Reviewerly/exHarmony huggingface dataset:
Context-aware adaptation of deep learning models for IoT devices

The rapid development of both Artificial Intelligence (AI) and the Internet of Things (IoT), has cultivated the new research area: the Artificial Intelligence of Things (AIoT). AIoT is used to deploy many different deep learning models on a variety of local IoT terminals including smartphones, wearables, and other embedded devices. Adapting to these dynamic and varied AIoT application scenarios, and the IoT platform resources (e.g., computation and storage resources) available in each diverse, requires a novel scheme for improving on device environmental adaptability. Deep learning models aim to dynamically adjust either the model structure, the calculation scheme, or both, of them specifically to adapt to the environment context. They must reduce costs and improve computational efficiency while creating negligible performance degradation. Specifically, an environmental adaptation evolution framework must actively and continuously assess the constantly changing environmental context including factors, such as application data, knowledge base, task-related performance requirements, and platform-imposed resource constraints. Then it must adopt on-demand model compression, model segmentation, and domain adaptation techniques to achieve a appropriate balance between the models performance and the environments budget. This paper focuses on making deep learning models for context-aware adaptation. We discuss the system architecture and core technologies solving this problem requires. We address research challenges in this area, and introduce our pilot research practice in this field.
Object Shape Measurement Based on Brox Optical Flow Estimation and Its Correction Method

In this work, a new method of measuring surface shape based on Brox optical flow estimation is presented. The measuring system consists of a projector, a measured object and a charge coupled device (CCD) camera. The grating fringes are projected onto the reference plane at a small angle. Two fringe images—before and after placing the measured object on the reference plane—are captured, respectively. Then, the optical flow field between two images is evaluated by using Brox optical flow algorithm. The theoretical relationship between the optical flow field and the height of the measured surface is established. According to the relationship, the height distribution of the measured object can be retrieved quickly without phase-to-height transformation. However, the calculated height distribution has been found to be deviated from its true value. To solve the problem, a correction scheme suitable for the optical flow method is proposed. By using the correction scheme, the accuracy of the calculated result is greatly improved. Simulations and experiments are completed to verify the feasibility of the proposed method and the accuracy of the correction method. The results show that the proposed method is more accurate than that of the Fourier transform method. Compared with traditional surface shape measurement, the optical flow method has some obvious advantages: (1) Only two frame images are required to recover the height distribution. (2) Relatively simple in measurement process and calculation so less time consuming. (3) Because the optical flow method contains time factor itself, it is more suitable for dynamic measurement. (4) No restrictions on projection pattern.
Impact of the malicious input data modification on the efficiency of quantum spatial search

In this paper we demonstrate that the efficiency of quantum algorithms can be significantly altered by malicious manipulation of the input data. We exemplify the possibility of attacks on quantum spatial search based on Szegedy walk. We achieve this by proposing a framework suitable for analysing efficiency of attacks on quantum search algorithms. We provide the analysis of proposed attacks for different models of random graphs.
Towards Ranking Geometric Automated Theorem Provers

The field of geometric automated theorem provers has a long and rich history, from the early AI approaches of the 1960s, synthetic provers, to today algebraic and synthetic provers.The geometry automated deduction area differs from other areas by the strong connection between the axiomatic theories and its standard models.In many cases the geometric constructions are used to establish the theorems' statements, geometric constructions are, in some provers, used to conduct the proof, used as counter-examples to close some branches of the automatic proof.Synthetic geometry proofs are done using geometric properties, proofs that can have a visual counterpart in the supporting geometric construction.With the growing use of geometry automatic deduction tools as applications in other areas, e.g. in education, the need to evaluate them, using different criteria, is felt.Establishing a ranking among geometric automated theorem provers will be useful for the improvement of the current methods/implementations. Improvements could concern wider scope, better efficiency, proof readability and proof reliability.To achieve the goal of being able to compare geometric automated theorem provers a common test bench is needed: a common language to describe the geometric problems; a comprehensive repository of geometric problems and a set of quality measures.
Quantum simulation scheme of two-dimensional xy-model Hamiltonian with controllable coupling

