Reviewerly/exHarmony huggingface dataset:
A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools

Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring of Technical Debt should be prioritized with respect to developing features or fixing bugs. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. We conducted a Systematic Literature Review of 557 unique papers published until 2020, following a consolidated methodology applied in software engineering. We included 44 primary studies. Different approaches have been proposed for Technical Debt prioritization, all having different goals and proposing optimization regarding different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We present an impact map of such factors. However, there is a lack of empirical and validated set of tools. We observed that Technical Debt prioritization research is preliminary and there is no consensus on what the important factors are and how to measure them. Consequently, we cannot consider current research conclusive. In this paper, we therefore outline different directions for necessary future investigations.
Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams

This work aims to connect two rarely combined research directions, i.e., non-stationary data stream classification and data analysis with skewed class distributions. We propose a novel framework employing stratified bagging for training base classifiers to integrate data preprocessing and dynamic ensemble selection methods for imbalanced data stream classification. The proposed approach has been evaluated based on computer experiments carried out on 135 artificially generated data streams with various imbalance ratios, label noise levels, and types of concept drift as well as on two selected real streams. Four preprocessing techniques and two dynamic selection methods, used on both bagging classifiers and base estimators levels, were considered. Experimentation results showed that, for highly imbalanced data streams, dynamic ensemble selection coupled with data preprocessing could outperform online and chunk-based state-of-art methods.
Robust semi-supervised nonnegative matrix factorization for image clustering

Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algorithms are sensitive to noisy data, or fail to fully utilize the limited supervised information. In this paper, a novel robust semi-supervised NMF method, namely correntropy based semi-supervised NMF (CSNMF), is proposed to solve these issues. Specifically, CSNMF adopts a correntropy based loss function instead of the squared Euclidean distance (SED) in constrained NMF to suppress the influence of non-Gaussian noise or outliers contaminated in real world data, and simultaneously uses two types of supervised information, i.e., the pointwise and pairwise constraints, to obtain the discriminative data representation. The proposed method is analyzed in terms of convergence, robustness and computational complexity. The relationships between CSNMF and several previous NMF based methods are also discussed. Extensive experimental results show the effectiveness and robustness of CSNMF in image clustering tasks, compared with several state-of-the-art methods.
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs). However, it remains an open problem how to integrate NAS with Application-Specific Integrated Circuits (ASICs), despite them being the most powerful AI accelerating platforms. The major bottleneck comes from the large design freedom associated with ASIC designs. Moreover, with the consideration that multiple DNNs will run in parallel for different workloads with diverse layer operations and sizes, integrating heterogeneous ASIC sub-accelerators for distinct DNNs in one design can significantly boost performance, and at the same time further complicate the design space. To address these challenges, in this paper we build ASIC template set based on existing successful designs, described by their unique dataflows, so that the design space is significantly reduced. Based on the templates, we further propose a framework, namely ASICNAS, which can simultaneously identify multiple DNN architectures and the associated heterogeneous ASIC accelerator design, such that the design specifications (specs) can be satisfied, while the accuracy can be maximized. Experimental results show that compared with successive NAS and ASIC design optimizations which lead to design spec violations, ASICNAS can guarantee the results to meet the design specs with 17.77%, 2.49×, and 2.32× reductions on latency, energy, and area and less than 1.6% accuracy loss. To the best of the authors’ knowledge, this is the first work on neural architecture and ASIC accelerator design co-exploration.
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization

We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, e.g., loss of lip-sync, unnatural facial and lip movements, etc. MDS is computed as the mean aggregate of dissimilarity scores between audio and visual segments in a video. Discriminative features are learnt for the audio and visual channels in a chunk-wise manner, employing the cross-entropy loss for individual modalities, and a contrastive loss that models inter-modality similarity. Extensive experiments on the DFDC and DeepFake-TIMIT Datasets show that our approach outperforms the state-of-the-art by up to 7%. We also demonstrate temporal forgery localization, and show how our technique identifies the manipulated video segments.
The impact of blockchain related name changes on corporate performance

This paper examines the impact of blockchain and crypto-related name changes on corporate and financial performance of the corporations. We document several pieces of evidence suggesting that companies who partake in such “crypto-exuberant” naming practices become more volatile and offer substantial and persistent stock market premiums as a reward for their corporate identity change. However, the retroactive name changes harm firm's short-term profitability and have a dampening effect on financial leverage of the company. This paper advances the Dotcom effect literature by providing novel results on the changing traditional pathways of price discovery and information flows after the announcement of corporate name changes to blockchain-related names. The identified contagion channels display that crypto-exuberant companies become more susceptible to cryptocurrency markets, which should interest regulators and investors.
Statistical Features based Optimized Technique for Copy Move Forgery Detection

