Reviewerly/exHarmony huggingface dataset:
Multi-dimensional clustering through fusion of high-order similarities

Clustering objects with heterogeneous attributes captured from different dimensions remains challenging in integrating the multiple dimensional information. Most of the current multi-dimensional clustering models pin on direct sample-wised similarity and fail to exploit hidden mutual affinity among different sampling spaces. Thus, it is hard to capture a legible cluster structure. To tackle this issue, we propose a High-order multi-dimensional Spectral Clustering method (HSC). The proposed HSC aims to learn a high-order similarity to characterize the intrinsic relationship among different dimensional spaces instead of the ordinary similarity. It then performs a clustering task within a latent space by jointly learning the high-order similarity and ordinary similarity. Extensive experiments over synthetic and real-world data sets show that the proposed HSC outperforms benchmark multi-dimensional methods in most scenarios and is capable of revealing a reliable structure concealed across multi-dimensional spaces.
Model-Distributed DNN Training for Memory-Constrained Edge Computing Devices

We consider a model-distributed learning framework in which layers of a deep learning model is distributed across multiple workers. To achieve consistent gradient updates during the training phase, model-distributed learning requires the storage of multiple versions of the layer parameters at every worker. In this paper, we design mcPipe to reduce the memory cost of model-distributed learning, which is crucial in memory-constrained edge computing devices. mcPipe uses an on-demand weight updating policy, which reduces the amount of weights that should be stored at workers. We analyze the memory cost of mcPipe and demonstrate its superior performance as compared to existing model-distributed learning mechanisms. We implement mcPipe in a real testbed and show that it improves the memory cost without hurting converge rate and computation cost.
PDE-Constrained Optimal Control Problems with Uncertain Parameters using SAGA

We consider an optimal control problem (OCP) for a partial differential equation (PDE) with random coefficients. The optimal control function is a deterministic, distributed forcing term that minimizes an expected quadratic regularized loss functional. For the numerical approximation of this PDE-constrained OCP, we replace the expectation in the objective functional by a suitable quadrature formula and, eventually, discretize the PDE by a Galerkin method. To practically solve such an approximate OCP, we propose an importance sampling version the SAGA algorithm [A. Defazio, F. Bach, and S. Lacoste-Julien, Advances in Neural Information Processing Systems 27, Curran Associates, Red Hook, NY, 2014, pp. 1646--1654], a type of stochastic gradient algorithm with a fixed-length memory term, which computes at each iteration the gradient of the loss functional in only one quadrature point, randomly chosen from a possibly nonuniform distribution. We provide a full error and complexity analysis of the proposed numerical scheme. In particular we compare the complexity of the generalized SAGA algorithm with importance sampling, with that of the stochastic gradient and the conjugate gradient (CG) algorithms, applied to the same discretized OCP. We show that SAGA converges exponentially in the number of iterations as for a CG algorithm and has a similar asymptotic computational complexity, in terms of computational cost versus accuracy. Moreover, it features good preasymptotic properties, as shown by our numerical experiments, which makes it appealing in a limited budget context.
Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.
Model-based Weight Quantization for Convolutional Neural Network Compression

Nowadays convolutional neural networks have many applications, from autonomous driving to the medical field. The need for fast deployment of the algorithms, reduced latency, while maintaining a reduced cost created a new paradigm - edge computing - to bring the computational power closer to the user. One limitation for the implementation of convolutional neural networks on edge devices is the large number of parameters, implying high memory usage. As the memory constraints are very strict on an edge device, the need to reduce the memory usage of convolutional neural networks arises. One way is to perform network weights quantization. We propose an adaptive model-based quantization method, with a parameterizable number of quantization intervals. Using the double exponential probability density function, the quantization intervals are determined by taking into account the histogram of trained network weights. For a hardware implementation, we modified a classical convolutional network architecture by replacing fully connected layers with convolutional layers, and changing activation functions. We performed a comparison of the performance of several convolutional network architectures, while the proposed quantization method was applied over the trained network weights. We present experimental results in terms of both accuracy and network size, considering the unquantized network and the quantized network using the proposed method and a non-adaptive one. Proposed method obtained a reduction of the model size to a quarter, in some cases without losing accuracy.
A Comparative Study of Underwater Marine Products Detection based on YOLOv5 and Underwater Image Enhancement

