Reviewerly/exHarmony huggingface dataset:
Pagoda: Towards Binary Code Privacy Protection with SGX-based Execute-Only Memory

Code disclosure remains a huge threat to the intellectual property (IP) of any software that is deployed in a remote, untrusted environment. In this threat model, attackers have complete control over the software stack, so software-only solutions for preventing code disclosure have been doomed to fail. A natural alternative is to employ trusted hardware, e.g., an enclave-based architecture such as Intel SGX. However, existing SGX frameworks assume the target application is in the trusted computing base, i.e., free of vulnerabilities which can be exploited to leak code. Making matters worse, simply porting to an enclave-based paradigm is impractical for enterprise-scale applications, incurring large performance overheads and compatibility issues.In this paper, we take a first step towards building a practical, SGX-based code privacy enforcement framework called Pagoda that supports unmodified applications with minimal performance overhead. The key insight of Pagoda is that placing only application code within the enclave prevents arbitrary code accesses, and at the same time avoids the usual performance and compatibility issues stemming from protecting data within enclaves. Pagoda achieves code privacy throughout the application’s lifetime, by loading and decrypting encrypted binaries into the enclave, and enforcing eXecute-Only-Memory (XOM) to block arbitrary accesses to the private code during its execution.We have built a prototype of Pagoda for Linux-based systems on Intel SGX. The performance evaluation on SPEC CPU2017 benchmarks shows that Pagoda incurs an average of 2.1% performance overhead when compared to native runs. To demonstrate its compatibility, we show that Pagoda can run a wide range of applications, from common server applications such as Lighttpd and Memcached, to complicated graphical applications such as Quake without any source code modification.
Real-time synthetic-to-real human detection for robotics applications

During the recent years, Deep Learning achieved exceptional performance in various computer vision tasks, paving auspicious research directions for its application in robotics. A key component for its exceptional performance is the availability of sufficient training data. However obtaining such amount of training data constitutes a challenging task, especially considering robotics applications. Thus, synthetic data have recently been regarded as a promising tool to overcoming the data availability problem. In this work we first build a synthetic human dataset, and then we train a lightweight model, capable of operating in real-time for high-resolution input on low-power GPUs, for discriminating between humans and non-humans. The target of this work is to assess the generalization of the model trained on synthetic data, to real data, and also to explore the effect of using (few) real images in the training phase. As it is shown through quantitative and qualitative results the use of only few real images can beneficially affect of the performance of the synthetic-to-real real-time model.
Boehm-Demers-Weiser Garbage Collection on Morello

The Boehm-Demers-Weiser collector is a conservative garbage collection scheme generally used for C/C++ applications. In this demo, we will show the collector running with C benchmark programs on a new CHERI-style hardware capability platform, specifically the Arm Morello system.
An Analysis of Public Hospital Services and Technologies 4.0: A Conceptual Framework for Health Management

The health 4.0 coming from the fourth industrial revolution technologies has influenced several segments in the private and public sectors. This research study analyzes the disruptive technologies and how these technologies can contribute to public health institutions, creating a conceptual management model. The research was developed through a literature review in the Web of Science database, where 75 articles were found based on search equations and pre-established filters. In total, 62 abstracts were selected for reading, five being excluded for not meeting the objectives, leaving 57 articles. Of these, 35 articles were read in full and the ten that met the scope were selected for analysis. The research period was between February and August 2021. A summary table of the articles found presents the results, helping to compose the findings, identifies disruptive technologies, and their contributions to hospital management. In a public health system patients are constantly admitted in the same health institutions. It is discussed how disruptive technologies can provide practical and innovative solutions that meet the desires of interested parties to maintain a constant and effective flow of care in a way that overcrowding does not occur. In the conclusion, the integration between health institutions and disruptive technologies can bring improvements in the work process and in the performance of professionals in the public hospitals. This integration can support theoretical elements for the preparation of project proposals and new management models based on health 4.0.
Empirical Study of the Model Generalization for Argument Mining in Cross-Domain and Cross-Topic Settings

