Reviewerly/exHarmony huggingface dataset:
“Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish

This paper tackles automatic detection of contradictions in Spanish within the news domain. Two pieces of information are classified as compatible, contradictory, or unrelated information. To deal with the task, the ES-Contradiction dataset was created. This dataset contains a balanced number of each of the three types of information. The novelty of the research is the fine-grained annotation of the different types of contradictions in the dataset. Presently, four different types of contradictions are covered in the contradiction examples: negation, antonyms, numerical, and structural. However, future work will extend the dataset with all possible types of contradictions. In order to validate the effectiveness of the dataset, a pretrained model is used (BETO), and after performing different experiments, the system is able to detect contradiction with a F1m of 92.47%. Regarding the type of contradictions, the best results are obtained with negation contradiction (F1m = 98%), whereas structural contradictions obtain the lowest results (F1m = 69%) because of the smaller number of structural examples, due to the complexity of generating them. When dealing with a more generalistic dataset such as XNLI, our dataset fails to detect most of the contradictions properly, as the size of both datasets are very different and our dataset only covers four types of contradiction. However, using the classification of the contradictions leads us to conclude that there are highly complex contradictions that will need external knowledge in order to be properly detected and this will avoid the need for them to be previously exposed to the system.
Large margin metric learning for multi-view vehicle re-identification

In this paper we propose a novel large margin metric learning scheme for vehicle re-identification (re-ID), which aims to maximize the distance margins among different vehicles in the embedding feature space. Inspired by the fact that in the embedding feature space vehicles with the same identity are usually scattered sparsely due to vehicles’ multi-view appearance, our method is designed to make samples with the same identities more compact and meanwhile project instances with different categories to separate regions in the embedding space. To make training process more efficient, in the first stage, only the Softmax Loss is adopted, and in the second stage, by computing classification hyperplanes between different vehicle identities, a large margin loss is defined to maximize the distances between training samples and their corresponding hyperplanes. Besides, a new sampling method is adopted to find the hardest samples which are close to their hyperplanes and a kernelized re-ranking method is applied to further boost the performance. Compared with state-of-the-art approaches, our method achieves superior results with efficient training and inference process and only the identity as supervision signal. Experimental results on three most popular datasets show that our system produce promising results, and notably on the VeRi-776 dataset, our method can reach the best record with Rank1 96.81% and mAP 80.95%.
Developing an IoT forensic methodology. A concept proposal

The adaptation of digital forensics solutions to the requirements and characteristics of the Internet of Things (IoT) is an ongoing process which has turn out to be quite demanding due to the novelty of this environment. The differences between the IoT and conventional scenarios in which forensic investigations used to took place, namely the desktop and the smart phone, are too great to be able to address IoT examinations by following a common approach. However, developing brand new solutions does not seem the best approach to follow either, since there are not many IoT-centered tools, and a drastic change might hinder the use of this new proposals in a court of law. Therefore, the development of solutions to ensure that IoT investigations are carried out in a complete and efficient manner might need to be performed by adapting the widely-accepted conventional ones to this new scenario. In this sense, this article proposes a concept methodology for conducting IoT investigations which uses a generic forensic model as a reference.
Integration of Web-Based and Mobile Application with QR Code implementation for the library management system

Abstract In recent years, the various institution has adopted the use of two-dimensional bar codes which are also known as Quick Response (QR) Code for information dissemination which can be decoded using smartphones, digital tablets, and other electronic devices. One of the institutions’ benefits from QR usage is the libraries to promote their services as well as in asset tagging. This paper proposed the use of QR codes on the existing library system in one of the private higher learning institutions in Malaysia for fast data retrieval at a minimal cost. Random functional acceptance tests (FAT) were conducted and improvements were made accordingly. In this proposed work, the physical location of the library assets can be found easily, fast retrieval of information, and hence the unnecessary wastage of time is avoided.
Large Scale, Actively Secure Computation from LPN and Free-XOR Garbled Circuits

We (MPC) protocol based on garbled circuits which is both actively secure and supports the free-XOR technique, and which has communication complexity O(n) per party. This improves on a protocol of Ben-Efraim, Lindell and Omri which only achieved passive security, without support for free-XOR. Our construction is based on a new variant of LPN-based encryption, but has the drawback of requiring a rather expensive garbling phase. To address this issue we present a second protocol that assumes at least n/c of the parties are honest (for an arbitrary fixed value c). This second protocol allows for a significantly lighter preprocessing, at the cost of a small sacrifice in online efficiency. We demonstrate the practicality of our evaluation phase with an implementation.
Providing Predictable Quality of Service in a Cloud-Based Web System

Cloud-computing web systems and services revolutionized the web. Nowadays, they are the most important part of the Internet. Cloud-computing systems provide the opportunity for businesses to undergo digital transformation in order to improve efficiency and reduce costs. The sudden shutdown of schools and offices during the pandemic of Covid 19 significantly increased the demand for cloud solutions. Load balancing and sharing mechanisms are implemented in order to reduce the costs and increase the quality of web service. The usage of those methods with adaptive intelligent algorithms can deliver the highest and a predictable quality of service. In this article, a new HTTP request-distribution method in a two-layer architecture of a cluster-based web system is presented. This method allows for the provision of efficient processing and predictable quality by servicing requests in adopted time constraints. The proposed decision algorithms utilize fuzzy-neural models allowing service times to be estimated. This article provides a description of this new solution. It also contains the results of experiments in which the proposed method is compared with other intelligent approaches such as Fuzzy-Neural Request Distribution, and distribution methods often used in production systems.
For the greater good? Data and disasters in a post-COVID world

