Reviewerly/exHarmony huggingface dataset:
Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias

Methods for Knowledge Base Completion (KBC) reason about a knowledge base (KB) in order to derive new facts that should be included in the KB. This is challenging for two reasons. First, KBs only contain positive examples. This complicates model evaluation which needs both positive and negative examples. Second, those facts that were selected to be included in the knowledge base, are most likely not an i.i.d. sample of the true facts, due to the way knowledge bases are constructed. In this paper, we focus on rule-based approaches, which traditionally address the first challenge by making assumptions that enable identifying negative examples, which in turn makes it possible to compute a rule's confidence or precision. However, they largely ignore the second challenge, which means that their estimates of a rule's confidence can be biased. This paper approaches rule-based KBC through the lens of PU-learning, which can cope with both challenges. We make three contributions.: (1) We provide a unifying view that formalizes the relationship between multiple existing confidences measures based on (i) what assumption they make about and (ii) how their accuracy depends on the selection mechanism. (2) We introduce two new confidence measures that can mitigate known biases by using propensity scores that quantify how likely a fact is to be included the KB. (3) We show through theoretical and empirical analysis that taking the bias into account improves the confidence estimates, even when the propensity scores are not known exactly.
Joint Rate Distortion Optimization with CNN-based In-Loop Filter For Hybrid Video Coding

Existing deep learning based approaches for coding artifact removal mainly focus on the relationship between reconstruction and original signals. Although the CNN-based in-loop filtering reveals significant potential, the filtering procedure is separated from the rate distortion optimization (RDO) in the block-based hybrid framework. In this paper, we propose a joint rate distortion optimization scheme [1] with CNN-based in-loop filter for the Versatile Video Coding (VVC), with the goal of excavating the potential of in-loop filtering. In particular, a joint rate distortion optimization is presented, to consider the CNN-based in-loop filtering in the coding unit. More specifically, the CNN-based in-loop filter is involved in the partitioning mode selection, and meanwhile the prediction mode determination process remains unchanged. The proposed joint optimization is only applied to the CUs, whose height and width are no larger than 64. To keep the encoding complexity as low as possible, a light-weight version of CNN-based model is provided to facilitate the encoding optimization. As shown in Table 1, the proposed method brings 9.61&#x0025; and 12.81&#x0025; BD-Rate gains for luma component under all intra and random access configurations, respectively, on VTM-11.0-nnvc platform.
Shrub Ensembles for Online Classification

Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successful online learning methods are Decision Tree (DT) ensembles. DT ensembles provide excellent performance while adapting to changes in the data, but they are not resource efficient. Incremental tree learners keep adding new nodes to the tree but never remove old ones increasing the memory consumption over time. Gradient-based tree learning, on the other hand, requires the computation of gradients over the entire tree which is costly for even moderately sized trees. In this paper, we propose a novel memory-efficient online classification ensemble called shrub ensembles for resource-constraint systems. Our algorithm trains small to medium-sized decision trees on small windows and uses stochastic proximal gradient descent to learn the ensemble weights of these `shrubs'. We provide a theoretical analysis of our algorithm and include an extensive discussion on the behavior of our approach in the online setting. In a series of 2~959 experiments on 12 different datasets, we compare our method against 8 state-of-the-art methods. Our Shrub Ensembles retain an excellent performance even when only little memory is available. We show that SE offers a better accuracy-memory trade-off in 7 of 12 cases, while having a statistically significant better performance than most other methods. Our implementation is available under https://github.com/sbuschjaeger/se-online .
Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic

We posit a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon any nonlinear function of the team's long-term state-action occupancy measure, i.e., a general utility. This subsumes the cumulative return but also allows one to incorporate risk-sensitivity, exploration, and priors. We derive the Decentralized Shadow Reward Actor-Critic (DSAC) in which agents alternate between policy evaluation (critic), weighted averaging with neighbors (information mixing), and local gradient updates for their policy parameters (actor). DSAC augments the classic critic step by requiring agents to (i) estimate their local occupancy measure in order to (ii) estimate the derivative of the local utility with respect to their occupancy measure, i.e., the ``shadow reward". DSAC converges to ϵ-stationarity in O(1/ϵ^2.5) or faster O(1/ϵ^2) steps with high probability, depending on the amount of communications. We further establish the non-existence of spurious stationary points for this problem, that is, DSAC finds the globally optimal policy. Experiments demonstrate the merits of goals beyond the cumulative return in cooperative MARL.
Tracking Down Misguiding Terms for Locating Bugs in Deep Learning-Based Software (Student Abstract)

