Reviewerly/exHarmony huggingface dataset:
Multi-label feature selection based on the division of label topics

Multi-label feature selection has attracted much attention from researchers and can reduce the high dimensionality of multi-label data. Previous multi-label methods consider the importance of labels equal, as a result, they choose the discriminative features based on the entire label set. In fact, there exists a latent semantic structure in the label set. Specifically, labels can be sorted into some central topics and some subordinate topics. Features regarding central topics should be chosen first and the number of them should be chosen more. To this end, we first explore the latent semantic structure according to spectral clustering. The labels are abstracted into several clusters named central clusters and subordinate clusters. Second, the importance of features with respect to the labels in each cluster is scored. Finally, we obtain the feature subset based on both the scores of features and the type of clusters. Comprehensive experiments demonstrate the superiority of the proposed method against seven state-of-the-art multi-label feature selection methods on fourteen benchmark multi-label data sets.
A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset

Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context.
Principal component analysis, hidden Markov model<scp>,</scp> and artificial neural network inspired techniques to recognize faces

Abstract Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal Components Analysis (PCA) using Eigen Faces, Hidden Markov Model (HMM) using Singular Value Decomposition, and Artificial Neural Network (ANN) using Gabor filters. These techniques are implemented and evaluated using various measuring metrics such as false acceptance, false recognition rate, and so on. We used ORL and Yale Face dataset to test the robustness of implemented algorithms. Results show that ANN model for face recognition outperforms the other two techniques by achieving more accurate results and shows the highest recognition rate of 97.49% on ORL database. Moreover, it is also observed that ANN model shows the minimum error count of about 2.502% on ORL database while it is 3.5% on Yale Face dataset. To evaluate further, the implemented techniques are compared with best known techniques in class implemented by various researchers.
On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based cyber-physical applications, and develops a scheduling solution to mitigate its effect. In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together with or ahead of those that are of higher priority. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> In current machine intelligence software, significant priority inversion occurs on the path from perception to decision-making, where the execution of underlying neural network algorithms does not differentiate between critical and less critical data. We describe a scheduling framework to resolve this problem, and demonstrate that it improves the system’s ability to react to critical inputs, while at the same time reducing platform cost.
Situating presence within extended reality for teacher training: Validation of the extended Reality Presence Scale (XRPS) in preservice teacher use of immersive 360 video

Abstract The use of video is commonplace for professional preparation in education and other fields. Research has provided evidence that the use of video in these contexts can lead to increased noticing and reflection. However, educators now have access to evolving forms of video such as 360 video. The purpose of this study was to adapt and validate an instrument for assessing immersive 360 video use in an undergraduate preservice teacher university training program. Data provided evidence of the validity of the Extended Reality Presence Scale (XRPS) for 360 video research in preservice teacher professional development. Moreover, evidence from the study suggests that those with higher feelings of presence are less likely to jump around (or twitch) while watching 360 videos. The main implications are that: a) the XRPS is a validated and reliable instrument and b) more research is needed to examine the presence and practices for in‐service and preservice teachers while watching 360 video. Practitioners notes What is already known about this topic? Instructional videos are widely used in preservice teacher training. 360 videos show promise for improving preservice teacher professional development in terms of immersion and presence. What this paper adds? An instrument for assessing 360 video teacher presence is presented (XRPS), targeting a current gap in the literature. Data provided evidence of the validity of the tool for future 360 video research and integration. Implications for practice and/or policy Practitioners can use XRPS for assessing preservice teachers’ experiences in immersive environments and evaluating 360 videos. Higher feelings of presence are associated with more focused viewpoints. Therefore, practitioners should support and facilitate this watching behavior. Higher scores of presence are associated with a perceived sense of agency and emotional attachment. Therefore, 360 videos should include design elements promoting these feelings.
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search

Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks. Instead of using hand-designed architectures, we propose to search for neural architectures that capture stronger image priors. Building upon a generic U-Net architecture, our core contribution lies in designing new search spaces for (1) an upsampling cell and (2) a pattern of cross-scale residual connections. We search for an improved network by leveraging an existing neural architecture search algorithm (using reinforcement learning with a recurrent neural network controller). We validate the effectiveness of our method via a wide variety of applications, including image restoration, dehazing, image-to-image translation, and matrix factorization. Extensive experimental results show that our algorithm performs favorably against state-of-the-art learning-free approaches and reaches competitive performance with existing learning-based methods in some cases.
Deepfake Detection through Deep Learning

Deepfakes allow for the automatic generation and creation of (fake) video content, e.g. through generative adversarial networks. Deepfake technology is a controversial technology with many wide reaching issues impacting society, e.g. election biasing. Much research has been devoted to developing detection methods to reduce the potential negative impact of deepfakes. Application of neural networks and deep learning is one approach. In this paper, we consider the deepfake detection technologies Xception and MobileNet as two approaches for classification tasks to automatically detect deepfake videos. We utilise training and evaluation datasets from FaceForensics++ comprising four datasets generated using four different and popular deepfake technologies. The results show high accuracy over all datasets with an accuracy varying between 91-98% depending on the deepfake technologies applied. We also developed a voting mechanism that can detect fake videos using the aggregation of all four methods instead of only one.
Semi-supervised classification on data streams with recurring concept drift and concept evolution

