Reviewerly/exHarmony huggingface dataset:
Information graphs and their use for Bayesian network graph construction

In this paper, we present the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where ‘deduction’ is used for defeasible ‘forward’ inference. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Our IG-formalism is intended to facilitate the construction of formal representations within AI systems by serving as an intermediary formalism between analyses performed using informal reasoning tools and formalisms that allow for formal evaluation. In this paper, we investigate the use of the IG-formalism as an intermediary formalism in facilitating Bayesian network (BN) graph construction. We propose a structured approach for automatically constructing from an IG a directed BN graph, together with qualitative constraints on the probability distribution represented by the BN. Moreover, we prove a number of formal properties of our approach and identify assumptions under which the construction of an initial BN graph can be fully automated.
Asymmetrical Game Design Approaches Solve Didactic Problems in VR Engineer Trainings

The use of VR in training groups for wind turbine engineers can cause didactic and practical problems. Integrating the whole group into the lesson and retaining attention and motivation while only one or two trainees wear a VR head mounted display (HMD) can be challenging for the trainer. Whereas VR HMDs isolate the users, engineering on wind turbines is a group effort. The problem is exacerbated when trainees need to use remote access to participate in the lesson, as can be the case under pandemic restrictions. We propose to use methods from asymmetrical game design and constructivist didactics to integrate participants without VR headsets into VR trainings for engineers.
Angular Margin Softmax Loss and Its Variants for Double Compressed AMR Audio Detection

Double compressed (DC) adaptive multi-rate (AMR) audio detection is an important but challenging audio forensic task which has received great attention over the last decade. Although the majority of the existing studies extract hand-crafted features and classify these features using traditional pattern matching algorithms such as support vector machines (SVM), recently convolutional neural network (CNN) based DC AMR audio detection system was proposed which yields very promising detection performance. Similar to any traditional CNN based classification system, CNN based DC AMR recognition system uses standard softmax loss as the training criterion. In this paper, we propose to use angular margin softmax loss and its variants for DC AMR detection problem. Although using angular margin softmax was originally proposed for face recognition, we adapt it to the CNN based end-to-end DC audio detection system. The angular margin softmax basically introduces a margin between two classes so that the system can learn more discriminative embeddings for the problem. Experimental results show that adding angular margin penalty to the traditional softmax loss increases the average DC AMR audio detection from 95.83% to 100%. It is also found that the angular margin softmax loss functions boost the DC AMR audio detection performance when there is a mismatch between training and test datasets.
Best Model and Performance of Bayesian Regularization Method for Data Prediction

Abstract The backpropagation algorithm has many training and activation functions that can be used to influence or maximize prediction results, all of which have their respective advantages and disadvantages. The purpose of this paper is to analyze one of the training functions of the backpropagation algorithm which can be used as a reference for use in data prediction problems in the form of models and best performance. The training function is the Bayesian Regularization method. This method is able to train the network by optimizing the Levenberg-Marquardt by updating the bias and weights. The research dataset used to analyze the data in this paper is Formal Education Participation in Indonesia 2015-2020 which consists of the School Participation Rate, the Gross Enrollment Rate, and the Pure Enrollment Rate. The 2015-2016 dataset is used as training data with a 2017 target, while the 2018-2019 dataset is the test data with a 2020 target. The models used are 2-10-1, 2-15-1, and 2-20-1. Based on the analysis and calculation process, the results of the 2-15-1 model are the best with an epoch of 217 iterations and an MSE of 0.00002945, this is because the epoch is not too large and has the smallest MSE compared to the other 2 models.
Use Case Testing: A Constrained Active Machine Learning Approach

As a methodology for system design and testing, use cases are well-known and widely used. While current active machine learning (ML) algorithms can effectively automate unit testing, they do not scale up to use case testing of complex systems in an efficient way. We present a new parallel distributed processing (PDP) architecture for a constrained active machine learning (CAML) approach to use case testing. To exploit CAML we introduce a use case modeling language with: (i) compile-time constraints on query generation, and (ii) run-time constraints using dynamic constraint checking. We evaluate this approach by applying a prototype implementation of CAML to use case testing of simulated multi-vehicle autonomous driving scenarios.
Uncertainty management with extra-functional qualities in multi-artefact co-evolution.

Metamodels are at the core of any model-driven projects and give the experts a linguistic means to abstractly represent the problem domain's instances.Metamodels are prone to modifications due to new insights emerging from the domain, improvements, and features being added to the modeling language over time.Metamodel evolution may induce severe repercussions over the related artifacts, which might need to be consistently adapted.The co-evolution problem poses another issue related to the epistemic uncertainty arising as a response to a metamodel change where many different consistency restoration procedures are possible, e.g., restoring models and transformations.In this work we rely on the notion of information loss (IL) to understand which migration combinations are preferable to others in coupled evolution, by offering a ranking of the possible solutions.The IL denotes that part of the knowledge contained in the source models cannot be conveyed or translated into the target models, using a selected transformation, resulting in a loss of information.Such aspect represents an extra-functional quality that can be used for ranking the selection of certain transformation migrations in the context of multiple available alternatives and in combination with model migrations.Information loss induced by a (migrated) transformation can be defined as the amount of information lost executing the (migrated) transformation compared to the original output model obtained by executing the original transformation.The proposed approach is supported by a prototype tool that we demonstrate and validate.
Operation-wise Attention Network for Tampering Localization Fusion

