Reviewerly/exHarmony huggingface dataset:
Choice logics and their computational properties

Qualitative Choice Logic (QCL) and Conjunctive Choice Logic (CCL) are formalisms for preference handling, with especially QCL being well established in the field of AI. So far, analyses of these logics need to be done on a case-by-case basis, albeit they share several common features. This calls for a more general choice logic framework, with QCL and CCL as well as some of their derivatives being particular instantiations. We provide such a framework, which allows us, on the one hand, to easily define new choice logics and, on the other hand, to examine properties of different choice logics in a uniform setting. In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. Our analysis also yields new results for QCL and CCL. For example, we show that the main reasoning task regarding preferred models of choice logic formulas is Θ2P-complete for QCL and CCL, while being Δ2P-complete for a newly introduced choice logic. The complexity of preferred model entailment for choice logic theories ranges from coNP to Π2P.
Artifact: Scalable Distributed Data Anonymization

We describe the artifact, publicly available at [1], that implements the proposal in [2], and the reproduction of the experimental results. It is an extended and distributed version of the Mondrian anonymization algorithm. Our solution anonymizes large datasets by partitioning data among workers in a distributed setting. It provides parallel execution on a dynamically chosen number of workers, limiting their interaction and data exchange.
‘Can the subaltern speak?’: COVID-19 and decolonial pedagogy in Palestinian universities

The online mode of education has created a space for a decolonial pedagogy that allows student liberation from in-class education which perpetuates students' passivity. Drawing on Freire's concepts of banking education, dialogue and democracy, Said's various works and concepts, including 'margins' and 'centres', and students' testimonies concerning online and in-class modes of education, we contend that online education shatters the hegemony of instructors over the process of education. While students air their proclivity for emancipation from the hegemonic intricacy of the traditional classroom, instructors also highlight the importance of dialogue, research and enriching discussions that subvert the teacher-student hierarchy. While some instructors used to follow research and dialogue in class, they encountered challenges in introducing topics related to sexuality, politics and religion. However, these issues have shored up during online education as the emphasis on the part of instructors and students has been on constructing arguments rather than memorising materials. Thus, the online education we have been forced to adopt is a call for a paradigm shift in the sense that the traditional mode of education perpetuates students' passivity and suppression of their voices in ways that resonate with the suppression of Palestinian voices by the Israeli-settler colonialism.
Weakly Supervised Regression Using Manifold Regularization and Low-Rank Matrix Representation

We solve a weakly supervised regression problem. Under “weakly” we understand that for some training points the labels are known, for some unknown, and for others uncertain due to the presence of random noise or other reasons such as lack of resources. The solution process requires to optimize a certain objective function (the loss function), which combines manifold regularization and low-rank matrix decomposition techniques. These low-rank approximations allow us to speed up all matrix calculations and reduce storage requirements. This is especially crucial for large datasets. Ensemble clustering is used for obtaining the co-association matrix, which we consider as the similarity matrix. The utilization of these techniques allows us to increase the quality and stability of the solution. In the numerical section, we applied the suggested method to artificial and real datasets using Monte-Carlo modeling.
Artefact Relation Graphs for Unit Test Reuse Recommendation

The reuse of artefacts is fundamental to software development and can reduce development cost and time as well as improve the quality of the output. For example, developers often create new tests from existing tests by copying and adapting them. However, reuse opportunities are often missed due to the cost of discovering suitable artefacts to reuse.Development artefacts form groups that have both internal connections between artefacts of the same type, and cross-group connections between artefacts of different types. When a pair of artefact groups are considered, the cross-group connections form a bipartite graph. This paper presents Rashid, an abstract framework to assist artefact reuse by predicting edges in these bipartite graphs. We instantiate Rashid with Relatest, an approach to assist developers to reuse tests. Relatest recommends existing tests that are closely related to a new function and can, therefore, be easily adapted to test the new function. Our evaluation finds that Relatest's recommendations result in an average 58% reduction in developer effort (measured in tokens), for 75% of functions, resulting in an overall saving of 43% of the effort required to create tests. A user study revealed that, on average, developers needed 10 minutes less to develop a test when given Relatest recommendations and all developers reported that the recommendations were useful.
A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies, such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud, one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services (CCS) that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework, which consists of three stages: identification of data security requirements, management of data security risks and evaluation of data security performance in CCS.
Time-capturing Dynamic Graph Embedding for Temporal Linkage Evolution

