Reviewerly/exHarmony huggingface dataset:
General information spaces: measuring inconsistency, rationality postulates, and complexity

Abstract AI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept of general information space which encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to an inconsistency equivalent propositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data. We demonstrate that for broad classes of distributions and classifiers, there exists a sample complexity gap between standard and robust classification. We quantify to what degree this gap can be bridged via leveraging unlabeled samples from a shifted domain by providing both upper and lower bounds. Moreover, we show settings where we achieve better adversarial robustness when the unlabeled data come from a shifted domain rather than the same domain as the labeled data. We also investigate how to leverage out-of-domain data when some structural information, such as sparsity, is shared between labeled and unlabeled domains. Experimentally, we augment two object recognition datasets (CIFAR-10 and SVHN) with easy to obtain and unlabeled out-of-domain data and demonstrate substantial improvement in the model's robustness against $\ell_\infty$ adversarial attacks on the original domain.
Distributed Power Trading System Based on Blockchain Technology

The power trading system has the characteristics of nonlinearity, dynamics, and complexity. Part of the business data in the trading system needs to be exposed to numerous external business systems. The traditional centralized power trading model has some problems, such as low data security and trust crisis of regulators. Blockchain technology provides prominent ideas for solving these problems. Firstly, the improved AdaBoost algorithm is used to predict the supply and demand gap of power trading nodes. Secondly, based on the fact that the information on the blockchain is only open to the power supply side, a two-stage game model of asymmetric information between the power supply side and the power user is constructed to capture the price competition behavior between them in order to find the Nash equilibrium price in two stages. Finally, the US PJM market electricity market data are used to carry out an example analysis to verify the effectiveness of the algorithm and model.
Robustness of Differentiable Neural Computer Using Limited Retention Vector-based Memory Deallocation in Language Model

Recurrent neural network (RNN) architectures have been used for language modeling (LM) tasks that require learning long-range word or character sequences.However, the RNN architecture is still suffered from unstable gradients on long-range sequences.To address the issue of long-range sequences, an attention mechanism has been used, showing state-of-theart (SOTA) performance in all LM tasks.A differentiable neural computer (DNC) is a deep learning architecture using an attention mechanism.The DNC architecture is a neural network augmented with a content-addressable external memory.However, in the write operation, some information unrelated to the input word remains in memory.Moreover, DNCs have been found to perform poorly with low numbers of weight parameters.Therefore, we propose a robust memory deallocation method using a limited retention vector.The limited retention vector determines whether the network increases or decreases its usage of information in external memory according to a threshold.We experimentally evaluate the robustness of a DNC implementing the proposed approach according to the size of the controller and external memory on the enwik8 LM task.When we decreased the number of weight parameters by 32.47%, the proposed DNC showed a low bits-per-character (BPC) degradation of 4.30%, demonstrating the effectiveness of our approach in language modeling tasks.
A multi-objective approach to determining the usefulness of papers in academic search

Purpose How to provide the most useful papers for searchers is a key issue for academic search engines. A lot of research has been carried out to address this problem. However, when evaluating the effectiveness of an academic search engine, most of the previous investigations assume that the only concern of the user is the relevancy of the paper to the query. The authors believe that the usefulness of a paper is determined not only by its relevance to the query but also by other aspects including its publication age and impact in the research community. This is vital, especially when a large number of papers are relevant to the query. Design/methodology/approach This paper proposes a group of metrics to measure the usefulness of a ranked list of papers. When defining these metrics, three factors, including relevance, publication age and impact, are considered at the same time. To accommodate this, the authors propose a framework to rank papers by a combination of their relevance, publication age and impact scores. Findings The framework is evaluated with the ACL (Association for Computational Linguistics Anthology Network) dataset. It demonstrates that the proposed ranking algorithm is effective for improving usefulness when two or three aspects of academic papers are considered at the same time, while the relevance of the retrieved papers is slightly down compared with the relevance-only retrieval. Originality/value To the best of the authors’ knowledge, the proposed multi-objective academic search framework is the first of its kind that is proposed and evaluated with a group of new evaluation metrics.
Modeling the distribution of heavy metals in lands irrigated by wastewater using satellite images of Sentinel-2

