Reviewerly/exHarmony huggingface dataset:
Fused DSConv: Optimizing Sparse CNN Inference for Execution on Edge Devices

Accelerating CNN on resource-constrained edge devices is becoming an increasingly important problem with the emergence of IoT and edge computing. This paper proposes an execution strategy and an implementation for efficient execution of CNNs. Our execution strategy combines two previously published, but not widely used, ideas - direct sparse convolution and fusion of two convolution layers. Together with a scheme for caching intermediate results, this results in a very efficient mechanism for speeding up inference after the model has been sparsified. We also demonstrate an efficient implementation that uses both multi-core and SIMD parallelism. Our experimental results demonstrate that our scheme significantly outperforms existing implementations on an edge device, while also scaling better in a server environment.
A novel robust Kalman filter with adaptive estimation of the unknown time-varying latency probability

To better model one-step randomly delayed measurements (ORDM) with unknown time-varying latency probability (UTLP) in linear systems with heavy-tailed measurement noise (HMN), a novel Normal-Gamma-Beta mixture (NGBM) distribution is presented. By introducing a Bernoulli random variable, the probability density function of the proposed NGBM distribution can be reformulated as a Gaussian hierarchical form. Based on this, a novel robust Kalman filter is designed using the variational Bayesian technique. A target tracking simulation verifies the potential of the proposed robust filter, which has higher filtering accuracy than existing cutting-edge filters and can adaptively estimate the UTLP. Furthermore, it is concluded that when HMN and ORDM concurrently exist, the HMN has more influence on the accuracy of the filter than the ORDM.
Simultaneous Learning and Planning using Rapidly Exploring Random Tree* and Reinforcement Learning

The paper proposes an approach to learn and plan simultaneously in a partially known environment. The proposed framework exploits the Voronoi bias property of Rapidly exploring Random Tree* (RRT*), which balances the exploration-exploitation in Reinforcement Learning (RL). RL is employed to learn policy (sequence of actions), while RRT* is planning simultaneously. Once policy is learned for a fixed start and goal, repeated planing for identical start and goal can be avoided. In case of any environmental uncertainties, RL dynamically adapts the learned policy with the help of RRT*. Apparently, both learning and planning complement each other to handle environmental uncertainties dynamically in real-time and online. Interestingly, more the proposed algorithm runs, its efficiency increases in terms of planning time and uncertainty handling capability over the contender algorithm (i.e., RRT*). Simulation results are shown to demonstrate the efficacy of the proposed approach.
Solving max-Archimedean t-norm interval-valued fuzzy relation equations

This paper discusses a new method for finding the complete set of tolerable solutions of max-Archimedean interval-valued fuzzy relation equations. According to the literature, three types of solution sets, namely; tolerable solution set, united solution set and controllable solution set can be identified with interval-valued fuzzy relation equations. The structure and the properties of the tolerable solution set are studied. The complete set of tolerable solutions can be characterized by one maximum solution and finitely many minimal solutions. An efficient method based on the concept of covering is proposed which computes all minimal solutions. The concept of covering is useful for large size problems in terms of computation. The proposed method is illustrated with some examples.
Trust Platforms: The Digitalization of Corporate Governance and the Transformation of Trust in Polycentric Space

This contribution explores the growing interest in transnational corporate accountability, arising out of the need to build trust in corporations. Emerging cultures of accountability and compliance, among other things, has driven the digitalization of corporate governance and the emergence of a polycentric order of transnational regulations (e.g., human rights due diligence instruments). While widely recognized conceptions of trust inform systems of decentralized, digital governance, the principles and practices of such governance recasts trust and its relationship to accountability within corporations. This contribution argues that this leads to “contradictions” between traditional systems of faith in the trustworthiness of the enterprise and its principal (human) and the emerging faith in the trustworthiness of systems of accountability from which enterprise trust can be objectively measured against an ideal-type, data-based metric. Trust in enterprises, then, become a function of trust in trust accountability systems. Competition among such trust accountability systems and the norms from which trust is measured produces polycentric regimes of transnational corporate accountability, and of measuring trustworthiness, that simultaneously enhances (through systems of exogenous objectification) and undermines (through incompatible standards and methods of trustworthiness) enterprise trust. Part I describes the problem and the challenge, one that arises from the near simultaneous shift in cultural expectations about trust from trust in character to trust in measurement, and the rise of cultures of data driven technologically complicated systems of compliance and accountability. Part Two then considers the transformation brought by efforts to respond that that challenge. These consisted of three closely interlinked trajectories: digitalization, complex compliance-accountability regimes, and the emergence of platforms as the space within which collectives of consumers and producers of governance and trust related data could interact. Part three then examines the consequences of the structures that have emerged as a product of those trajectories. The first is the constitution of polycentric governance standards around which entities must now navigate and the second is the detachment of trust from the entity that is its subject. That is, it considers the rise of trust platforms as the autonomous pint of engagement through which the trustworthiness of corporate governance is negotiated.
Sparse Gated Mixture-of-Experts to Separate and Interpret Patient Heterogeneity in EHR data