Dynamic graph embedding learns representation vectors for vertices and edges in a graph that evolves over time. We aim to capture and embed the evolution of vertices' temporal connectivity. Existing work studies the vertices' dynamic connection changes but neglects the time it takes for edges to evolve, failing to embed temporal linkage information into the evolution of the graph. To capture vertices' temporal linkage evolution, we model dynamic graphs as a sequence of snapshot graphs, appending the respective timespans of edges (ToE). We co-train a linear regressor to embed ToE while inferring a common latent space for all snapshot graphs by a matrix-factorization-based model to embed vertices' dynamic connection changes. Vertices' temporal linkage evolution is captured as their moving trajectories within the common latent representation space. Our embedding algorithm converges quickly with our proposed training methods, which is very time efficient and scalable. Extensive evaluations on several datasets show that our model can achieve significant performance improvements, i.e. 22.98% on average across all datasets, over the state-of-the-art baselines in the tasks of vertex classification, static and time-aware link prediction, and ToE prediction.
How Does Updatable Learned Index Perform on Non-Volatile Main Memory?

Recent work on learned index opens a new research direction for index structures. By exploiting the data distribution, a read-only learned index can achieve much better time and space performance than B+-Trees. A number of recent studies propose enhanced learned index structures that provide various levels of support for insertions and deletions. Among them, ALEX, an updatable adaptive learned index, provides the most comprehensive support, allowing on-the-fly insertions and dynamically adjusting the index structure similar to the B+-Tree. In this paper, we study learned index from a new perspective, trying to understand how ALEX performs on Non-Volatile Main Memory (NVM). We analyze the insertion behaviors of ALEX and experimentally evaluate its performance on a real machine equipped with Intel Optane DC Persistent Memory. We find that learning data distributions in learned index makes it feasible to create very large leaf nodes, and reduce the tree height drastically for better search performance. However, this design choice causes a large number of NVM writes for insertion operations, even for the well optimized ALEX design.
Multiview Subspace Dual Clustering

A single clustering refers to the partitioning of data such that the similar data are assigned into the same group, whereas the dissimilar data are separated into different groups. Recently, multiview clustering has received significant attention in recent years. However, most existing works tackle the single-clustering scenario, which only use single clustering to partition the data. In practice, nevertheless, the real-world data are complex and can be clustered in multiple ways depending on different interpretations of the data. Unlike these methods, in this article, we apply dual clustering to multiview subspace clustering. We propose a multiview dual-clustering method to simultaneously explore consensus representation and dual-clustering structure in a unified framework. First, multiview features are integrated into a latent embedding representation through a multiview learning process. Second, the dual-clustering segmentation is incorporated into the subspace clustering framework. Finally, the learned dual representations are assigned to the corresponding clusterings. The proposed approach is efficiently solved using an alternating optimization scheme. Extensive experiments demonstrate the superiority of our method on real-world multiview dual- and single-clustering datasets.
Efficient Bias Estimation in Airborne Video Georegistration for Ground Target Tracking

Accurate georegistration is required in airborne surveillance of ground targets since even a small bias in sensor registration may result in a large error in the converted target geolocation. Thus, this article addresses the problem of estimating and correcting the sensor biases in target geolocation. The effect of sensor biases on target geolocation is modeled and decoupled from the true target geolocation. By assuming that the biases are small, the effect of biases is separately addressed by a linear term in the converted target geolocation. Based on this model, two computationally efficient methods are proposed to estimate the biases. The converted target geolocation can then be corrected based on the bias estimates. Simulation results demonstrate the effectiveness of the proposed methods in estimating sensor biases and correcting target geolocations as well as their high computational efficiency.
High-accuracy handwriting recognition based on improved CNN algorithm

