Reviewerly/exHarmony huggingface dataset:
Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification

The smartphone-based human activity recognition (HAR) systems are not capable to deliver high-end performance for challenging applications. We propose a dedicated hardware-based HAR system for smart military wearables, which uses a multilayer perceptron (MLP) algorithm to perform activity classification. To achieve the flexible and efficient hardware design, the inherent MLP architecture with parallel computation is implemented on FPGA. The system performance has been evaluated using the UCI human activity dataset with 7767 feature samples of 20 subjects. The three combinations of a dataset are trained, validated, and tested on ten different MLP models with distinct topologies. The MLP design with the 7-6-5 topology is finalized from the classification accuracy and cross entropy performance. The five versions of the final MLP design (7-6-5) with different data precision are implemented on FPGA. The analysis shows that the MLP designed with 16-bit fixed-point data precision is the most efficient MLP implementation in the context of classification accuracy, resource utilization, and power consumption. The proposed MLP design requires only 270 ns for classification and consumes 120 mW of power. The recognition accuracy and hardware results performance achieved are better than many of the recently reported works.
Graphite: Iterative Generative Modeling of Graphs

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite, an algorithmic framework for unsupervised learning of representations over nodes in large graphs using deep latent variable generative models. Our model parameterizes variational autoencoders (VAE) with graph neural networks, and uses a novel iterative graph refinement strategy inspired by low-rank approximations for decoding. On a wide variety of synthetic and benchmark datasets, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification. Finally, we derive a theoretical connection between message passing in graph neural networks and mean-field variational inference.
Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment

Several energy transition plans attempt to establish low-carbon practices towards a circular bio-economy in order to reduce greenhouse gas emissions. However, most actions only try to assuage the impacts of climate change without improving the resource flows generated by human activities. In this paper, we propose a semi-quantitative assessment of the impacts of biowaste-based energy transition by engaging all relevant social stakeholders' evaluation in the strategic plan. This holistic approach models a Decision Support System (DSS) to effectively evaluate the interplay of local and sectoral low-carbon actions. Regional energy alliances and stakeholders are used for participatory modeling to promote the buildup of the learning base of this DSS. The core pillar of the DSS involves the application of advanced features of soft computing for the development of a Fuzzy Cognitive Map (FCM) that elicits the inter-causalities of the critical factors affecting the energy transitions towards bio-economy options. The concepts participating in the map are established by experts, and their interrelations via a learning process that utilizes survey statistics. The strands of research include scenarios to highlight the effect of energy provision to urbanization and the increase of urban actors (social, technological, political) in influencing the decision making related to low-carbon policies. Particularly, we study a use case of a Greek region that, despite its munificent agricultural production, also disclosures a stimulated manufacturing economy sector. The proposed decision making tool uses analytics and optimization algorithms to guide competent authorities and decision makers to sustainable energy transitioning towards decarbonization.
Norm-Based Curriculum Learning for Neural Machine Translation

A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence. The norm-based sentence difficulty takes the advantages of both linguistically motivated and model-based sentence difficulties. It is easy to determine and contains learning-dependent features. The norm-based model competence makes NMT learn the curriculum in a fully automated way, while the norm-based sentence weight further enhances the learning of the vector representation of the NMT. Experimental results for the WMT’14 English-German and WMT’17 Chinese-English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x).
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis

In this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, and thereby propose a multi-task deep learning framework to solve all these three problems simultaneously in a multi-modal conversational scenario. We, at first, manually annotate the recently released multi-modal MUStARD sarcasm dataset with sentiment and emotion classes, both implicit and explicit. For multi-tasking, we propose two attention mechanisms, viz. Inter-segment Inter-modal Attention (Ie-Attention) and Intra-segment Inter-modal Attention (Ia-Attention). The main motivation of Ie-Attention is to learn the relationship between the different segments of the sentence across the modalities. In contrast, Ia-Attention focuses within the same segment of the sentence across the modalities. Finally, representations from both the attentions are concatenated and shared across the five classes (i.e., sarcasm, implicit sentiment, explicit sentiment, implicit emotion, explicit emotion) for multi-tasking. Experimental results on the extended version of the MUStARD dataset show the efficacy of our proposed approach for sarcasm detection over the existing state-of-the-art systems. The evaluation also shows that the proposed multi-task framework yields better performance for the primary task, i.e., sarcasm detection, with the help of two secondary tasks, emotion and sentiment analysis.
Private Set Intersection in the Internet Setting from Lightweight Oblivious PRF

We present a new protocol for two-party private set intersection (PSI) with semi-honest security in the plain model and one-sided malicious security in the random oracle model. Our protocol achieves a better balance between computation and communication than existing PSI protocols. Specifically, our protocol is the fastest in networks with moderate bandwidth (e.g., 30–100 Mbps). Considering the monetary cost (proposed by Pinkas et al. in CRYPTO 2019) to run the protocol on a cloud computing service, our protocol also compares favorably. Underlying our PSI protocol is a new lightweight multi-point oblivious pesudorandom function (OPRF) protocol based on oblivious transfer (OT) extension. We believe this new protocol may be of independent interest.
A sensitive dynamic mutual encryption system based on a new 1D chaotic map

