Reviewerly/exHarmony huggingface dataset:
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We don’t require to define the label of a class a-priori, rather the CAS loss clusters regions with similar appearance together in a weakly-supervised manner. Furthermore, we show that the CAS loss function is sparse, bounded, and robust to class-imbalance. We first apply our CAS loss function with fully-convolutional ResNet101 and DeepLab-v3 architectures to the binary segmentation problem of salient object detection. We investigate the performance against the state-of-the-art methods in two settings of low and high-fidelity training data on seven salient object detection datasets. For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%. For high-fidelity training data (correct class labels) class-agnostic segmentation models perform as good as the state-of-the-art approaches while beating the state-of-the-art methods on most datasets. In order to show the utility of the loss function across different domains we then also test on general segmentation dataset, where class-agnostic segmentation loss outperforms competing losses by huge margins.
Interact: IT infrastructure energy and cost analyzer tool for data centers

The environmental impact of data centers has become a major concern. In their Q1 2021 report, Intel estimated that there are around 100 million servers deployed globally. In this paper we provide an overview of Interact, a machine learning tool that helps data center operators easily analyze, manage, and optimize the energy, cost, and Carbon footprint of their current servers and infrastructure. We discuss in detail the machine learning algorithms and the energy models used. We also demonstrate the key features of the tool and how it can help data centers become more sustainable by reducing server inefficiencies.
<scp>MIFAS</scp>: <scp>Multi‐source</scp> heterogeneous information fusion with adaptive importance sampling for link prediction

Abstract Link prediction plays an important role in constructing knowledge graph. Recently, graph representation learning models yield state‐of‐the‐art results. However, existing models concentrate merely on triples or graph structures and mostly ignore textual descriptions, resulting in incomplete or partial information. In this paper, we propose a novel graph representation learning model to address this challenge, namely multi‐source heterogeneous information fusion with adaptive importance sampling. Our model leverages multiple sources, such triple, graph structure and textual description, and generate rich‐attribute embeddings for entities, encapsulating relations simultaneously. We also propose an adaptive importance sampling algorithm to boost aggregation of useful features from local neighbours. Additionally, we also boost node aggregation of useful features from local neighbours by adaptive importance sampling algorithm in our model. Experimental results on two benchmark datasets show that our proposed model significantly outperforms state‐of‐the‐art methods.
Medical-Modality Super-resolution for increased visualisation of Intracranial Tissue Details and Structural Details

Numerous efforts have been made to produce high-resolution images for medical imaging equipment. The procedure for image acquisition in the medical imaging field however not invariably produce high quality images that can be beneficial for clinical diagnosis. Super resolution (SR) imaging has become a common area of research in medical imaging in particular nowadays. Because of its wide practical applications, image SR has drawn great interest in the domain of image processing community. The purpose of image SR is to produce high quality images from the low-quality counterparts. The field of imaging has seen significant improvements in resolution and image quality over the past few decades, with the aid of improved effective super resolution algorithms. The study proposes an efficient SR method based on wiener filtering via adaptive line search method. The proposed method initially employs a wiener filtering which recovers the feature of the images by inverse filtering of low-quality source images. Further, an adaptive line search method is utilized for fast convergence, in which an approximate analytical term of step size is proposed in order to prevent us from setting it empirically. In addition, a proposed line search method further modifies in order to be more adaptive under various SR circumstances. In the end, the method uses a recursive filtering in transform domain which adequately helps to retains the edges of the source images. An experimental evaluation is performed on numerous medical image data sets. In terms of both quantitative metrics and visual analysis, the proposed strategy exhibits higher performance as compares to prevailing state-of-the-art methods.
Unsupervised Domain Expansion for Visual Categorization

Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA seeks for a deep representation that is both discriminative and domain-invariant. While UDA focuses on the target domain, we argue that the performance on both source and target domains matters, as in practice which domain a test example comes from is unknown. In this article, we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model’s performance on the source domain. We propose Knowledge Distillation Domain Expansion (KDDE) as a general method for the UDE task. Its domain-adaptation module can be instantiated with any existing model. We develop a knowledge distillation-based learning mechanism, enabling KDDE to optimize a single objective wherein the source and target domains are equally treated. Extensive experiments on two major benchmarks, i.e., Office-Home and DomainNet, show that KDDE compares favorably against four competitive baselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our study also reveals that the current UDA models improve their performance on the target domain at the cost of noticeable performance loss on the source domain.
Multi-Server Private Linear Computation with Joint and Individual Privacy Guarantees

This paper considers the problem of multi-server Private Linear Computation, under the joint and individual privacy guarantees. In this problem, identical copies of a dataset comprised of K messages are stored on N non-colluding servers, and a user wishes to obtain one linear combination of a D-subset of messages belonging to the dataset. The goal is to design a scheme for performing the computation such that the total amount of information downloaded from the servers is minimized, while the privacy of the D messages required for the computation is protected. When joint privacy is required, the index set of these D messages must be kept private, and when individual privacy is required, the identity of each individual message required for the computation must be kept private. In this work, we characterize the capacity, which is defined as the maximum achievable download rate, under both joint and individual privacy requirements. Our converse proofs are based on reduction from two variants of the multi-server Private Information Retrieval problem with side information. Our achievability schemes build up on our recently proposed schemes for single-server Private Linear Transformation and the multi-server private computation scheme proposed by Sun and Jafar. Using similar techniques, we also establish bounds on the capacity for the cases in which the user wants to compute multiple (more than one) linear combinations of a D-subset of messages.
How to Explain It to Facility Managers? A Qualitative, Industrial User Research Study for Explainability

In this research, we focus on a building management system (BMS) and the facility managers as users. The application under research is a BMS which reports building related incidents to facility manager (e.g., an air conditioning system is not working properly), including causal factors and related measurements. The information should enable facility managers to select and initiate a responsive action. We wanted to know whether facility managers have the same or different expectations for a BMS application, using an machine-learning (ML) based technology (producing uncertain results), compared to the use of a rule-based technology (producing certain results). We interviewed four facility managers. The research found out that all four interviewed facility managers have different expectations for the use of ML-based technology, compared to a rule-based technology with the following differences: D.1) Show several options per incident. D.2) Annotate each option with a confidence level. D.3) Provide detailed information for each option (outside-in content), expressed in the building technology domain. Two observations are resulting from our case study. ML-generated content enables facility managers to select a proposed incident option and to validate a proposed incident option against their experience. Proposed options with a high confidence level that are validated by facility manager as correct build up even more trust in ML-based technology. Options with a high confidence level which is validated as incorrect can receive feedback by the facility manager, so that the ML-based technology can learn and improve. The limited research indicates that explainability content needs to be expressed as "outside-in content". The result also suggests that a user-centered approach is needed to identify which content is outside-in content which effectively enables option selection and option validation. Such an outside-in content can be used to specify requirements for the development of an ML-technology.
An Analysis of Transaction Handling in Bitcoin

Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fee and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin.Specifically, the analysis consists of two parts. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii).
Study of Overfitting through Activation Functions as a Hyper-parameter for Image Clothing Classification using Neural Network

Often neural network models having a high classification accuracy on the training data suffers a dent when deployed in real-world scenarios which may be attributed to overfitting. Overfitting occurs when the model starts taking into its account every miniscule detail of the training data and thus becomes incapable to generalize the learning over unknown data instances resulting in many misclassifications. Tuning the hyper-parameters of the model while training can help bridge the gap. This paper studies the contribution of activation function in neural networks as a hyper-parameter to help estimate the gap between training and testing accuracy. This predominantly brings to light that use of different activation functions while building a neural network can affect the performance of the model in the metric of testing accuracy. Our study reflected the least overfitting to be demonstrated by softmax activation function for our neural network which performed image clothing classification over fashion MNIST dataset and the maximal by hard sigmoid activation function among the thirteen experimented ones.
A Decentralized Framework for Managing Task Allocation in Distributed Software Engineering

