Reviewerly/exHarmony huggingface dataset:
Infrastructure in Code: Towards Developer-Friendly Cloud Applications

The popularity of cloud technologies has led to the development of a new type of applications that specifically target cloud environments. Such applications require a lot of cloud infrastructure to run, which brought about the Infrastructure as Code approach, where the infrastructure is also coded using a separate language in parallel to the main application. In this paper, we propose a new concept of Infrastructure in Code, where the infrastructure is deduced from the application code itself, without the need for separate specifications. We describe this concept, discuss existing solutions that can be classified as Infrastructure in Code and their limitations, and then present our own framework called Kotless — an extendable cloud-agnostic serverless framework for Kotlin that supports two cloud providers, three DSLs, and two runtimes. Finally, we showcase the usefulness of Kotless by demonstrating its efficiency in migrating an existing application to a serverless environment.
Emergence of Content-Agnostic Information Processing by a Robot Using Active Inference, Visual Attention, Working Memory, and Planning

Generalization by learning is an essential cognitive competency for humans. For example, we can manipulate even unfamiliar objects and can generate mental images before enacting a preplan. How is this possible? Our study investigated this problem by revisiting our previous study (Jung, Matsumoto, & Tani, 2019), which examined the problem of vision-based, goal-directed planning by robots performing a task of block stacking. By extending the previous study, our work introduces a large network comprising dynamically interacting submodules, including visual working memory (VWMs), a visual attention module, and an executive network. The executive network predicts motor signals, visual images, and various controls for attention, as well as masking of visual information. The most significant difference from the previous study is that our current model contains an additional VWM. The entire network is trained by using predictive coding and an optimal visuomotor plan to achieve a given goal state is inferred using active inference. Results indicate that our current model performs significantly better than that used in Jung et al. (2019), especially when manipulating blocks with unlearned colors and textures. Simulation results revealed that the observed generalization was achieved because content-agnostic information processing developed through synergistic interaction between the second VWM and other modules during the course of learning, in which memorizing image contents and transforming them are dissociated. This letter verifies this claim by conducting both qualitative and quantitative analysis of simulation results.
Multistage quantum swapping of vacuum-one-photon entanglement

Vacuum-one-photon entanglement can serve as an alternative to many protocols implemented by two-photon states, and also exhibits its own practical advantages. Quantum swapping plays a central role in both constructing photonic quantum computers and quantum networks. However, the phase-sensitivity in free space makes it very challenging to scale quantum swapping of vacuum-one-photon entanglement up to multistage, especially to large-scale quantum networks. Here, we implement multistage quantum swapping of vacuum-one-photon entanglement on a three-dimensional reconfigurable photonic chip. We also propose and experimentally demonstrate a prototype of a fully connected multipartite network based on multistage quantum swapping. Our results provide an integrated, scalable, and portable solution of multistage quantum swapping, showing the potential as a crucial element of the all-photonic quantum information processing.
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures

The unprecedented performance of deep neural networks (DNNs) has led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition. Nevertheless, deploying such AI models across commodity devices faces significant challenges: large computational cost, multiple performance objectives, hardware heterogeneity and a common need for high accuracy, together pose critical problems to the deployment of DNNs across the various embedded and mobile devices in the wild. As such, we have yet to witness the mainstream usage of state-of-the-art deep learning algorithms across consumer devices. In this paper, we provide preliminary answers to this potentially game-changing question by presenting an array of design techniques for efficient AI systems. We start by examining the major roadblocks when targeting both programmable processors and custom accelerators. Then, we present diverse methods for achieving real-time performance following a cross-stack approach. These span model-, system- and hardware-level techniques, and their combination. Our findings provide illustrative examples of AI systems that do not overburden mobile hardware, while also indicating how they can improve inference accuracy. Moreover, we showcase how custom ASIC- and FPGA-based accelerators can be an enabling factor for next-generation AI applications, such as multi-DNN systems. Collectively, these results highlight the critical need for further exploration as to how the various cross-stack solutions can be best combined in order to bring the latest advances in deep learning close to users, in a robust and efficient manner.
Learning Of Linear Video Prediction Models In A Multi-Modal Framework For Anomaly Detection

