Reviewerly/exHarmony huggingface dataset:
An Open-Source Suite of Causal AI Tools and Libraries

We propose to accelerate use-inspired basic research in causal AI through a suite of causal tools and libraries that simultaneously provides core causal AI functionality to practitioners and creates a platform for research advances to be rapidly deployed. In this presentation, we describe our contributions towards an open-source causal AI suite. We describe some of their applications, the lessons learned from their usage, and what is next.
UnCommonSense in Action! Informative Negations for Commonsense Knowledge Bases

Knowledge bases about commonsense knowledge i.e., CSKBs, are crucial in applications such as search and question answering. Prominent CSKBs mostly focus on positive statements. In this paper we show that materializing important negations increases the usability of CSKBs. We present Uncommonsense, a web portal to explore informative negations about everyday concepts: (i) in a research-focused interface, users get a glimpse into results-per-steps of the methodology; (ii) in a trivia interface, users can browse fun negative trivia about concepts of their choice; and (iii) in a query interface, users can submit triple-pattern queries with explicit negated relations and compare results with significantly less relevant answers from the positive-only baseline. It can be accessed at:https://uncommonsense.mpi-inf.mpg.de/.
Data-Efficient Graph Learning Meets Ethical Challenges

Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users and items under recommender system applications. These graph learning- based methods are data hungry, which brought a significant data efficiency challenge. In this proposal, I introduce my PhD research from three aspects: 1) Efficient privacy-preserving recommendation for imbalanced data. 2) Efficient recommendation model training for Insufficient samples. 3) Explainability in the social recommendation. Challenges and solutions of the above research problems have been proposed in this proposal.
Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation

Knowledge graphs (KGs) have been widely used in recommendation systems to improve recommendation accuracy and interpretability effectively. Recent research usually endows KG reasoning to find the multi-hop user-item connection paths for explaining why an item is recommended. The existing path-finding process is well designed by logic-driven inference algorithms, while there exists a gap between how algorithms and users perceive the reasoning process. Factually, human thinking is a natural reasoning process that can provide more proper and convincing explanations of why particular decisions are made. Motivated by the Dual Process Theory in cognitive science, we propose a cognition-aware KG reasoning model CogER for Explainable Recommendation, which imitates the human cognition process and designs two modules, i.e., System~1 (making intuitive judgment) and System~2 (conducting explicit reasoning), to generate the actual decision-making process. At each step during the cognition-aware reasoning process, System~1 generates an intuitive estimation of the next-step entity based on the user's historical behavior, and System~2 conducts explicit reasoning and selects the most promising knowledge entities. These two modules work iteratively and are mutually complementary, enabling our model to yield high-quality recommendations and proper reasoning paths. Experiments on three real-world datasets show that our model achieves better recommendation results with explanations compared with previous methods.
AutoML for Deep Recommender Systems: Fundamentals and Advances

Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Recommender systems aim to identify a set of items that best match users' explicit or implicit preferences, by utilizing the user and item interactions to improve the accuracy. With the fast advancement of deep neural networks (DNNs) in the past few decades, recommendation techniques have achieved promising performance. However, we still meet three inherent challenges to design deep recommender systems (DRS): 1) the majority of existing DRS are developed based on hand-crafted components, which requires ample expert knowledge recommender systems; 2) human error and bias can lead to suboptimal components, which reduces the recommendation effectiveness; 3) non-trivial time and engineering efforts are usually required to design the task-specific components in different recommendation scenarios.
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms

Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a toolQDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate thatQDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube.com/watch?v=loh0_-7XLW4.
AgAsk: A Conversational Search Agent for Answering Agricultural Questions

While large amounts of potentially useful agricultural resources (journal articles, manuals, reports) are available, their value cannot be realised if they cannot be easily searched and presented to the agriculture users in a digestible form.AgAsk is a conversational search system for the agricultural domain, providing tailored answers to growers questions. AgAsk is underpinned by an efficient and effective neural passage ranking model fine-tuned on real world growers' questions. An adaptable, messaging-style user interface is deployed via the Telegram messaging platform, allowing users to ask natural language questions via text or voice, and receive short natural language answers as replies.
Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference

As the state-of-the-art graph learning models, the message passing based neural networks (MPNNs) implicitly use the graph topology as the "pathways" to propagate node features. This implicit use of graph topology induces the MPNNs' over-reliance on (node) features and high inference latency, which hinders their large-scale applications in industrial contexts. To mitigate these weaknesses, we propose the Graph Explicit Neural Network (GENN) framework. GENN can be flexibly applied to various MPNNs and improves them by providing more efficient and accurate inference that is robust in feature-constrained settings. Specifically, we carefully incorporate recent developments in network embedding methods to efficiently prioritize the graph topology for inference. From this vantage, GENN explicitly encodes the topology as an important source of information to mitigate the reliance on node features. Moreover, by adopting knowledge distillation (KD) techniques, GENN takes an MPNN as the teacher to supervise the training for better effectiveness while avoiding the teacher's high inference latency. Empirical results show that our GENN infers dramatically faster than its MPNN teacher by 40x-78x. In terms of accuracy, GENN yields significant gains (more than 40%) for its MPNN teacher when the node features are limited based on our explicit encoding. Moreover, GENN outperforms the MPNN teacher even in feature-rich settings thanks to our KD design.
BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs

Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in directed graphs. Specifically, given a directed graph and query node as input, the goal is to recommend top- nodes that have a high likelihood of a link with the query node. Here we propose BLADE, a novel GNN to model directed graphs. In order to jointly capture link likelihood and link direction, we employ an asymmetric loss function and learn dual embeddings for each node, by appropriately aggregating features from its neighborhood. In order to achieve optimal performance on both low and high-degree nodes, we employ a biased neighborhood sampling scheme that generates locally varying neighborhoods which differ based on a node's connectivity structure. Extensive experimentation on several open-source and proprietary directed graphs show that BLADE outperforms state-of-the-art baselines by 6-230% in terms of HitRate and MRR for the node recommendation task and 10.5% in terms of AUC for the link direction prediction task. We perform ablation study to accentuate the importance of biased neighborhood sampling employed in generating higher quality recommendations for both low-degree and high-degree query nodes. Further, BLADE delivers significant improvement in revenue and sales as measured through an A/B experiment.
Developing and Evaluating Graph Counterfactual Explanation with GRETEL

The black-box nature and the lack of interpretability detract from constant improvements in Graph Neural Networks (GNNs) performance in social network tasks like friendship prediction and community detection. Graph Counterfactual Explanation (GCE) methods aid in understanding the prediction of GNNs by generating counterfactual examples that promote trustworthiness, debiasing, and privacy in social networks. Alas, the literature on GCE lacks standardised definitions, explainers, datasets, and evaluation metrics. To bridge the gap between the performance and interpretability of GNNs in social networks, we discuss GRETEL, a unified framework for GCE methods development and evaluation. We demonstrate how GRETEL comes with fully extensible built-in components that allow users to define ad-hoc explainer methods, generate synthetic datasets, implement custom evaluation metrics, and integrate state-of-the-art prediction models.
DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network

To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.
Partimento Imitation and Fugue

Abstract This chapter explains how contrapuntal structures are implied in many partimenti, and provides instruction in discovering these structures. Points of imitation are explained, shown in partimenti, and studied through exercises. Partimenti by Nicola Sala, Fedele Fenaroli, Niccolò Zingarelli, Giovanni Paisiello, Giacomo Insanguine, Giacomo Tritto, and Leonardo Leo appear in this chapter. The reader encounters the technique of partimento fugue and is led through exercises in realizing them.
An Exploratory Study on Code Smells during Code Review in OSS Projects: A Case Study on OpenStack and WikiMedia