The use of information technology during the COVID-19 pandemic raises significant questions around the protection of personal data in a disaster. This paper considers how the clear benefits of using and sharing such data in disaster scenarios can be achieved while recognising an individual's right to privacy through examining the experiences of Taiwan and New Zealand. These states represent two successful COVID-19 response strategies which utilised different approaches to the use of technology. In Taiwan, the response made significant use of personal data and information technology. New Zealand, by contrast, has relied upon stringent lockdowns and extreme limits upon personal freedoms. The paper considers the different approaches to data and privacy that underpinned these responses and considers whether New Zealand can learn from the Taiwanese experience in future disaster planning. In doing so, the paper concludes by examining the wider question of when and if the community's expectation of a safe environment should trump the rights of individuals to retain personal data both in the context of pandemics and in other emergency or disaster scenarios. It also raises deeper questions, exposed by the COVID-19 response, about whether our current approach to privacy is sustainable in the digital age.
Ontology-Based Process Modelling-with Examples of Physical Topologies

Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic approach to process modelling. We do not aim toward a single modelling domain, but we enable to address specific application domains and object inheritance. We start with reductionism and demonstrate how the basic entities are depending on the targeted application domain. We use directed graphs to capture process models, and we introduce a new concept, which we call ”tokens” that enables us to extend the context beyond physical systems. The network representation is hierarchical so as to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy, making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a network of networks. We identify two distinct network communication ports, namely ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behaviour equations. Linking the behaviours to the basic entities defines the alphabet of a graphical language. We use this graphical language to represent processes, which has proven to be efficient and valuable. A set of three examples demonstrates the power of the graphical language. The Process Modelling framework (ProMo) implements the ontology-centred approach to process modelling and uses the graphical language to construct process models.
Person Tracking Using <scp>Ankle‐Level LiDAR</scp> Based on Enhanced <scp>DBSCAN</scp> and <scp>OPTICS</scp>

Abstract Along with the progress of deep learning techniques, people tracking using video cameras became easy and accurate. However, privacy and security issues are not enough to be concerned with vision‐based monitoring. People may not be tolerated surveillance cameras installed everywhere in our daily life. A camera‐based system may not work robustly in unusual situations such as smoke, fogs, or darkness. To cope with these problems, we propose a two‐dimensional (2D) LiDAR‐based people tracking technique based on clustering algorithms. A LiDAR sensor is a prominent approach for tracking people without disclosing their identity, even under challenging conditions. For tracking people, we propose modified density‐based spatial clustering of applications with noise (DBSCAN) and ordering points to identify cluster structure (OPTICS) algorithms for clustering 2D LiDAR data. We have confirmed that our approach significantly improves the accuracy and robustness of people tracking through the experiments. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Guiding feature model evolution by lifting code-level dependencies

Feature models are a de facto standard for representing the commonalities and variability of product lines and configurable software systems. Requirements-level features are commonly implemented in multiple source code artifacts, resulting in complex dependencies at the code level. As developers add and evolve features frequently, it is challenging to keep feature models consistent with their implementation. This article thus presents an approach combining feature-to-code mappings and code dependency analyses to inform engineers about possible inconsistencies. We focus on code-level changes requiring updates in feature dependencies and constraints. Our approach uses static code analysis and a variation control system to lift complex code-level dependencies to feature models. We present the suggested dependencies to the engineer in two ways: directly as links between features in a feature model and as a heatmap visualizing the dependency changes of all features in a model. We evaluate our approach by analyzing the evolution history of two case study systems: the Pick-and-Place Unit and the KePlast product line of our industry partner KEBA AG. We present results of evaluating the utility (RQ1) and performance (RQ2) of our approach, as well as the quality of the suggestions (RQ3).
Debiasing Graph Representations via Metadata-Orthogonal Training

In real world graphs, the formation of edges can be associated with certain sensitive features of the nodes (e.g. gender, community, reputation). In this paper we argue that when such associations exist, any downstream Graph Neural Network (GNN) will be implicitly biased by these structural correlations. To allow control over this phenomenon, we introduce the Metadata-Orthogonal Node Embedding Training (MONET) unit, a general neural network module for performing training-time linear debiasing of graph embeddings. MONET operates by ensuring that the node embeddings are trained on a hyperplane orthogonal to that of the node features (metadata). Unlike debiasing approaches in similar domains, our method offers exact guarantees about the correlation between the resulting embeddings and any sensitive metadata. We illustrate the effectiveness of MONET though our experiments on a variety of real world graphs against challenging baselines (e.g. adversarial debiasing), showing superior performance in tasks such as preventing the leakage of political party affiliation in a blog network, and preventing the gaming of embedding-based recommendation systems.
Video anomaly detection method based on future frame prediction and attention mechanism