This paper proposes a method for performing future-frame prediction and anomaly detection on video data in a multi-modal framework based on Dynamic Bayesian Networks (DBNs). In particular, odometry data and video data from a moving vehicle are fused. A Markov Jump Particle Filter (MJPF) is learned on odometry data, and its features are used to aid the learning of a Kalman Variational Autoencoder (KVAE) on video data. Consequently, anomaly detection can be performed on video data using the learned model. We evaluate the proposed method using multi-modal data from a vehicle performing different tasks in a closed environment.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation [ K ] -interaction [ L ] -matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance–response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context–response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.
Human-Robot Motion Control Application with Artificial Intelligence for a Cooperating YuMi Robot

This paper deals with the application focused on motion control for a cooperating YuMi robot. The use of sensors on gloves or other controls to control robots may restrict both operation of the robot and the operator’s activities. Therefore, research focusing on camera-based monitoring of body parts or hand gestures is becoming increasingly popular. The presented approach is based on using a camera to recognize hand gestures and implementing artificial intelligence to control a YuMi robot with Python using a TCP/IP connection. The program can be expanded by integrating other IoT devices to help control the robot and collect data useful for a needed application.
Rethinking Genre Classification With Fine Grained Semantic Clustering

Movie genre classification is an active research area in machine learning; however, the content of movies can vary widely within a single genre label. We expand these ‘coarse’ genre labels by identifying ‘fine-grained’ contextual relationships within the multi-modal content of videos. By leveraging pre-trained ‘expert’ networks, we learn the influence of different combinations of modes for multi-label genre classification. Then, we continue to fine-tune this ‘coarse’ genre classification network self-supervised to sub-divide the genres based on the multi-modal content of the videos. Our approach is demonstrated on a new multi-moda137,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes pre-computed audio, location, motion, and image embeddings.
Edge-Aware Superpixel Segmentation with Unsupervised Convolutional Neural Networks

Superpixels provide an efficient representation of images, and are applicable for subsequent vision tasks. In this paper, we propose an edge-aware superpixel algorithm based on an unsupervised convolutional neural network (CNN). Noticing that to adhere the boundaries of objects is one of the most essential characteristics of superpixels, we propose an entropy-based edge-aware term, which helps fit the differential model of the pixel-superpixel soft-assignment matrix predicted from CNN to image gradients, i.e. generate boundary-aligning superpixels. The proposed algorithm yields more boundary-adhering superpixels, and experimental results on BSDS500 show the effectiveness of the proposed edge-aware term.
Alleviating Challenges Related to FDA-Approved Medical Wearables Using Blockchain Technology

Medical wearables are an emerging technology that is proceeding its early wellness segment. Medical wearables can provide clinically relevant data and are approved by the Food and Drug Administration (FDA). Some of the FDA-approved medical wearables include the electrocardiogram (EKG) monitor, continuous blood glucose monitor, and smart pill. Wearable technology today simply collects data and stores it in a centralized cloud system. The influx of patient-generated health data (PGHD) and exchange of health data on the network bring challenges around security, privacy, interoperability, and data integrity. In this article, we describe those challenges and propose a conceptual model leveraging blockchain to improve security, privacy, interoperability, and data integrity of FDA-approved medical wearable devices. Our model emphasizes distribution and encryption of data, smart contracts, and permissioned blockchain-based architecture. We then discuss validation and contributions of the proposed model via a case-based scenario.
Comparative analysis of HAR datasets using classification algorithms