Bugs in source files (SFs) may cause software malfunction, inconveniencing users and even leading to catastrophic accidents. Therefore, the bugs in SFs should be found and fixed quickly. However, from hundreds of candidate SFs, finding buggy SFs is tedious and time consuming. To lessen the burden on developers, deep learning-based bug localization (DLBL) tools can be utilized. Text terms in bug reports and SFs play an important role. However, some terms provide incorrect information and degrade bug localization performance. Therefore, those terms are defined here as "misguiding terms," and an explainable-artificial-intelligence-based identification method is proposed. The effectiveness of the proposed method for DLBL was investigated. When misguiding terms were removed, the mean average precision of the bug localization model improved by 33% on average.
Pathology Image Compression Based on JPEG2000, Multi-Resolutional Human Perception and the Region of Interest Predictions

To achieve high efficiency of remote pathology image browsing in telemedicine, efficient image compression coding is required. In this work, we establish a visibility threshold (VT) model, which considers multi-resolution and different visual qualities jointly. Based on this model, we propose an image coding method, which operates adaptively according to the required resolutions and visual qualities, under the JPEG2000 standard for the whole-slide pathology images (WSIs). Particularly, we calculated the average standard deviation of the wavelet coefficient from lung squamous cell carcinoma (LSCC) WSIs and implemented in the VT calculations, as well as the masking factor calculation in each wavelet subband to count for the masking effect. The objective metric, HDR-VDP 3.0.6 score was implemented to evaluate to proposed encoding method. The averaged resulting scores across 100 LSCC WSIs in various visual qualities are plotted in Fig. 1, where half of the full resolution was used. The results from the mean-squared-error (MSE) based encoder at the same average bit rates are also shown. It can be seen that the predicted visual quality of decoded image from the proposed method is improved, compared with the MSE based method. Additionally, we also propose a weighting model to adjust the VTs. The encoder with the adjusted VTs concentrates on retaining the visual quality for the regions, where the lesion probabilities predicted with deep neural networks are high. A representative comparison of the decoded WSI blocks from the encoders with the adjusted and the non-adjusted VTs is shown in Fig. 2, where the adjusted VTs are shown to retain important features in decoding results while further reduce the coding bit rates.
Reinforcement Learning of Causal Variables Using Mediation Analysis

We consider the problem of acquiring causal representations and concepts in a reinforcement learning setting. Our approach defines a causal variable as being both manipulable by a policy, and able to predict the outcome. We thereby obtain a parsimonious causal graph in which interventions occur at the level of policies. The approach avoids defining a generative model of the data, prior pre-processing, or learning the transition kernel of the Markov decision process. Instead, causal variables and policies are determined by maximizing a new optimization target inspired by mediation analysis, which differs from the expected return. The maximization is accomplished using a generalization of Bellman's equation which is shown to converge, and the method finds meaningful causal representations in a simulated environment.
Analysis of The Elementary School Teachers’ Ability to Use Technology on The Mathematics Online Learning

In Indonesia, the implementation of online mathematics learning does not always run smoothly, there are various obstacles that occur, such as the lack of gadgets owned by teachers or students, internet networks are still weak, etc. So that the readiness of mathematics teachers in managing online learning is very necessary, especially in utilizing technology. Thus, it is necessary to analyze the readiness of mathematics teachers in carrying out online learning. In this study, we aim to describe (1) online mathematics learning in primary schools during the COVID-19 pandemic; (2) the ability of elementary school teachers to use technology during online learning, especially in learning mathematics; and (3) the content that teachers use to present online learning. We conducted research by distributing an online survey to 56 primary school teachers, over a period of approximately two weeks of data collection. The findings in this study are that most mathematics teachers in schools still use whatsApp and google meet or zoom media in conducting online learning, elementary school teachers still have not mastered learning technology well, and most teachers are still limited to using video downloads from YouTube, PPT and conventional books to present learning.
Cryptocurrency Legality and Regulations – An International Scenario

As cryptocurrencies become more widely used, so we need to have regulations put in place to regulate them.The terrain is always changing, and keeping up with the rules in several jurisdictions is difficult.In this paper the author looks at the global situation in terms of the current regulatory regime for cryptocurrencies and their trading on crypto exchanges.The authour further analayzed how some of the world's largest economies are attempting to handle crypto assets to provide us with insight into the need for and emerging trends in crypto regulation.
Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey

The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research network based on the common data model (CDM) has emerged, enabling collaborative international medical research without sharing patient-level data. Although the CDM database for each institution is built inside a firewall, the risk of re-identification requires management. Hence, this study aims to elucidate the perceptions CDM users have towards CDM and risk management for re-identification.The survey, targeted to answer specific in-depth questions on CDM, was conducted from October to November 2020. We targeted well-experienced researchers who actively use CDM. Basic statistics (total number and percent) were computed for all covariates.There were 33 valid respondents. Of these, 43.8% suggested additional anonymization was unnecessary beyond, "minimum cell count" policy, which obscures a cell with a value lower than certain number (usually 5) in shared results to minimize the liability of re-identification due to rare conditions. During extract-transform-load processes, 81.8% of respondents assumed structured data is under control from the risk of re-identification. However, respondents noted that date of birth and death were highly re-identifiable information. The majority of respondents (n = 22, 66.7%) conceded the possibility of identifier-contained unstructured data in the NOTE table.Overall, CDM users generally attributed high reliability for privacy protection to the intrinsic nature of CDM. There was little demand for additional de-identification methods. However, unstructured data in the CDM were suspected to have risks. The necessity for a coordinating consortium to define and manage the re-identification risk of CDM was urged.
Learning Noise-independent Speech Representation for High-quality Voice Conversion for Noisy Target Speakers

Building a voice conversion system for noisy target speakers, such as users providing noisy samples or Internet found data, is a challenging task since the use of contaminated speech in model training will apparently degrade the conversion performance. In this paper, we leverage the advances of our recently proposed Glow-WaveGAN and propose a noise-independent speech representation learning approach for high-quality voice conversion for noisy target speakers. Specifically, we learn a latent feature space where we ensure that the target distribution modeled by the conversion model is exactly from the modeled distribution of the waveform generator. With this premise, we further manage to make the latent feature to be noise-invariant. Specifically, we introduce a noise-controllable WaveGAN, which directly learns the noise-independent acoustic representation from waveform by the encoder and conducts noise control in the hidden space through a FiLM module in the decoder. As for the conversion model, importantly, we use a flow-based model to learn the distribution of noise-independent but speaker-related latent features from phoneme posteriorgrams. Experimental results demonstrate that the proposed model achieves high speech quality and speaker similarity in the voice conversion for noisy target speakers.
Design of Compensation System Using Adhered Method and Overlapping Method at PT XY DKI Jakarta

Compensation is one of the essential factors that can motivate employees to improve performance. Compensation provided by the company must be comparable to the company's workload, job risk, and the competencies that accompany the position. The Company is obliged to provide compensation both financially and non-financially so that employees' standard of living needs can be met. Therefore, it is necessary to design an appropriate compensation system based on the value of the job (job value). The object of this study is XY Company, located in DKI Jakarta. XY company is engaged in education, namely online English courses and early childhood tutor franchise, as well as in the culinary field, namely online courses in the pasta pizza business. The research methods used are quantitative and qualitative methods sourced in scientific journals and trusted articles. This research focuses on the compensation system. The method of processing and analyzing data used to compile the job value and job grading-based compensation system in XY companies is to use Compensable Factors analysis using hay method, Salary Mapping Adhered, and Salary Mapping Overlapping. This study shows that the effective compensation system to be implemented by PT XY is to use overlapping methods. The division of salary overlapping methods brings more benefits because the salary increase is less significant than the adhered method
Verifiable Conjunctive Dynamic Searchable Symmetric Encryption With Forward and Backward Privacy

Abstract Dynamic searchable symmetric encryption (DSSE) with forward and backward privacy makes it possible to perform search on the outsourced encrypted database efficiently while still allowing updates under acceptable leakage. Current forward and backward private DSSE (FB-DSSE) scheme proposed by Zuo et al. cannot support conjunctive keyword query and the cloud server needs to be honest-but-curious. Recent FB-DSSE scheme supporting conjunctive keyword query proposed by Patranabis et al. cannot verify search results. On the other hand, searchable symmetric encryption scheme proposed by Wang et al. that supports conjunctive keyword query and the verification of search results cannot achieve forward and backward privacy. The problem of constructing a verifiable conjunctive FB-DSSE scheme is still open. In this paper, we propose a verifiable conjunctive dynamic searchable symmetric encryption scheme (VCDSSE). VCDSSE is a FB-DSSE scheme that additionally supports the verification of search results and conjunctive keyword query. We revisit homomorphic MAC to enable efficient verification of search results, adopt the technique of oblivious cross-tags to achieve conjunctive keyword query and utilize state chain to ensure forward and backward privacy. The formal security analysis and performance evaluation demonstrate that VCDSSE is secure and practical as compared with Mitra scheme in terms of search time.
On the benefits and limits of incremental build of software configurations

