Reviewerly/exHarmony huggingface dataset:
An explainable machine learning pipeline for backorder prediction in inventory management systems

research-article Share on An explainable machine learning pipeline for backorder prediction in inventory management systems Authors: Charis Ntakolia Hellenic Air Force Academy, Greece Hellenic Air Force Academy, GreeceView Profile , Christos Kokkotiis University of Thessaly, Greece University of Thessaly, GreeceView Profile , Serafeim Moustakidis AIDEAS OÜ, Estonia AIDEAS OÜ, EstoniaView Profile , Elpiniki Papageorgiou University of Thessaly, Greece University of Thessaly, GreeceView Profile Authors Info & Claims PCI 2021: 25th Pan-Hellenic Conference on InformaticsNovember 2021 Pages 229–234https://doi.org/10.1145/3503823.3503866Online:22 February 2022Publication History 0citation79DownloadsMetricsTotal Citations0Total Downloads79Last 12 Months79Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning

Healthcare disparities in multiethnic medical data is a major challenge; the main reason lies in the unequal data distribution of ethnic groups among data cohorts. Biomedical data collected from different cancer genome research projects may consist of mainly one ethnic group, such as people with European ancestry. In contrast, the data distribution of other ethnic races such as African, Asian, Hispanic, and Native Americans can be less visible than the counterpart. Data inequality in the biomedical field is an important research problem, resulting in the diverse performance of machine learning models while creating healthcare disparities. Previous researches have reduced the healthcare disparities only using limited data distributions. In our study, we work on fine-tuning of deep learning and transfer learning models with different multiethnic data distributions for the prognosis of 33 cancer types. In previous studies, to reduce the healthcare disparities, only a single ethnic cohort was used as the target domain with one major source domain. In contrast, we focused on multiple ethnic cohorts as the target domain in transfer learning using the TCGA and MMRF CoMMpass study datasets. After performance comparison for experiments with new data distributions, our proposed model shows promising performance for transfer learning schemes compared to the baseline approach for old and new data distributation experiments.
pyCLAMs: An integrated Python toolkit for classifiability analysis

In data-driven discriminative tasks, classifiability analysis is an often-neglected and implicit step. It answers the fundamental question: does the dataset possess sufficient between-class differences? To measure the dataset’s classifiability degree, we develop pyCLAMs (python package for CLassifiabilty Analysis Metrics). pyCLAMs has integrated existing classifiability complexity metrics (e.g., Fisher discriminant ratio, overlapping region volume, distribution topology) and extends more metrics/statistics, such as BER (Bayes error rate, irreducible error), ES (effect size), Person’s r, Spearman’s rho, Kendall’s tau, IG (information gain, mutual information), ANOVA (Analysis of Variance), MANOVA (Multivariate ANOVA), MWW (Mann–Whitney–Wilcoxon test), KS (Kolmogorov–Smirnov test), etc. The current version of pyCLAMs supports 68 metrics. We recommend researchers use pyCLAMs for a precursory assessment for their classification tasks.
Consistent Cell Tracking in Multi-frames with Spatio-Temporal Context by Object-Level Warping Loss

Multi-object tracking is essential in biomedical image analysis. Most methods follow a tracking-by-detection approach that involves using object detectors and learning the appearance feature models of the detected regions for association. Although these methods can learn the appearance similarity features to identify the same objects among frames, they have difficulties identifying the same cells because cells have a similar appearance and their shapes change as they migrate. In addition, cells often partially overlap for several frames. In this case, even an expert biologist would require knowledge of the spatial-temporal context in order to identify individual cells. To tackle such difficult situations, we propose a cell-tracking method that can effectively use the spatial-temporal context in multiple frames by using long-term motion estimation and an object-level warping loss. We conducted experiments showing that the proposed method outperformed state-of-the-art methods under various conditions on real biological images.
Natural Language Video Moment Localization Through Query-Controlled Temporal Convolution