In this research paper, we propose a novel one-dimensional chaotic system based on the fraction of cosine over sine (1-DFCS). Evaluation of 1-DFCS indicates the existence of a complex chaotic behavior, an infinite chaotic range, and a high initial state sensitivity. We further propose a new sensitive dynamic mutual image encryption scheme (SDME) using 1-DFCS. SDME is designed with a dynamic diffusion and confusion, which gives a unique encryption process for each image. To ensure the dynamicity, SDME is enriched with a new proposed plain image sensitivity function (PISF). PISF uses the 1-DFCS, plain image, and secret key, which makes it unpredictable and sensitive to a tiny change. PISF is recommended to many image encryption schemes to raise their ability toward the differential attacks. Besides, we propose a mutual part encryption technique. This technique refers to two parties encrypting each other, where a small change in one part reflects the other part. These three characteristics make our scheme able to achieve a high-security level in a single round, which provides a combination of security and time efficiency. The experiment results prove that SDME provides better performance when compared with several state-of-the-art image encryption systems.
Why’s Everyone on TikTok Now? The Algorithmized Self and the Future of Self-Making on Social Media

The video-sharing social media platform TikTok has experienced a rapid rise in use since its release in 2016. While its popularity is undeniable, at the first glance, it seems to offer features already available on previously existing and well-established platforms such as Instagram, YouTube, and Facebook. To understand processes of self-making on TikTok, we undertake two methods of data collection: a walkthrough of the app and its surrounding environment, and 14 semistructured participant interviews. A qualitative analysis of this data finds three distinct themes emerge: (1) awareness of the algorithm, (2) content without context, and (3) self-creation across platforms. These results show that TikTok departs from existing platforms in the model of self-making it engenders, which we term “the algorithmized self”—a complication of the pre-existing “networked self” framework.
Deep graph regularized non-negative matrix factorization for multi-view clustering

Multi-view clustering is an unsupervised method which aims to enhance the clustering performance by combining the knowledge from multiple view data. Non-negative matrix factorization (NMF) is one of the most favourable multi-view clustering methods due to its strong representation ability of non-negative data. However, NMF only factorizes the data matrix into two non-negative factor matrices, which may limit its ability to learn higher level and more complex hierarchical information. To overcome this shortcoming, in this paper, we propose a multi-view clustering method based on deep graph regularized non-negative matrix factorization (MvDGNMF). MvDGNMF is able to extract more abstract representation by constructing a multilayer NMF model with graph Laplacian regularization and drive the last layer representation from each view to a common consensus representation. Meanwhile, an efficient algorithm using alternating multiplicative update rules is developed. Furthermore, in order to demonstrate the effectiveness of this proposed method, we employ several open datasets including image and text datasets to evaluate the clustering performance of MvDGNMF and the state-of-art methods.
Toward Trust in Internet of Things Ecosystems: Design Principles for Blockchain-Based IoT Applications

The Internet of Things (IoT) describes the concept of physical objects equipped with identifying, sensing, networking, and processing capabilities being connected to the Internet. Architectures for the IoT typically rely on transmitting data to centralized cloud servers for processing. Although cloud services are supposed to enhance the IoT in storage, computation, and communication capabilities, this approach often generates isolated data silos and requires trust in third parties operating the cloud servers, which become single point of failure. In addition, centralized cloud-based applications lack transparency and allow for undetected manipulation and concealment of IoT data. To overcome these downsides, we develop and evaluate a blockchain-based IoT sensor data logging and monitoring system, employing a design science research approach. In this article, we show that such systems should provide modularity, data parsimony, and availability in addition to domain-specific principles. The prototype improves data integrity and availability but uncovers challenges, such as high operating costs through smart contract computation fees. Furthermore, semistructured interviews with practitioners allowed us to derive insights for developing blockchain-based IoT ecosystems and reveal that cooperation with organizations is key for transferring solutions into production. We contribute to the IoT knowledge base by providing design principles as well as managerial and technological recommendations.
Billiards-inspired optimization algorithm; a new meta-heuristic method

This paper introduces a new physics-based meta-heuristic algorithm called Billiards-inspired Optimization Algorithm (BOA). The optimization process of this algorithm resembles the billiards game. Each solution candidate is considered as a multi-dimensional billiards ball and the best obtained solutions as pockets. When the balls encounter other balls, vector algebra and conservation laws determine final positions of the balls in optimization search space. In order to evaluate the validity of the proposed approach, twenty-three mathematical functions and seven constrained engineering benchmark problems are studied. The results indicated that the BOA can play an effective role in the field of optimization.
Neural architecture search

Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress...
A new fractional-order chaos system of Hopfield neural network and its application in image encryption

In this work, we propose a new fractional-order chaotic system based on the model of 4-neurons-based Hopfield Neural Network (HNN). By using Adomain decomposition method, the proposed fractional-order chaotic system is solved. With the orders changing, the proposed fractional-order system shows rich dynamical characteristics. Then, based on the pseudo-random numbers (PRNs) generated by the proposed system, a new construction method of multiple hash index chain is designed. And a new image encryption algorithm is designed according to the multiple hash index chain. The safety test results show that the design encryption algorithm has higher security performance. Finally, the 4-neurons-based HNN fractional-order system is implemented by Multisim circuit simulation. The experimental results show the feasibility of the theoretical analysis.
On the Power and Limitations of Random Features for Understanding Neural Networks