This paper proposes a impoved convolutional neural network model based on the Adam optimizer for network training and replacing the commonly used activation function ReLU with the advanced activation function PReLU. The algorithm will address the current problems of low accuracy and low efficiency of handwritten digit recognition based on SVM(Support Vector Machines) classifier and nearest neighbor classification techniques. In the model training, the Dropout regularization method is used to improve the generalization ability of the model and reduce overfitting; the MNIST dataset of handwritten digits is used to explore the effects of ReLU activation function and PReLU activation function on the accuracy and convergence of the model; the accuracy is compared with that of other handwritten digit recognition techniques. The experimental results show that the enhanced CNN model outperforms other recognition techniques with high algorithm convergence and an accuracy of 99.60% when trained on the MNIST dataset.
Blockchain and Homomorphic Encryption For Digital Copyright Protection

Internet, especially mobile Internet, brings a new channel for digital content distribution. However, all the new technology can make users easily share, modify and redistribute the digital contents out of the owner's control, which have created new intellectual property issues. In this paper, we use blockchain and homomorphic encryption to implement a detailed copyright auction system, which not only can protect the digital contents copyright from the view of the contents owner, but also from the privacy and data security from the end clients. For homomorphic encryption, different algorithms are compared from the performance of key generation, encryption and decryption. Moreover, the security proof and statement of the whole procedure are also discussed in detailed in the paper.
Learning a Low-Complexity Channel Estimator for One-Bit Quantization

A low-complexity convolutional neural network (CNN) channel estimator has been proposed recently, which was designed based on assumptions on the underlying channel model. In this work, we investigate how one-bit quantized observations affect this CNN estimator. In contrast to many other approaches, we propose a technique to obtain only one CNN estimator for a whole range of signal-to-noise ratio (SNR) values. We compare the performance of this estimator with a linear minimum mean square error (LMMSE) estimator based on the Bussgang decomposition and also with a state-of-the-art maximum a posteriori (MAP) approach, which exploits an approximate sparsity of the channels.
Causal Inference Methods and their Challenges: The Case of 311 Data

The main purpose of this paper is to illustrate the application of causal inference method to administrative data and the challenges of such application. We illustrate by applying Bayesian networks method to 311 data from Miami-Dade County, Florida (USA). The 311 centers provide non-emergency services to residents. The 311 data are large and granular. We aim to explore the equity issues and biases that might exist in this particular type of service requests. As a case study, the relationship between population characteristics (independent variables) and request volume and completion time (dependent variables) is examined to identify the disparities, if any, from the observational data. The empirical analysis shows that there are no biases in services provided to any specific demographic, socioeconomic, or geographical groups. However, the administrative data do have various challenges for inferring causality due to missing or impure data, inadequacy, and latent confounders. The precautions of applying causal techniques to analyzing administrative data like 311 are discussed.
A Deep Neural Network Approach using Convolutional Network and Long Short Term Memory for Text Sentiment Classification

The current emotion-based text categorization method incorporates a lot of deep learning, such as LSTM (Long short term memory) and CNN (Convolutional neural network) algorithms. The traditional algorithm extracts relatively few text features, so the performance of the algorithm can be improved. Based on this fact, this paper decided to adopt a text sentiment prediction method based on CNN and LSTM. First, the user's words are converted into vectors by the frequency of occurrence of the words, and the user's words are convoluted by the CNN to extract the feature information in the user text. Then, further feature extraction of the CNN convolved data by LSTM enables more dimensional information of the text to be used for classification. The experimental results show that the model constructed by this method is more effective in extracting multidimensional features on user text, and effectively optimizes the traditional algorithm. Compared with the CNN model and the LSTM model, the performance is improved.
Robust and Dynamic Graph Convolutional Network For Multi-view Data Classification

