Reviewerly/exHarmony huggingface dataset:
Flexible Sensor Suite Integrated Into Textile for Calcium Ion and Fall Detection

Studies show that excessive loss of sweat calcium is associated with the deterioration of bone mineral density in young athletes during vigorous intensity exercise. Low levels of bone mineral density cause a higher risk of bone fracture, meaning that a single fall can be devastating. This letter reports a smart compression sock that detects calcium levels in sweat and simultaneously scans for a fall and alerts the user accordingly. The sock is washable, and the device is removable using a Velcro pad attached to the inner side of the sock. An electrochemical sensor detects the calcium levels, while a triaxial accelerometer detects a fall event. When paired with a fall detection algorithm, the accelerometer quickly determines if a fall has occurred based on the differential acceleration. The calcium and fall detection units are interfaced with a microcontroller that sends the information to a user interface platform via the Blynk Internet of Things cloud. If the calcium level is above/below the critical value or a fall is detected, a notification along with the user's GPS location is sent to authorized emergency contacts so that the user can be attended to immediately. In addition, the electronic components were soldered onto a flexible printed circuit board. Such an integrated flexible system will allow the continuous monitoring of athletes to drive just-in-time intervention.
Multi-modality relation attention network for breast tumor classification

Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Within the proposed network, a novel multi-modality relation attention module improves the discriminative ability of single-modality features by exploring the correlation information between two modalities. In addition, a module ensures the classification consistency of ADC and DWI modality, thus improving robustness to noise. Experimental results on our database demonstrate that the proposed method is effective for breast tumor classification, and outperforms existing multi-modality fusion methods. The AUC, accuracy, specificity, and sensitivity are 85.1%, 86.7%, 83.3%, and 88.9% respectively.
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer from unfair performance gain because they lack adequate insights of local distribution to customize the global model. To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents), thus to enhance their perception of local data; 2) based on this paradigm, we design an uncertainty-based data-relation metric to ensure that selected helpers can provide trustworthy pseudo labels instead of misleading the local training; 3) to mitigate the network overload introduced by helper searching, we further develop a helper selection protocol to achieve efficient communication with acceptable performance sacrifice. Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partially labeled data, especially in highly heterogeneous setting.
Intertextuality 2.0

Abstract Intertextuality captures the idea that all texts and conversations—and by extension, other creations such as images—are linked to other texts and conversations (and other creations), and that people, through making and interpreting such links, construct and infer meanings. Metadiscourse, which broadly refers to discourse about discourse, captures the notion that one important function of language is to communicate about itself. While scholars have long recognized the interrelatedness of the two theoretical concepts, existing studies have tended to focus on one or the other, thus leaving underexplored the specific ways in which these phenomena are intertwined at the micro-interactional level, especially online, and for what purposes. This interactional sociolinguistic study contributes to filling this gap by demonstrating how specific intertextual linking strategies, both linguistic (e.g., word repetition, deictic pronouns) and multimodal (e.g., emojis, symbols, and GIFs), are mobilized by posters participating on weight loss discussion boards. These strategies serve as a resource to accomplish the metadiscursive activities, targeted at various levels of discourse, through which participants construct shared understandings, negotiate the group’s interactional norms, and facilitate engagement in the group’s primary shared activity: exchanging information about, and providing support in, weight loss, healthful eating, and related issues. Intertextuality 2.0 provides microanalysis of discourse in a multimodal digital discourse context, or in “discourse 2.0”; in so doing, it advocates a dual understanding of intertextuality in the sense that its companion, metadiscourse, must be elevated in studies of intertextuality if we are to fully understand its role in (contemporary digital) discourse.
Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran

Resources/reserves classification is crucial for block model creation utilised in mine planning and feasibility study. Selection of estimation methods is an essential part of mineral exploration and mining activities. In other word, resources classification is an issue for mining companies, investors, financial institutions and authorities, but it remains subject to some confusion. The aim of this paper is to determine a resources classification for a Cu block model generated by an Ordinary Kriging (OK) and a Concentration-Volume (C-V) fractal modelling based on estimated variance in Eastern Kahang Cu-Mo porphyry deposit, Central Iran. Variography, block modelling and cell declustering for dataset with respect to Cu concentrations as the main target in this deposit were conducted firstly. Then, Cu distribution model was carried out by the OK and estimated variances were calculated for all voxels. According to a C-V log-log plot, three populations for estimated variances were detected. ‘’Measured’’ resources contain voxels with estimated variances lower than 0.08 and more than 7 samples. Estimated variances varied between 0.08 and 0.24 in which more than 3 samples were engaged for estimation of ‘’Indicated’’ resources. ‘’Inferred’’ resources include estimated variances over 0.24 which are located in marginal parts of this deposit. Results derived via this study reveal that the C-V fractal modelling can be used for resources classification in different ore deposits.
Dual SVM Training on a Budget

We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-based) solvers. Dual subspace ascent methods like sequential minimal optimization are attractive for their good adaptation to the problem structure, their fast convergence rate, and their practical speed. By incorporating a budget constraint into a dual algorithm, our method enjoys the best of both worlds. We demonstrate considerable speed-ups over primal budget training methods.
Sensor-Classifier Co-Optimization for Wearable Human Activity Recognition Applications

Advances in integrated sensors and low-power electronics have led to an increase in the use of wearable devices for health and activity monitoring applications. These devices have severe limitations on weight, form-factor, and battery size since they have to be comfortable to wear. Therefore, they must minimize the total platform energy consumption while satisfying functionality (e.g., accuracy) and performance requirements. Optimizing the platform-level energy efficiency requires considering both the sensor and processing subsystems. To this end, this paper presents a sensor-classifier co-optimization technique with human activity recognition as a driver application. The proposed technique dynamically powers down the accelerometer sensors and controls their sampling rate as a function of the user activity. It leads to a 49% reduction in total platform energy consumption with less than 1% decrease in activity recognition accuracy.
MODELLING OF CONCRETE COMPRESSIVE STRENGTH ADMIXED WITH GGBFS USING GENE EXPRESSION PROGRAMMING

Several studies have established that strength development in concrete is not only determined by the water/binder ratio, but it is also affected by the presence of other ingredients. With the increase in the number of concrete ingredients from the conventional four materials by addition of various types of admixtures (agricultural wastes, chemical, mineral and biological) to achieve a desired property, modelling its behavior has become more complex and challenging. Presented in this work is the possibility of adopting the Gene Expression Programming (GEP) algorithm to predict the compressive strength of concrete admixed with Ground Granulated Blast Furnace Slag (GGBFS) as Supplementary Cementitious Materials (SCMs). A set of data with satisfactory experimental results were obtained from literatures for the study. Result from the GEP algorithm was compared with that from stepwise regression analysis in order to appreciate the accuracy of GEP algorithm as compared to other data analysis program. With R-Square value and MSE of ‌-0.94 and 5.15 respectively, The GEP algorithm proves to be more accurate in the modelling of concrete compressive strength.
Practical stochastic separation theorems for product distributions

Stochastic separation theorems provide mathematical foundation for construction an extremely efficient mechanism for error correction in artificial intelligence systems. They imply that this mechanism works even if the number of points in the dataset is exponentially large in terms of dimension of the underlying space. However, in most such theorems in the literature, the bound for the size of the dataset in terms of dimension is either inexplicit or impractical for large but not extremely large dimensions (such as few hundreds or one thousand). In this work, we derive much less restrictive estimates for dataset size in terms of dimension, which still sufficient to guarantee Fisher separability with large probability, provided that data follow product distributions in the unit cube.
Operatic Configurations in the Digital Age