A chalenge in developing machine learning models for patient risk prediction involves addressing patient heterogeneity and interpreting the model outcome in clinical settings. Patient heterogeneity manifests as clinical differences among homogeneous patient subtypes in observational datasets. The discovery of such subtypes is helpful in precision medicine, where different risk factors from different patient would contribute differently to disease development and thus personalized treatment. In this paper, we use a Mixture-of-Experts (MoE) model and specifically couple it with a sparse gating network to handle patient heterogeneity for prediction and to aid interpretation of patient subtype separation. In experiment we show that with this sparsity we can improve the risk prediction. We therefore conduct empirical study to understand why and how the model learn to subtype patients from sparse training.
An Effective NTRU-Based Fully Homomorphic Encryption Scheme

Fully homomorphic encryption (FHE) supports arbitrary computations on ciphertexts without decryption to protect users’ privacy. However, currently, there are still some shortcomings in research studies on FHE. For example, the NTRU-based FHE scheme constructed using the approximate eigenvector method requires complex matrix multiplications, and the power-of-two cyclotomic ring cannot prevent subfield attacks. To address these problems, this paper proposed a NTRU-based FHE scheme constructed based on the power-of-prime cyclotomic ring and made the following improvements: (1) the power-of-prime cyclotomic ring is immune to subfield attacks; (2) complex matrix multiplications are replaced with matrix-vector multiplications to modify the ciphertext forms and decryption structures, so as to gain advantages in storage, transportation, and computations; (3) the single instruction multiple data (SIMD) technology is introduced, and homomorphic operations are executed through the Chinese remainder theorem, further improving the scheme computation and storage efficiency. The ciphertext of the scheme is in a form of a vector, and no key exchange is required for homomorphic operations. In addition, this scheme can eliminate the decisional small polynomial ratio (DSPR) assumption under certain conditions and only relies on the ring learning with errors (RLWE) assumption. The standard security model can prove that this scheme is secure against chosen-plaintext (IND-CPA) attacks. Compared with similar schemes, the proposed scheme improves the efficiency at least by a factor of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mrow> <mi>l</mi> <mi>φ</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>x</mi> </mrow> </mfenced> </mrow> <mo>/</mo> <mi>d</mi> <mo>+</mo> <mtext> </mtext> <mn>1</mn> </math> and quadratically decreases the noise growth rate.
Consistency for Functional Encryption

In functional encryption (FE) a sender, Alice, encrypts plaintexts for which a receiver, Bob, can obtain functional evaluations, while Charlie is responsible for initializing the encryption keys and issuing the decryption keys. Standard notions of security for FE deal with a malicious Bob and guarantee the confidentiality of Alice's messages despite the leakage that occurs due to the functional keys that are revealed to the adversary via various forms of indistinguishability experiments that correspond to IND-CPA, IND-CCA and simulation-based security.In this work we provide a complete and systematic investigation of Consistency, a natural security property for FE, that deals with attacks that can be mounted by Alice, Charlie or a collusion of the two against Bob. We develop three main types of consistency notions according to which set of parties is corrupted and investigate their relation to the standard security properties of FE. To validate our different consistency types, we extend the universally composable framework for FE by Matt and Maurer (CSF 2015) and we show that our consistency notions naturally complement FE security by proving how they imply (and are implied by) UC security depending on which set of parties is corrupted; in this way we demonstrate a complete characterization of consistency for FE. Finally, we provide explicit constructions that achieve consistency efficiently either directly via a construction based on MDDH for specific function classes of inner products over a modulo group or generically for all the consistency types via compilers using standard cryptographic tools.
Automatic View Selection in Graph Databases

Recently, several works have studied the problem of view selection in graph databases. However, existing methods cannot fully exploit the graph properties of views, e.g., supergraph views and common subgraph views, which leads to a low view utility and duplicate view content. To address the problem, we propose an extended graph view that persists all the edge-induced subgraphs to answer the subgraph and supergraph queries simultaneously. Furthermore, we present the graph gene algorithm (GGA), which relies on a set of view transformations to reduce the view space and optimize the view benefit. Extensive experiments on real-life and synthetic datasets demonstrated GGA outperformed other selection methods in both effectiveness and efficiency.
TreeNet: A Hierarchical Deep Learning Model to Facilitate Edge Intelligence for Resource-Constrained Devices

