Reviewerly/exHarmony huggingface dataset:
Reaching for the Sky: Maximizing Deep Learning Inference Throughput on Edge Devices with AI Multi-Tenancy

The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive growth in data generation at the edge of the Internet over the past decade. Intelligent real-time analysis of such a high volume of data, particularly leveraging highly accurate deep learning (DL) models, often requires the data to be processed as close to the data sources (or at the edge of the Internet) to minimize the network and processing latency. The advent of specialized, low-cost, and power-efficient edge devices has greatly facilitated DL inference tasks at the edge. However, limited research has been done to improve the inference throughput (e.g., number of inferences per second) by exploiting various system techniques. This study investigates system techniques, such as batched inferencing, AI multi-tenancy, and cluster of AI accelerators, which can significantly enhance the overall inference throughput on edge devices with DL models for image classification tasks. In particular, AI multi-tenancy enables collective utilization of edge devices’ system resources (CPU, GPU) and AI accelerators (e.g., Edge Tensor Processing Units; EdgeTPUs). The evaluation results show that batched inferencing results in more than 2.4× throughput improvement on devices equipped with high-performance GPUs like Jetson Xavier NX. Moreover, with multi-tenancy approaches, e.g., concurrent model executions (CME) and dynamic model placements (DMP), the DL inference throughput on edge devices (with GPUs) and EdgeTPU can be further improved by up to 3× and 10×, respectively. Furthermore, we present a detailed analysis of hardware and software factors that change the DL inference throughput on edge devices and EdgeTPUs, thereby shedding light on areas that could be further improved to achieve high-performance DL inference at the edge.
Porphyry Copper: Revisiting Mineral Resource Assessment Predictions for the Andes

A mineral resource assessment of porphyry copper deposits in the Andes Mountains of South America was done in 2005 in cooperation with geological surveys in South America. The study identified 590 million metric tons (Mt) of copper in identified resources. Continued exploration and development in the region over a 15-year period provide an opportunity to compare the predicted assessment results with new discoveries and resource growth in previously known deposits. The 2005 assessment estimated that 145 undiscovered deposits could contain a mean of 750 Mt of copper. The actual number of deposits increased (2005 to 2020) from 69 to 120 and the amount of identified copper resources increased from 590 Mt to 1600 Mt. Although most of the new deposits and copper resources are concentrated in Miocene-Pliocene and Eocene-Oligocene mineral belts, new deposits have been discovered in Jurassic and Cretaceous mineral belts. Resource growth in porphyry copper deposits known in the Andes in 2005 (1100 Mt copper) exceeds copper resources in new discoveries since 2005 (490 Mt copper) by a factor of 2.
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime

Most differential privacy mechanisms are applied (i.e., composed) numerous times on sensitive data. We study the design of optimal differential privacy mechanisms in the limit of a large number of compositions. As a consequence of the law of large numbers, in this regime the best privacy mechanism is the one that minimizes the Kullback-Leibler divergence between the conditional output distributions of the mechanism given two different inputs. We formulate an optimization problem to minimize this divergence subject to a cost constraint on the noise. We first prove that additive mechanisms are optimal. Since the optimization problem is infinite dimensional, it cannot be solved directly; nevertheless, we quantize the problem to derive nearoptimal additive mechanisms that we call "cactus mechanisms" due to their shape. We show that our quantization approach can be arbitrarily close to an optimal mechanism. Surprisingly, for quadratic cost, the Gaussian mechanism is strictly suboptimal compared to this cactus mechanism. Finally, we provide numerical results which indicate that cactus mechanisms outperform Gaussian and Laplace mechanisms for a finite number of compositions.The full proofs can be found in the extended version at [1]. This paper is Part I in a pair of papers, where Part II is [2].
Basic ideas and categorical means of activity theory