In distributed software development, planning and managing fair and transparent task allocation is both critical and challenging. The objective of this paper is to propose a decentralized blockchain-oriented, transparent task allocation framework to improve the quality of the task allocation process. It addresses the concerns of (i) enhancing collaboration, (ii) inhibiting knowledge vaporization, and (iii) reducing documentation problems. The proposed method is a novel two-fold process: First, it identifies and categorizes tasks exhibiting different dependencies and complexities to create equal task clusters based on their dependency type, difficulty, cost, and time. Second, it uses a blockchain-oriented framework to broadcast, check bid validity, allow developers to bid on tasks matching their roles and expertise, evaluate, and announce the winner for task allocation using smart contracts. Results of experimentation, surveys, and interviews with software practitioners conclude that the proposed solution is transparent and effective in allocating tasks (with Cranach’s alpha of 0.894) at a low cost of contract execution in a distributed software development environment. Overall, the proposed approach will have a positive and significant impact in industrial settings.
Graph Community Infomax

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.
Smart Musical Instruments preset sharing: an ontology-based data access approach

Interoperability represents an important aspect in research dealing with the emerging class of smart musical instruments (SMIs). To date, no interoperable file format for the exchange of content produced by heterogeneous SMIs has been defined yet. This paper proposes a solution to the issue of sharing presets among heterogeneous SMIs, which are used to conFigure an SMI. The heterogeneity of SMIs may come from the type, structure and implementation of the SMI’s embedded system, its sound engine and sensor interface. The presented solution is based on the “ontology-based data access” paradigm and leverages the existing Smart Musical Instruments Ontology. This approach allows one to share presets between heterogeneous SMIs by mapping information about the configuration of an instrument to the concepts of the ontology. Thanks to this approach, SMIs developers can implement programs that convert proprietary formats for the configuration of the instrument into a common format for SMIs, and vice versa. We present the general architecture and workflow of this approach, and we describe an implementation for it which involves the sharing of presets among two heterogeneous smart guitars.
Enabling Collaborative Interaction with 360° Panoramas between Large-scale Displays and Immersive Headsets

Head mounted displays (HMDs) can provide users with an immersive virtual reality (VR) experience, but often are limited to viewing a single environment or data set at a time. In this paper, we describe a system of networked applications whereby co-located users in the real world can use a large-scale display wall to collaborate and share data with immersed users wearing HMDs. Our work focuses on the sharing of 360° surround-view panoramic images and contextual annotations. The large-scale display wall affords non-immersed users the ability to view a multitude of contextual information and the HMDs afford the ability for users to immerse themselves in a virtual scene. The asymmetric virtual reality collaboration between immersed and non-immersed individuals can lead to deeper under-standing and the feeling of a shared experience. We will highlight a series of use cases – two digital humanities projects that capture real locations using a 360° camera, and one scientific discovery project that uses computer generated 360° surround-view panoramas. In all cases, groups can benefit from both the immersive capabilities of HMDs and the collaborative affordances of large-scale display walls, and a unified experience is created for all users.
Digital Communication in the Inclusive Classroom

Digital communication is being extensively used, and during COVID-19 pandemic, it has transformed the way teaching is delivered and how learning happens which became even more problematic for children with learning difficulties. The digitalization of education during the lockdown period has forced teachers, children, and parents to develop and enhance their digital skills to maintain and keep ensuring efficient learning. Digital communication can be provided in the educational system by the simple use of email or WhatsApp groups up to the integration of complex digitalized learning programs and software adapted to the specific educational needs of each student. Being digital natives, students nowadays seem more engaged if learning is mediated by the use of digital communication tools. They are opened and interested in participating in educational activities that are technology-based.
Secure Framework Based on Blockchain for E-Learning During COVID-19

