Reviewerly/exHarmony huggingface dataset:
Secure Non-interactive Reducibility is Decidable

Secure Non-Interactive Reductions (SNIR) is a recently introduced, but fundamental cryptographic primitive. The basic question about SNIRs is how to determine if there is an SNIR from one 2-party correlation to another. While prior work provided answers for several pairs of correlations, the possibility that this is an undecidable problem in general was left open. In this work we show that the existence of an SNIR between any pair of correlations can be determined by an algorithm. At a high-level, our proof follows the blueprint of a similar (but restricted) result by Khorasgani et al. But combining the spectral analysis of SNIRs by Agrawal et al. (Eurocrypt 2022) with a new variant of a "junta theorem" by Kindler and Safra, we obtain a complete resolution of the decidability question for SNIRs. The new junta theorem that we identify and prove may be of independent interest.
SPARCS: Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop

Existing work in physical robot caregiving is limited in its ability to provide long-term assistance. This is majorly due to (i) lack of well-defined problems, (ii) diversity of tasks, and (iii) limited access to stakeholders from the caregiving community. We propose Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop (SPARCS) to address these challenges. SPARCS is a framework for physical robot caregiving comprising (i) Building Blocks, models that define physical robot caregiving scenarios, (ii) Structured Workflows, hierarchical workflows that enable us to answer the Whats and Hows of physical robot caregiving, and (iii) SPARCS-box, a web-based platform to facilitate dialogue between all stakeholders. We collect clinical data for six care recipients with varying disabilities and demonstrate the use of SPARCS in designing well-defined caregiving scenarios and identifying their care requirements. All the data and workflows are available on SPARCS-box. We demonstrate the utility of SPARCS in building a robot-assisted feeding system for one of the care recipients. We also perform experiments to show the adaptability of this system to different caregiving scenarios. Finally, we identify open challenges in physical robot caregiving by consulting care recipients and caregivers. Supplementary material can be found at emprise.cs.cornell.edu/sparcs.
Inference with Deep Gaussian Process State Space Models

In this paper, we address the problem of sequential processing of observations modeled by deep Gaussian process state space models. First, we introduce the model where the Gaus-sian processes are based on random features and where both the transition and observation functions of the models are unknown. Then we propose a method that can estimate the unknowns of the model. The method allows for incremental learning of the system without requiring all the historical information. We also propose an ensemble version of the method, where each member of the ensemble has its own set of features. We show with computer simulations that the method can track the latent states up to scale and rotation.
(Nondeterministic) Hardness vs. Non-malleability

We present the first truly explicit constructions of non-malleable codes against tampering by bounded polynomial size circuits. These objects imply unproven circuit lower bounds and our construction is secure provided $$\textsf{E}$$ requires exponential size nondeterministic circuits, an assumption from the derandomization literature. Prior works on NMC for polysize circuits, either required an untamperable CRS [Cheraghchi, Guruswami ITCS'14; Faust, Mukherjee, Venturi, Wichs EUROCRYPT'14] or very strong cryptographic assumptions [Ball, Dachman-Soled, Kulkarni, Lin, Malkin EUROCRYPT'18; Dachman-Soled, Komargodski, Pass CRYPTO'21]. Both of works in the latter category only achieve non-malleability with respect to efficient distinguishers and, more importantly, utilize cryptographic objects for which no provably secure instantiations are known outside the random oracle model. In this sense, none of the prior yields fully explicit codes from non-heuristic assumptions. Our assumption is not known to imply the existence of one-way functions, which suggests that cryptography is unnecessary for non-malleability against this class. Technically, security is shown by non-deterministically reducing polynomial size tampering to split-state tampering. The technique is general enough that it allows us to construct the first seedless non-malleable extractors [Cheraghchi, Guruswami TCC'14] for sources sampled by polynomial size circuits [Trevisan, Vadhan FOCS'00] (resp. recognized by polynomial size circuits [Shaltiel CC'11]) and tampered by polynomial size circuits. Our construction is secure assuming $$\textsf{E}$$ requires exponential size $$\varSigma _4$$ -circuits (resp. $$\varSigma _3$$ -circuits), this assumption is the state-of-the-art for extracting randomness from such sources (without non-malleability). Several additional results are included in the full version of this paper [Eprint 2022/070]. First, we observe that non-malleable codes and non-malleable secret sharing [Goyal, Kumar STOC'18] are essentially equivalent with respect to polynomial size tampering. In more detail, assuming $$\textsf{E}$$ is hard for exponential size nondeterministic circuits, any efficient secret sharing scheme can be made non-malleable against polynomial size circuit tampering. Second, we observe that the fact that our constructions only achieve inverse polynomial (statistical) security is inherent. Extending a result from [Applebaum, Artemenko, Shaltiel, Yang CC'16] we show it is impossible to do better using black-box reductions. However, we extend the notion of relative error from [Applebaum, Artemenko, Shaltiel, Yang CC'16] to non-malleable extractors and show that they can be constructed from similar assumptions. Third, we observe that relative-error non-malleable extractors can be utilized to render a broad class of cryptographic primitives tamper and leakage resilient, while preserving negligible security guarantees.
Towards Real-World HDRTV Reconstruction: A Data Synthesis-Based Approach

Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several drawbacks on modeling the realistic degradation: information over-preservation, color bias and possible artifacts, making the trained reconstruction networks hard to generalize well to real-world cases. To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions. In specific, we design a conditioned two-stream network with prior tone mapping results as a guidance to synthesize SDRTVs by both global and local transformations. To train the data synthesis network, we form a novel self-supervised content loss to constraint different aspects of the synthesized SDRTVs at regions with different brightness distributions and an adversarial loss to emphasize the details to be more realistic. To validate the effectiveness of our approach, we synthesize SDRTV-HDRTV pairs with our method and use them to train several HDRTV reconstruction networks. Then we collect two inference datasets containing both labeled and unlabeled real-world SDRTVs, respectively. Experimental results demonstrate that, the networks trained with our synthesized data generalize significantly better to these two real-world datasets than existing solutions.
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models. As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical. A complementary way towards robustness is to introduce a rejection option, allowing the model to not return predictions on uncertain inputs, where confidence is a commonly used certainty proxy. Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones. This intriguing property sheds light on using coupling strategies to better detect and reject adversarial examples. We evaluate our rectified rejection (RR) module on CIFAR-10, CIFAR-10-C, and CIFAR-100 under several attacks including adaptive ones, and demonstrate that the RR module is compatible with different adversarial training frameworks on improving robustness, with little extra computation.
Voice Recognition Chat bot for Consumer Product Applications

The concept and implementation of a voice recognition chat bot for customer assistance is presented in this paper. Chat bots are automated tools that aid in the replication of human behaviour in a dialogue. They also aid humans in a discussion or when they have a question about something on the internet. They can be used to communicate between clients and the system in web-services. The bot’s questions are analysed using AI techniques, and suitable replies are gathered from a database. The output is provided in the form of speech and text. New questions that aren’t in the database are processed and added to the database for future inquiries. As a result, we give a review of the strategies used to create Chatbots in this work. A few examples of chatbot design are also explored to help understand how chatbots work and what kind of techniques are available for developing chatbots. With the rapid advancement of Chatbot technology, it is envisaged that it would be able to supplement human limitations and increase productivity.
Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Artificial Intelligence-based applications have been increasingly deployed in every field of life including smart homes, smart cities, healthcare services, and autonomous systems where personal data is collected across heterogeneous sources and processed using ”black-box” algorithms in opaque centralised servers. As a consequence, preserving the data privacy and security of these applications is of utmost importance. In this respect, a modelling technique for identifying potential data privacy threats and specifying countermeasures to mitigate the related vulnerabilities in such AI-based systems plays a significant role in preserving and securing personal data. Various threat modelling techniques have been proposed such as STRIDE, LINDDUN, and PASTA but none of them is sufficient to model the data privacy threats in autonomous systems. Furthermore, they are not designed to model compliance with data protection legislation like the EU/UK General Data Protection Regulation (GDPR), which is fundamental to protecting data owners’ privacy as well as to preventing personal data from potential privacy-related attacks. In this article, we survey the existing threat modelling techniques for data privacy threats in autonomous systems and then analyse such techniques from the viewpoint of GDPR compliance. Following the analysis, We employ STRIDE and LINDDUN in autonomous cars, a specific use-case of autonomous systems, to scrutinise the challenges and gaps of the existing techniques when modelling data privacy threats. Prospective research directions for refining data privacy threats & GDPR-compliance modelling techniques for autonomous systems are also presented.
SCALES