Objective: Open-source software (OSS) has become an important choice for developing software applications, and its usage has exponentially increased in recent years. Although many OSS systems have shown high reliability in terms of their functionality, they often exhibit several quality issues. Since most developers focus primarily on meeting clients’ functional requirements within the appropriate deadlines, the outcome suffers from poor design and implementation practices. This issue can also manifest as software code smells, resulting in a variety of quality issues such as software maintainability, comprehensibility, and extensibility. Generally speaking, OSS developers use code reviews during their software development to discover flaws or bugs in the updated code before it is merged with the code base. Nevertheless, despite the harmful impacts of code smells on software projects, the extent to which developers do consider them in the code review process is unclear in practice. Methods: To better understand the code review process in OSS projects, we gathered the comments of code reviewers who specified where developers should fix code smells in two OSS projects, OpenStack and WikiMedia, between 2011 and 2015. Results: Our findings indicate that most code reviewers do not pay much attention to code smells. Only a few code reviewers have attempted to motivate developers to improve their source code quality in general. The results also show that there is an increasing tendency to provide advice concerning code smells corrections over time. Conclusion: We believe that this study's findings will encourage developers to use new software engineering practices, such as refactoring, to reduce code smells when developing OSS.
Bass Motions in Invertible Counterpoint

Abstract This chapter explains the ways in which common bass motions may be combined in invertible counterpoint, resulting in the rapid construction of complex contrapuntal structures. The chapter explains that the motions C5 and Cascade are often combined with descending scales to form passages of rich harmony and counterpoint. Many exercises allow the reader to practice combining bass motions at the keyboard.
TASTA: Text‐Assisted Spatial and Temporal Attention Network for Video Question Answering

Video question answering (VideoQA) is a typical task that integrates language and vision. The key for VideoQA is to extract relevant and effective visual information for answering a specific question. Information selection is believed to be necessary for this task due to the large amount of irrelevant information in the video, and explicitly learning an attention model can be a reasonable and effective solution for the selection. Herein, a novel VideoQA model called Text‐Assisted Spatial and Temporal Attention Network (TASTA) is proposed, which shows the great potential of explicitly modeling attention. TASTA is made to be simple, small, clean, and efficient for clear performance justification and possible easy extension. Its success is mainly from two new strategies of better using the textual information. Experimental results on a large and most representative dataset, TGIF‐QA, show the significant superiority of TASTA w.r.t. the state‐of‐the‐art and demonstrate the effectiveness of its key components via ablation studies.
On measuring inconsistency in definite and indefinite databases with denial constraints

Real-world databases are often inconsistent. Although there has been an extensive body of work on handling inconsistency, little work has been done on measuring inconsistency in databases. In this paper, building on work done on measuring inconsistency in propositional knowledge bases, we explore inconsistency measures (IMs) for definite and indefinite databases with denial constraints. We first introduce database IMs that are inspired by well-established methods to quantify inconsistency in propositional knowledge bases, but are tailored to the relational database context where data is generally the reason for inconsistency, not the integrity constraints. Then, we analyze the compliance of the database IMs with rationality postulates for both definite and indefinite databases. Finally, we investigate the complexity of the inconsistency measurement problem as well as of the problems of deciding whether the inconsistency is lower than, greater than, or equal to a given threshold for both the definite and the indefinite cases.
Accurate edge-preserving stereo matching by enhancing anisotropy

Stereo matching has attracted substantial research attention, as it plays an important role in many visual tasks such as autonomous driving. Despite the remarkable progress has been made by all kinds of stereo matching methods, they still suffer in some challenging scenes like disparity discontinuities. The root cause of blurred depth boundaries lies in information diffusion across edge boundaries. Aiming at this problem, this paper presents a novel edge-preserving stereo matching method by enforcing anisotropy, where information is allowed to propagate throughout the image except through large discontinuities. Specifically, we propose an intra-scale cost aggregation algorithm to smooth out noise in homogeneous regions while well preserving strong edges in the guided image. And weighted averaging scheme, where weights are calculated according to the variances of pixels in different overlapping support windows, is utilized for enhancing anisotropy to improve accuracy at disparity discontinuities. We carry out comprehensive experiments on Middlebury public datasets to demonstrate the accuracy and edge-preserving property of our method. Qualitative and quantitative performance evaluation on Middlebury data sets demonstrate the superior performance of our method for strong edge preservation with 20.77% decreases in wrong matching ratios in discontinuous regions.
Basic of Artificial Neural Network