The pollution of heavy metals is considered as one of the main problems of using wastewater for irrigation purposes. Frequent experimental measurements, time, and cost are essential for evaluating the pollution of heavy metals in a large area. Thus, using satellite images and establishing a relationship between the images and concentration of heavy metals can be regarded as a solution for estimating the polluted area. In this study, the image of the Sentinel-2 satellite was used to evaluate the heavy metals pollution of the area irrigated with wastewater in south of Tehran. For this aim, 30 soil-surface samples irrigated with raw wastewater were collected in the area. After preparing the samples, the concentration of Pb, Cu, and Ni was determined using atomic absorption spectroscopy. Then, the relationship between the heavy metal concentration, reflection on the bands or rating of the bands at the corresponding sampling points was determined by applying the stepwise regression method. In addition, the developed models were applied to the satellite image for zoning the heavy metal concentrations in the studied area. Finally, the accuracy of the developed models was examined by Root-Mean-Square Error (RMSE) and Pearson correlation coefficients. The results indicated that the amounts of RMSE for the equations of Pb, Cu, and Ni were 1.90, 2.54, and 1.59 respectively, while the amounts of R were 0.81, 0.75, and 0.73 for these metals representing a promising match between the predicted and measured results of the models.
Lazy Stream Manipulation in Prolog via Backtracking: The Case of 2P-Kt

The ability to lazily manipulate long or infinite streams of data is an essential feature in the era of data-driven artificial intelligence. Yet, logic programming technologies currently fall short when it comes to handling long or infinite streams of data. In this paper, we discuss how Prolog can be reinterpreted as a stream processing tool, and re-designed around an abstract state-machine capable of lazily manipulating streams of data via backtracking.
Requirements engineering: Foundation for software quality (REFSQ2020)

Many organizations undertake large-scale projects of application migration due to availability of scalable and cost-efficient technologies. Such legacy application migration projects are very complex since the process involves in-depth profiling of the applications.During the initial profiling phase, it is imperative to understand the underlying complexities of individual applications, as well as the interdependencies among applications in the organization. This analysis phase can take considerable time and effort, depending on number and complexity of the applications. The main goal of this paper is to provide a framework that provides a cost-effective and quick approach to study the interdependencies between legacy applications with minimal prior knowledge of application usage.In this paper, we propose a framework that uses community detection algorithms and other established techniques from graph theory, to discover interdependencies of legacy applications within an organization, group these highly interdependent legacy applications in clusters, and finally sequence the clusters for migration to a modern platform. We study the proposed framework through three case studies, using network datasets from a large US organization.The experimental results from the proposed framework suggests that legacy applications can be grouped into clusters with high interdependencies between each other. Also, the framework shows how organizations can then appropriately sequence the clusters of legacy applications into a phase-wise migration project, thereby reducing migration costs.The proposed framework provides a valuable design input to organizations on how to determine the interdependencies between the various legacy applications that are in scope for migration to a modern platform. Such large-scale migration projects can be simplified and broken down to use a systematic approach, thereby reducing migration costs and data integrity challenges.
Is the relationship between corruption and domestic investment non-linear in Nigeria? Empirical evidence from quarterly data

This study employs the ARDL estimation method to investigate whether corruption and domestic investment relationship is non-linear in Nigeria using quarterly data over the 1996-2019 period. Other alternative estimation techniques such as the CCR, DOLS and FMOLS were used to check for consistency of the results. The results demonstrate that corruption-domestic relationship is non-linear. Although domestic investment reduces with an improvement in the corruption index (reducing corruption), a further reduction in corruption raises the domestic investment. Other significant determinants of domestic investment include income level, oil prices and inflation rate. Based on these outcomes, this study recommends policies to reduce corruption to raise domestic investment.
A Cybersecurity Guide for Using Fitness Devices