With the development of deep learning technology, a large number of new technologies for video anomaly detection have emerged. This paper proposes a video anomaly detection algorithm based on the future frame prediction using Generative Adversarial Network (GAN) and attention mechanism. For the generation model, a U-Net model, is modified and added with an attention module. For the discrimination model, a Markov GAN discrimination model with self-attention mechanism is proposed, which can affect the generator and improve the generation quality of the future video frame. Experiments show that the new video anomaly detection algorithm improves the detection performance, and the attention module plays an important role in the overall detection performance. It is found that the more the attention modules are appliedthe deeper the application level is, the better the detection effect is, which also verifies the rationality of the model structure used in this project.
Towards an Automatic Speech-Based Diagnostic Test for Alzheimer’s Disease

Automatic Speech Recognition (ASR) is widely used in many applications and tools. Smartphones, video games, and cars are a few examples where people use ASR routinely and often daily. A less commonly used, but potentially very important arena for using ASR, is the health domain. For some people, the impact on life could be enormous. The goal of this work is to develop an easy-to-use, non-invasive, inexpensive speech-based diagnostic test for dementia that can easily be applied in a clinician’s office or even at home. While considerable work has been published along these lines, increasing dramatically recently, it is primarily of theoretical value and not yet practical to apply. A large gap exists between current scientific understanding, and the creation of a diagnostic test for dementia. The aim of this paper is to bridge this gap between theory and practice by engineering a practical test. Experimental evidence suggests that strong discrimination between subjects with a diagnosis of probable Alzheimer’s vs. matched normal controls can be achieved with a combination of acoustic features from speech, linguistic features extracted from a transcription of the speech, and results of a mini mental state exam. A fully automatic speech recognition system tuned for the speech-to-text aspect of this application, including automatic punctuation, is also described.
RICE: A method for quantitative mammographic image enhancement

We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the ‘neighbourhood’ for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system.
A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user's location. Hence, it will pose a huge threat to user's privacy if the sensing locations are published without protection. As usual, the user's location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user's sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user's location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism.
Digital Transformation and Transparency in Wine Supply Chain Using OCR and DLT

This paper describes an effort to utilize IoT, OCR, and blockchain technology to create wine track and trace system evaluated in a real-life environment. The research is focused on digital transformation in traditional wine supply chain, using computer vision to read the existing serial numbers labeled on bottles, so as to uniquely identifying individual bottles of wine and track the item life-cycle. The system provides mobile app to allow end consumers to scan each wine bottle and learn more about that particular product instance, its origin, authenticity, rating and potentially other characteristics. Status of each bottle is updated every time it has been scanned. To ensure the transparency of the recorded data and information immutability, the blockchain technology is used to record all relevant information into the ledger, e.g. all status updates for each item. The developed service enables tracking of a bottle throughout the supply chain, from a producer to a consumer, where traceability story is still not finalized. The consumed serial numbers are kept in the ledger, to avoid counterfeit scenario when one “bottle” (i.e. one serial number) is being sold multiple times. The life-cycle of bottle and its status changes are recorded, and the authenticity ensured facilitating verifiable identity of the authorized handlers and cutting edge cryptography, thus providing additional trust in the solution and transparency to all stakeholders.
Navigation of Omni-Directional Mobile Robot in Unstructured Environments using Fuzzy Logic Control

Among the existing control schemes, Fuzzy Logic (FL) is advantageous in offering robustness to uncertainty and noise in sensor readings. However, the performance of FL-based navigation strategies is well-known to be bounded by localminima risks in polygonal environments with nonconvex configurations. Here, we propose a twin-stage Fuzzy Logic scheme for autonomous navigation of mobile robots. Our exhaustive computational and real-world experiments using Robotino, an omnidirectional mobile robot architecture shows the potential to avoid local-minima and obstacle collisions, enabling the computationally efficient and adaptive FL-based behavioural modules for mobile robots.
Using Past Data to Warm Start Active Machine Learning: Does Context Matter?

Despite the abundance of data generated from students' activities in virtual learning environments, the use of supervised machine learning in learning analytics is limited by the availability of labeled data, which can be difficult to collect for complex educational constructs. In a previous study, a subfield of machine learning called Active Learning (AL) was explored to improve the data labeling efficiency. AL trains a model and uses it, in parallel, to choose the next data sample to get labeled from a human expert. Due to the complexity of educational constructs and data, AL has suffered from the cold-start problem where the model does not have access to sufficient data yet to choose the best next sample to learn from. In this paper, we explore the use of past data to warm start the AL training process. We also critically examine the implications of differing contexts (urbanicity) in which the past data was collected. To this end, we use authentic affect labels collected through human observations in middle school mathematics classrooms to simulate the development of AL-based detectors of engaged concentration. We experiment with two AL methods (uncertainty sampling, L-MMSE) and random sampling for data selection. Our results suggest that using past data to warm start AL training could be effective for some methods based on the target population's urbanicity. We provide recommendations on the data selection method and the quantity of past data to use when warm starting AL training in the urban and suburban schools.