Reviewerly/exHarmony huggingface dataset:
The Human Likeness of Government Chatbots – An Empirical Study from Norwegian Municipalities

While chatbots represent a potentially useful supplement to government information and service provision, transparency requirements imply the need to make sure that this technology is not confused with human support. However, there is a knowledge gap concerning whether and how government chatbots indeed represent a risk of such confusion, in spite of their resemblance with human conversation. To address this gap, we have conducted a study of a Norwegian municipality chatbot including interviews with 16 chatbot users and 18 municipality representatives, as well as analysis of > 2600 citizen dialogues. Interviews with citizen and municipality representatives suggested that citizens typically understood well the chatbot capabilities and limitations, though municipality representatives reported on some examples of humanizing the chatbot in its early phases of deployment. Dialogue analyses indicated that citizens have a markedly utilitarian style in their communication with the chatbot, suggesting limited anthropomorphizing of the chatbot.
Enhancing GNN Feature Modeling for Document Information Extraction Using Transformers

Business documents are used every day by all kinds and sizes of companies and administrations, even if most of these entities have several information systems where the documents are digitilized in different formats (json, xml, database tables, ...), there still an important number of business documents that require manual processing which costs a lot and is very time consuming. Extracting key-value information from business documents is a challenging problem due to the variety of document types and templates, in this work we will deal with the problem as a graph node classification problem using a multi “graph transformer" layers, we propose a graph construction method that focuses on the most relevant neighbours of every node while reducing the size of the graph and we use a document transformer embedding combined with some spatial and textual feature to give a better representation to each node. Our contribution in this work was to conceive a graph neural network (GNN) achieving the highest results comparing to the rest of GNN models dealing with the same problem to our knowledge, the model is small (53,6K parameters) comparing to the recent models using transformers architectures (hundreds of millions of parameters) which is very suitable for applications when storage constraints are present, it also has a limited impact on the environment and represent an alternative to build greener AI systems. We experiment our model on the SROIE ICDAR receipts dataset where we got an important F1 score compared to other graph neural network (GNN) based models.
On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift

This paper focuses on the implementation details of the baseline methods and a recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming data under class-prior shift. LIMES achieves superior performance over the baseline methods, especially concerning the minimum-across-day accuracy, which is important for the users of the system. In this work, the key measures to facilitate reproducibility and enhance the credibility of the results are described.
Promoting Reproducibility of Research Results in International Events (Report from the $$4^{th}$$ RRPR)

Following the fourth edition of the workshop on Reproducible Research in Pattern Recognition (RRPR) at the International Conference on Pattern Recognition (ICPR), this paper reports the main discussions that were held during and after the workshop. In particular, the integration of reproducible research inside an international conference was the first main axis of reflection. Further discussions addressed the ways of initiating or imposing reproducible research, as well as the problem of performance comparisons of published research papers that emerges due to the fact that the reported results are often based on different implementations and datasets.
Fake Review Recognition Using an SVM Model

This paper aims to develop a fake product review recognition system on Amazon using Python code. e-Commerce websites form an ever-expanding portion of the global business market, and reviews play a vital role in deciding the authenticity and quality of a product. Reviews and comments play a vital role in the e-commerce domain contributing to a product’s and in turn a seller’s good or bad reputation. There are often fake reviews posted on these platforms which ultimately result in either raising or dropping the ratings of these products. To promote products and disparage rivals, immoral actors hire bot farms, purchase legitimate accounts, and use Sybil accounts. Many businesses employ people to post fictitious favourable reviews about their goods or services or unfairly critical reviews about the goods or services of their rivals. This procedure provides inaccurate information to potential buyers of these goods; hence we need a way to identify and eliminate bogus reviews. Moreover. It directly affects the reliability of both the seller as well as the platform. Through this project, we aim to utilize SVM techniques in Python script to differentiate fake reviews from authentic ones, which ultimately decide the quality of the product and reliability of the seller.
Companion Paper: Deep Saliency Map Generators for Multispectral Video Classification

This is the companion paper for the ICPR 2022 Paper “Deep Saliency Map Generators for Multispectral Video Classification”, that investigates the applicability of three saliency map generators on multispectral video input data. In addition to implementation details of modifications for the investigated methods and the used neural network implementations, the influence of the parameters and a more detailed insight in the training and evaluation process is given.
Assessing Forgetfulness in Data Stream Learning – The Case of Hoeffding AnyTime Tree Algorithm

