experimentally study the performance achieved by various approaches for creating a GraphQL server. In this article, we discuss the design considerations of the benchmark, describe its main components (data schema; query templates; performance metrics), and analyze the benchmark in terms of statistical properties that are relevant for defining concrete experiments. Thereafter, we present experimental results obtained by applying the benchmark in three different use cases, which demonstrates the broad applicability of LinGBM.
Entanglement Universality of TGX States in Qubit-Qutrit Systems

We prove that all states (mixed or pure) of qubit-qutrit ($2\times 3$) systems have entanglement-preserving unitary (EPU) equivalence to a compact subset of true-generalized X (TGX) states called EPU-minimal TGX states which we give explicitly. Thus, for any spectrum-entanglement combination achievable by general states, there exists an EPU-minimal TGX state of the same spectrum and entanglement. We use I-concurrence to measure entanglement and give an explicit formula for it for all $2\times 3$ minimal TGX states (a more general set than EPU-minimal TGX states) whether mixed or pure, yielding its minimum average value over all decompositions. We also give a computable I-concurrence formula for a more general family called minimal super-generalized X (SGX) states, and give optimal decompositions for minimal SGX states and all of their subsets.