In the current research and development era, Human Activity Recognition (HAR) plays a vital role in analyzing the movements and activities of a human being. The main objective of HAR is to infer the current behaviour by extracting previous information. Now-a-days, the continuous improvement of living condition of human beings changes human society dramatically. To detect the activities of human beings, various devices, such as smartphones and smart watches, use different types of sensors, such as multi modal sensors, non-video based and video-based sensors, and so on. Among the entire machine learning approaches, tasks in different applications adopt extensively classification techniques, in terms of smart homes by active and assisted living, healthcare, security and surveillance, making decisions, tele-immersion, forecasting weather, official tasks, and prediction of risk analysis in society. In this paper, we perform three classification algorithms, Sequential Minimal Optimization (SMO), Random Forest (RF), and Simple Logistic (SL) with the two HAR datasets, UCI HAR and WISDM, downloaded from the UCI repository. The experiment described in this paper uses the WEKA tool to evaluate performance with the matrices, Kappa statistics, relative absolute error, mean absolute error, ROC Area, and PRC Area by 10-fold cross validation technique. We also provide a comparative analysis of the classification algorithms with the two determined datasets by calculating the accuracy with precision, recall, and F-measure metrics. In the experimental results, all the three algorithms with the UCI HAR datasets achieve nearly the same accuracy of 98%.The RF algorithm with the WISDM dataset has the accuracy of 90.69%,better than the others.
Adaptive heterogeneous multi-robot collaboration from formal task specifications

Efficiently coordinating different types of robots is an important enabler for many commercial and industrial automation tasks. Here, we present a distributed framework that enables a team of heterogeneous robots to dynamically generate actions from a common, user-defined goal specification. In particular, we discuss the integration of various robotic capabilities into a common task allocation and planning formalism, as well as the specification of expressive, temporally-extended goals by non-expert users. Models for task allocation and execution both consider non-deterministic outcomes of actions and thus, are suitable for a wide range of real-world tasks including formally specified reactions to online observations. One main focus of our paper is to evaluate the framework and its integration of software modules through a number of experiments. These experiments comprise industry-inspired scenarios as motivated by future real-world applications. Finally, we discuss the results and learnings for motivating practically relevant, future research questions.
Polar representation-based cell nucleus segmentation in non-small cell lung cancer histopathological images

Image segmentation is a major area of interest within the field of medical image analysis and processing. In the last few decades, cell nucleus segmentation in non-small lung cancer histopathological images has spawned considerable critical attention. However, most of the previously presented studies have only been concerned with revealing the representation of contours in Cartesian coordinates and suffer from a lack of clarity in connecting the points into a whole contour. Bearing this in mind, we propose a polar representation-based nucleus segmentation model by leveraging the fully convolutional one-stage object detection. In general, center classification and length regression are simultaneously adapted to yield the contour of the nucleus in a polar coordinate. To evaluate the performance of our approach, the comparing experiments are conducted between the state-of-the-art deep learning-based object detection algorithms and the proposed method on one manually collected dataset. Experimental results demonstrate that our model outperformed the state-of-the-art both in efficiency and effectiveness.
Efficient side-channel attacks beyond divide-and-conquer strategy

Side channel attacks exploit physical information that leaks from a cryptographic device, for example power, to extract secret information, such as secret keys. While such attacks are effective for small keys with 8 or 16 bits, they are not viable in practice, however, where keys are much larger, such as 128 bits in AES 128 or more. In order to scale these attacks, some form of divide-and-conquer strategy is typically used, where the attacker divides the key into subkeys and attempts to recover the subkeys separately and then combines them to form the key. In this paper we address two problems that render divide-and-conquer based attacks largely ineffective in practice: First, the power leakage models are inaccurate because of the noise caused by computation involving the remaining portion of the key. We show how better leakage models can improve the accuracy of the resulting leakage models. Second, naïvely combining the recovered subkeys during the attack phase is ineffective, as errors in the subkey recoveries compound. We show how leakage models from other stages of the cryptographic computation can be leveraged to validate (we call this “reinforce”) the choice of recovered subkeys. Experiments using AES 128 leakage data show that (1) leakage models that use the entire key are far superior over subkey-based models, (2) that reinforcement of subkey selection through validation with leakage in a single additional round of the encryption process is both efficient and highly effective (performance improvements up to 240% in key recovery rate in some cases), and (3) the benefits of using more than one additional round are negligible. This work is paving the way towards the study of data-driven system identification techniques to be applied in side-channel attacks.
An Efficient E2E Crowd Verifiable E-Voting System