We study a scheme of quantum simulator for two-dimensional xy-model Hamiltonian. Previously the quantum simulator for a coupled cavity array spin model has been explored, but the coupling strength is fixed by the system parameters. In the present scheme several cavity resonators can be coupled with each other simultaneously via an ancilla qubit. In the two-dimensional Kagome lattice of the resonators the hopping of resonator photonic modes gives rise to the tight-binding Hamiltonian which in turn can be transformed to the quantum xy-model Hamiltonian. We employ the transmon as an ancilla qubit to achieve in situ controllable xy-coupling strength.
Stochastic Method for Skeleton Based Human Action Diagnostics

In recent years, modeling of human actions and activity patterns for recognition or detection of the special situation has attracted a significant research interest.We present our approach for abnormal human action recognition as a sequence of intermediate states.We propose to decompose each action into a sequence of discrete intermediate states and to present state transitions as a stochastic process.Each state is described with the joint locations of a human skeleton.Actions are described with Hidden Markov Model based on the found states and its interconnections.As a result, we combine our stochastic model of human actions with intermediate states described via skeleton joints.Convolutional Neural Network is employed to learn skeleton features for intermediate state recognition.Viterbi algorithm is employed to find model parameters.We implemented proposed methods in a framework for human abnormal action recognition and tested our approach on two samples: MPII Human Pose Dataset and exam footages.
Localization Network and End-to-End Cascaded U-Nets for Kidney Tumor Segmentation

Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging.This is partly due to its challenging manual annotation and great medical impact.Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets-collectively denoted TuNet-utilizing the resulting kidney masks for the consecutive tumor segmentation.The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.
Deeply Integrating C11 Code Support into Isabelle/PIDE

We present a framework for C code in C11 syntax deeply integrated into the Isabelle/PIDE development environment. Our framework provides an abstract interface for verification back-ends to be plugged-in independently. Thus, various techniques such as deductive program verification or white-box testing can be applied to the same source, which is part of an integrated PIDE document model. Semantic back-ends are free to choose the supported C fragment and its semantics. In particular, they can differ on the chosen memory model or the specification mechanism for framing conditions. Our framework supports semantic annotations of C sources in the form of comments. Annotations serve to locally control back-end settings, and can express the term focus to which an annotation refers. Both the logical and the syntactic context are available when semantic annotations are evaluated. As a consequence, a formula in an annotation can refer both to HOL or C variables. Our approach demonstrates the degree of maturity and expressive power the Isabelle/PIDE subsystem has achieved in recent years. Our integration technique employs Lex and Yacc style grammars to ensure efficient deterministic parsing. We present two case studies for the integration of (known) semantic back-ends in order to validate the design decisions for our back-end interface.
Information Anxieties and Information Distrust: The effects of Overload Information about COVID – 19

Covid-19 is the world's main focus, so the mass media is competing to provide details information. The amount of information related to Covid-19 in the communities raises information overload. The purpose of the study is to know the effects of overload information about Covid-19 on the information of anxiety and distrust of information about the people of Semarang. This research uses quantitative methods. The samples in this study were one hundred and fifty seven respondents which is acquired using random sampling. The Data in this research was obtained through online questionnaires. Data is presented descriptively because in this study using an open-ended questionnaire. The results showed that overload information about Covid-19 triggers the occurrence of anxiety information. The anxiety of the information itself has an impact on the rise of laziness to searching information and fatigue of information, where both lead to information distrust.
Segmenting multi-intent queries for spoken language understanding

With the rising popularity of voice assistants, automatic speech recognition (ASR) systems play a crucial role in translating speech to text in order to enablenatural language understanding (NLU) models to process human commands. However, NLU models lack the proper resources to handle certain natural human speechcharacteristics, such as dividing or segmenting several intents expressed in one spo-ken sentence. This study presents an innovative approach to sentence boundarysegmentation from ASR output using a neural network model. We improve on previous attempts by removing the need for complex model output postprocessing, aswell as reporting higher accuracy than previous studies on the subject.
iPregel: Strategies to Deal with an Extreme Form of Irregularity in Vertex-Centric Graph Processing