Deep learning has achieved remarkable successes in various areas such as computer vision and natural language processing. Many sophisticated models have been proposed to improve performance by designing a significant number of layers of neurons. As an emerging research area, edge intelligence tries to bring intelligence to the network edge by integrating edge computing and AI technologies and it has gained wide attention for its lower latency and better privacy preservation features. Nevertheless, training and inferencing deep neural networks require intensive computation power and time, making it quite challenging to run the models on the resource-constrained edge devices. In this paper, we propose a deep learning model, namely TreeNet, based on task decomposition. After obtaining a task, we would not fit the entire task but decompose the task into disjoint sub-tasks to reduce the complexity of the required deep learning model (it could be divided multiple times if necessary). We first fit the original dataset mapping to different sub-tasks and then fit the mapping of each sub-task to the category of the original dataset that it contains. During the running of the model, we dynamically call the low-level classifier based on the inference result of the high-level classifier. When the inference result of the high-level classifier is unreliable, we send the input sample to the cloud server for processing. We use several popular datasets to study the TreeNet architecture and show that it can process most of the input data while achieving high inference accuracy and significantly decreasing the total amount of calculation.
Fuzzy contrastive learning for online behavior analysis

With the prevalence of smart devices, billions of people are accessing digital resource in their daily life. Online user-behavior modeling, as such, has been actively researched in recent years. However, due to the data uncertainty (sparse-ness and skewness), traditional techniques suffer from certain drawbacks, such as relying on labor-intensive expertise or prior knowledge, lacking of interpretability and transparency, and expensive computational cost. As a step toward bridging the gap, this paper proposes a fuzzy-set based contrastive learning algorithm. The general idea is to design an end-to-end learning framework of optimizing representation from contrastive samples. The proposed algorithm is characterized by three main modules, including data augmentation, fuzzy encoder, and semi-supervised optimization. More precisely, data augmentation is used to produce contrastive (positive and negative) samples based on anchor ones. The fuzzy encoder is introduced to fuzzify (or encode) latent representation of those contrastive samples, while the semi-supervised learning is then implemented to optimize the fuzzy encoder and minimize the training loss simultaneously. The advantage of the proposed algorithm includes no requirement of domain knowledge, preserving the transparency and interpretability of result, and computational effectiveness. Experimental results, based on a real-world app usage dataset, demonstrate that the applicability and flexibility of the proposed algorithm, compared with other state-of-the-art methods.
Solar-Powered Trash Collecting Boat with Solar Power Prediction using Machine Learning and Human-Computer Interface

The power from the sun is contemplated to be one of the most apt renewable energy sources to replace fossil fuels in the marine field. Also, the growing pollution due to trash deposits in the water bodies of our neighbourhoods has increased for the past few years, endangering aquatic life and freshwater resources. This work aims to build a solar-powered boat with maximum efficiency, to inculcate a cleanup system to collect the trash on the water surface using a conveyor belt mechanism within the boat and to develop a user-friendly website for the consumer in order to provide them with the necessary energy details. Such small-scale boats can be initially used in backwater/ small seas for small distance transportation, travel between islands and for cleaning heavily polluted backwaters and adjacent ponds. The implementation of such boats can lead to reduced pollution across the water bodies.
Contrapuntal Composition and Autonomous Style Development of Organum Motets by using AntsOMG

Based on a previously proposed meta-framework, called ants on multiple graphs (AntsOMG), this paper further investigates into certain more complicated creative behavior, in which creators interact with their own creations in order to complete works and elevate the creation forms to a higher level, via computational mechanisms. Aiming to achieve the goal of this study, the target music genre, organum motets, is adopted because its composition process requires iterative interactions with composed music and quite resembles the creative behavior under investigation. AntsOMG is employed as a fundamental component for developing models and conducting creations according to the developed models. With the assistance of AntsOMG, the proposed approach firstly develops style models for section scheme planning and accordingly plans section schemes for the organum motets to be composed. Secondly, following the section scheme, cantus firmus is composed and also used to generate the graph models to be developed into the style models required for composing the corresponding contrapuntal parts for the polyphonic sections of the organum motets. For finalizing each contrapuntal section, a genetic algorithm is utilized to introduce variety and diversity. The outcomes demonstrate that contrapuntal composition and autonomous style development of organum motets can be achieved by the proposed approach. The contribution of this paper is twofold. First, the presented implementation is immediately applicable to compose unlimited amount of organum motets, which may not be possible for human composers. Second, the success of the proposal may shed light on gaining further understandings of complicated creative behavior.
Bayesian learning with variable prior

