Reviewerly/exHarmony huggingface dataset:
Learning migration models for supporting incremental language migrations of software applications

A Legacy system can be defined as a system that significantly resists modification and evolution. According to the literature, there are two main strategies to migrate a legacy system: (a) to replace the legacy system by a new one, (b) to incrementally migrate parts from the legacy system to the new one. Incremental migration allows developers to better control the risks that may occur during the migration process. However, this strategy is more complex because it requires decomposition of the legacy system into different parts, e.g. a set of files, and to define the order of migration of them along the migration process. To our knowledge, there is no approach to support developers on those activities. This paper presents an approach, named MigrationExp, to support incremental language migrations of applications from one source language to another target language. MigrationExp recommends the files that should be migrated first in a particular migration iteration. As a novelty, our approach relies on a ranking model learned, using a learning-to-rank algorithm, from migrations made by developers. We validate our approach in the context of the migrations of Android apps, from Java to Kotlin, a new official language for Android. We train our model using migrations of Java code to Kotlin written by developers on open-source applications. The results show that, on the task of proposing files to migrate, our approach outperforms a previous migration strategy proposed by Google, in terms of its ability to accurately predict empirically observed migration orders. Since most Android applications are written in Java, we conclude that approaches to support developers such as MigrationExp may significantly impact the development of Android applications.
Research on privacy protection of dummy location interference for Location-Based Service location

Location privacy refers to the individual private and sensitive location information involved in the user’s access to location services. Achieving location privacy protection has become a hot topic of research. However, existing location privacy protection schemes are susceptible to background knowledge attack, edge information attack, and homogeneity attack, on the one hand, and strict constraint on the number of neighbors, on the other hand. To address these deficiencies, a dummy location interference privacy protection algorithm for Location-Based Service location is proposed. To begin with, the dummy location candidate set is constructed based on using WordNet structure to guarantee semantic differentiation, randomly selecting offset location, and conforming to probability similarity; next, the dummy location set is filtered out by discretizing dummy locations based on the Heron formula; finally, the secure anonymity set is constructed according to the anonymity level. Experiments show that the algorithm enhances the privacy protection strength and improves the security of location privacy. Meanwhile, the communication volume and time overhead are reduced and the practicality is boosted by taking into account the sparse and dense environment of location points.
BLOCKCHAIN TECHNOLOGY AND ITS IMPACT ON THE GLOBAL ECONOMY

In this paper, we will be reviewing the research published by Dr. BurcuSakÄ±z (Istanbul AydÄ±n University, Turkey) and Prof. Dr. AyÅŸenHiÃ§Gencer (Beykent University, Turkey) on Blockchain Technology and its Impact on the Global Economy at the International Conference on Eurasian Economies (2019) (Gencer, 2019). We will be including a summary of the research article, findings, and concepts discussed. The paper starts with a quote that Data has acquired the position to be the most valuable resource. Smartphones and high-speed internet have resulted in an explosion of data. The paper denotes that innovative AI techniques can extract meaningful information from the data. There is a need to share the knowledge, made possible only through an autonomous and decentralized mechanism for fair sharing policy and just hoarded by Corporates around the world. This article covers the significance of Blockchain in our economy today. It addresses the functioning of Blockchain and some popular opinion on it. It also includes the history and evolution and concludes by stating how a single white paper published in 2008 brought forth a revolution.
Gorgias Cloud: On-line Explainable Argumentation

Gorgias Cloud offers argumentation-based decision making as a service.The service includes an integrated development environment for the theories, testing and execution based on user scenarios, and, finally, an API for use by user applications.Gorgias is a structured argumentation framework where arguments are constructed using a basic scheme of argument rules.Two types of arguments are constructed within a Gorgias argumentation theory: object-level arguments and priority arguments expressing a preference between other arguments.Admissible composite arguments supporting a claim typically include both types of arguments.The Gorgias framework was introduced in [1], extended in [2] and applied to a variety of real-life application problems in [3].The Gorgias system allows us to code argumentation theories of the form described above and subsequently query the system to find out if there is an admissible (composite) argument that supports a desired Claim.The system of Gorgias has been publicly available since 2003 and has been used by several research groups to develop prototype real-life applications of argumentation in a variety of application domains.Today, it is available as a service over the internet with Gorgias Cloud, which provides an integrated environment for developing applications of argumentation with three novel features:
Paleolimnological evaluation of metal(loid) enrichment from oil sands and gold mining operations in northwestern Canada