Methodological research is devoted to the coverage of intellectual sources, basic fundamental ideas, categorical means, scientific-subject resources, systemic and actually polystructural foundations, reproduction-activity schemes, logical principles and organization-actional norms, as well as invariants of reflexive, naturalistic and actional positioning of the general theory of activity. It summarizes the results of fulfillment of the second major program of the Moscow Methodological Circle by an informal group of self-identified methodologists led by G.P. Shchedrovitsky, namely the creation of the theory of activity and the establishment of actional approach in philosophical-scientific discourse since the 60s of the last century. It is noteworthy that this theory is based on a systematic approach with its attributive characteristics of polystructurality, developmental functionality, morphological and process integrity, productively uses its categorical means and conceptual capabilities of the scientific subject as an epistemological system. In general, the category of activity from the regulatory lever is transformed into a system-forming center of worldviews of broad ontological ideas and from the objective principle – into the main subject of research-construction. First of all, this is confirmed by the schemes of “reproduction of activity and transmission of culture”, “act of activity” and “reflexive way out”, which in complementarity reveal the methodological resources of thinking as activity, ie reveal the mechanism of transmission of cultural norms, paradigms, and actually, ways and means of thinking itself in the socio-cultural, historical course of everyday life. Among the nuclear innovations of methodological thinking, reflection is substantiated, which is initiated by problematic situations and at the same time is a specific mechanism for the development of activity due to the variety of forms and organizations of human cooperation. As a result, the enrichment is got not only by the methodological project itself, which advocates a kind of “totalization” of reflection and reflexive “closure” of the whole aggregate sphere of activity, but also by the prospect of the third major intellectual program emerging in the late 1980s - system-thought-activity methodology or STA-methodology.
Fuzzy-PI Controller Tuned With ICA: applied to 2 DOF robot control trajectory

The main aim of this study is to introduce Fuzzy-PI based imperialist competitive algorithm (ICA) for the control of trajectory of 2 DOF robot planar. The dynamics that describes the robot is complex, non-linear and coupled model, thus it is a good benchmark model to test the efficiently and the robustness of the prosed schema. Fuzzy-PI is based on rather simple mathematical tools, which adequately investigate the ambiguities and reflect uncertainties. ICA is devoted to find the near optimal parameters of fuzzy logic (scaling factor) and PI parameters and its performance is compared to grey wolf optimizer (GWO), Whale optimizer algorithm (WOA) and teaching learning based optimization (TLBO). In order to test the robustness (the presence of external perturbation) and the efficiently of the proposed approach the PID controller is also tuned with ICA. The involved comparative study of our work shows that ICA was the outstanding one and the proposed controller outperform PID-ICA before and after the robustness test.
End-to-End Cell Recognition by Point Annotation

Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification. Recent weakly-supervised methods usually estimate probability density maps for cell recognition. However, in dense cell scenarios, their performance can be limited by pre- and post-processing as it is impossible to find a universal parameter setting. In this paper, we introduce an end-to-end framework that applies direct regression and classification for preset anchor points. Specifically, we propose a pyramidal feature aggregation strategy to combine low-level features and high-level semantics simultaneously, which provides accurate cell recognition for our purely point-based model. In addition, an optimized cost function is designed to adapt our multi-task learning framework by matching ground truth and predicted points. The experimental results demonstrate the superior accuracy and efficiency of the proposed method, which reveals the high potentiality in assisting pathologist assessments.
Deformable Part Region Learning for Object Detection

In a convolutional object detector, the detection accuracy can be degraded often due to the low feature discriminability caused by geometric variation or transformation of an object. In this paper, we propose a deformable part region learning in order to allow decomposed part regions to be deformable according to geometric transformation of an object. To this end, we introduce trainable geometric parameters for the location of each part model. Because the ground truth of the part models is not available, we design classification and mask losses for part models, and learn the geometric parameters by minimizing an integral loss including those part losses. As a result, we can train a deformable part region network without extra super-vision and make each part model deformable according to object scale variation. Furthermore, for improving cascade object detection and instance segmentation, we present a Cascade deformable part region architecture which can refine whole and part detections iteratively in the cascade manner. Without bells and whistles, our implementation of a Cascade deformable part region detector achieves better detection and segmentation mAPs on COCO and VOC datasets, compared to the recent cascade and other state-of-the-art detectors.
Image Interpolation Based on 2D-DWT with Novel Regularity-Preserving Algorithm Using RLS Adaptive Filters