The application of target detection algorithms in underwater images has not been very effective due to the quality problems such as image blurring and color incongruity that often exist in underwater images. In this paper, the YOLOv5 algorithm is used as a target detection network model, which is trained by using underwater marine products dataset and combined with six underwater image enhancement recovery algorithms to enhance and recover the images before they are detected, an attempt is made to improve the deep learning based target detection method at the input side. Finally, the impact of different underwater image enhancement algorithms on the YOLOv5-based target detection algorithm is compared and summarized through experiments, and the specific image enhancement methods can effectively improve the detection performance of the algorithm under different image background environments.
A Collaborative Method for Scoping Software Product Lines: A Case Study in a Small Software Company

SPL scoping is the activity for bounding Software Product Lines (SPL), gathering heterogeneous knowledge from diverse sources. For achieving an agreement among different stakeholders, a commonalty scope must be understood and committed to. However, gathering this knowledge from stakeholders with individual interests is a complex task. This paper reports the experience of scoping the SPL of a small Colombian software company, applying and evaluating a collaborative method called CoMeS-SPL. The company was looking to develop a set of products from a product previously developed with great potential to be adapted and sold to different customers. From a collaborative relationship university–enterprise model, the research groups that developed CoMeS-SPL proposed to use it answering to the company needs for defining an organization-suitable reuse scope around its platform called CORA. Both parties joined in the scoping co-production of the first SPL of the company. This method implied that the company would perform new tasks and involve other roles different for those who are used to defining the scope of a single product. The company actors considered that they obtained a useful scope and perceived the collaboration as valuable because they shared different knowledge and perspectives. The researchers were able to provide feedback on their proposed model, identifying successes and aspects to improve. The experience allowed strengthening the ties of cooperation with the company, and new projects and consultancies are being carried out.
3D brain tumor segmentation using a two-stage optimal mass transport algorithm

Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper. The first stage of a two-stage OMT (TSOMT) procedure transforms the brain into a unit solid ball. The second stage transforms the unit ball into a cube, as it is easier to apply a 3D convolutional neural network to rectangular coordinates. Small variations in the local mass-measure stretch ratio among all the brain tumor datasets confirm the robustness of the transform. Additionally, the distortion is kept at a minimum with a reasonable transport cost. The original [Formula: see text] dataset is thus reduced to a cube of [Formula: see text], which is a 76.6% reduction in the total number of voxels, without losing much detail. Three typical U-Nets are trained separately to predict the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) from the cube. An impressive training accuracy of 0.9822 in the WT cube is achieved at 400 epochs. An inverse TSOMT method is applied to the predicted cube to obtain the brain results. The conversion loss from the TSOMT method to the inverse TSOMT method is found to be less than one percent. For training, good Dice scores (0.9781 for the WT, 0.9637 for the TC, and 0.9305 for the ET) can be obtained. Significant improvements in brain tumor detection and the segmentation accuracy are achieved. For testing, postprocessing (rotation) is added to the TSOMT, U-Net prediction, and inverse TSOMT methods for an accuracy improvement of one to two percent. It takes 200 seconds to complete the whole segmentation process on each new brain tumor dataset.
A Higher-Level Security Scheme for Key Access on Cloud Computing