The recently proposed YOSO model is a groundbreaking approach to MPC, executable on a public blockchain, circumventing adaptive player corruption by hiding the corruption targets until they are worthless. Players are selected unpredictably from a large pool to perform MPC subtasks, in which each selected player sends a single message (and reveals their identity). While YOSO MPC has attractive asymptotic complexity, unfortunately, it is concretely prohibitively expensive due to the cost of its building blocks. We propose a modification to the YOSO model that preserves resilience to adaptive server corruption, but allows for much more efficient protocols. In SCALES (Small Clients And Larger Ephemeral Servers) only the servers facilitating the MPC computation are ephemeral (unpredictably selected and "speak once"). Input providers (clients) publish problem instance and collect the output, but do not otherwise participate in computation $$\text {SCALES}$$ offers attractive features, and improves over YOSO in outsourcing MPC to a large pool of servers under adaptive corruption. We build $$\text {SCALES}$$ from Rerandomizable Garbling Schemes (RGS). RGS is a contribution of independent interest with additional applications.
On the use of fine-grained vulnerable code statements for software vulnerability assessment models

Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on leveraging such detection outputs for data-driven SV assessment to give information about exploitability, impact, and severity of SVs. The information is important to understand SVs and prioritize their fixing. Using large-scale data from 1,782 functions of 429 SVs in 200 real-world projects, we investigate ML models for automating function-level SV assessment tasks, i.e., predicting seven Common Vulnerability Scoring System (CVSS) metrics. We particularly study the value and use of vulnerable statements as inputs for developing the assessment models because SVs in functions are originated in these statements. We show that vulnerable statements are 5.8 times smaller in size, yet exhibit 7.5--114.5% stronger assessment performance (Matthews Correlation Coefficient (MCC)) than non-vulnerable statements. Incorporating context of vulnerable statements further increases the performance by up to 8.9% (0.64 MCC and 0.75 F1-Score). Overall, we provide the initial yet promising ML-based baselines for function-level SV assessment, paving the way for further research in this direction.
Learning From Heterogeneous Data Based on Social Interactions Over Graphs

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning, several useful strategies have been developed, which solve decision making problems through local cooperation across distributed agents and allow them to learn from streaming data. However, traditional social learning strategies rely on the fundamental assumption that each agent has significant prior knowledge of the underlying distribution of the observations. In this work we overcome this issue by introducing a machine learning framework that exploits social interactions over a graph, leading to a fully data-driven solution to the distributed classification problem. In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples; in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers. We show that the SML strategy enables the agents to learn consistently under this highly-heterogeneous setting and allows the network to continue learning even during the prediction phase when it is deciding on unlabeled samples. The prediction decisions are used to continually improve performance thereafter in a manner that is markedly different from most existing static classification schemes where, following training, the decisions on unlabeled data are not re-used to improve future performance.
An Applied Holistic Landmark with Deep Learning for Thai Sign Language Recognition

Thai Sign Language is the main language for communication with the hearing impaired in Thailand. It uses hand or finger signs or gestures, as well as facial expressions and body movements, to convey the meaning of spoken words to people who are deaf or hard of hearing. This research is an attempt to develop a Thai Sign Language interpreter with the holistic approach of landmark detection for hand, facial, and body movements with various types of deep learning algorithms for both recognition and classification. With this approach, we propose a method to recognize the Thai sign language using holistic landmarks with deep learning algorithms. The result shows we can have an accuracy of over 90 percent.
Radial basis function network for aggregating predictions of k-nearest neighbors local models generated based on independent data sets