How much can be learned from a noisy signal about the state of the world not only depends on the accuracy of the signal, but also on the distribution of the prior. Therefore, we define a general information system as a tuple consisting of both a signal technology and a prior. In this paper we develop a learning order for general information systems and characterize the order in two different ways: first, in terms of the dispersion of posterior beliefs about state quantiles and, second, in terms of the value of learning for two different classes of decision makers. The first class includes all agents with quasi-linear quantile preferences, and the second class contains all agents with supermodular quantile preferences.
Practical state estimation model and algorithm based on kernel density theory

This paper presented a practical state estimation model and algorithm based on the kernel density theory (PKDE), which owns better performance than the existing methods. First, a PKDE was presented, which transforms the calculation of the kernel bandwidth of each measurement to that of each state variable. Therefore, the number of kernel bandwidths required to be determined is greatly reduced as the number of state variables are much less than that of the measurements. The feasibility of the PKDE model is supported by a proposition which is strictly proved. Second, an approach for choosing the appropriate kernel bandwidth was presented through the integration of the optimal bandwidth method based on the rule of thumb, the initial bandwidth selection method and the correcting bandwidth selection of the state variables. Third, PKDE’s robustness to bad data is further enhanced by a strategy to correct the measurements according to the hidden component of the measurement error. The simulation results finally verify the effectiveness of the proposed models and algorithms.
Feasibility Study of Multi Autonomous Mobile Robots (AMRs) Motion Planning in Smart Warehouse Environment

This paper aims to do a feasibility study of the motion planning solutions of autonomous mobile robots suitable for smart warehouses in Industry 4.0 environment. It looks at the motion planning solutions like Rapidly exploring Random Tree [RRT] algorithm, Vector Field Histogram [VFH] technique for a single Autonomous mobile robot for warehousing environment and why it can’t be applicable for a large warehouse. Further the paper looks at motion planning solutions of multi-agent AMR systems like leader follower, bio-inspired modelling, multi-robot collision avoidance solutions for the warehouse environment that are used to improve the warehouse productivity. The performance of both type of the motion planning algorithm was measured using an evaluation metric based on industry standard obstacle detection and avoidance. Among the studied methods, the highest motion performance of 93.3 % is achieved in simulations.
Inferences on enacted understanding: using immersive technologies to assess intuitive physical science knowledge

Purpose This study aims to advance the proposal to use immersive virtual learning environments to stimulate and reveal deep-seated knowledge about science, giving instructors and researchers unique possibilities for assessing and identifying intuitive physical science knowledge. Aside from the ability to present rich and dynamic stimuli, these environments afford bodily enactment of people’s understanding, which draws less from declarative knowledge stores and more from everyday experiences with the physical world. Design/methodology/approach The authors ground their proposal in a critical review of the impact of stimulus and task characteristics of traditional physics inventories. Using a grounded theory approach, the authors present classifications and interpretations of observed bodily enactments of physics understandings in a study where participants enacted their understanding of force and motion of space in an immersive, interactive mixed reality (MR) environment. Findings The authors find that instances of these action categories can be interpreted as relating to underlying knowledge, often identified by other studies. The authors thus replicate a number of prior findings, which provide evidence to establish validation for using MR simulation as a tool for identifying people’s physical intuitions. Research limitations/implications This study targeted only a few specific physical science scenarios. Further, while a number of key insights about student knowledge came from the analysis, many of the observations are mere leads in need of further investigation and interpretation rather than core findings. Originality/value Immersive digital learning environments are primarily used for instruction. The authors propose to use and design them for assessment as well. This paper should prompt more research and development in this direction.
Poisoning Attacks via Generative Adversarial Text to Image Synthesis

A poisoning attack is where the adversary can inject a small fraction of poisoning instances into the training data used to train a machine learning model to compromise the performance. Poison attacks can significantly affect the learning process and performance as the model is trained on incorrect data. We have seen many works on data poisoning over the years, but it is limited to few deep learning networks. In this work, we introduce a novel approach by leveraging Generative Adversarial Text to Image Synthesis to create poison attacks against machine learning classifiers. Our approach has three components, which are the generator, discriminator, and the target classifier. We performed an extensive experimental evaluation that proves our attack's efficiency to compromise machine learning classifiers including deep networks.