The popularity of wearable devices is growing exponentially, with consumers using these for a variety of services. Fitness devices are currently offering new services such as shopping or buying train tickets using contactless payment. In addition, fitness devices are collecting a number of personal information such as body temperature, pulse rate, food habits and body weight, steps-distance travelled, calories burned and sleep stage. Although these devices can offer convenience to consumers, more and more reports are warning of the cybersecurity risks of such devices, and the possibilities for such devices to be hacked and used as springboards to other systems. Due to their wireless transmissions, these devices can potentially be vulnerable to a malicious attack allowing the data collected to be exposed. The vulnerabilities of these devices stem from lack of authentication, disadvantages of Bluetooth connections, location tracking as well as third party vulnerabilities. Guidelines do exist for securing such devices, but most of such guidance is directed towards device manufacturers or IoT providers, while consumers are often unaware of potential risks. The aim of this paper is to provide cybersecurity guidelines for users in order to take measures to avoid risks when using fitness devices.
BCT-AA: A Survey of Blockchain Technology-Based Applications in Context With Agribusiness

Today blockchain technology (BCT) has almost covered every sector from MedChain to blockchain-powered IBM. There are numbers of blockchain based applications coined out, after the invention of world-famous cryptocurrency. This survey paper presents many previous works in the field of blockchain technology, and its applications related to business. The paper also discusses the merits and scenarios through current advancement of the agribusiness sector. It is observed that the transparency and trust of blockchain technology with its popular network architecture (using blocks of data and information) can benefit agribusiness in several dimensions. We have also proposed a blockchain-based agribusiness model for information transparency and improving the efficiency of the public sector.
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge

With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem in graph learning. Existing works usually perform the attack in a white-box fashion: they need to access the predictions/labels to construct their adversarial loss. However, the inaccessibility of predictions/labels makes the white-box attack impractical for a real graph learning system. This paper promotes current frameworks in a more general and flexible sense -- we consider the ability of various types of graph embedding models to remain resilient against black-box driven attacks. We investigate the theoretical connection between graph signal processing and graph embedding models, and formulate the graph embedding model as a general graph signal process with a corresponding graph filter. Therefore, we design a generalized adversarial attack framework: GF-Attack. Without accessing any labels and model predictions, GF-Attack can perform the attack directly on the graph filter in a black-box fashion. We further prove that GF-Attack can perform an effective attack without assumption on the number of layers/window-size of graph embedding models. To validate the generalization of GF-Attack, we construct GF-Attack on five popular graph embedding models. Extensive experiments validate the effectiveness of GF-Attack on several benchmark datasets.
MODEL OF STUDENTS’ PROJECT ACTIVITY IN THE DIGITAL SPACE ON THE EXAMPLE OF DISCIPLINES OF THE PEDAGOGICAL CYCLE