Many efforts around the world have emerged on regulations concerning personal management data guarantee, being one of them related to the ‘Right to Be Forgotten’. There are many divergences on what type of data must be considered in this matter. If some governmental policy interprets that some data collected in a given domain is property of an individual, and this individual has the right to request forgetfulness of this data portion, this data must be erased from third-party tools and services, including e-government services. One important challenge in this scenario is when these data portions have been used for constructing machine learning-based models, as the knowledge composing these models were partially obtained by the data to be forgotten. Moreover, there can be of special interest when it is demanded to a company to forget huge parts of their source data, which can lead to lower quality estimators. So, it is fundamental to present machine learning tools to support these types of policies as well as investigating the impact of data forgetting to machine learning-based estimators. In this paper, we investigate the impact of these learning and forgetting policies in Data Stream Learning (DSL) using an algorithm called Hoeffding AnyTime Tree (HATT). This is an interesting algorithm as it incorporates the ability to negatively weighting instances, which can be seen as a property of data forgetting. We subject the HATT algorithm to 4 levels of forgetting and investigate the impact of data forgetting in the obtained predictive performance. They are compared against control instances (upper and lower bound) of the HATT algorithm using four non-stationary stream datasets. Our results showed that as the forgetting rate increases, the model approaches the lower bound behavior in terms of accuracy for 2 out of 4 datasets, indicating that this is a promising approach.
Finding Suitable Data Mining Techniques for Software Development Effort Estimation

An organization's new projects all go through an analysis process. The data gathered throughout the study serve as the cornerstone for important choices about complexity, resources, frameworks, timetables, costs, etc. Numerous methods have been developed throughout time to make the project analysis phase simpler, but most of them are still insufficient when it comes to the accuracy of the results. Without a precise analysis technique in place, even initiatives with clear goals might unravel in the later stages. Software project management still faces challenges in producing accurate and trustworthy estimates of software effort, particularly in the early stages of the software life cycle when the information available is more categorical than numerical. Predicting the number of person-hours, or months, required for software development is seen as a difficult task in Software Effort Estimation (SEE). Project cancellation or project failure is the outcome of overestimating or underestimating the software effort. Although useful sizing tools and methods derived from function points don't take into consideration the unique project management culture of a business. Data processing techniques are being investigated as a substitute estimation method as a result of these shortcomings in recent years. This research aims to propose a mixture method of functional sizing measurement and three data processing methods for effort estimation at the first stage of projects: Generalized Linear Models (GLM), Deep Learning Neural Networks (DLNN), and Decision Trees - Gradient Boosting Machine (GBM). These models’ estimation accuracies were contrasted so as to assess their potential value for implementation within businesses. Additionally, a combined strategy that mixes the output of the many algorithms is usually recommended so as to enhance prediction accuracy and forestall the incidence of over-fitting.
A General Unbiased Training Framework for Deep Reinforcement Learning

Deep reinforcement learning (DRL) combines deep neural networks with reinforcement learning which enables agents to learn the best actions in virtual environment in order to attain their goals. In DRL, experience replay enables reinforcement learning agents to memorize and reuse past experiences, and it has been widely used to stabilize the training process by randomizing over training data and removing correlations in the observation sequence. However, existing experience replay lacks importance sampling to remove the bias errors induced by samples following different probabilities. Since there is no way to gain knowledge about the transition distribution in model-free learning, we propose a novel training framework to amend the distribution of the samples from the beginning. Specifically, we employ Monte-carlo sampling to obtain the input states, and then follow the Markov decision process to obtain four-tuple transition slots. The network parameters are updated synchronously using a batch of independently and identically distributed (IID) transition slots instead of experience replay. This training framework optimizes the unbiased approximation of loss function whose estimation exactly matches the real probability distribution of data inputs without importance sampling, and thus have overwhelming advantages of sample efficiency and convergence rate over existing DRL training framework. Since it only changes the mechanism of collecting and exploiting samples, it can be generalized to reinforcement learning algorithms. Moreover, we propose several algorithms under our new framework to deal with typical discrete and continuous scenarios. These algorithms prove to be far more efficient, and provide examples for existing and future algorithms on how to apply our framework.
Domain-Compatible Synthetic Data Generation for Emergency Vehicle Detection