In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.
From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study

Program comprehension is a vital skill in software development. This work investigates program comprehension by examining the eye movement of novice programmers as they gain programming experience over the duration of a Java course. Their eye movement behavior is compared to the eye movement of expert programmers. Eye movement studies of natural text show that word frequency and length influence eye movement duration and act as indicators of reading skill. The study uses an existing longitudinal eye tracking dataset with 20 novice and experienced readers of source code. The work investigates the acquisition of the effects of token frequency and token length in source code reading as an indication of program reading skill. The results show evidence of the frequency and length effects in reading source code and the acquisition of these effects by novices. These results are then leveraged in a machine learning model demonstrating how eye movement can be used to estimate programming proficiency and classify novices from experts with 72% accuracy.
Structure-Aware Parameter-Free Group Query via Heterogeneous Information Network Transformer

Owing to a wide range of important applications, such as team formation, dense subgraph discovery, and activity attendee suggestions on online social networks, Group Query attracts a lot of attention from the research community. However, most existing works are constrained by a unified social tightness k (e.g., for k-core, or k-plex), without considering the diverse preferences of social cohesiveness in individuals. In this paper, we introduce a new group query, namely Parameter-free Group Query (PGQ), and propose a learning-based model, called PGQN, to find a group that accommodates personalized requirements on social contexts and activity topics. First, PGQN extracts node features by a GNN-based method on Heterogeneous Activity Information Network (HAIN). Then, we transform the PGQ into a graph-to-set (Graph2Set) problem to learn the diverse user preference on topics and members, and find new attendees to the group. Experimental results manifest that our proposed model outperforms nine state-of-the-art methods by at least 51% in terms of F1-score on three public datasets.
No-Reference Quality Assessment of Night-Time Images Via the Analysis of Local and Global Features

This paper focuses on the quality assessment of the night-time images, which is technically vulnerable to diverse and sophisticated visual distortions, and proposes an effective no-reference image quality assessment method through comprehensive analysis of local and global image attributes. Several quality-aware features are extracted through the study of image brightness, contrast, structure and color. Specifically, the features related to brightness and contrast are extracted through the analysis of local information, while the others are extracted through the analysis of global information. The quality assessment model for night-time images is built by mapping the selected features into subjective ratings via the support vector regression. Extensive experiments conducted on public night-time image database demonstrates the superiority of the proposed method.
Automated User Experience Testing through Multi-Dimensional Performance Impact Analysis

Although there are many automated software testing suites, they usually focus on unit, system, and interface testing. However, especially software updates such as new security features have the potential to diminish user experience. In this paper, we propose a novel automated user experience testing methodology that learns how code changes impact the time unit and system tests take, and extrapolate user experience changes based on this information. Such a tool can be integrated into existing continuous integration pipelines, and it provides software teams immediate user experience feedback. We construct a feature set from lexical, layout, and syntactic characteristics of the code, and using Abstract Syntax Tree-Based Embeddings, we can calculate the approximate semantic distance to feed into a machine learning algorithm. In our experiments, we use several regression methods to estimate the time impact of software updates. Our open-source tool achieved a 3.7% mean absolute error rate with a random forest regressor.
Quantum Convolutional Neural Network For Image Classification

In this paper we propose a MERA inspired ternary unitary circuit feature extraction method based on Quantum Convolutional Neural Network(QCNN) in the TensorFlow quantum framework for binary image classification. The image data is properly downscaled with the ternary unitary circuits before fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. High or appropriate entangled state corresponds to the high separated weight function. When entanglement is reduced, the classification result can then be directly obtained from the qubit. We trained quantum classifiers with QCNN and with two hybrid quantum-classical QCNN models and their performance are compared against that of a classic CNN. The results on the breast cancer image dataset show that the proposed QCNN with the proposed feature extraction outperformed the classic CNN in terms of recognition rate 90%-93% over 83%-85% on performance.
Hierarchical Attention Image-Text Alignment Network For Person Re-Identification

Description based Person Re-identification (Re-ID) is a crucial cross-modality task that aims at retrieving a specific person for the given textual description. Existing description based Re-ID methods focus on learning robust representations to effectively measure the similarity between the global features of two modalities. However, such global mapping disregards semantic consistencies between local visual and linguistic features. Further, there are major challenges of alignment uncertainty that occur due to poor correspondence of text-image pairs and text complexity arising due to the irrelevant words. Towards this, we propose an end-to-end Hierarchical Attention Image-Text Alignment Network, named as HAITA-Net. Our model comprises of: i) a hierarchical attention alignment network to determine the potential relationships of image content and textual information at different levels, namely, word-patch level, phrase-patch level, and sentence-image level for addressing alignment uncertainty; ii) a new strategy of Term Frequency-Inverse document Frequency thresholding to extract the salient tokens to alleviate the challenge of text complexity. The network is optimized via joint weighted hierarchical attention loss and cross-modal loss in an end-to-end manner. Extensive experiments demonstrate the effectiveness of our method.
Single-photon Mach-Zehnder interferometry for High Schools