The article presents a model of project activities of applicants for higher education in the context of distance-digital learning using the example of disciplines of the pedagogical training cycle. The analysis of the scientific sources devoted to various aspects of project activity and especially in digital educational space is carried out. The theoretical provisions of the project activities in the digital space are described and the tasks that applicants can perform within this activity are presented. The process of implementation of project activities in the conditions of forced distance learning is highlighted on the example of 2nd year students of the School of Foreign Languages of V. N. Karazin Kharkiv National University in the 2019/2020 academic year, and attention is focused on the stages of creating and supporting this activity by a lecturer and a student. These stages include: preparatory (communication between applicants and the teacher), operational (project creation), correction (making changes and additions) and final (public defense and registration of the results). The discussion section reveals the prospects and risks of implementing project activities in the digital learning space. The conclusions provide recommendations for the effective organization of project activities in the digital learning space on the example of disciplines of the pedagogical cycle of training: previous preparatory work of the teacher to clarify the requirements for studying the course and organizing project activities (the emphasis here is on group monitors due to the large number of students: an average of 230 people); bright presentation of requirements and previous achievements in creating creative works (projects); defining a clear sequence of actions of this process; explanation of the sequence of actions and communication between the participants of the process teacher-student, teacher-group of students, student-student; support for feedback in consultations, in messengers (with an emphasis on convenience for all parties); current verification of the obtained results, i.e. support and correction of creative works with obligatory explanation of problematic moments for applicants; organization of public defenses of creative works (projects) on proven digital communication platforms; adherence to the principles of academic integrity in the context of distance digital learning through verification of works in generally recognized digital information verification programs; allocation of time for separate consultations for research projects as such, which require special attention due to their complexity; the use of cloud services for recording the results of the progress of applicants and the like.
Electronic Resource Management: The Quest for Systems and Standards

A few years ago Cornell University Libraries’ strategic plan included the bold and startling prediction of a “mostly digital environment” for that library system by 2005. Libraries are obviously in the midst of a profound shift toward reliance on electronic resources. Meanwhile user behavior and attitudes seem to have been changing even more quickly. One fundamental management problem that some of these systems were meant to address is simply the need to describe larger and larger numbers of bibliographic databases, and present that information to users. As electronic resources have become pervasive, formal license agreements have also come to supplement or supersede copyright law as the basis for defining and determining their appropriate use. The emerging interest among libraries in electronic resource management systems prompted Adam Chandler and the author to establish in 2001 a “Web Hub” to exchange information about local systems and foster communication among interested librarians.
Muesli: Combining Improvements in Policy Optimization

We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the-art performance on Atari. Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation speed comparable to model-free baselines. The Atari results are complemented by extensive ablations, and by additional results on continuous control and 9x9 Go.
Design and Implementation of Visually Impaired Assistant system

The need for independent living is recognised in the case of visually impaired people who face a variety of issues, including social isolation.They are forced to live in strange environments with no means of assistance. Today's tech world should supply them with this assistance for easier socialization. Our aim is to facilitate the life of visually impaired people who could lack the technology to help them in their lives. This project is proposed to focus on the development of a mobile application that uses voice commands and text to voice technology to enable users to interact with the mobile application. The user captures the item they want to identify using the smartphone's camera and pick the image they want to upload for processing. The image chosen then will undergo through pre-processing techniques depending on the feature the user chose, the features include Money recognition CNN MobileNet architecture was used to analyze the Egyptian banknotes and Color and patterns recognition CNN resnet50 architecture was used to analyze the color and pattern of the clothing. As well, the application allows the user to register or login using facial recognition to further facilitate the experience for users. The application is designed to be effective and can be easily navigated by visually impaired users. ResNet50 architecture produce the highest accuracy in patterns and colors cloth recognition, Mobilenet architecture produce the highest accuracy in currency recognition.
Assessment strategies for online learning. Engagement and authenticity

Reseña del libro: Assessment strategies for online learning. Engagement and authenticity, de Dianne Conrad y Jason Openo.
Dataset Condensation with Differentiable Siamese Augmentation

In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large training sets into significantly smaller synthetic sets which can be used to train deep neural networks from scratch with minimum drop in performance. Inspired from the recent training set synthesis methods, we propose Differentiable Siamese Augmentation that enables effective use of data augmentation to synthesize more informative synthetic images and thus achieves better performance when training networks with augmentations. Experiments on multiple image classification benchmarks demonstrate that the proposed method obtains substantial gains over the state-of-the-art, 7% improvements on CIFAR10 and CIFAR100 datasets. We show with only less than 1% data that our method achieves 99.6%, 94.9%, 88.5%, 71.5% relative performance on MNIST, FashionMNIST, SVHN, CIFAR10 respectively. We also explore the use of our method in continual learning and neural architecture search, and show promising results.