Copy Move Forgery Detection (CMFD) is helpful to detect copied and pasted areas in one image, it plays a crucial role in legal evidence, forensic investigation and in many more places. In this paper, CMFD method is proposed using Steerable Pyramid Transform (SPT), Grey Level Co-occurrence Matrix (GLCM) and Optimized Naive Bayes Classifier (ONBC). The suspected image is given to SPT to obtain different orientations, from all suspected image orientations GLCM features are extracted. These features are used to train ONBC as well as to classify ONBC. Wide range of tests conducted on CoMoFoD, MICC_F and CASIA v1.0 databases using proposed algorithm and performance is measured in terms TPR and FNR. It shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.
Predicting the Perceptual Quality of Point Cloud: A 3D-to-2D Projection-Based Exploration

Point cloud is emerged as a promising media format to represent realistic 3D objects or scenes in applications, such as virtual reality, teleportation, etc. How to accurately quantify the subjective point cloud quality for application-driven optimization, however, is still a challenging and open problem. In this paper, we attempt to tackle this problem in a systematic means. First, we produce a fairly large point cloud dataset where ten popular point clouds are augmented with seven types of impairments (e.g., compression, photometry/color noise, geometry noise, scaling) at six different distortion levels, and organize a formal subjective assessment with tens of subjects to collect mean opinion scores (MOS) for all 420 processed point cloud samples (PPCS). We then try to develop an objective metric that can accurately estimate the subjective quality. Towards this goal, we choose to project the 3D point cloud onto six perpendicular image planes of a cube for the color texture image and corresponding depth image, and aggregate image-based global (e.g., Jensen-Shannon (JS) divergence) and local features (e.g., edge, depth, pixel-wise similarity, complexity) among all projected planes for a final objective index. Model parameters are fixed constants after performing the regression using a small and independent dataset previously published. The proposed metric has demonstrated the state-of-the-art performance for predicting the subjective point cloud quality compared with multiple full-reference and no-reference models, e.g., the weighted peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM) and natural image quality evaluator (NIQE). The dataset is made publicly accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://smt.sjtu.edu.cn</uri> or <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://vision.nju.edu.cn</uri> for all interested audiences.
A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data

Nowadays, the field of mobile robotics has experienced an important evolution and these robots are more commonly proposed to solve different tasks autonomously. The use of visual sensors has played an important role in mobile robotics tasks during the past few years due to the advances in computer vision hardware and algorithms. It is worth remarking the use of AI tools to solve a variety of problems in mobile robotics based on the use of images either as the only source of information or combining them with other sensors such as laser or GPS. The improvement of the autonomy of mobile robots has attracted the attention of the scientific community. A considerable amount of works have been proposed over the past few years, leading to an extensive variety of approaches. Building a robust model of the environment (mapping), estimating the position within the model (localization) and controlling the movement of the robot from one place to another (navigation) are important abilities that any mobile robot must have. Considering this, this review focuses on analyzing these problems; how researchers have addressed them by means of AI tools and visual information; and how these approaches have evolved in recent years. This topic is currently open and a large number of works can be found in the related literature. Therefore, it can be of interest making an analysis of the current state of the topic. From this review, we can conclude that AI has provided robust solutions to some specific tasks in mobile robotics, such as information retrieval from scenes, mapping, localization and exploration. However, it is worth continuing to develop this line of research to find more integral solutions to the navigation problem so that mobile robots can increase their autonomy in large, complex and heterogeneous environments.
Euclidean and Hermitian Hulls of MDS Codes and Their Applications to EAQECCs

In this paper, we construct several classes of maximum distance separable (MDS) codes via generalized Reed-Solomon (GRS) codes and extended GRS codes, where we can determine the dimensions of their Euclidean hulls or Hermitian hulls. It turns out that the dimensions of Euclidean hulls or Hermitian hulls of the codes in our constructions can take all or almost all possible values. As a consequence, we can apply our results to entanglement-assisted quantum error-correcting codes (EAQECCs) and obtain several new families of MDS EAQECCs with flexible parameters. The required number of maximally entangled states of these MDS EAQECCs can take all or almost all possible values. Moreover, several new classes of q-ary MDS EAQECCs of length n > q + 1 are also obtained.
Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training

Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of data-driven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://github.com/lidq92/MDTVSFA.
Big data: the end of the scientific method?