This paper proposes a novel method for the image interpolation problem based on two-dimensional discrete wavelet transform (DWT) with the edge preserving approach. The purpose of this method is to consider two contrasting issues of over-smoothing and creation of spurious edges at the same time, and offer a novel solution based on statistical dependencies of image sub-bands, and noise behavior. The offered method has a multi-faceted approach for the problem; by sub-band coding, it handles each 2D-DWT image sub-band with a different solution. For LH and HL sub-bands, two algorithms work together in order to preserve regularity. Area_Check algorithm is a four-phase edge-preserving algorithm that aims to recognize and interpolate separating lines of environments and edgy regions in the best possible way. On the other hand, RLS_AVG algorithm interpolates smooth surfaces of the image by keeping the regularity of the image without over-smoothing. In this regard, the offered algorithm has a great power to counter jaggies and annoying artifacts. In the end, in order to demonstrate the capability, and performance of the proposed method, the final results in various metrics are compared with the results of the most famous and the newest image interpolation methods.
Integrating an academic management system with blockchain: A case study

This paper reports the design, implementation, and experimental phases of the EU H2020 QualiChain pilot “Staffing the Public Sector—The Case of Portugal”. The overall purpose of this pilot is to ensure the authenticity and integrity of the diplomas for all involved stakeholders and therefore to contribute to solving the diploma counterfeiting and/or falsification that is a great threat to the recruitment of qualified personnel. The main innovative aspect of this solution is the integration that is offered between an Academic Management System (the Fenix.edu platform) and a Blockchain (Ethereum) to automatically deploy diplomas. This solution helps the involved stakeholders trust the diplomas provided. The case study involves four different stakeholders and studies, specifically, the increase in their satisfaction in terms of diploma control, diploma veracity, and diploma credibility. The developed system was tested with external participants who were asked to follow a set of guidelines and complete a survey to assess their perceptions. All system interactions were recorded, and the data were analyzed. The results indicated that the participants successfully executed the guidelines and that a perception increase toward diploma control, veracity, and credibility was identified.
Unsupervised High-Resolution Portrait Gaze Correction and Animation

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels. To address this issue, we first create two new portrait datasets: CelebGaze and high-resolution CelebHQGaze. Second, we formulate the gaze correction task as an image inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze Animation Module (GAM). Moreover, we propose an unsupervised training strategy, i.e., Synthesis-As-Training, to learn the correlation between the eye region features and the gaze angle. As a result, we can use the learned latent space for gaze animation with semantic interpolation in this space. Moreover, to alleviate both the memory and the computational costs in the training and the inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM and GAM. Extensive experiments validate the effectiveness of our method for both the gaze correction and the gaze animation tasks in both low and high-resolution face datasets in the wild and demonstrate the superiority of our method with respect to the state of the arts. Code is available at https://github.com/zhangqianhui/GazeAnimationV2
RNNSC: Recurrent Neural Network-Based Stereo Compression Using Image and State Warping

Stereo images are used in various applications, such as autonomous driving, surveillance, robotics, and 3D-TV. Those images are captured by two horizontally adjacent cameras, capturing a scene from two different points of view. In this work, we propose an end-to-end trainable recurrent neural network (RNN) for stereo image compression, which we call RNNSC. The RNN allows variable compression rates without retraining of the network due to the iterative nature of the recurrent units. The proposed method makes use of the redundancies, to reduce the overall bit rate. Each image in the stereo pair has its separate encoder and decoder network similar to [1]. We propose to share the mutual information between the stereo pair networks by warping the hidden states of one codec network to the other with the help of disparity information that is coded and transmitted independently via JPEG2000. Moreover, we also improve the quality of the shared mutual information by eliminating wrong information by estimating and applying occlusion maps which are computed with a convolutional neural network without direct supervision. The proposed method outperforms all tested image codecs on MS-SSIM, a perceptual metric capturing the structural quality of an image, as shown in Table 1.
Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm.
Perceptual quality assessment of panoramic stitched contents for immersive applications: a prospective survey