Let me start with a seeming paradox. Beginning in December 2006, the rise of “Live in HD” broadcasts from New York’s Metropolitan Opera (and, soon thereafter, other major houses) has made some commentators worry, once again, about the survival of opera. As New York Times critic Anthony Tommasini paradigmatically argued in 2013, broadcasts into the cinema offer a fundamentally different experience from prior forms of operatic remediation because of the former’s combination of audiovisual immersion and communal—even live—viewing. Thus, Tommasini feared, not only might HD broadcasts lure audiences away from the opera house, but they also blur a sense of “what the real thing is.”1 The cinema is cast as opera’s angel of death. Ironically, a number of film scholars have simultaneously raised concerns about the future of the cinema itself, which they see endangered by the recent proliferation of digital technologies.2 Partly in response, Francesco Casetti in 2015 (re)defined cinema by focusing not on its enabling technologies but on the experience it affords—an experience constituted by three fundamental aspects: an enclosed environment, the onscreen creation of a world, and “an audience immersed in viewing.”3 In his analysis, this cinematic experience has now “relocated” to new devices and situations, summarily marked by a loss of darkness. Hence Casetti’s verdict: “No longer capable of aiding the other arts, it is now cinema that is in need of assistance.”4 This diagnosis could make us ponder whether HD opera broadcasts perhaps promote cinema more than they do opera; whether the novel content and added boon of a live event, simulcast (by now) in over seventy countries, lend assistance and cultural cachet to an ailing technical medium.5 But I want to emphasize instead Casetti’s own rescue operation—his call to adjust our idea of the cinema: it lives on, he holds, in multiple medial configurations, and it reveals its essence precisely through these new relocations. Such a conceptual sleight of hand is elegant; but it would surely not appease Tommasini, concerned as he is with the embodied live performance that the cinema has arguably always been missing (at least since the rise of sound film).
The effect of data-driven learning activities on young EFL learners’ processing of English idioms

This chapter reports on an empirical study with young EFL learners (12–15 years old), investigating the development of their competence in relation to processing and comprehending idioms through data-driven learning (DDL). Sixty young EFL learners were exposed to authentic non-concordance examples from a corpus before studying concordance lines and extended sentences as part of a 16-week programme of study. Data analysis of pre- and post-achievement tests on idioms showed significant improvement in participants’ overall ability to deduce idiomatic meaning from context. The chapter also includes relevant activities and practical guidance on how corpus-based activities and DDL can improve young EFL learners’ processing of idioms, which can prove helpful to EFL instructors and materials developers considering implementing data-driven learning with young EFL learners.
A Survey

Recently, human stress is rapidly increasing. The school-college students, job professionals, and many people those work under pressure. In last few decades, research is going on how to predict people under pressure or feeling relax with his/her duty. In survey it is evaluated, sentiment analysis will work to find emotions or feelings about their daily life. By analyzing social media network like Facebook, Twitter, and other networking sites where user can share personal feelings like happy, angry, stressed, relaxed, or any other emotion to express human life events or views regarding any topic. On social networking sites, a huge number of informal messages are posted every day, also blogs or discussion forums are also available. Emotions appear to be frequently vital in these texts for expressing friendship, and the presentation of social support as a part of opinions or view. In this article, a survey is done on existing techniques which are working to find sentiment analysis of textual data. In the textual data, the positive and negative sentences have to be found to check the emotions of the user. The survey also finds the natural language processing, the lexical parser, sentiment analysis, the classifier algorithm and some different kinds of Twitter datasets. It is found that 85% work completed on sentiment analysis and categorized the sentences as positive or negative.
FlexAEAD v1.1 -A Lightweight AEAD Cipher with Integrated Authentication

This paperdescribes a symmetrical block cipher family – FlexAEAD v1.1. This is an updated version of the work presented as round 1 candidate on the contest for NIST lightweight crypto standardization process. It was engineered to be lightweight, consuming less computational resources than other ciphers and to work with different block and key sizes. Other important characteristic is to integrate the authentication on its basic algorithm. This approach is helps to reduce the resource needs. The algorithm capacity to resist against linear and different cryptanalysis attacks was evaluated. The FlexAEAD also supports the authentication of the Associated Data (AD). The version 1.1 makes the algorithm resistant to iterated differential attacks. It also resolves a padding attack on the AD that allowed messages to have the same tag if the last AD block was filled with zeros.
Head Motion Classification for Single-Accelerometer Virtual Reality Hardware