Mining non-stationary stream is a challenging task due to its unique property of infinite length and dynamic characteristics let alone the issues of concept drift, concept evolution and limited labeled data. Although more attention has been attracted on the issues of concept drift and evolution in data streams, however, most of existing methods are supervised in nature, which probably result in a worse classification performance and lower efficiency in the case with scarcity of labeled data. Thus, in this paper, we proposed a semi-supervised framework with recurring concept drift and novel class detection called ESCR, which aims to detect recurring concept drift and concept evolution in data streams with partially labeled data. It is firstly built on an ensemble model consisted of several clustering-based classifiers. In terms of this framework, we adopt Jensen–Shannon divergence based change detection technique on classifier confidence score instead of classification error rate to detect recurring concept drifts. Meanwhile, we take concept evolution into consideration by monitoring the outliers with strong cohesion. Moreover, we further improve the execution efficiency of our framework by exploiting the recursive function and dynamic programming. Finally, extensive experiments conducted on both benchmark and synthetic data sets demonstrate the effectiveness and efficiency of our proposed semi-supervised framework in the handling of data streams with recurring concept drifts and concept evolution, as compared to several well-known semi-supervised data stream classification methods.
IoU-uniform R-CNN: Breaking through the limitations of RPN

Region Proposal Network (RPN) is the cornerstone of two-stage object detectors. It generates a sparse set of object proposals and alleviates the extrem foreground-background class imbalance problem during training. However, we find that the potential of the detector has not been fully exploited due to the IoU distribution imbalance and inadequate quantity of the training samples generated by RPN. With the increasing intersection over union (IoU), the exponentially smaller numbers of positive samples would lead to the distribution skewed towards lower IoUs, which hinders the optimization of detector at high IoU levels. In this paper, to break through the limitations of RPN, we propose IoU-Uniform R-CNN, a simple but effective method that directly generates training samples with uniform IoU distribution for the regression branch as well as the IoU prediction branch. Besides, we improve the performance of IoU prediction branch by eliminating the feature offsets of RoIs at inference, which helps the NMS procedure by preserving accurately localized bounding box. Extensive experiments on the PASCAL VOC and MS COCO dataset show the effectiveness of our method, as well as its compatibility and adaptivity to many object detection architectures. The code is made publicly available at https://github.com/zl1994/IoU-Uniform-R-CNN.
Online Rain/Snow Removal From Surveillance Videos

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MinghanLi/OTMSCSC_matlab_2020</uri> .
Federated vs. Centralized Machine Learning under Privacy-elastic Users: A Comparative Analysis

The proliferation of machine learning (ML) applications has lately witnessed a considerable shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to typical Centralized learning (CL) whereby the involved (large amounts of) training data are centrally gathered to train models, the load of training tasks is distributed across a set of capable mobile learners at the expense of their own energy. The idea of Federated learning (FL) has emerged as a privacy-preserving mechanism suggesting that the ML model parameters rather than data, are sent over the network to a central point of aggregation. However, when relaxing the privacy concerns, the debate strongly relates to the available network resources. Interestingly, the sofar theoretical or even experimental comparison of the two approaches overlooks network conditions and remains of low realism. In this work we rely on past measurement studies to introduce a realistic system model that accounts for all involved mobile network conditions such as bandwidth and data availability (af-fecting training accuracy and model aggregation) as well as user mobility patterns (affecting data loss). A dedicated simulation framework we have developed replays rich mobile-traces allowing for a comprehensive comparison of the two ML approaches over a large set of training data shedding light on network-resources utilization, energy efficiency and training convergence. Intuitively, our results suggest that the ratio between the employed raw data and the corresponding ML model shapes the conditions under which FL acts as a network-efficient alternative to CL. Interestingly enough, asymmetry in data availability across users as well as their varying number are shown to hardly affect the FL approach in traffic and energy needs, pointing both to its promising potential and the need for further research.
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning

Reinforcement learning (RL) combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two-player board games. However, to the best of our knowledge, current research has yet to produce a result that has surpassed human-level performance in modern complex fighting games. This is due to the inherent difficulties with real-time fighting games, including: vast action spaces, action dependencies, and imperfect information. We overcame these challenges and made 1v1 battle AI agents for the commercial game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Blade and Soul</i> . The trained agents competed against five professional gamers and achieved a winning rate of 62%. This article presents a practical RL method that includes a novel self-play curriculum and data skipping techniques. Through the curriculum, three different styles of agents were created by reward shaping and were trained against each other. Additionally, this article suggests data-skipping techniques that could increase data efficiency and facilitate explorations in vast spaces. Since our method can be generally applied to all two-player competitive games with vast action spaces, we anticipate its application to game development including level design and automated balancing.
Interpretable Machine Learning Tools: A Survey