Recent advancements in generative models have led to significant improvements in the quality of generated images, making them virtually indistinguishable from real ones. However, using AI generated images for training robust computer vision models for real-world applications, especially object detection in road scene perception, is still a challenge. AI generated images usually lack the required diversity and scene complexity where specific objects appear with critically low frequency in the available real datasets. An example of such applications is the detection of emergency vehicles like police cars, fire trucks, and ambulances in road scenes. These vehicles appear with drastically low frequencies in available datasets. Successfully generating synthetic images of road scenes that include these types of vehicles and using them in training downstream models would prove useful for autonomous driving vehicles, mitigating safety concerns on the road. To address this, this paper proposes a new approach for synthetically generating diverse, complex, and domain-compatible images of emergency vehicles in road scenes by employing a diffusion-based generative model pretrained on a generic dataset. We investigate the impact of using generated synthetic images in the performance of downstream object detection models. Finally, we thoroughly discuss challenges of generating synthetic datasets with the proposed approach.
New Results on Algebraic Constructions of Extremal Graph Theory and Implementations of New Algorithms of Postquantum Cryptography

Recently established lower bound on the girth of the known small world graphs A(n, q) motivates new constructions of A(n, q) based stream ciphers. We suggest graph based algorithm of symmetric encryption with the resistance to linearisation attacks. It allows us to introduce a new protocol based cryptosystem of Noncommutative Cryptography based on platform of cubic automorphisms of $$F_q[x_1, x_2, \dots , x_n]$$ .
Digital Watermarking Method for Copyright Protection of Deep Neural Networks

This paper proposes a new method for copyright protection of deep neural networks designed for solving image classification tasks. The main idea of the method is to embed digital watermarks into the deep model by fine-tuning on a unique set of images, called triggers, represented in the form of pseudo-holographic signals (pseudo-holograms). A pseudo-hologram is a two-dimensional sinusoidal signal that encodes a binary sequence of arbitrary length. By changing the phase of each sinusoid, it is possible to form various pseudo-holograms encoding the same binary sequence. The proposed watermarking method consists in construction of a training set by producing a required number of pseudo-holograms on the basis of binary sequences, which are unique for each class. Thus, the class label assigned to each pseudo-hologram depend on the sequence encoded in it. The procedure of watermark verification is performed by sending various random pseudo-holograms as model input and evaluating the accuracy of classification. High rate of successful predictions indicates that input images are constructed based on the identification key of the legal owner. Experimental studies confirm the efficiency of the method for various model architectures and prove the compliance with all quality criteria required for the methods of deep model watermarking.
Low-Cost Model-Free Deep Reinforcement Learning on Continuous Control

Training agents to perform reinforcement learning (RL) is a difficult task in most cases since they are often misguided by the reward signal, getting into invalid states and behaving in unwanted ways when exploring and interacting with the environment. Sometimes the cost needs to be considered when optimizing the performance during RL training. An additional cost signal from the environment may help to lead agents towards the desired actions, maximizing the expected return at low cost. In most practical situations, the state or action space is usually large or continuous, which makes model-free training more difficult if too much constraint is given for low cost. Therefore, in continuous Markov Decision Processes, we present a notion to optimize the set objective function at low cost with free exploration, which avoids the performance degrading. In this paper, we propose a novel approach to replace the objective and cost with surrogate functions, as well as applying a state-dependent multiplier to provide a highly efficient tradeoff between them.
Adoption of the Organization of Help According to the Use Case Technique in the Open-Source Software Development Process

Open source is a way of working that goes beyond traditional software production. The number of non-developer users of open-source software (OSS) is growing, which has led to an increase in decentralized OSS production. OSS communities do not know how to apply usability techniques in the development process. The goal of our research is to incorporate the usability technique Organization of help by Use Case (OACU) into the OSS Sleek project. For this purpose, we participate as volunteers and have the authorization of the developer of the Sleek project. We used the case study research method during the application of the technique and solicited the participation of the OSS community. The biggest challenge in applying this technique was to involve expert users. To this end, external users with intermediate-advanced knowledge in projects similar to Sleek were recruited through e-mail dissemination. As a result, we identified the adverse conditions that complicated the application of the technique and modified them for application in OSS projects. We can conclude that the results obtained were favorable as the modifications made helped to apply the OACU technique.
Vision Based Machine Learning Algorithms for Out-of-Distribution Generalisation