To date, the number of studies that address the generalization of argument models is still relatively small. In this study, we extend our stacking model from argument identification to an argument unit classification task. Using this model, and for each of the learned tasks, we address three real-world scenarios concerning the model robustness over multiple datasets, different domains and topics. Consequently, we first compare single-datset learning (SDL) with multi-dataset learning (MDL). Second, we examine the model generalization over completely unseen dataset in our cross-domain experiments. Third, we study the effect of sample and topic sizes on the model performance in our cross-topic experiments. We conclude that, in most cases, the ensemble learning stacking approach is more stable over the generalization tests than a transfer learning DistilBERT model. In addition, the argument identification task seems to be easier to generalize across shifted domains than argument unit classification. This work aims at filling the gap between computational argumentation and applied machine learning with regard to the model generalization.
Conversations Towards Practiced AI – HCI Heuristics

Artificial intelligence (AI) has become one of the most critical features of our techno culture, and by extension, a key element in many user-facing technologies. However, User Interface (UI) and Experience Designers (XD) lack the necessary understanding of AI, as well as the variances of its application but most importantly, they lack guiding principles for the day-to-day activities of design. Through the prism of existing Design Thinking framework, a knowledge pathway that intersects with AI technology, in pursuit of practical design heuristics, is necessary. Such heuristics must enable Designers to respond to the call for growing and emergent regulations around AI; they must enable the continued adherence of human centred design, privileging human over machine, and they must empower Designers in AI design conversations. Given these circumstances, there is also a need for both research and education that seeks to investigate all the ways in which AI influences human computer interaction (HCI) and design, broadly. This paper takes a step towards acknowledging the lack of practiced AI-HCI knowledge frameworks that can guide Designers through the Design Thinking phases when designing technologies that deploy AI with user facing technologies, in varying contexts and domains. Further, the growing levels of concerns around AI, often with detrimental effects and negative human outcomes, make the matter mission critical and forces the acknowledgment that we need guidance, as appendages to existing Design Thinking frameworks and which can keep pace with the rapid rates of technological advances.
Camera Mouse: Sound-Based Activation as a New Approach to Click Generation

In the rapid development of HCI technology, mouse-replacement interfaces have innovated the user interaction experience with graphical user interfaces, specifically for those with neuromuscular diseases. By employing click actuation modalities that do not require a mouse for performing the tasks of pointing and clicking, the individuals are provided with a solution for effective interaction with computers. We present an alternative click actuation modality called “sound-click recognition” for the users of Camera Mouse, a mouse-replacement interface that tracks user motion. Using the sound-click recognition feature, users can generate a left click of the mouse based on the volume of the sound they generate. Furthermore, we present the findings of the evaluation of Camera Mouse while employing two input methods including sound-click recognition and dwell-time selection to analyze the user performance with each approach through side-by-side comparison. The metrics used to measure performance are generated by the GoFitts evaluation tool and are movement time(ms), throughput(bits/s), error rate (%), and target re-entries.
Adaptive Destruction Learning for Fine-grained Visual Classification

Fine-grained visual classification severs as an important task in artificial intelligence and greatly improves our daily life. However, this task is very challenging due to the difficulties in learning robust local features for categories with subtle visual differences. To address this issue, destruction learning, which focuses on destroying image global information and then reconstructing the entire objects, has been increasingly studied owing to its implicit local feature learning manner. Nevertheless, existing destruction learning approaches utilize a random image patch shuffling scheme to destroy the global information and easily bring in undesirable shuffling results. In this paper, we devise a novel adaptive destruction learning approach for fine-grained visual classification. Our method can actively learn the optimal shuffling matrix for each specific image, while simultaneously learning the optimal deep visual network. Both the shuffling matrix and deep visual network are trained in an end-to-end manner. The experimental results demonstrate that the proposed method achieves competitive state-of-the-art accuracy, with 89.8%, 95.3%, 93.6%, and 86.6% top-1 classification accuracy on CUB-200-2011, Stanford-Cars, FGVC-Aircraft, and Stanford-Dog, respectively.
DICE

This article deals with serious game design methods. More specifically, it focuses on the following question: is there any universal series of steps to design a serious game? Or is the availability of several different design methods unavoidable? To try to answer this question, we will study a corpus of ten design methods suited to Serious Games. Most of these theoretical tools are coming from or aimed to industry professionals. This literary review will allow us to perform a comparative analysis over the various steps used by these methods, in order to build a generic model for the design process of Serious Games: the Design / Imagine / Create / Evaluate (D.I.C.E.) model.
GViT: Locality enhanced Vision Transformer using Spectral Graph Convolutional Network