Abundant reserves of metals and oil have spurred large-scale mining developments across northwestern Canada during the past 80 years. Historically, the associated emissions footprint of hazardous metal(loid)s has been difficult to identify, in part, because monitoring records are too short and sparse to have characterized their natural concentrations before mining began. Stratigraphic analysis of lake sediment cores has been employed where concerns of pollution exist to determine pre-disturbance metal(loid) concentrations and quantify the degree of enrichment since mining began. Here, we synthesize the current state of knowledge via systematic re-analysis of temporal variation in sediment metal(loid) concentrations from 51 lakes across four key regions spanning 670 km from bitumen mining in the Alberta Oil Sands Region (AOSR) to gold mining (Giant and Con mines) at Yellowknife in central Northwest Territories. Our compilation includes upland and floodplain lakes at varying distances from the mines to evaluate dispersal of pollution-indicator metal(loid)s from bitumen (vanadium and nickel) and gold mining (arsenic and antimony) via atmospheric and fluvial pathways. Results demonstrate ‘severe’ enrichment of vanadium and nickel at near-field sites (≤20 km) within the AOSR and ‘severe’ (near-field; ≤ 40 km) to ‘considerable’ (far-field; 40–80 km) enrichment of arsenic and antimony due to gold mining at Yellowknife via atmospheric pathways, but no evidence of enrichment of vanadium or nickel via atmospheric or fluvial pathways at the Peace-Athabasca Delta and Slave River Delta. Findings can be used by decision makers to evaluate risks associated with contaminant dispersal by the large-scale mining activities. In addition, we reflect upon methodological approaches to be considered when evaluating paleolimnological data for evidence of anthropogenic contributions to metal(loid) deposition and advocate for proactive inclusion of paleolimnology in the early design stage of environmental contaminant monitoring programs.
OBTracker: Visual Analytics of Off-ball Movements in Basketball

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.
Daft-Exprt: Cross-Speaker Prosody Transfer on Any Text for Expressive Speech Synthesis

This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art for cross-speaker prosody transfer on any text. This is one of the most challenging, and rarely directly addressed, task in speech synthesis, especially for highly expressive data. Daft-Exprt uses FiLM conditioning layers to strategically inject different prosodic information in all parts of the architecture. The model explicitly encodes traditional low-level prosody features such as pitch, loudness and duration, but also higher level prosodic information that helps generating convincing voices in highly expressive styles. Speaker identity and prosodic information are disentangled through an adversarial training strategy that enables accurate prosody transfer across speakers. Experimental results show that Daft-Exprt significantly outperforms strong baselines on inter-text cross-speaker prosody transfer tasks, while yielding naturalness comparable to state-of-the-art expressive models. Moreover, results indicate that the model discards speaker identity information from the prosody representation, and consistently generate speech with the desired voice. We publicly release our code and provide speech samples from our experiments.
Knowledge Distillation for Discourse Relation Analysis

Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension. It can be categorized into explicit and implicit discourse relation recognition (EDRR and IDRR). Due to the lack of connectives, IDRR remains to be a big challenge. In this paper, we take the first step to exploit the knowledge distillation (KD) technique for discourse relation analysis. Our target is to train a focused single-data single-task student with the help of a general multi-data multi-task teacher. Specifically, we first train one teacher for both the top and second level relation classification tasks with explicit and implicit data. We then transfer the feature embeddings and soft labels from the teacher network to the student network. Extensive experimental results on the popular PDTB dataset proves that our model achieves a new state-of-the-art performance. We also show the effectiveness of our proposed KD architecture through detailed analysis.
Blockchain Empowered Federated Learning with Edge Computing for Digital Twin Systems in Urban Air Mobility

The recent emergence of advanced computing technologies has opened a world of possibilities for bridging virtual-physical spaces in the future of urban air mobility (UAM). Digital twin technology (DT) introduces a coupled virtual-physical asset system that evolves over time in their digital and physical state-spaces associated with the real-time exchange of observed data and control inputs for high-fidelity operational services in UAM, such as traffic management, or vehicle maintenance. On the other hand, edge computing has been envisioned as a dominant computing paradigm in future UAM infrastructures to enable infinitesimal-latency processing of massive and heterogeneous data acquired from ubiquitous devices and vehicles. Furthermore, federated learning (FL) has recently been proposed to resolve the performance and security problems of traditional AI techniques featured by centralized data collection and training to offer a decentralized and cooperative data training pattern among a huge number of devices/vehicles (aka., agents) in multi agent DT systems for UAM. Lastly, the blockchain has emerged as a ledger technology in which data and processes are divided into tiny data blocks and concatenated into a series to reinforce the security and scalability of decentralized computing patterns. Realizing the advances and integration of the emerging technologies mentioned above is of paramount importance for the development of DT systems in UAM. Previous studies have not completely considered the integration and harmony of these existing technologies for the development of UAM DT systems. There has also been a significant lack in comprehension and integration of emerging technologies for the development of UAM-DT systems. In that context, this study proposes a comprehensive integrated UAM-DT platform and solution of FL with the blockchain in edge computing for the development of UAM-DT systems, called BlockFE-DT. First, we introduce the adoption of a probabilistic graphical model as a formal mathematical foundation of coupled digital-physical twin systems for UAM, particularly for unmanned aircrafts. Afterwards, we review the fundamental concepts of FL, blockchain, and edge computing technologies to explore and propose an integration framework of FL with the blockchain in edge computing in the context of UAM-DT systems. Key issues in the integration are assimilated including explainable AI, dependability, and security. This work provides ground-breaking concepts and architectures for a blockchain empowered FL framework with edge computing in future UAM-DT systems.
DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images

Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted calculation, where the weights are decided by the learned interdependencies among all channels. To verify the performance of the DGANet, we conduct breast lesion detection experiments on our collected data set of 7040 ultrasound images and a public data set of breast ultrasound images. YOLOv3, RetinaNet, Faster R-CNN, YOLOv5, and YOLOX are used as comparison models. The results indicate that DGANet outperforms the comparison methods by 0.2%-5.9% in total mean average precision.
The Story time Dataset: Simulated Videotelephony Clips for Quality Perception Research

To study people's natural behavior during different conditions of audiovisual quality, we usually invite people into a lab and let them talk to each other. In such conversation settings, not only the media quality impacts the quality perception, but, e.g., social aspects of a real conversation are reflected by individual conversational and rating behavior. Hence, to study quality perception in conversational settings, we try to create an environment that isolates the media quality from such outside factors and is consistent for each participant in the lab. Therefore, we created a dataset of simulated videotelephony clips to act as stimuli in quality perception research. The dataset consists of four different stories in the German language that are told through ten consecutive parts, each about 10 seconds long. Each of these parts is available in four different quality levels, ranging from perfect to stalling. All clips (FullHD, H.264 / AAC) are actual recordings from end-user video-conference software to ensure ecological validity and realism of quality degradation. To ensure consistency among different clips of the same quality level, each video has been scored using VMAF and POLQA and selected to match predefined selection criteria. To analyze the perceived quality of the clips, we conducted a user study (N=25) and evaluated perceived quality, interest in the stories, and speaker engagement. Results validate the consistency of the quality levels of the video clips. Apart from a detailed description of the methodological approach, we contribute the entire stimuli dataset containing 160 videos and all rating scores for each file.
A structured-light 3D sensor-based shape measurement method for casting allowance evaluation

The evaluation of casting allowance currently relies on manual operation, which is time-consuming and unstable. The structured-light three-dimensional (3D) sensor-based 3D shape measurement technology is characterized by non-contact, high accuracy, and fast measuring speed, which provides the complete 3D shape of casting for accurate allowance evaluation. However, measuring a complex shape casting still requires numerous manual operations, including attaching marks, planning the measurement process, and data processing. To solve these problems, a robot-driven structured-light 3D sensor-based measurement method is proposed, with a six-degree of freedom (6-DOF) industrial robot and a turntable to control the structured-light 3D measurement sensor, to transform the 3D results from different measuring viewpoints into a coherent coordinate system, and to form a complete 3D shape of the casting. Then, the mechanical processing allowance will be calculated by comparing the 3D measurement result and the standard CAD model automatically. The experiment results show that the proposed method is accurate and efficient, and the casting allowance evaluation time is about ten times faster than that of manual operation. Moreover, the proposed method would provide valuable experience for other industrial applications.
External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64−0.72) for all women, 0.67 (0.61−0.72) for White women, and 0.70 (0.65−0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method.
Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Models

Vision-Language Pre-training (VLP) models have achieved state-of-the-art performance in numerous cross-modal tasks. Since they are optimized to capture the statistical properties of intra- and inter-modality, there remains risk to learn social biases presented in the data as well. In this work, we (1) introduce a counterfactual-based bias measurement CounterBias to quantify the social bias in VLP models by comparing the [MASK]ed prediction probabilities of factual and counterfactual samples; (2) construct a novel VL-Bias dataset including 24K image-text pairs for measuring gender bias in VLP models, from which we observed that significant gender bias is prevalent in VLP models; and (3) propose a VLP debiasing method FairVLP to minimize the difference in the [MASK]ed prediction probabilities between factual and counterfactual image-text pairs for VLP debiasing. Although CounterBias and FairVLP focus on social bias, they are generalizable to serve as tools and provide new insights to probe and regularize more knowledge in VLP models.