There are many computer vision applications including object segmentation, classification, object detection, and reconstruction for which machine learning (ML) shows state-of-the-art performance. Nowadays, we can build ML tools for such applications with real-world accuracy. However, each tool works well within the domain in which it has been trained and developed. Often, when we train a model on a dataset in one specific domain and test on another unseen domain known as an out of distribution (OOD) dataset, models or ML tools show a decrease in performance. For instance, when we train a simple classifier on real-world images and apply that model on the same classes but with a different domain like cartoons, paintings or sketches then the performance of ML tools disappoints. This presents serious challenges of domain generalisation (DG), domain adaptation (DA), and domain shifting. To enhance the power of ML tools, we can rebuild and retrain models from scratch or we can perform transfer learning. In this paper, we present a comparison study between vision-based technologies for domain-specific and domain-generalised methods. In this research we highlight that simple convolutional neural network (CNN) based deep learning methods perform poorly when they have to tackle domain shifting. Experiments are conducted on two popular vision-based benchmarks, PACS and Office-Home. We introduce an implementation pipeline for domain generalisation methods and conventional deep learning models. The outcome confirms that CNN-based deep learning models show poor generalisation compare to other extensive methods.
Investigating the Stability of SMOTE-Based Oversampling on COVID-19 Data

Predictive analytic methods for medical diagnosis can be helpful in supporting decision-making of medical treatment, which in turn reduce the need for medical experts’ attention. However, imbalanced data problems often exist in medical diagnosis datasets and negatively impact the models’ predictive performance. The results of learning algorithms on imbalanced data are biased and often cause over-fitting of the majority class. The Synthetic Minority Over-sampling Technique (SMOTE) was proposed to deal with this over-fitting challenge. The application of SMOTE requires the over-sampling of the minority class(es). However, there are vague guidelines on how much oversampling on the minority class is suitable. Therefore, experiments on oversampling using SMOTE with different oversampling ratio setups are done on a medical diagnosis dataset. It is observed that the increase in oversampling rate will reduce the accuracy and precision. Oversampling to a uniform level and excessive oversampling can cause poorer performance. Both recall and precision should be considered based on the costs when deciding the best oversampling percentage.
Localizing and Idiomatizing Nonidiomatic Python Code with Deep Learning

It is a common mistake to neglect high-level constructs while programming, often made by programmers new to Python. Such bad practice leads to verbose and nonidiomatic code, which generally also runs slower. Our goal is to refactor code containing such low-level snippets while preserving the original behaviour of the program. We present a method to localize and idiomatize nonidiomatic parts in Python source code. Localization is formulated as a sequence tagging task, where each line is either part of a low-level snippet or not. The task is solved by a recurrent neural network. Generating the idiomatic counterparts from nonidiomatic code is tackled by a sequence-to-sequence model with attention. The method was evaluated on a real-world dataset containing programs written by students, and it outperformed previous work with higher precision and F1-score.
An Improved CNN Model for Image Forgery Detection

As Digital Image forgery is alarmingly flourishing nowadays, a serious insight to detect and classify such forgeries are highly essential. Passive image forgery detection techniques have gained lot of interest among researchers and have achieved tremendous advancements in cracking the technologically advanced image forgery tools. At present Deep Learning based detection methods are mostly adopted because of its automatic feature extraction capabilities. This paper highlights a method to improve the performance of a Convolutional Neural Network model in the context of Image Forgery Detection application. The study can be extended to analyze some of the best CNN based deep learning models which are good at classification and the best one which fits in to the image forgery detection application can be identified. To achieve this, VGG19, one among the best classification CNN model is considered and the experimental results applying the proposed approach shows that the model has improved performance regarding the performance metrics such as accuracy, precision, recall and F1-Score. The experimental analysis is carried out using popular image forgery datasets. The architecture is implemented using Keras API on Tensor flow using Python 3 programming language and the simulation is run on Google Colab using GPU runtime.