Abstract The superposition states, allowed by quantum theory, are dramatically diverse with respect to classical states and represent the true quantum states. By using a Mach-Zehnder interferometer we experimentally show how a single photon in a polarization superposition state can produce interference. The experimental setup includes a strongly attenuated laser source, polarizing beam splitters, half-waveplates, polarizers and single-photon detectors. The experimental activity has been performed with high-school students and teachers in several different contexts.
Adaptive Online Learning for the Autoregressive Integrated Moving Average Models

This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models without hyperparameters. The regret analysis and experiments on both synthetic and real-world datasets show that the performance of the proposed algorithms can be guaranteed in both theory and practice.
Filtering Empty Camera Trap Images in Embedded Systems

Monitoring wildlife through camera traps produces a massive amount of images, whose a significant portion does not contain animals, being later discarded. Embedding deep learning models to identify animals and filter these images directly in those devices brings advantages such as savings in the storage and transmission of data, usually resource-constrained in this type of equipment. In this work, we present a comparative study on animal recognition models to analyze the trade-off between precision and inference latency on edge devices. To accomplish this objective, we investigate classifiers and object detectors of various input resolutions and optimize them using quantization and reducing the number of model filters. The confidence threshold of each model was adjusted to obtain 96% recall for the nonempty class, since instances from the empty class are expected to be discarded. The experiments show that, when using the same set of images for training, detectors achieve superior performance, eliminating at least 10% more empty images than classifiers with comparable latencies. Considering the high cost of generating labels for the detection problem, when there is a massive number of images labeled for classification (about one million instances, ten times more than those available for detection), classifiers are able to reach results comparable to detectors but with half latency. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Anchor-based Bilingual Word Embeddings for Low-Resource Languages

Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text.MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs.For low resource languages training MWEs monolingually results in MWEs of poor quality, and thus poor bilingual word embeddings (BWEs) as well.This paper proposes a new approach for building BWEs in which the vector space of the high resource source language is used as a starting point for training an embedding space for the low resource target language.By using the source vectors as anchors the vector spaces are automatically aligned during training.We experiment on English-German, English-Hiligaynon and English-Macedonian.We show that our approach results not only in improved BWEs and bilingual lexicon induction performance, but also in improved target language MWE quality as measured using monolingual word similarity.
Blockchain Technology in Tourism Management

The tourist activity realized early on the advantages in the adoption of technology for the performance of its activities. With the emergence of the technological environment provided by Industry 4.0, blockchain technology emerges as the most disruptive and consequently more challenging technology for the tourism industry, since it has excellent potential to leverage innovation in this activity, whether in terms of products, processes, management, marketing, or institutionally. A bibliometric analysis was carried out to investigate affected studies in the tourism area, including the hotel, restaurant, marketing, and other sectors. Application of blockchain technology may be the key to boost tourism since its applicability presents several opportunities for tourism stakeholders, adds security and confidence in business transactions, can influence changes in the tourism value system, in terms of the value creation for customers, value creation for business, and in the tourism distribution chain.
P‐65: The Thickness of Pressure Sensitive Adhesive and Modulus Influence on Foldable Display's Cover Mechanical Performance

Pencil hardness and bendability tradeoff in foldable displays is one of the main problem in terms of reliability. As solution, an optimized laminate that includes the use of high modulus pressure sensitive adhesive and conventional hard coating was experimentally and analytically studied. Results showed an increase of pencil hardness without compromising bendability.
The Integration of the Blockchain Technology Into Wine Tourism

The aim of this study is to offer some suggestions to both the tourism industry and the practitioners in terms of making wine tourism and wine supply chain more transparent and traceable. From this point of view, in the current study, in which the blockchain technology in wine supply chain is examined, first the technology of block chain has comprehensively been assessed, and later the operational principles of blockchain technology are discussed. Afterwards, the blockchain technology was attempted to be integrated into wine tourism and wine supply chain. Hence, a new model was proposed. In this respect, the outputs that can be obtained via the use of blockchain technology in wine tourism and wine supply chain are revealed in this study.
Does Code Structure Affect Comprehension? On Using and Naming Intermediate Variables

Intermediate variables can be used to break complex expressions into more manageable smaller expressions, which may be easier to understand. But it is unclear when and whether this actually helps. We conducted an experiment in which subjects read 6 mathematical functions and were supposed to give them meaningful names. 113 subjects participated, of which 58% had 3 or more years of programming work experience. Each function had 3 versions: using a compound expression, using intermediate variables with meaningless names, or using intermediate variables with meaningful names. The results were that in only one case there was a significant difference between the two extreme versions, in favor of the one with intermediate variables with meaningful names. This case was the function that was the hardest to understand to begin with. In two additional cases using intermediate variables with meaningless names appears to have caused a slight decrease in understanding. In all other cases the code structure did not make much of a difference. As it is hard to anticipate what others will find difficult to understand, the conclusion is that using intermediate variables is generally desirable. However, this recommendation hinges on giving them good names.