Software projects use build systems to automate the compilation, testing, and continuous deployment of their software products. As software becomes increasingly configurable, the build of multiple configurations is a pressing need, but expensive and challenging to implement. The current state of practice is to build independently (a.k.a., clean build) a software for a subset of configurations. While incremental build has been studied for software evolution and relatively small changes of the source code, it has surprisingly not been considered for software configurations. In this exploratory study, we examine the benefits and limits of building software configurations incrementally, rather than always building them cleanly. By using five real-life configurable systems as subjects, we explore whether incremental build works, outperforms a sequence of clean builds, is correct w.r.t. clean build, and can be used to find an optimal ordering for building configurations. Our results show that incremental build is feasible in 100% of the times in four subjects and in 78% of the times in one subject. In average, 88.5% of the configurations could be built faster with incremental build while also finding several alternatives faster incremental builds. However, only 60% of faster incremental builds are correct. Still, when considering those correct incremental builds with clean builds, we could always find an optimal order that is faster than just a collection of clean builds with a gain up to 11.76%.
CLAIRE: Enabling Continual Learning for Real-time Autonomous Driving with a Dual-head Architecture

Autonomous vehicles rely on a pre-trained object detector to perceive surroundings. However, when never seen before scenarios are encountered, late decisions may result in hard braking due to perceived threats. Image sequences leading to such a situation provide the potential to learn and improve over time. Yet instant re-training on board with all prior training data is infeasible given computational, storage and power constraints. What’s more, exposure of a pre-trained CNN to only images of the new scenario is known to result in “catastrophic forgetting” for already learned features.This work makes several contributions: A novel lightweight dual-head detection network architecture is proposed to overcome forgetting and to support fast on-board continual learning on small sets of new images and assesses the feasibility of continual learning methods for autonomous driving. A sensitivity study on the quality and quantity of continually learned images for our dual-head technique is performed, including an assessment of its real-time suitability. Experiments show that our method’s accuracy is improved by up to 13% and performance increases by 5.8X over a state-of-the-art continual learning framework. This makes it suitable for autonomous driving scenarios with real-time constraints. Source code is made available via Github.
Artificial Intelligence Techniques in E-Commerce: The Possibility of Exploiting them in Saudi Arabia

E-Commerce has transformed business as we know over the past few decades. The rapid increasing use of the Internet and the strong purchasing power in Saudi Arabia have had a strong impact on the evolution of E-Commerce in the country. Saudi Arabia is yet another country that will release artificial intelligence power to fuel its growth in the economic world. Recently, artificial intelligence (AI) applications that can facilitate e-commerce processes have been widely used. The impact of using artificial intelligence (AI) concepts and techniques on the efficiency of e-commerce, particularly has been overlooked by many prior studies. In this paper, a literature review was conducted to explore and investigate possible applications of AI in E-Commerce that can help Saudi Arabian businesses.
An Efficient Hybrid Model for Acute Myeloid Leukaemia detection using Convolutional Bi-LSTM based Recurrent Neural Network

The major aim of this research is to identify the AML early from the morphological images. The proposed approach detects AML through four major stages, namely pre-processing, segmentation, feature extraction and classification. The pre-processing stage is employed for image enhancement. The segmentation process is utilized for segmenting the nucleus as well as the cell mask. When the human is affected by AML, the nucleus shape is asymmetrical, and texture is altered. The features achieved by the feature extraction process are given as the input of the classifier. The classification is attained by using the hybrid convolutional bidirectional LSTM-based recurrent neural network network. Although there are several optimisation algorithms proposed to solve different segmentation problems in the literature, to achieve the classified result more optimal, the slime mould optimisation algorithm is utilised. The data sets to detect AML were collected from Munich University Hospital. The experimental investigation is conducted, and from the experimental outcomes, it is examined that the proposed AML detection has obtained a sensitivity of 95.12%, a specificity of 93.72%, an accuracy of 97.95%, a precision of 94.64%, Matthews correlation coefficient of 98.2% and recall of 95.78%. This indicates that the proposed technique outperforms other state-of-art methods.