The recent advancements in the field of Virtual Reality (VR) and Augmented Reality (AR) have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology (Soft Tech). VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360° imagery that widely used in the education, gaming, entertainment, and production sector. The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360° images, in fact a minor visual distortion can significantly degrade the overall quality. Thus, to ensure the quality of constructed panoramic contents for VR and AR applications, numerous Stitched Image Quality Assessment (SIQA) methods have been proposed to assess the quality of panoramic contents before using in VR and AR. In this survey, we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date. For better understanding, the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches. Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task. Further, we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents. In last, we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.
Framing digital competence in media work – The case of Finland

Purpose This article aims to study digital competence (DC) in media work. Design/methodology/approach The authors utilize frame analysis to investigate how DC is framed in media work using 30 semi-structured interviews as data with journalists in Finland. Findings The authors identify three main frames of DC in the context of media work. The individual attitude frame emphasizes employees' attitudes toward DC, the team-level support frame underlines the need for support in the work community and the organizational-level practice frame highlights enablers of and organizations' requirements for digital competence. Practical implications First, media workers' DC is necessary to enhance work efficiency and accomplish tasks and may be supported with supportive management practices. Second, the findings suggest that DC should be understood and analyzed as a multi-level issue. Third, the findings suggest that appropriate continuing education and study opportunities were needed. Besides formal arrangements for learning, informal contexts of learning are important. Originality/value The article contributes to media studies and studies on the digitalization of work by taking account of the organizational, team and individual levels in discussing digital competence in the news media sector.
Using Deep Learning for Positive Reviews Prediction in Explainable Recommendation Systems

In the recent years, recommender systems have begun to attract the attention of many online-based companies. While these systems are being developed to provide users with better recommendations, they suffer from the lack of explain-ability. The explainable recommendation systems are developed to solve the problem of why certain products or services are recommended to a particular user. However, less attention has been attracted for predicting positive reviews from the whole data in the context of explainable recommendation. Therefore, in this paper, we focus on developing a model that uses deep learning for predicting positive reviews in explainable recommendation systems. It enables users to get not only intuitive explanations for the recommended items, but also to get more transparency by investigating whether the explanations are positive ones. To evaluate the proposed model, we conduct experiments on a benchmark dataset from Amazon. Experimental results demonstrate the efficacy of the proposed model against the baselines.
Identification of structural control factors of primary gold ore occurrences by method of unmanned aeromagnetic survey by the example of the Neryungrisky district of Yakutia

The long-term development of the geophysical industry, in which the methods of magnetometry are in maximum demand, as the simplest in instrumental and methodological execution, has determined the development of remote measurement methods implemented both from space and airborne carriers. The necessity to use the latter as an obligatory component of field surveys, providing coverage of significant areas, determines the need for using the unmanned low-tonnage carriers. Their use is implemented to search for predictive elements of structural (spatial, genetic) control of endogenous gold ore occurrences that allow predictive constructions, i.e., solving the problem of increasing gold reserves, which is being performed within the framework of federal programs. The purpose of the survey is to develop a system of instrumental and subsequent interpretation approaches in the organization of unmanned magnetometer survey, implemented for structural and geological mapping by the example of the Neryungrinsky district of Yakutia. Within the framework of the digital model formation of the relief and the anomalous magnetic field, a survey method using an unmanned aircraft, its technical characteristics are considered; the analysis and the author's modification of the office analysis of magnetometry data are performed. Based on the obtained materials, a physical and geological model of the investigated area was created, which is presented in the form of a geological and structural cut, accompanied by the physical characteristics of the structural and material complexes. The refinement of the physical and geological model was implemented by a joint morphostructural analysis of the remote base and the anomalous magnetic field using the results of quantative interpretation of the anomalous magnetic field. The result of the study is presented by an updated geological basis with the allocation of promising ore sites for their detailing as part of the planned large-scale geological and geophysical surveys. The scientific novelty of the work consists in the synthesis of the tried and tested methods of unmanned aeromagnetometric measurements and geostructural reconstructions, which allow the processing of both potential and non-potential geofields.