In this paper, we propose a graph vision transformer, named GViT, which can efficiently combine the local and global connectivity between image patches. It is motivated by observations that local interactions between image patches can be de-scribed by an undirect graph adjacency matrix. To insert the locality into a standard vision transformer, we directly add a spectral graph convolutional network that extracts local information by a simple matrix multiplication and a Squeeze-and-Excitation block that models inter-dependencies between channels in Feed Forward Network of the transformer. Extensive experiments show that the GViT attains the best accuracy/throughput trade-off compared to original ViT and CNN enhanced ViT. Moreover, the proposed locality mechanism can be easily plugged into a wide range of vision transformers which ensures its broad application in ViT based visual tasks.
Modified GAN for Natural Occlusion Detection and Inpainting of Raw Footage From Video Surveillance

Low resolution and occlusion are mainly prominent in images taken from certain unconstrained environments such as raw footage from video surveillance. In this work, a deep generative adversarial network for joint face completion and face super-resolution is proposed. It will be really useful in the current COVID-19 scenario as people wearing masks are a common sight. Given an input of a low-resolution face image with occlusion, the generator aims to recover a high-resolution face image without occlusion. The discriminator uses a set of carefully designed losses to assure the high quality of the recovered high-resolution face images without occlusion. Experimental results on CelebA database show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution and face completion, and shows good generalization ability in cross-database testing. MSSIM showed an accuracy of around 80% for test cases. The recorded values of generator adversarial loss, generator pixel loss, and discriminator loss are 0.93, 0.10, and 0.003, respectively.
EDE-NAS: An Eclectic Differential Evolution Approach to Single-Path Neural Architecture Search

Convolutional neural networks (CNNs) are a very prevalent and powerful deep learning paradigm. In recent years, many neural architecture search (NAS) methods have been developed to automate the design process of CNN architectures, significantly reducing human effort. Among various search techniques, differential evolution (DE), as a popular evolutionary computation algorithm, has advantages of fewer control variables, fast convergence and powerful optimization capability. However, existing DE-based NAS methods simply use conventional search operators, and do not consider the global and local information in the search process well, thus failing to achieve satisfactory results. In this paper, we propose an eclectic DE approach for NAS that can make good use of the search capability of DE. The architectural parameters are encoded into two parts according to their ranges. A discrete mutation operator is proposed to evolve the part that has a small search space, while a versatile mutation operator is devised for the other part with a large search space. The proposed DE algorithm can well balance the global and local search, and yields better overall results than most compared methods with a single-path CNN architecture design based on basic operations on four benchmark image classification datasets.
Mobile cloud computing using unpredictable queriable cryptography

This study looks at the safety issue of outsourcing user resources from the cloud. There is a specific combination encryption system to search for authenticated user information in the cloud. The framework facilitates parallel fused keyword searches besides matched performance, two essential considerations for realistic searchable encryption. A disorderly transformation approach is proposed to help stable indexing, preservation, and query fudge our keyword. A safe sharing list is also generated to identify the corresponding outcomes while protecting user data protection and privacy, saving user mobile device energy. Our strategy means that even the cloud vendor who is semi-trustful in our situation is privately protected for the customer. The suggested approach is intended to effectively recover the encrypted data stored remotely for mobile cloud scenarios using cryptography. Extensive tests are carried out, and test findings indicate that the proposed solution is reliable and ideal for a stable cloud server.
Autonomous driving quality assurance with data uncertainty analysis

Deep Learning (DL) based self-driving systems are vigorously developing in big companies. While, as several serious accidents were reported with life- and property-loss, the issue of robustness in DL-based self-driving systems inspires great attention, especially facing with some high-risk cases, like adversarial inputs or corner case scenarios in driving. Considering the existing methods are cumbersome in real driving, therefore this paper proposed a novel and simple way which studies data's uncertainty to rise alarm for manual checking. This method developed a metric describing corner case with respect to DL models, and subsequently evaluated data uncertainty. Experiments on a self-driving system verified the feasibility and usefulness of the proposed method. Code in this paper is released [1].
A Secure Task Matching Scheme in Crowdsourcing Based on Blockchain