We argue that the boldest claims of Big Data are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of Big Data are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, nonlocality and hyperdimensions which one encounters frequently in multiscale modelling.
TextCaps: A Dataset for Image Captioning with Reading Comprehension

Image descriptions can help visually impaired people to quickly understand the image content. While we made significant progress in automatically describing images and optical character recognition, current approaches are unable to include written text in their descriptions, although text is omnipresent in human environments and frequently critical to understand our surroundings. To study how to comprehend text in the context of an image we collect a novel dataset, TextCaps, with 145k captions for 28k images. Our dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase, requiring spatial, semantic, and visual reasoning between multiple text tokens and visual entities, such as objects. We study baselines and adapt existing approaches to this new task, which we refer to as image captioning with reading comprehension. Our analysis with automatic and human studies shows that our new TextCaps dataset provides many new technical challenges over previous datasets.
Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost

Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, accessible unlabeled data are not used for model training in most conventional methods. Here, we propose to unify unlabeled sample selection and model training towards minimizing labeling cost, and make two contributions towards that end. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data during the training stage. Second, we propose a consistency-based sample selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. We conduct extensive experiments on image classification tasks. The experimental results on CIFAR-10, CIFAR-100 and ImageNet demonstrate the superior performance of our proposed method with limited labeled data, compared to the existing methods and the alternative AL and SSL combinations. Additionally, we also study an important yet under-explored problem – “When can we start learning-based AL selection?”. We propose a measure that is empirically correlated with the AL target loss and is potentially useful for determining the proper starting point of learning-based AL methods .
CrossTalk: Speculative Data Leaks Across Cores Are Real

Recent transient execution attacks have demonstrated that attackers may leak sensitive information across security boundaries on a shared CPU core.Up until now, it seemed possible to prevent this by isolating potential victims and attackers on separate cores.In this paper, we show that the situation is more serious, as transient execution attacks can leak data across different cores on many modern Intel CPUs.We do so by investigating the behavior of x86 instructions, and in particular, we focus on complex microcoded instructions which perform offcore requests.Combined with transient execution vulnerabilities such as Micro-architectural Data Sampling (MDS), these operations can reveal internal CPU state.Using performance counters, we build a profiler, CROSSTALK, to examine the number and nature of such operations for many x86 instructions, and find that some instructions read data from a staging buffer which is shared between all CPU cores.To demonstrate the security impact of this behavior, we present the first cross-core attack using transient execution, showing that even the seemingly-innocuous CPUID instruction can be used by attackers to sample the entire staging buffer containing sensitive data -most importantly, output from the hardware random number generator (RNG)across cores.We show that this can be exploited in practice to attack SGX enclaves running on a completely different core, where an attacker can control leakage using practical performance degradation attacks, and demonstrate that we can successfully determine enclave private keys.Since existing mitigations which rely on spatial or temporal partitioning are largely ineffective to prevent our proposed attack, we also discuss potential new mitigation techniques.
Optimal Contract Design for Efficient Federated Learning With Multi-Dimensional Private Information

As an emerging machine learning technique, federated learning has received significant attention recently due to its promising performance in mitigating privacy risks and costs. While most of the existing work of federated learning focused on designing learning algorithm to improve training performance, the incentive issue for encouraging users' participation is still under-explored. This paper presents an analytical study on the server's optimal incentive mechanism design, in the presence of users' multi-dimensional private information (e.g., training cost and communication delay). Specifically, we consider a multi-dimensional contract-theoretic approach, with a key contribution of summarizing users' multi-dimensional private information into a one-dimensional criterion that allows a complete order of users. We further perform the analysis in three information scenarios to reveal the impact of information asymmetry levels on server's optimal strategy and minimum cost. We show that weakly incomplete information does not increase the server's cost (comparing with the complete information scenario) when training data is IID, but it in general does when data is non-IID. Furthermore, the optimal mechanism design under strongly incomplete information is much more challenging, and it is not always optimal for the server to incentivize the group of users with the lowest training cost and delay to participate.
Exploring How Older Adults Use a Smart Speaker–Based Voice Assistant in Their First Interactions: Qualitative Study

Background Smart speaker–based voice assistants promise support for the aging population, with the advantages of hands-free and eyes-free interaction modalities to handle requests. However, little is known about how older adults perceive the benefits of this type of device. Objective This study investigates how older adults experience and respond to a voice assistant when they first interact with it. Because first impressions act as strong predictors of the overall attitude and acceptability of new technologies, it is important to understand the user experiences of first exposure. Methods We conducted semistructured interviews with 18 people 74 years and older who had never used a smart speaker before, investigating the patterns of use, usability issues, and perspectives that older adults have when using a voice assistant for the first time. Results The overall first response to a voice assistant was positive, thanks to the simplicity of a speech-based interaction. In particular, a positive and polite response to complete the interaction with a voice assistant was prevalent, such as expressing gratitude or giving feedback about the quality of answers. Two predominant topics of commands made in the first interaction include asking health care–related questions and streaming music. However, most of the follow-up reactions were unfavorable because of the difficulty in constructing a structured sentence for a command; misperceptions about how a voice assistant operates; and concerns about privacy, security, and financial burdens. Overall, a speech-based interaction was perceived to be beneficial owing to its efficiency and convenience, but no other benefits were perceived. Conclusions On the basis of the findings, we discuss design implications that can positively influence older adults' first experiences with a voice assistant, including helping better understand how a voice assistant works, incorporating mistakes and common interaction patterns into its design, and providing features tailored to the needs of older adults.