The goal of natural language video moment localization is to locate a short segment of a long, untrimmed video that corresponds to a description presented as natural text. The description may contain several pieces of key information, including subjects/objects, sequential actions, and locations. Here, we propose a novel video moment localization framework based on the convolutional response between multimodal signals, i.e., the video sequence, the text query, and subtitles for the video if they are available. We emphasize the effect of the language sequence as a query about the video content, by converting the query sentence into a boundary detector with a filter kernel size and stride. We convolve the video sequence with the query detector to locate the start and end boundaries of the target video segment. When subtitles are available, we blend the boundary heatmaps from the visual and subtitle branches together using an LSTM to capture asynchronous dependencies across two modalities in the video. We perform extensive experiments on the TVR, Charades-STA, and TACoS benchmark datasets, demonstrating that our model achieves state-of-the-art results on all three.
Inpaint2Learn: A Self-Supervised Framework for Affordance Learning

Perceiving affordances –the opportunities of interaction in a scene, is a fundamental ability of humans. It is an equally important skill for AI agents and robots to better understand and interact with the world. However, labeling affordances in the environment is not a trivial task. To address this issue, we propose a task-agnostic framework, named Inpaint2Learn, that generates affordance labels in a fully automatic manner and opens the door for affordance learning in the wild. To demonstrate its effectiveness, we apply it to three different tasks: human affordance prediction, Location2Object and 6D object pose hallucination. Our experiments and user studies show that our models, trained with the Inpaint2Learn scaffold, are able to generate diverse and visually plausible results in all three scenarios.
Approaches to Quality Education in Tertiary Sector: An Empirical Study Using PLS-SEM

Quality assurance in education has been a critical issue in both private and public higher education in all emerging economies since World War II. Available theories on quality education are numerous, but their strategic models are sparsely sustained. The main objective of this study is, therefore, to articulate and to empirically test a conceptual model to assure and sustain quality in tertiary education grounding on a theory base presented by Chohen and Ball (2006). The model has three parts: independent components, i.e., quality inputs such as quality teachers, quality students, and quality staffs; mediating component, i.e., quality process such as quality programs; and target component, i.e., quality product such as quality education. Data (n = 97 private universities) were gathered from a report of the university grant commission (UGC), Bangladesh. The data were analysed following PLS-SEM using SmartPLS3 and SPSS software. Findings indicate that all of the predictors appear to be robust in predicting quality education. The results also indicate that the mediating construct has a significant partial mediating role in enhancing quality education in private universities. These outcomes will help academic authorities formulate the right policy option in assuring quality education in higher education institutes in Bangladesh.
Towards the use of Participatory Methods in Cybersecurity research in rural Africa: A grassroots Approach

As a research domain, Cybersecurity has been around for a long time. However, research methods and outputs have been deeply rooted in western epistemologies, thus leaving pockets of communities in rural Africa at possible risk. Even though some research on the African continent aims to address cybersecurity awareness, they still utilize a top-down approach, with interventions designed and developed for rural end users often without their input. This position paper proposes a new research focus in cybersecurity research within rural communities in Africa and for cybersecurity research in rural African communities. Participatory methods have been utilized in human-computer interactions research with greater impact of end-product adoptability, sense of ownership of communities involved and overall design implications for design research as a whole. We therefore argue that for cybersecurity research to have sustainable effects for these communities on the fringes of society, a participatory design approach needs to be considered in cybersecurity research.
Security Requirements Classification into Groups Using NLP Transformers

This study presents an implementation of sentencelevel classification of security requirements into predefined groups. The method of this paper suggests using three models: BERT, XLNET, and DistilBERT for classification task and figures out evaluation metrics such as precision, recall, and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score. We compiled a new dataset of 1086 security requirements of 7 classes collected from multiple existing datasets, such as PURE, SecReq and Riaz's dataset. The best-achieved result is DistilBERT’s 78% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score on the multiclass classification task. The main contribution of this study is the new multiclass dataset of security requirements and an example of how a deep transformer model can be used for requirements elicitation, which can be used as a basis for further improvement.
Power Grid Field Violation Recognition Algorithm Based on Enhanced YOLOv5

