Reviewerly/exHarmony huggingface dataset:
A formal proof and simple explanation of the QuickXplain algorithm

In his seminal paper of 2004, Ulrich Junker proposed the QuickXplain algorithm, which provides a divide-and-conquer computation strategy to find within a given set an irreducible subset with a particular (monotone) property. Beside its original application in the domain of constraint satisfaction problems, the algorithm has since then found widespread adoption in areas as different as model-based diagnosis, recommender systems, verification, or the Semantic Web. This popularity is due to the frequent occurrence of the problem of finding irreducible subsets on the one hand, and to QuickXplain's general applicability and favorable computational complexity on the other hand. However, although (we regularly experience) people are having a hard time understanding QuickXplain and seeing why it works correctly, a proof of correctness of the algorithm has never been published. This is what we account for in this work, by explaining QuickXplain in a novel tried and tested way and by presenting an intelligible formal proof of it. Apart from showing the correctness of the algorithm and excluding the later detection of errors (proof and trust effect), the added value of the availability of a formal proof is, e.g., (i) that the workings of the algorithm often become completely clear only after studying, verifying and comprehending the proof (didactic effect), (ii) that the shown proof methodology can be used as a guidance for proving other recursive algorithms (transfer effect), and (iii) the possibility of providing "gapless" correctness proofs of systems that rely on (results computed by) QuickXplain, such as numerous model-based debuggers (completeness effect).
AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments

The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers.To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation.We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis.We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022.The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively.As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition.According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems

Machine learning (ML) algorithms have been widely developed to enable real-time security assessment for large-scale electricity grids. However, it also has been extensively recognized that the ML models are vulnerable to adversarial examples, which are slightly perturbed instances that can mislead the classifications but cannot be distinguished by human eyes. Considering the adversarial examples against the ML-based security assessment, they must not only cause misclassification but also follow the power balance and transfer limits and bypass the bad data detection. To this end, this paper proposes a novel concept named physics-constrained robustness that aims to computing a lower-bound of adversarial perturbations, evaluating the vulnerability of the ML-based intelligent security assessment (ISA) for power systems. A general optimization problem is formulated to compute the physics-constrained robustness of ISA with the mislabeling, power balance, power limitation, and invisible constraints. The analytical results and comparing relationship of ISA’s robustness with different constraints are provided. By using the static security assessment as an example, we provide explicit formulations to evaluate the physics-constrained robustness of. Finally, extensive experiments are conducted to evaluate the physics-constrained robustness of ISA in static and dynamic cases with the real-world load profiles from New York State and provide suggestions to select the ML models and parameters.
Blockchain Technology Transforms Digital Marketing by Growing Consumer Trust

Surges in the use of e-commerce channels, together with smartphone apps, have catalysed the rise of business-to-consumer commerce, changed the nature of businesses and the way they generate value. These emerging innovations also modified the nature of brand marketing, making it easier to have a larger scope and more targeting to improve brand confidence and increase consumer loyalty. Today, the Internet makes it possible for businesses to go further into their current markets, develop new virtual markets and create new demand. Such diverse consumer interaction uses emerging tools to concentrate on the audience in more granular ways. In this study, the use of Blockchain technology is discussed as it is projected to affect digital marketing practices. Blockchain technology promotes disintermediation, combats click manipulation, fortifies trust and accountability, guarantees privacy safety, empowers security and encourages loyalty services that deliver exclusive experiences to their consumers. We demonstrate how Blockchain technology functions as an evolutionary breakthrough that empowers a consumer-centric mentality. In this chapter, we look at the potential of Blockchain technology from a marketing point of view. To understand the role of trust in Blockchain adoption in digital marketing, peer-reviewed articles were selected using the following search terms: ‘Blockchain’, ‘big data’ and ‘digital marketing’. This review of the literature found that trust is a key driver to consumer–brand relations when implementing Blockchain technology in digital marketing.
Open-source software product line extraction processes: the ArgoUML-SPL and Phaser cases

Abstract Software Product Lines (SPLs) are rarely developed from scratch. Commonly, they emerge from one product when there is a need to create tailored variants, or from existing variants created in an ad-hoc way once their separated maintenance and evolution become challenging. Despite the vast literature about re-engineering systems into SPLs and related technical approaches, there is a lack of detailed analysis of the process itself and the effort involved. In this paper, we provide and analyze empirical data of the extraction processes of two open-source case studies, namely ArgoUML and Phaser. Both cases emerged from the transition of a monolithic system into an SPL. The analysis relies on information mined from the version control history of their respective source-code repositories and the discussion with developers that took part in the process. Unlike previous works that focused mostly on the structural results of the final SPL, the contribution of this study is an in-depth characterization of the processes. With this work, we aimed at providing a deeper understanding of the strategies for SPL extraction and their implications. Our results indicate that the source code changes can range from almost a fourth to over half of the total lines of code. Developers may or may not use branching strategies for feature extraction. Additionally, the problems faced during the extraction process may be due to lack of tool support, complexity on managing feature dependencies and issues with feature constraints. We made publicly available the datasets and the analysis scripts of both case studies to be used as a baseline for extractive SPL adoption research and practice.
Experimental Analysis of Methods Used to Solve Linear Regression Models