Abstract Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance.
Protecting Student Privacy with Synthetic Data from Generative Adversarial Networks

Educational data requires layers of protection that prohibit easy access to sensitive student data. However, the additional layers of security hinder research that relies on educational data to progress. In this paper, a Least Squares GAN (LSGAN) is proposed to create synthetic student performance datasets based on a master dataset without recreating samples. Synthetic data is less likely to be traced back to a student thereby reducing privacy issues. Two feature subsets were considered in the study: sequential, and all features. GANs trained on the sequential data produced new datasets that were representative of student performance from the training dataset, while the GAN trained on all features was not able to capture characteristics from the dataset. Based on the results, the synthetic dataset can provide an alternative unrestricted source of data without compromising student privacy.
Enabling Pervasive Federated Learning using Vehicular Virtual Edge Servers

Recent works have proposed various distributed federated learning (FL) systems for the edge computing paradigm. These FL algorithms can assist pervasive applications in various aspects, e.g., decision making, pattern recognition, and behavior prediction. Existing solutions do not efficiently support the training based on the real-time location-specific data, because fundamentally, the “data collection” problem is rarely studied in the context of FL systems. To address this problem, we present a novel system, VC-SGD (Vehicular Clouds-Stochastic Gradient Descent), which seamlessly integrates the emerging concept of vehicular clouds with an edge-based FL. We show that by using vehicular clouds as virtual edge servers, VC-SGD is able to effectively support FL algorithms that use real-time location-specific data. We develop a general simulator that uses SUMO to simulate vehicle mobility and MXNet to perform real training. We use our simulator to verify the efficacy of VC-SGD. The experimental results demonstrate that VC-SGD improves over existing solutions.
Case Discrimination: Self-supervised Feature Learning for the Classification of Focal Liver Lesions

Deep Learning provides exciting solutions to problems in medical image analysis and is regarded as a key method for future applications. However, only a few annotated medical image datasets exist compared to numerous natural images. A solution to this problem is transfer learning using ImageNet. However, because the domain of ImageNet is different from that of medical images, the results of transfer learning are not always good. Therefore, we propose a model to investigate transfer learning by self-supervised learning using medical images. It is widely known that the results of Computerized Tomography (CT) scan are 3D volume images. There are lots of slices in CT or Magnetic Resonance Imaging scan images. So why not make these slices to a class? It is imperative to formulate this intuition as a self-supervised feature learning at the case-level. The results of our experiment demonstrated that, under self-supervised feature learning settings, our method surpasses the transfer learning method that uses ImageNet for classification. By experimenting with unannotated datasets, our method is remarkable for consistently improving test performance with a few annotated data. By fine-tuning the learned features, we obtained competitive results for self-supervised learning and classification tasks.
Comparison of Automated Machine Learning Tools for SMS Spam Message Filtering

Short Message Service (SMS) is a very popular service used for communication by mobile users. However, this popular service can be abused by executing illegal activities and influencing security risks. Nowadays, many automatic machine learning (AutoML) tools exist which can help domain experts and lay users to build high-quality ML models with little or no machine learning knowledge. In this work, a classification performance comparison was conducted between three automatic ML tools for SMS spam message filtering. These tools are mljar-supervised AutoML, H2O AutoML, and Tree-based Pipeline Optimization Tool (TPOT) AutoML. Experimental results showed that ensemble models achieved the best classification performance. The Stacked Ensemble model, which was built using H2O AutoML, achieved the best performance in terms of Log Loss (0.8370), true positive (1088/1116), and true negative (281/287) metrics. There is a 19.05% improvement in Log Loss with respect to TPOT AutoML and 5.56% improvement with respect to mljar-supervised AutoML. The satisfactory filtering performance achieved with AutoML tools provides a potential application for AutoML tools to automatically determine the best ML model that can perform best for SMS spam message filtering.