Electronic voting (e-voting), compared with article voting, has advantages in several aspects. Among those benefits, the ability to audit the electoral process at every stage is one of the most desired features of an e-voting system. In Eurocrypt 2015, Kiayias, Zacharias, and Zhang proposed a new E2E verifiable e-voting system that for the first time provides E2E verifiability without relying on external sources of randomness or the random oracle model; the main advantage of such system is in the fact that election auditors need only the election transcript and the feedback from the voters to pronounce the election process unequivocally valid. Unfortunately, their system comes with a huge performance and storage penalty for the election authority (EA) compared to other e-voting systems such as Helios. The main reason is that due to the way the EA forms the proof of the tally result, it is required to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precompute</i> a number of ciphertexts for each voter and each possible choice of the voter. The performance penalty on the EA appears to be intrinsic to the approach: voters cannot compute an enciphered ballot themselves because there seems to be no way for them to prove that it is a valid ciphertext. In this work, we construct a new e-voting system that retains similar strong E2E characteristics (but against computational adversaries) while completely eliminating the performance and storage penalty of the EA. Our construction has similar performance to Helios and is practical. The privacy of our construction relies on the SXDH assumption over bilinear groups via complexity leveraging.
Task-Sensitive Concept Drift Detector with Constraint Embedding

Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound influence on the performance of trained models. Most of the available literature on drift detection employs either supervised methods, that requires true labels during inference time, or unsupervised, that aim for any changes in the data distribution. We propose a novel task-sensitive semi-supervised drift detection framework, which uses label information to train a model, but detects drifts during inference time only when they affect the model performance, without the ground truth label information. It utilizes a constrained low-dimensional embedding representation of the input data. This way, the dimensionality of the input data is reduced, and the learned representation is best suited for the classification task. We propose two change detectors, which are customized for our framework, but any method to detect a change in the statistic of a data stream can be chosen freely. Experimental evaluation on nine benchmarks datasets, with different types of artificially induced drift, demonstrates that the proposed framework can reliably detect drifts. Furthermore, in our studies we empirically demonstrated that the investigated drift detectors combined with the proposed framework, consistently outperform the other state-of-the-art unsupervised drift detection approaches.
Training Generative Adversarial Networks via Stochastic Nash Games

Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging, it is fundamental to design reliable algorithms to compute an equilibrium. In this article, we propose a stochastic relaxed forward-backward (SRFB) algorithm for GANs, and we show convergence to an exact solution when an increasing number of data is available. We also show convergence of an averaged variant of the SRFB algorithm to a neighborhood of the solution when only a few samples are available. In both cases, convergence is guaranteed when the pseudogradient mapping of the game is monotone. This assumption is among the weakest known in the literature. Moreover, we apply our algorithm to the image generation problem.
Rotation-Invariant Task-Aware Spatial Disentanglement in Rotated Ship Detection Based on the Three-Stage Method

The &#x201C;shared head for classification and localization&#x201D; (sibling head) has been leading the fashion of the object detection community over the past years. However, the article by Song <i>et al</i>. (2020) found that there is a spatial misalignment between the two object functions of sibling head and proposed an operator called task-aware spatial disentanglement (TSD) to decouple the classification and regression from the spatial dimension by generating two disentangled proposals. But there are two problems in applying TSD to detecting rotated objects. The first problem is that the deformable pooling with the disentangled proposals in TSD is not rotation-invariant. The second problem is that the angle offset is not considered when getting the disentangled proposals. To address the above problems, we propose a rotation-invariant TSD (RITSD) method. The proposed method adopts the rotation-invariant deformable pooling and angle offset learning when getting region-of-interest (RoI) features. Considering the rotated anchor increases the computational burden and training time, we use a three-stage backbone that first utilizes horizontal proposals to obtain rotated proposals and then obtains the predicted rotated bounding boxes. Finally, detailed ablation studies on our collected dataset and HRSC2016 were performed to demonstrate the feasibility and superiority of our proposed method. From the heatmap, we find the features of near bow and stern areas are beneficial for classification and the side areas of ships are good at bounding box regression.