Head motions classification applied to virtual reality (VR) systems is still an open problem without a leading pattern recognition solution. In contrary to typical motion capture pattern recognition problem in this case we use only single inertial measurement unit (IMU) sensor. Head motions that we want to recognize in VR systems might be both natural head motions like nodding or shaking head (they might be used while interacting with VR avatars) and also elements of head-based navigation system or interface. The second type of actions is more challenging because it might contains actions that generate motion trajectories that do not appear in real-life, though they have to be possible to execute only by using a head. In this paper we propose a trajectory-based motion features description that is utilized by dynamic time warping (DTW) classificator. The training of the classificator requires using modified dynamic time warping barycenter averaging (DBA) heuristic algorithm which utilizes quaternions to represents rotations. The proposed pattern recognition system together with its evaluation on the set of head motions acquired by VR system is our original contribution. We have evaluated our method on dataset consisted of 8 types of motions performed by two persons (there are 160 motions samples). In leave-one-out evaluation we have obtained very good results: only 10% of one and 15% of another action has been incorrectly classified, while remaining 6 actions classes had been 100% correctly classified. Both dataset and implementation of proposed method can be downloaded, due to this our experiment can be reproduced.
A Novel Steganography Algorithm Based on Instance Segmentation

Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing s... | Find, read and cite all the research you need on Tech Science Press
Influencers and Social Media Recommender Systems: Unfair Commercial Practices in EU and US Law

In the digital society, the availability of information is both a blessing and a curse. Particularly on social media, the question of how information spreads is mostly engulfed in the commercial interests of platforms who rely on algorithmic governance in managing information flows, and the adversarial approach taken by their users to game algorithms in the pursuit of their own benefits. The interaction between social media platforms and content creators who are engaged in monetization has barely received any scholarly attention. YouTubers deal with issues such as shadow-banning, a practice that entails diverting traffic from some channels and videos on policy grounds, and Instagrammers are increasingly scared of an outright ban from the platform. In growing their online presence, content creators depend on platforms making them visible, which is done through recommender systems. This paper aims to shed light onto how recommender systems affect influencers (also referred to as content creators) in a three step approach. First, it looks at how recommender systems work, and how they are applied by social media platforms to the activity of content creators. Second, the paper discusses the potential unfair competition issues that arise out of the use of recommender systems in relation to content creators. Third, it reflects on the suitability of existing frameworks on unfair competition in the EU and US to tackle the tensions arising between content creators and social media platforms. The study innovates existing literature in two ways. It focuses on content creators not as the perpetrators of harms against consumers or citizens, but as the potential victims of social media platforms, thereby providing an illustration of legal issues relating to social media influencers beyond advertising law. Moreover, it revolves around content moderation as an expression of algorithmic governance, with a particular focus on the impact of this governance not on the users-as-audience, but on the users-as-producers, who monetize content as a living.
Phase Reconstruction with Iterated Hilbert Transforms

We discuss theoretical and practical issues of data-driven phase reconstruction approaches for nonlinear oscillatory systems by means of the geometric technique of embeddings and protophase-to-phase transformation. In this chapter, we introduce a natural extension of the well-studied Hilbert transform by iteration. The novel approach, termed iterated Hilbert transform embeddings, implements central assumptions underlying phase reconstruction and allows for exact demodulation of purely phase modulated signals. Here, we examine the performance of the novel method for the more challenging situation of generic phase-amplitude modulated signals of a simple nonlinear oscillatory system. In particular we present the benefits of the approach for secondary phase analysis steps illustrated by reconstruction of the phase response curve. Limitations of the approach are disussed for a noise-driven phase dynamics.
Graph Homomorphism Convolution

In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from $F$ to $G$, where $G$ is a graph of interest (e.g. molecules or social networks) and $F$ belongs to some family of graphs (e.g. paths or non-isomorphic trees). We show that graph homomorphism numbers provide a natural invariant (isomorphism invariant and $\mathcal{F}$-invariant) embedding maps which can be used for graph classification. Viewing the expressive power of a graph classifier by the $\mathcal{F}$-indistinguishable concept, we prove the universality property of graph homomorphism vectors in approximating $\mathcal{F}$-invariant functions. In practice, by choosing $\mathcal{F}$ whose elements have bounded tree-width, we show that the homomorphism method is efficient compared with other methods.