The area of e-learning has been positively influencing by the tremendous development of information and communication technology (ICTs). However, due to widespread COVID-19, the educational model has recently been switched from the conventional classroom to e-learning. Choosing the proper e-learning method and safeguarding e-learning data and the environment are still the main issues that need to be tackle. This paper aims to direct e-learning Practitioners to choose the appropriate e-learning tool and provide a comprehensive structure for protecting the privacy and protection of e-learning data and the environment. The purpose of this research aims to assist e-learning practitioners in selecting the appropriate e-learning tool and to provide a secure structure for the protection of e-learning data and the environment. To visualize the impact of COVID-19 on training across the globe, actual-time facts are accrued and analyzed. During COVID-19, the developing call for e-mastering is studying, and a complete taxonomy gives to make e-mastering practitioners aware of contemporary answers for distance mastering. A contrast of extensively used e-mastering gear provides to help with the choice of e-mastering equipment consistent with the necessities of the institution. A Blockchain-primarily based e-mastering platform proposes to help e-mastering designers cope with e-mastering facts and environmental protection. The proposed framework expects to deliver a good answer for growing an excellent and open mastering online training surroundings and may conquer the deficiencies because of college closures throughout COVID-19.
Seeds of SEED: Preventing Priority Inversion in Instruction Scheduling to Disrupt Speculative Interference

Speculative side-channel attacks consist of two parts: The speculative instructions that abuse speculative execution to gain illegal access to sensitive data and the side-channel instructions that leak the sensitive data. Typically, the side-channel instructions are assumed to follow the speculative instructions and be dependent on them. Speculative side-channel defenses have taken advantage of these facts to construct solutions where speculative execution is limited only under the presence of these conditions, in an effort to limit the performance overheads introduced by the defense mechanisms.Unfortunately, it turns out that only focusing on dependent instructions enables a new set of attacks, referred to as “speculative interference attacks”. These are a new variant of speculative side-channel attacks, where the side-channel instructions are placed before the point of misspeculation and hence before any illegal speculative instructions. As this breaks the previous assumptions on how speculative side-channel attacks work, this new attack variant can be used to bypass many of the existing defenses.We argue that the root cause of speculative interference is a priority inversion between the scheduling of older, bound to be committed, and younger, bound to be squashed instructions, which affects the execution order of the former. This priority inversion can be caused by affecting either the readiness of a not-yet-ready older instruction or the issuing priority of an older instruction after it becomes ready. We disrupt the opportunity for speculative interference by ensuring that current defenses adequately prevent the interference of younger instructions with the availability of operands to older instructions and by proposing an instruction scheduling policy to preserve the priority of ready instructions. As a proof of concept, we also demonstrate how the prevention of scheduling-priority inversion can safeguard a specific defense, Delay-on-Miss, from the possibility of speculative interference attacks. We first discuss why it is susceptible to interference attacks and how this can be corrected without introducing any additional performance costs or hardware complexity, with simple instruction scheduling rules.
A Closer Look

The breach of users' location privacy can be catastrophic. To provide users with privacy protections, numerous location privacy methods have been developed in the last two decades. While several studies surveyed existing location privacy methods, the lack of comparative, empirical evaluations imposes challenges for adopting location privacy by applications and researchers who may not be privacy experts. This study fills the gap by conducting a comparative evaluation among a range of location privacy methods with real-world datasets. To evaluate utility, we consider different types of measures, e.g., distortion and mobility metrics; to evaluate privacy protection, we design two empirical privacy risk measures via inference and re-identification attacks. Furthermore, we study the computational overheads inflicted by location privacy in CPU time and memory requirement. The results are thoroughly examined in our work and show that it is possible to strike a balance between utility and privacy when sharing location data with untrusted servers.
On the existence of quaternary Hermitian LCD codes with Hermitian dual distance 1

For k≥2 and a positive integer d0, we show that if there exists no quaternary Hermitian linear complementary dual [n,k,d] code with d≥d0 and Hermitian dual distance greater than or equal to 2, then there exists no quaternary Hermitian linear complementary dual [n,k,d] code with d≥d0 and Hermitian dual distance 1. As a consequence, we generalize a result by Araya, Harada and Saito on the nonexistence of some quaternary Hermitian linear complementary dual codes.