Over the last decade, the vertex-centric programming model has attracted significant attention in the world of graph processing, resulting in the emergence of a number of vertex-centric frameworks. Its simple programming interface, where computation is expressed from a vertex point of view, offers both ease of programming to the user and inherent parallelism for the underlying framework to leverage. However, vertex-centric programs represent an extreme form of irregularity, both inter and intra core. This is because they exhibit a variety of challenges from a workload that may greatly vary across supersteps, through fine-grain synchronisations, to memory accesses that are unpredictable both in terms of quantity and location. In this paper, we explore three optimisations which address these irregular challenges; a hybrid combiner carefully coupling lock-free and lock-based combinations, the partial externalisation of vertex structures to improve locality and the shift to an edge-centric representation of the workload. We also assess the suitability of more traditional optimisations such as dynamic load-balancing and software prefetching. The optimisations were integrated into the iPregel vertex-centric framework, enabling the evaluation of each optimisation in the context of graph processing across three general purpose benchmarks common in the vertex-centric community, each run on four publicly available graphs covering all orders of magnitude from a million to a billion edges. The result of this work is a set of techniques which we believe not only provide a significant performance improvement in vertex-centric graph processing, but are also applicable more generally to irregular applications.
Variasi Bahasa, Singkatan, dan Kesalahan Ejaan Pada Fitur Market Place di Facebook

The purposes of this study were to answer the following questions: (1) What are the forms of language variations found on the market place feature on Facebook? (2) What are the forms of word abbreviations found on the market place features on Facebook? (3) What are spelling errors in the market place feature on Facebook? This study used a qualitative method referring to the analyses of screenshots and data records. The results of this study revealed the following: 1) There are three variations of language, including Indonesian and English language variations, Indonesian and Javanese variations, and the variations of Indonesian, English, and Javanese. 2) There are two types of word abbreviations, namely Indonesian abbreviations and English abbreviations. 3) There are four forms of spelling errors, including a) Erroneous use of capital letters, b) erroneous use of the in-front letter, c) Errors in writing periods (.) and commas (,), and d) Errors in quotation marks (". .. ") and abbreviations or apostrophes ('').
Softmax Deep Double Deterministic Policy Gradients

A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can negatively affect the performance. Although the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm mitigates the overestimation issue, it can lead to a large underestimation bias. In this paper, we propose to use the Boltzmann softmax operator for value function estimation in continuous control. We first theoretically analyze the softmax operator in continuous action space. Then, we uncover an important property of the softmax operator in actor-critic algorithms, i.e., it helps to smooth the optimization landscape, which sheds new light on the benefits of the operator. We also design two new algorithms, Softmax Deep Deterministic Policy Gradients (SD2) and Softmax Deep Double Deterministic Policy Gradients (SD3), by building the softmax operator upon single and double estimators, which can effectively improve the overestimation and underestimation bias. We conduct extensive experiments on challenging continuous control tasks, and results show that SD3 outperforms state-of-the-art methods.
Contrastive Variational Reinforcement Learning for Complex Observations

Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environments remains a major challenge. This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles complex visual observations in DRL. CVRL learns a contrastive variational model by maximizing the mutual information between latent states and observations discriminatively, through contrastive learning. It avoids modeling the complex observation space unnecessarily, as the commonly used generative observation model often does, and is significantly more robust. CVRL achieves comparable performance with state-of-the-art model-based DRL methods on standard Mujoco tasks. It significantly outperforms them on Natural Mujoco tasks and a robot box-pushing task with complex observations, e.g., dynamic shadows. The CVRL code is available publicly at https://github.com/Yusufma03/CVRL.
On Linear Learning with Manycore Processors