In recent years machine learning (ML) systems have been deployed extensively in various domains. But most MLbased frameworks lack transparency. To believe in ML models, an individual needs to understand the reasons behind the ML predictions. In this paper, we provide a survey of open-source software tools that help explore and understand the behavior of the ML models. Also, these tools include a variety of interpretable machine learning methods that assist people with understanding the connection between input and output variables through interpretation, validate the decision of a predictive model to enable lucidity, accountability, and fairness in the algorithmic decision making policies. Furthermore, we provide the state-of-the-art of interpretable machine learning (IML) tools, along with a comparison and a brief discussion of the implementation of those IML tools in various programming languages.
SSS-PR: A short survey of surveys in person re-identification

Person re-identification (re-id) addresses the problem of whether “a query image corresponds to an identity in the database” and is believed to play a fundamental role in security enforcement in the near future, particularly in crowded urban environments. Due to many possibilities in selecting appropriate model architectures, datasets, and settings, the performance reported by the state-of-the-art re-id methods oscillates significantly among the published surveys. Therefore, it is difficult to understand the mainstream trends and emerging research difficulties in person re-id. This paper proposes a multi-dimensional taxonomy to categorize the most relevant researches according to different perspectives and tries to unify the categorization of re-id methods and fill the gap between the recently published surveys. Furthermore, we discuss the open challenges with a focus on privacy concerns and the issues caused by the exponential increase in the number of re-id publications over the recent years. Finally, we discuss several challenging directions for future studies.
Knowledge modeling: A survey of processes and techniques

Knowledge modeling is an important step in building knowledge-based applications. Understanding the processes of knowledge modeling and the techniques involved can help developers to grasp the knowledge modeling task as a whole and improve the efficiency of execution and management of modeling tasks. However, previous reviews on knowledge modeling mainly focus on ontology-based knowledge modeling. At present, there is no research work to summarize nonontology knowledge modeling methods, nor to systematically summarize the processes and techniques of knowledge modeling. In this paper, the processes, techniques, and characteristics of knowledge modeling methods based on ontology and nonontology are surveyed. Three research questions related to knowledge modeling are proposed. (1) What methods can be used for knowledge modeling? (2) What processes are involved in knowledge modeling? (3) What techniques are used in the processes of knowledge modeling? By answering these questions, the results of the survey help developers choose appropriate knowledge modeling methods in their work and complete modeling tasks effectively. Meanwhile, it is also conducive to the research work of improving knowledge modeling methods in the future.
Alteration Mineralogy of the Zhengguang Epithermal Au-Zn Deposit, Northeast China: Interpretation of Shortwave Infrared Analyses During Mineral Exploration and Assessment

Abstract Alteration mineralogy from shortwave infrared (SWIR) spectroscopy was compared with X-ray diffraction (XRD) analyses for samples from the Zhengguang intermediate sulfidation epithermal Au-Zn deposit, eastern Central Asian orogenic belt, northeast China. The SWIR and XRD analyses indicate that alteration minerals in the vein-adjacent halo mainly comprise quartz, illite, and locally pyrite (QIP) and chlorite, whereas samples from the pervasive propylitic alteration of host basaltic andesite lava contain epidote, chlorite, carbonate, montmorillonite, and locally illite. SWIR mineral identifications from automated mineral identification software may not always be accurate; thus, the results should be validated by the user. The wavelength position of the Al-OH (~2,200 nm; wAlOH) absorption feature can be used to approximate the composition of illite or white mica. However, caution is required when using the wAlOH value to assess paleotemperatures, as the composition of illite can be influenced by the composition of the host rocks or the hydrothermal fluid. In addition, values of the illite spectral maturity (ISM; ratio of the depth of the ~2,200 nm minima divided by the ~1,900 nm minima) can be affected by the presence of other hydrous minerals, quartz-sulfide veins, and absorption intensity (which can be a function of rock coloration). Despite these cautions, the spatial distribution and variation of the wAlOH and ISM values for illite suggest that the high paleotemperature hydrothermal upflow zones related to the Zhengguang Au-Zn deposit were located below ore zones I and IV, which are predicted to be proximal to the intrusive center of the system.
Rethinking data collection for person re-identification: active redundancy reduction

Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification (re-ID) models. To alleviate such a problem, we present an active redundancy reduction (ARR) framework via training an effective re-ID model with the least labeling efforts. The proposed ARR framework actively selects informative and diverse samples for annotation by estimating their uncertainty and intra-diversity, thus it can significantly reduce the annotation workload. Moreover, we propose a computer-assisted identity recommendation module embedded in the ARR framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out on several public re-ID datasets to demonstrate the existence of data redundancy. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model.