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes.Regression is one of the most important types of supervised machine learning, in which labeled data is used to build a prediction model, regression can be classified into three different categories: linear, polynomial, and logistic.In this research paper, different methods will be implemented to solve the linear regression problem, where there is a linear relationship between the target and the predicted output.Various methods for linear regression will be analyzed using the calculated Mean Square Error (MSE) between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods, including least-square fit; Feed-Forward Artificial Neural Network (FFANN), and Cascade Feed-Forward Artificial Neural Network (CFFANN), and recommendations will be raised.The proposed method has been tested in this research on random data samples, and the results were compared with the results of the most common method, which is the linear multiple regression method.It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.
Zero-Shot Recommendation as Language Modeling

Recommendation is the task of ranking items (e.g. movies or products) according to individual user needs. Current systems rely on collaborative filtering and content-based techniques, which both require structured training data. We propose a framework for recommendation with off-the-shelf pretrained language models (LM) that only used unstructured text corpora as training data. If a user u liked Matrix and Inception, we construct a textual prompt, e.g. "Movies like Matrix, Inception, $${<}m{>}$$ ” to estimate the affinity between u and m with LM likelihood. We motivate our idea with a corpus analysis, evaluate several prompt structures, and we compare LM-based recommendation with standard matrix factorization trained on different data regimes. The code for our experiments is publicly available ( https://colab.research.google.com/drive/...?usp=sharing ).
OSSARA: Abandonment Risk Assessment for Embedded Open Source Components

Systems with unmaintained embedded open source software (OSS) components are vulnerable to severe risks. This article introduces the OSS Abandonment Risk Assessment model to help companies avoid potentially dire consequences.
Towards Explainability for AI Fairness

Abstract AI explainability is becoming indispensable to allow users to gain insights into the AI system’s decision-making process. Meanwhile, fairness is another rising concern that algorithmic predictions may be misaligned to the designer’s intent or social expectations such as discrimination to specific groups. In this work, we provide a state-of-the-art overview on the relations between explanation and AI fairness and especially the roles of explanation on human’s fairness judgement. The investigations demonstrate that fair decision making requires extensive contextual understanding, and AI explanations help identify potential variables that are driving the unfair outcomes. It is found that different types of AI explanations affect human’s fairness judgements differently. Some properties of features and social science theories need to be considered in making senses of fairness with explanations. Different challenges are identified to make responsible AI for trustworthy decision making from the perspective of explainability and fairness.
Fuzzy Differential Privacy Theory and Its Applications in Subgraph Counting

Transportation networks are essential to the operation of societies and economies. Protecting the privacy of sensitive information is a meaningful conception in sustainable transport when mining the transportation data. In data mining, differential privacy (DP) has provable privacy guarantees for releasing sensitive data by introducing randomness into query results. However, it suffers from significant accuracy loss of outputs when the query has high sensitivity (e.g., triangle counting). The reason is that the range of random perturbation to each query result in DP is too large. It consists of all possible output values for a query that forms a large or even unbounded interval. However, when impose perturbation only in a small neighborhood of the true query result, the similarity measure based on randomness in DP fails. Thereupon, we introduce fuzziness into DP to formulate new models which have smaller disturbance via fuzzy similarity measures. In this article, we establish a novel and general theory of private data analysis, fuzzy differential privacy (FDP). The new theory FDP aims to acquire a more flexible tradeoff between the accuracy of outputs and the privacy-preserving level of data. FDP combines DP with fuzzy set theory by introducing fuzziness into the query results and characterizing similarities between outputs via multiple fuzzy similarity measures. From this perspective, DP can be viewed as a special case of FDP with probabilistic similarity measure. Compared with DP, FDP has three superiorities: 1) most fuzzy similarity measures in FDP support sliding window perturbation strategies we proposed, which refer to perturbation in a small neighborhood of the query results; 2) FDP adds noise to the query results only according to a fraction of all possible neighboring datasets; and 3) the fuzzy similarity with valued in [0,1] quantifies the privacy protection level intuitively. These three points enable more accurate outputs while providing provable and intuitive privacy guarantees. As for subgraph counting, the state-of-the-art method is ladder framework in DP. We illustrate FDP mechanisms by applying them to a common application in subgraph counting–triangle/4-cliques counting. Experiments show that FDP is effective and efficient with smaller output errors than DP.
Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health Intervention