Abstract Aiming at the problem that Yolov5 is difficult to accurately detect backlighting and small samples in national grid power transmission and distribution operations, studies the illegal behavior technology of “two wear and one wear” in power grid operation, and proposes a violation recognition algorithm based on the enhanced YOLOv5. First, add a new detection layer and use the BiFPN (bi-directional feature pyramid network) layer for feature fusion, so that feature layers of different scales can better learn the weight distribution and enhance the fusion ability. Secondly, add CBAM (Convolutional Block Attention Module) module before the output detection layer feature map to make full use of channel and spatial information to achieve better model accuracy and recall. The experimental results on the six self-made data sets show that the mAP of this method is 91.0%, which is 5.6% higher than the original algorithm, indicating that the model has stronger predictive ability and robustness for the identification of personnel and safety tools in transmission and distribution scenarios.
Convincing 3D Face Reconstruction from a Single Color Image under Occluded Scenes

The last few years have witnessed the great success of generative adversarial networks (GANs) in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction works often pursue higher resolutions and ignore occlusion. We study the problem of detailed 3D facial reconstruction under occluded scenes. This is a challenging problem; currently, the collection of such a large scale high resolution 3D face dataset is still very costly. In this work, we propose a deep learning based approach for detailed 3D face reconstruction that does not require large-scale 3D datasets. Motivated by generative face image inpainting and weakly-supervised 3D deep reconstruction, we propose a complete 3D face model generation method guided by the contour. In our work, the 3D reconstruction framework based on weak supervision can generate convincing 3D models. We further test our method on the MICC, Florence and LFW datasets, showing its strong generalization capacity and superior performance.
A Framework for Blockchain and Fogging-based Efficient Authentication in Internet of Things

IoT paradigm has laid the foundation to interconnect billions of objects for gathering, storing, and analyzing data. In an IoT complex ecosystem, maintaining the security and privacy of objects as well as data becomes a critical issue. Efficient authentication of IoT objects to each other is restricted by resource-constrained IoT objects. A recent approach to authentication is to integrate blockchain technology, fog computing to develop a lightweight, robust authentication framework for IoT systems. Several works have adopted this integration to improve their authentication solutions for IoT systems. This paper aims to present a review of the authentication frameworks that integrate blockchain and fog computing technologies. In light of the review conclusions, the paper proposes a fogging-based authentication framework using blockchain to enhance IoT system's security, and scalability. The proposed framework employs fog nodes to support IoT devices with close computation and storage resources. Moreover, it adopts multi-level blockchain platforms to support cross-domain authentication among IoT systems. Finally, the paper discusses the trade-offs of adopting blockchain and fogging-based authentication frameworks in IoT and shows how the proposed framework could balance those trade-offs.
Using multivalued logic for qualitative data analysis

Abstract The paper considers a logical approach to data analysis to solve the classification problem. The studied data is a set of objects and their features. As a rule, this is scattered heterogeneous information and it is not enough for a reasonable application of probabilistic models. Therefore, logical algorithms are considered, which under certain conditions may be more adequate. For an expressive formal representation of the relationship between objects and their attributes, multivalued logic is used, and the number of values depends on a specific attribute. Therefore, a system of operations is proposed for variables with different domains of definition. As a result, a decisive function is built, which is a classifier of objects that are present in the studied data. The properties and capabilities of this function are analyzed. It is shown that the logical function, which is a conjunction in the space of rules connecting given objects with their characteristic features, uniquely characterizes the initial data, divides the subject area into classes, possesses modifiability properties, meets the requirements of completeness and consistency in a given area. The paper also proposes an algorithm for its implementation.
Multi-criteria Based Cloud Service Selection Model Using Fuzzy Logic for QoS

Due to the expansion of web users and increase demand for Cloud in the public domain, a cloud service provider (CSP) tries to fulfill the exclusive functionalities and specifications required by different users which is a challenging task. There are numerous challenges such as migration of all the applications and data from one Cloud to another can drop-off the computing capabilities offered by the Cloud platform because different Cloud service providers (CSP) provides cloud services at different pricing categories with a different set of choices where one cloud charge low cost for storage but high cost for computation or vice-versa. In this paper, a Fuzzy based Cloud selection model is proposed using cost, capacity, performance, security and maintenance as key parameters to improve QoS experience of the cloud users.
Special Issue “Multi-Agent Systems”: Editorial

Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. Despite heterogeneity, those domains share common requirements such as autonomy, structured interaction, mobility, and openness—which are well suited for MAS. Therein, in fact, goal-oriented processes can enter and leave the system dynamically and interact with each other according to structured protocols. This special issue gathers 17 contributions spanning from agent-based modelling and simulation to applications of MAS in situated and socio-technical systems.
Trusted and Secure Blockchain-Based Durable Medium Electronic Service

A novel, trusted, and secure durable medium electronic service is proposed in the paper. The proposed idea joins cryptographic methods (such as signing with an electronic seal and data encryption) with blockchain techniques. The e-service and blockchain databases were implemented on the TTP side, which made the presented concept trusted and secure. The proposed electronic service was oriented towards practical implementations, and it has commonly been developed together with a company from the cybersecurity field (which is considered a TTP in the proposed approach). The concept has been designed to meet the requirements of Polish law (i.e., the conditions and regulations related to the implementation of the durable medium in Poland); nevertheless, it can easily be adapted for other regions. The functionality of the presented e-service is illustrated by the example case study.
LegalVis: Exploring and Inferring Precedent Citations in Legal Documents

To reduce the number of pending cases and conflicting rulings in the Brazilian Judiciary, the National Congress amended the Constitution, allowing the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a set of understandings that both Executive and lower Judiciary branches must follow. The STF's justices frequently cite the 58 existing BPs in their decisions, and it is of primary relevance that judicial experts could identify and analyze such citations. To assist in this problem, we propose LegalVis, a web-based visual analytics system designed to support the analysis of legal documents that cite or could potentially cite a BP. We model the problem of identifying potential citations (i.e., non-explicit) as a classification problem. However, a simple score is not enough to explain the results; that is why we use an interpretability machine learning method to explain the reason behind each identified citation. For a compelling visual exploration of documents and BPs, LegalVis comprises three interactive visual components: the first presents an overview of the data showing temporal patterns, the second allows filtering and grouping relevant documents by topic, and the last one shows a document's text aiming to interpret the model's output by pointing out which paragraphs are likely to mention the BP, even if not explicitly specified. We evaluated our identification model and obtained an accuracy of 96%; we also made a quantitative and qualitative analysis of the results. The usefulness and effectiveness of LegalVis were evaluated through two usage scenarios and feedback from six domain experts.
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inverting a pretrained generator, where an encoder is trained ad hoc after the generator is trained in a two-stage fashion. In this paper, we focus on style-based generators asking a scientific question: Does forcing such a generator to reconstruct real data lead to more disentangled latent space and make the inversion process from image to latent space easy? We describe a new methodology to train a style-based autoencoder where the encoder and generator are optimized end-to-end. We show that our proposed model consistently outperforms baselines in terms of image inversion and generation quality. Supplementary, code, and pretrained models are available on the project website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Adversarial Robustness of Deep Sensor Fusion Models

We experimentally study the robustness of deep camera-LiDAR fusion architectures for 2D object detection in autonomous driving. First, we find that the fusion model is usually both more accurate, and more robust against single-source attacks than single-sensor deep neural networks. Furthermore, we show that without adversarial training, early fusion is more robust than late fusion, whereas the two perform similarly after adversarial training. However, we note that single-channel adversarial training of deep fusion is often detrimental even to robustness. Moreover, we observe cross-channel externalities, where single-channel adversarial training reduces robustness to attacks on the other channel. Additionally, we observe that the choice of adversarial model in adversarial training is critical: using attacks restricted to cars’ bounding boxes is more effective in adversarial training and exhibits less significant cross-channel externalities. Finally, we find that joint-channel adversarial training helps mitigate many of the issues above, but does not significantly boost adversarial robustness.
Anonymous communication protocol over quantum networks

We propose a W state-based protocol for anonymously transmitting quantum messages in a quantum network. Different from the existing protocols [A. Unnikrishnan, et al., Phys. Rev. Lett. 122, 240501 (2019)], the proposed protocol can be effectively implemented in the network only equipped with quantum channels and regular broadcast channels. Throughout the design procedure, we develop three sub-protocols using the W state, including the quantum collision detection protocol and the quantum notification protocol. Moreover, together with the conventional anonymous entanglement protocol, the whole anonymous communication protocol has been constructed. Finally, we examine the correctness and security of the proposed quantum anonymous communication protocol.