In this work, we construct a key access management scheme that seamlessly transitions any hierarchical-like access policy to the digital medium. The proposed scheme allows any public cloud system to be used as a private cloud. We consider the data owner an entity consisting of several organization units. We provide a secure method for each user of this entity to access the public cloud from both inside and outside the company's network. The idea of our key access control scheme, which is based on Shamir's secret sharing algorithm and polynomial interpolation method, is suitable especially for hierarchical organizational structures. It offers a secure, flexible, and hierarchical key access mechanism for organizations utilizing mission-critical data. It also minimizes concerns about moving mission-critical data to the public cloud and ensures that only users with sufficient approvals from the same or higher privileged users can access the key by making use of the topological ordering of a directed graph, including self-loop. Main overheads such as public and private storage needs are reduced to a tolerable level, and the key derivation is computationally efficient. From a security perspective, our scheme is both resistant to collaboration attacks and provides key indistinguishability security. Since the key does not need to be held anywhere, the problem of a data breach based on key disclosure risk is also eliminated.
A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

Violence against women and children is a public health issue of pandemic proportions. It is estimated that one in every three women worldwide has experienced physical, emotional, or sexual violence. Similarly, each year one out of two children are victims of some form of violence including domestic aggression and bullying. Due to the widespread use of the Internet and social media, women and children are now vulnerable to other types of violence such as cyber-bullying and online sexual or emotional harassment. To help alleviate this social problem, the use of computer science and related technologies has been leveraged in recent years. The Internet of Things, artificial intelligence, ubiquitous and mobile computing, pattern recognition, cloud computing and similar technologies, have been used to formulate solutions to detect and prevent violent acts against women and children. In this paper, a systematic review of some of the efforts that can help address the problem of violence against women and children is presented. This paper describes the current state-of-the-art of these contributions and identifies trends, architectures, technologies, and current open challenges. The survey was developed using a literature review of academic documents published from 2010 to 2020. The contributions were categorized into four application domains: online detection, offline detection, safety, and education. These contributions were further categorized based on the computer science approaches and technologies used: artificial intelligence, Internet of Things, and digital serious games.
License Plate Image Reconstruction Based on Generative Adversarial Networks

License plate image reconstruction plays an important role in Intelligent Transportation Systems. In this paper, a super-resolution image reconstruction method based on Generative Adversarial Networks (GAN) is proposed. The proposed method mainly consists of four parts: (1) pretreatment for the input image; (2) image features extraction using residual dense network; (3) introduction of progressive sampling, which can provide larger receptive field and more information details; (4) discriminator based on markovian discriminator (PatchGAN) can make a more accurate judgment, which guides the generator to reconstruct images with higher quality and details. Regarding the Chinese City Parking Dataset (CCPD) dataset, compared with the current better algorithm, the experiment results prove that our model has a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and less reconstruction time, which verifies the feasibility of our approach.
Standardizing AI - The Case of the European Commission's Proposal for an Artificial Intelligence Act

On April 21, 2021, the European Commission presented its long-awaited proposal for a Regulation “laying down harmonized rules on Artificial Intelligence”, the so-called “Artificial Intelligence Act” (hereinafter: AIA). Most of the provisions of the AIA deal with high-risk systems, setting out obligations on providers, users and other participants across the AI value chain, establishing in particular, conformity assessment procedures to be followed for each type of high-risk AI system.At the heart of the proposal is the idea of co-regulation through standardization based on the New Legislative Framework (NLF). According to Recital (61) AIA, “[s]tandardization should play a key role to provide technical solutions to providers to ensure compliance with this Regulation”. Accordingly, this chapter provides a critical analysis of the proposal, discussing in particular, how the envisaged system of co-regulation, standardization and certification could contribute to European governance of AI and address the manifold ethical and legal concerns of (high-risk) AI systems.
The use of anonymized and aggregated telecom mobility data by a public health agency during the COVID-19 pandemic: Learnings from both the operator and agency perspective