Recently, a spate of papers have provided positive theoretical results for training over-parameterized neural networks (where the network size is larger than what is needed to achieve low error). The key insight is that with sufficient over-parameterization, gradient-based methods will implicitly leave some components of the network relatively unchanged, so the optimization dynamics will behave as if those components are essentially fixed at their initial random values. In fact, fixing these \emph{explicitly} leads to the well-known approach of learning with random features (e.g. \citep{rahimi2008random,rahimi2009weighted}). In other words, these techniques imply that we can successfully learn with neural networks, whenever we can successfully learn with random features. In this paper, we formalize the link between existing results and random features, and argue that despite the impressive positive results, random feature approaches are also inherently limited in what they can explain. In particular, we prove that random features cannot be used to learn \emph{even a single ReLU neuron} (over standard Gaussian inputs in $\reals^d$ and $\text{poly}(d)$ weights), unless the network size (or magnitude of its weights) is exponentially large in $d$. Since a single neuron \emph{is} known to be learnable with gradient-based methods, we conclude that we are still far from a satisfying general explanation for the empirical success of neural networks. For completeness we also provide a simple self-contained proof, using a random features technique, that one-hidden-layer neural networks can learn low-degree polynomials.
Differential Privacy Has Disparate Impact on Model Accuracy

Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data. The cost of differential privacy is a reduction in the model's accuracy. We demonstrate that in the neural networks trained using differentially private stochastic gradient descent (DP-SGD), this cost is not borne equally: accuracy of DP models drops much more for the underrepresented classes and subgroups. For example, a gender classification model trained using DP-SGD exhibits much lower accuracy for black faces than for white faces. Critically, this gap is bigger in the DP model than in the non-DP model, i.e., if the original model is unfair, the unfairness becomes worse once DP is applied. We demonstrate this effect for a variety of tasks and models, including sentiment analysis of text and image classification. We then explain why DP training mechanisms such as gradient clipping and noise addition have disproportionate effect on the underrepresented and more complex subgroups, resulting in a disparate reduction of model accuracy.
GroupFace: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition

In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch. We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature. The proposed method provides self-distributed labels that balance the number of samples belonging to each group without additional human annotations, and learns the group-aware representations that can narrow down the search space of the target identity. We prove the effectiveness of the proposed method by showing extensive ablation studies and visualizations. All the components of the proposed method can be trained in an end-to-end manner with a marginal increase of computational complexity. Finally, the proposed method achieves the state-of-the-art results with significant improvements in 1:1 face verification and 1:N face identification tasks on the following public datasets: LFW, YTF, CALFW, CPLFW, CFP, AgeDB-30, MegaFace, IJB-B and IJB-C.
Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence

In their traditional role, digital assistants in technology-mediated services provide customers with information, guidance, and suggestions. However, as the opportunities offered by technology and artificial intelligence increase, digital assistants can also provide emotional support, which refers to empathetic, reassuring expressions for customers who have failed or succeeded in fulfilling a task. We show across four experiments that emotional support offered by a digital assistant increases customer satisfaction (Study 1 and 2) and persistence (Study 3 and 4) in using technology-mediated services. The increase in satisfaction occurs via the perceived warmth of the digital assistant, and the increase in persistence via the serial mediation of perceived warmth and satisfaction. Further, the results of a moderated serial mediation show that the effect on persistence only occurs when a digital (but not when a human) assistant provides emotional support in technology-mediated services. Finally, the effect of emotional support on persistence occurs independently of the digital assistant's embodiment. Practitioners learn how to imbue technology-mediated services with a human touch, inducing favorable customer outcomes.
Feasibility of satellite-to-ground continuous-variable quantum key distribution

Establishing secure communication links at a global scale is a major potential application of quantum information science but also extremely challenging for the underlying technology. While milestone experiments using satellite-to-ground links and exploiting singe-photon encoding for implementing quantum key distribution have shown recently that this goal is achievable, it is still necessary to further investigate practical solutions compatible with classical optical communication systems. Here we examine the feasibility of establishing secret keys in a satellite-to-ground downlink configuration using continuous-variable encoding, which can be implemented using standard telecommunication components certified for space environment and able to operate at high symbol rates. Considering a realistic channel model and state-of-the-art technology, and exploiting an orbit subdivision technique for mitigating fluctuations in the transmission efficiency, we find positive secret key rates for a low-Earth-orbit scenario, while finite-size effects can be a limiting factor for higher orbits. Our analysis determines regions of values for important experimental parameters where secret key exchange is possible and can be used as a guideline for experimental efforts in this direction.
Discovering Visual Patterns in Art Collections With Spatially-Consistent Feature Learning

Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. Our key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style invariant matching, and can be used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artworks, we also demonstrate improvement on localization on the Oxford5K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.