In this article, a new classification method using neural networks for dispersed data from independent sources - local decision tables - is proposed. The presented method uses a modified k-nearest neighbors algorithm and a radial basis function neural network. Prediction vectors are generated by the modified k-nearest neighbors algorithm for all local decision tables, which are then passed to the neural network for a final decision. Comparative analysis of the error level and structural complexity were carried on the proposed method and a classification method which uses a modified k-nearest neighbors algorithm and a multi-layer perceptron. Results obtained shows that the proposed method generates unambiguous decisions and achieves lower error with less structural complexity as compared to the classification method which uses a modified k-nearest neighbors algorithm and the multi-layer perceptron.
LogicWiSARD: Memoryless Synthesis of Weightless Neural Networks

Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons.As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications.This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely eliminating memories and arithmetic circuits and utilizing only logic functions.The approach creates compressed minimized implementations by converting trained WNN nodes from lookup tables to logic functions.The proposed LogicWiSARD is implemented in FPGA and ASIC technologies to illustrate its suitability for edge inference.Experimental results show more than 80% reduction in energy consumption when the proposed LogicWiSARD model is compared with a multilayer perceptron network (MLP) of equivalent accuracy.Compared to previous work on FPGA implementations for WNNs, convolutional neural networks, and binary neural networks, the energy savings of LogicWiSARD range between 32.2% and 99.6%.
Cloud Adoption Framework

The article is focused on the development of a cloud adoption framework that will allow organizations to test the existence of recommended controls according to the stage of the cloud transition life cycle and control area. At the same time, based on these tests, it is possible to determine the level of maturity of the organization and recommend measures to improve the quality of decision-making related to cloud adoption. Besides, this framework may serve as a guideline and checklist for the cloud adoption process. The framework contains 122 controls in total divided into 8 phases. The authors leveraged especially the findings from existing and already published research that focused on the area of cloud adoption and two cloud provider-specific frameworks that are Cloud Adoption Framework for Microsoft Azure and Amazon Cloud Adoption Framework. The article also describes a case study on which the framework was validated.
Multi-View Adjacency-Constrained Hierarchical Clustering

This paper explores the problem of multi-view clustering, which aims to promote clustering performance with multi-view data. The majority of existing methods have problems with parameter adjustment and high computational complexity. Moreover, in the past, there have been few works based on hierarchical clustering to learn the granular information of multiple views. To overcome these limitations, we propose a simple but efficient framework: Multi-view adjacency-Constrained Hierarchical Clustering (MCHC). Specifically, MCHC mainly consists of three parts: including the Fusion Distance matrices with Extreme Weights (FDEW); adjacency-Constrained Nearest Neighbor Clustering (CNNC); and the internal evaluation Index based on Rawls' Max-Min criterion (MMI). FDEW aims to learn a fusion distance matrix set, which not only uses complementary information among multiple views, but exploits the information from each single view. CNNC is utilized to generate multiple partitions based on FDEW, and MMI is designed for choosing the best one from the multiple partitions. In addition, we propose a parameter-free version of MCHC (MCHC-PF). Without any parameter selection, MCHC-PF can give partitions at different granularity levels with a low time complexity. Comprehensive experiments tested on eight real-world datasets validate the superiority of the proposed methods compared with the 13 current state-of-the-art methods.
Design for Visitor Authentication Based on Face Recognition Technology Using CCTV

Recently, image recognition technology using deep learning has improved significantly, and security systems and home services that use biometric information such as fingerprints, iris scans, and face recognition are attracting attention. In particular, user authentication methods that utilize face recognition have been studied at length. This study presents a visitor authentication technology that uses CCTV with a Jetson Nano and webcam. In the preprocessing phase for face recognition, face data with 7 features that can be identified as a person are collected using CCTV. The collected dataset goes through the annotation process to classify the data, and facial features are detected using deep learning. If there are four or more detected features, the image data is determined to be a person, and the visitor’s face is matched with stored user data in detail using 81 feature vectors. Additionally, the security of the access control system was enhanced by implementing logging functions such as recording the face of the visitor, the number of visitors, and the time of the visit. This paper implements a visitor authentication system using a Jetson Nano and evaluates performance by analyzing the accuracy and detection speed of the system. The tiny-YOLOv3 in the Jetson Nano was effective in real-time verification for the real-time face authentication system with an average detection speed of 6.5 FPS and 86.3% accuracy. Through this study, we designed a system based on deep learning technology that recognizes and authenticates the face of a user during the visitor access process and controls user access.