Abstract The COVID-19 pandemic and associated measures implemented have rapidly changed how people move about and behave in society. Utilizing data on people’s mobility could provide unique and valuable insights to governments and institutions to better manage the crisis. These entities, however, have not traditionally had access to, nor the experience of applying, continuous anonymized and aggregated data on people mobility. This article aims to show how the Public Health Agency in Sweden successfully collaborated with a Nordic Telecoms operator to make use of such data during the COVID-19 pandemic. Specifically, it investigates how the collaboration started, approaches used to go from data to insight, outcomes and impact, and lessons learned on both sides. Telia, the largest telecom operator in the Nordics, had an existing product commercially available that provided anonymized and aggregated insights about people’s movement. Several challenges existed within Telia as it was the first time worldwide a collaboration with a Public Health Agency would take place and social benefits had to be weighed against commercial and reputational risks. The hypothesis at the beginning of the pandemic was that the solution could be adapted to fit the needs of policymakers and the internal challenges could be overcome, while providing a meaningful contribution to the fight against the virus. The results show that it is possible to both form a mutually beneficial collaboration between a telecom operator and a public institution, and to make use of mobility data in evidence-based policymaking without compromising applicable personal data protection laws.
Intention Prediction and Human Health Condition Detection in Reaching Tasks with Machine Learning Techniques

Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human–robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) and random forest (RF) in: (i) predicting the subject’s intention of moving towards a specific direction among a set of possible choices, (ii) detecting if the subject is moving according to a healthy or pathological pattern, and in the case of discriminating the damage location (left or right hemisphere). Data were captured with wearable electromagnetic sensors, and a sub-section of the acquired signals was required for the analyses. The possibility of detecting with which arm (left or right hand) the motion was performed, and the sensitivity of the MLT to variations in the length of the signal sub-section were also evaluated. LDA and RF prediction accuracies were compared: Accuracy improves when only healthy subjects or longer signals portions are considered up to 11% and at least 10%, respectively. RF reveals better estimation performance both as intention predictor (on average 59.91% versus the 62.19% of LDA), and health condition detector (over 90% in all the tests).
Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors

Computational chemistry is the leading application to demonstrate the advantage of quantum computing in the near term. However, large-scale simulation of chemical systems on quantum computers is currently hindered due to a mismatch between the computational resource needs of the program and those available in today’s technology. In this paper we argue that significant new optimizations can be discovered by co-designing the application, compiler, and hardware. We show that multiple optimization objectives can be coordinated through the key abstraction layer of Pauli strings, which are the basic building blocks of computational chemistry programs. In particular, we leverage Pauli strings to identify critical program components that can be used to compress program size with minimal loss of accuracy. We also leverage the structure of Pauli string simulation circuits to tailor a novel hardware architecture and compiler, leading to significant execution overhead reduction by up to 99%. While exploiting the high-level domain knowledge reveals significant optimization opportunities, our hardware/software framework is not tied to a particular program instance and can accommodate the full family of computational chemistry problems with such structure. We believe the co-design lessons of this study can be extended to other domains and hardware technologies to hasten the onset of quantum advantage.
LLM: Learning Cross-Modality Person Re-Identification via Low-Rank Local Matching

Existing methods of cross-modality person re-identification usually use set-level global feature constraints to reduce the cross-modality discrepancy. However they often ignore the modality-specific and modality-shared local feature matching. Modality-specific local matching could help extract discriminative identity-consistent features and alleviate spatial misalignment, and modality-shared local matching could make identity-consistent features be correlated in two modalities and mitigate the modality misalignment. Therefore, we establish the intra-modality and inter-modality co-occurrence relation between identity parts. We observe the modality-specific and modality-shared local feature matching of identity parts as intra-modality and inter-modality low-rank relation-finding problems. Therefore, in this work, we propose low-rank local matching (LLM) approach to establish the intra-modality and inter-modality co-occurrence relation between identity parts. First, in order to reinforce identity-consist features in two modalities and correlate these cross-modality identity-consist features together, the local matching (LM) module is designed to estimate partial co-occurrence probability by measuring local feature similarity. Moreover, we propose cross-modality triplet-center loss (CTLoss) which adds global constraint for each class distribution in embedding space to alleviate dramatic data expansion due to modality variance. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-art. Our code is released on https://github.com/fegnyujian/LLM.