A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the efficient training of generalized linear models on these machines. We propose a novel approach for achieving parallelism which we call Heterogeneous Tasks on Homogeneous Cores (HTHC). It divides the problem into multiple fundamentally different tasks, which themselves are parallelized. For evaluation, we design a detailed, architecture-cognizant implementation of our scheme on a recent 72-core Knights Landing processor that is adaptive to the cache, memory, and core structure. Our library efficiently supports dense and sparse datasets as well as 4-bit quantized data for further possible gains in performance. We show benchmarks for Lasso and SVM with different data sets against straightforward parallel implementations and prior software. In particular, for Lasso on dense data, we improve the state-of-the-art by an order of magnitude.
Robustness-optimized quantum error correction

Abstract Quantum error correction (QEC) codes are usually designed to correct errors regardless of their physical origins. In large-scale devices, this is an essential feature. In smaller-scale devices, however, the main error sources are often understood, and this knowledge could be exploited for more efficient error correction. Optimizing the QEC protocol is therefore a promising strategy in smaller devices. Typically, this involves tailoring the protocol to a given decoherence channel by solving an appropriate optimization problem. Here we introduce a new optimization-based approach, which maximizes the robustness to faults in the recovery. Our approach is inspired by recent experiments, where such faults have been a significant source of logical errors. We illustrate this approach with a three-qubit model, and show how near-term experiments could benefit from more robust QEC protocols.
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning

In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false estimations due to local plane fitting from incomplete point clouds. In this paper, we propose a novel Map Generation technique from the viewpoint of information theory, to boost the slight 3D expression differences from strong personality variations. First, we examine the HDR depth data to extract the discriminative dynamic range $r_{dis}$, and maximize the entropy of $r_{dis}$ to a global optimum. Then, to prevent the large deformation caused by over-enhancement, we introduce a depth distortion constraint and reduce the complexity from $O(KN^2)$ to $O(KNτ)$. Furthermore, the constrained optimization is modeled as a $K$-edges maximum weight path problem in a directed acyclic graph, and we solve it efficiently via dynamic programming. Finally, we also design an efficient Facial Attention structure to automatically locate subtle discriminative facial parts for multi-scale learning, and train it with a proposed loss function $\mathcal{L}_{FA}$ without any facial landmarks. Experimental results on different datasets show that the proposed method is effective and outperforms the state-of-the-art 2D+3D FER methods in both FER accuracy and the output entropy of the generated maps.
Neural Architecture Search for Extreme Multi-label Text Classification

Extreme classification and Neural Architecture Search (NAS) are research topics which have recently gained a lot of interest. While the former has been mainly motivated and applied in e-commerce and Natural Language Processing (NLP) applications, the NAS approach has been applied to a small variety of tasks, mainly in image processing. In this study, we extend the scope of NAS to the task of extreme multilabel classification (XMC). We propose a neuro-evolution approach, which was found to be the most suitable for a variety of tasks. Our NAS method automatically finds architectures that give competitive results with respect to the state of the art (and superior to other methods) with faster convergence. In addition, we perform analysis of the weights of the architecture blocks to provide insight into the importance of different operations that have been selected by the method.
Meta Automatic Curriculum Learning

A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has been identified as a key ingredient for good generalization, which pushed researchers towards using rich procedural task generation systems controlled through complex continuous parameter spaces. In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible. Since it is hard to get prior knowledge on such task spaces, many ACL algorithms explore the task space to detect progress niches over time, a costly tabula-rasa process that needs to be performed for each new learning agents, although they might have similarities in their capabilities profiles. To address this limitation, we introduce the concept of Meta-ACL, and formalize it in the context of black-box RL learners, i.e. algorithms seeking to generalize curriculum generation to an (unknown) distribution of learners. In this work, we present AGAIN, a first instantiation of Meta-ACL, and showcase its benefits for curriculum generation over classical ACL in multiple simulated environments including procedurally generated parkour environments with learners of varying morphologies. Videos and code are available at https://sites.google.com/view/meta-acl .
Semantic signatures for large-scale visual localization

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called “semantic signature” is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI’18. A more efficient retrieval protocol is presented with additional experiment results.