As crowdsourcing continues to evolve, researchers explored task matching in crowdsourcing extensively. However, the privacy issues such as task content of publishers and ability or interest of worker in task matching are often overlooked. Also, the identity of the task publisher/worker needs to be protected. To address the above issues, we propose a secure task matching scheme in crowdsourcing based on blockchain in this paper. Firstly, we implement multi-publisher/multi-worker task matching in the scheme while protecting task content privacy. Meanwhile, we take advantage of the immutability of the blockchain to ensure the reliability of publishing/matching results. We utilize the smart contract for task publishing/matching without human intervention. Finally, the scheme is shown to be secure and feasible through theoretical and comprehensive performance evaluations.
A Novel Approach to Time Series Complexity via Reservoir Computing

When working with time series, it is often beneficial to have an idea as to how complex the signal is. Periodic, chaotic and random signals (from least to most complex) may each be approached in different ways, and knowing when a signal can be identified as belonging to one of these categories can reveal a lot about the underlying system. In the field of time series analysis, permutation entropy has emerged as one of the premier measures of time series complexity due to its ability to be calculated from data alone. We propose an alternative method for calculating complexity based on the machine learning paradigm of reservoir computing, and how the outputs of these neural networks capture similar information regarding signal complexity. We observe similar behaviour in our proposed measure to both the Lyapunov exponent and permutation entropy for well known dynamical systems. Additionally, we assess the dependence of our measure on key hyperparameters of the model, drawing conclusions about the invariance of the measure and possible implications on informing network structure.
Detect Compiler Inserted Run-time Security Checks in Binary Software

Our goal in this work is to develop a mechanism to determine the presence of targeted compiler-based or automated rules-based run-time security checks in any given binary. Our generalized approach relies on several key insights. First, instructions added by automated checks likely follow just one or only a few fixed patterns or templates at every insertion point. Second, any security check will guard some interesting or vulnerable program structure, like return addresses, indirect jumps/calls, etc., and the placement of the security check will inform about the nature of the check. By contrast, we would not expect ordinary user code to follow any single pattern at every such interesting program location. Our technique to detect automated security checks in binary code does not rely on known code signatures that can change depending on the language, the compiler, and the security check. We implement and evaluate our technique, and present our results, observations, and challenges in this work.
Split-based Feedback Network for Image Super-Resolution

Most existing image super-resolution (SR) methods has achieved superior performance. However, the contrastive learning, which commonly be used in high-level tasks, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution network based feedback mechanism to learn abstract representations to explore high information in the representation space. Specifically, we first use the hidden states and constraints in RNN to achieve feedback network. Then, a contrastive learning is used to conduct representation learning by pulling the final SR image to the high resolution image and push the final image to intermediate images. In addition, we introduce a split based feedback block (SPFB) to reduce the redundancy of models for inference acceleration, where the tolerate features with similar patterns but require less computation. Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.
MARKET BASKET ANALYSIS ON TRANSACTION DATA USING THE APRIORI ALGORITHM

This research aims to get information about the relationship between sales patterns carried out by CV. Dian Abadi Jaya workshop by using APRIORI algorithms through transaction data sets carried out by customers. The subject of research is a record of shopping cart transactions made by customers, namely vehicle parts sales transactions and vehicle repair service transactions. The data collection techniques used are interviews and documentation. The criteria used in this research are a minimum of frequent itemset of 20 transactions with support criteria of 1,7%, confidence value of 40% and lift ratio value above 1. The results of the research have produced 9 sales pattern relationships with the highest confidence of 100%. The results that have been obtained are expected to help the CV. Dian Abadi Jaya workshop in making a decision for the next sale.
Vehicle detection method based on improved YOLOv3

Most of the current vehicle detection models based on depth learning have many problems such as large parameters, high computational complexity, and most lightweight target detection models can not meet the actual work requirements in accuracy. Aiming at this problem, this paper proposes a vehicle detection method based on improved YOLOv3. In order to reduce model parameters and speed up network inference, CSPNet and Ghost modules are added to the Backbone part of YOLOv3; Expand the feature input size of the feature fusion network, add the interval fusion structure of four times of up sampling on the basis of the original two times of up sampling, and use Depth Separable Convolution instead of conventional convolution to further compress the model parameters. The experimental results show that the accuracy of the improved model is 1.5% higher than that before the improvement, the detection speed is 23.0ms faster, and the model has good robustness, which can meet the detection requirements of different scenes.