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.
A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading. The results show that our framework has achieved pathologist-level accuracy in diagnosis, can generate pathology reports with tumor indicators, and provide pathologists with interpretable auxiliary diagnoses
A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive

Small file processing in Hadoop is one of the challenging task. The performance of the Hadoop is quite good when dealing with large files because they require lesser metadata and consume less memory. But while dealing with enormous amount of small files, metadata grows linearly and Name Node memory gets overloaded hence overall performance of the Hadoop degrades. This paper presents a dual merge technique HB-EHA (Hash Based-Extended Hadoop Archive), that will resolve the small file issue of Hadoop and provide an excellent solution for massive small files that are generated in the health care management applications. The proposed technique merges the small files using two-level compaction, therefore, the size of metadata at the name node gets reduced and less memory will be used. The indexing will be carried out over the archives and files can be accessed after merging in real-time. Index files in the proposed approach can read partially that improves the name node memory usage and also offers the file appending capability in the existing archive. The proposed technique first creates Hadoop archive from the small files and then uses two special hash functions i.e. SSHF (Scalable-Splittable Hash Function) and HT-MMPHF (Hollow Trie Monotone Minimal Perfect Hash Function), SSHF is used to dynamically distribute the archives meta-data to the associated slave index files, and these slave index files will be further written to the final index files, the order of the meta-data in final index file will be preserved by the HT-MMPHF. The evaluation outcome exhibit that the proposed technique is 13% & 17% faster than HDFS with caching enabled and disabled respectively, and 38% & 47% faster than the HAR with caching and without caching, respectively. While comparing with the map file, the proposed technique is 28 & 35 times faster with caching and without caching, respectively. HB-EHA is a maximum of 40% & 28% faster than the HBAF with and without caching, respectively.
Micro-Behavior Encoding for Session-based Recommendation

Session-based Recommendation (SR) aims to predict the next item for recommendation based on previously recorded sessions of user interaction. The majority of existing approaches to SR focus on modeling the transition patterns of items. In such models, the so-called micro-behaviors describing how the user locates an item and carries out various activities on it (e.g., click, add-to-cart, and read-comments), are simply ignored. A few recent studies have tried to incorporate the sequential patterns of micro-behaviors into SR models. However, those sequential models still cannot effectively capture all the inherent interdependencies between micro-behavior operations. In this work, we aim to investigate the effects of the micro-behavior information in SR systematically. Specifically, we identify two different patterns of micro-behaviors: “sequential patterns” and “dyadic relational patterns”. To build a unified model of user micro-behaviors, we first devise a multigraph to aggregate the sequential patterns from different items via a graph neural network, and then utilize an extended self-attention network to exploit the pair-wise relational patterns of micro-behaviors. Extensive experiments on three public real-world datasets show the superiority of the proposed approach over the state-of-the-art baselines and confirm the usefulness of these two different micro-behavior patterns for SR.
A Heuristic-Based Task Scheduling Policy for QoS Improvement in Cloud

Cloud computing is a big step in the parallel and distributed computing that offers pervasive access to the entire stack of computing resources located in the data center via the Internet in a virtualized manner. QoS is an important research direction in the cloud and is a collection of constraints that meets the service level agreement (SLA) between the users and service providers. These constraints are waiting time, completion time, response time, makespan, resource utilization, effective utilization of bandwidth, and load balancing. This work presents a distribution plan for transferring task loads to different virtual machines (VMs) with an efficient load balancing mechanism that is best suited in heterogeneous environments and improve the QoS parameter of users and cloud vendors simultaneously. In this paper, the expected uniform load of tasks that can be mapped to a particular VMs is calculated and after that, the optimal average completion time (OACT) of expected uniform tasks load to each VM is calculated.
SpeechSplit2.0: Unsupervised Speech Disentanglement for Voice Conversion without Tuning Autoencoder Bottlenecks

SpeechSplit can perform aspect-specific voice conversion by disentangling speech into content, rhythm, pitch, and timbre using multiple autoencoders in an unsupervised manner. However, SpeechSplit requires careful tuning of the autoencoder bottlenecks, which can be time-consuming and less robust. This paper proposes SpeechSplit2.0, which constrains the information flow of the speech component to be disentangled on the autoencoder input using efficient signal processing methods instead of bottleneck tuning. Evaluation results show that SpeechSplit2.0 achieves comparable performance to SpeechSplit in speech disentanglement and superior robustness to the bottleneck size variations. Our code is available at https://github.com/biggytruck/SpeechSplit2.