An information processing paradigm called an Artificial Neural Network (ANN) is based on how biological nervous systems, like the brain, process information. The information processing system's novel structure is the paradigm's most important feature. It is made up of many highly interconnected processing elements (neurons) that work together to solve particular problems. Similar to humans, ANNs learn from examples. Through a learning process, an ANN is set up for a specific use, like pattern recognition or data classification. Changing the neuronal synaptic connections is necessary for learning in biological systems. This is also true for ANNs. This paper provides an overview of the Artificial Neural Network, its operation, and its training. Additionally, it describes the advantages and applications of ANN.
An Analysis of Cryptocurrency, Bitcoin and the Future

A technology that was developed eight years ago is cryptocurrency, which is an encrypted, peer-to-peer network for facilitating digital barter. The first and most widely used cryptocurrency, Bitcoin, is paving the way as a disruptive technology for decades-old financial payment systems that have remained unchanged. Although cryptocurrencies are unlikely to replace conventional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing barriers to exchange rates and standard national currencies. The market in which a technology aims to improve is almost entirely responsible for its success. Technology advances at a rapid rate. By creating a fee-free trading system, cryptocurrencies may revolutionize digital trade markets. A SWOT analysis of Bitcoin is provided, which sheds light on some of the most recent happenings and trends that may have an impact on whether or not Bitcoin contributes to a paradigm shift in the economic system.
Intensity Tomography Method for Secure and High-Performance Quantum Key Distribution

Decoy-state method has greatly filled in loopholes from multi-photon pulses in the field of quantum key distribution (QKD) and it has been employed in almost all practical QKD systems in recent years. However, security and performance of the practical decoy-state QKDs are still limited by inaccurate modulations such as random intensity fluctuation and correlated intensity fluctuation. In this work, an intensity tomography method is proposed as a countermeasure against these fluctuations. It has an ability to tightly bound the unknown density matrices of photon-number state and accurately calibrate the fluctuations by measuring the decoy states with a local single-photon detector. We verified the tomography method by experiments and simulations. The results indicated that our method can efficiently mitigate the flaws from these fluctuations to improve the protocol performance and practical security. Noting that these fluctuations are inevitable in practical QKDs, our method would pave the avenue for QKD's practical applications.
Photo Semantic Understanding and Retargeting by a Noise-Robust Regularized Topic Model

Retargeting aims at displaying a photo with an arbitrary aspect ratio, wherein the visually/semantically prominent objects are appropriately preserved and visual distortions can be well alleviated. Conventional retargeting models are built upon the visual perception of photos from a family of pre-specified communities ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , “portrait”), wherein the underlying community-specific features are not learned explicitly. Thus they cannot appropriately retarget aerial photos, which contains a rich variety of objects with different scales. In this work, a novel aerial photo retargeting framework is designed by encoding the deep features from automatically detected Google Maps communities into a regularized probabilistic model. Specifically, we first propose an enhanced matrix factorization (MF) algorithm to calculate communities based on million-scale Google Maps pictures, for each of which deep feature is learned simultaneously. The enhanced MF incorporates label denoising, between-communities correlation, and deep feature encoding collaboratively. Subsequently, a probabilistic model called LTM is designed that quantifies the spatial layouts of multiple Google Maps communities in the underlying hidden space. To alleviate the overfitting from Google Maps communities with imbalanced numbers of aerial photos, a regularizer is added into the LTM. Finally, by leveraging the regularized LTM, we shrink the test photo horizontially/vertically to maximize the posterior probability of the retargted photo. Comprehensive subjective evaluations and visualizations have demonstrated the advantages of our method. Besides, our calculate Google Maps communities are competitively consistent with the ground truth, according to the quantitative comparisons on the 2M Google Maps photos.