Reviewerly/exHarmony huggingface dataset:
On Trustworthy Scalable Hardware/Software Platform Design

The continuously growing importance of today’s technology paradigms such as the Internet of Things (IoT) and the new 5G/6G standard open up unique features and opportunities for smart systems and communication devices. Famous examples are edge computing and network slicing. Generational technology upgrades provide unprecedented data rates and processing power. At the same time, these new platforms must address the growing security and privacy requirements of future smart systems. This poses two main challenges concerning the digital processing hardware. First, we need to provide integrated trustworthiness covering hardware, runtime, and the operating system. Whereas integrated means that the hardware must be the basis to support secure runtime and operating system needs under very strict latency constraints. Second, applications of smart systems cover a wide range of requirements where "one- chip-fits-all" cannot be the cost and energy effective way forward. Therefore, we need to be able to provide a scalable hardware solution to cover differing needs in terms of processing resource requirements.In this paper, we discuss our research on an integrated design of a secure and scalable hardware platform including a runtime and an operating system. The architecture is built out of composable and preferably simple components that are isolated by default. This allows for the integration of third-party hardware/software without compromising the trusted computing base. The platform approach improves system security and provides a viable basis for trustworthy communication devices.
A QoE-Aware Nonlinear Fuzzy Radio Resource Management Approach for Revenue Enhancement

In this article, the problem of radio resource management is addressed by adjusting the tradeoff between two key quality of experience (QoE) influencing factors, namely transmission rate, and service price. To this end, a piecewise utility function is employed to assess the user QoE in three different quality classes. It comprises the combination of two utility functions corresponding to the transmission rate and the service price. A low-complexity fuzzy-based approach is then introduced to reallocate additional resources to dissatisfied users to increase the number of satisfied users. The simulation results indicate the efficiency and applicability of the novel approach in terms of increasing the service provider revenue while providing approximately the same overall QoE and power consumption. The results also show proposed approach achieves an improvement of at least 15% in total service provider revenue on average compared to other methods.
Efficient Model Selection in Switching Linear Dynamic Systems by Graph Clustering

The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes. In this paper, a computationally tractable graph representation is proposed for a switching linear dynamic system (SLDS) along with the solution of a minimum-sum optimization problem for clustering to reduce the switching mode cardinality offline, before collecting measurements. It is shown that upon perfect mode detection, the induced error caused by mode clustering can be quantified exactly in terms of the dissimilarity measures in the proposed graph structure. Numerical results verify that clustering based on the proposed framework effectively reduces model complexity given uncertain mode detection and that the induced error can be well approximated if the underlying assumptions are satisfied.
Cruel Parallel: Towards the Designing of a Dynamic Game Adaptation for VR-Based Survival Horror Games

This study explored the body motion trackers of a virtual reality device in dynamically adjusting game elements for survival horror games. The proposed approach utilizes the hand and head motions to quantify the player’s uncanny feelings for updating scary effects, including the game ambiance and the sequence of uncanny events, in a survival horror game. The user study results show that the proposed dynamic game adaptation approach had the intended impact on the players. In particular, the players moderately experienced the correlation between their scary feelings and the change in the sound effect and the monster’s behavior. The heartbeat sound got louder, and the monster became more aggressive as the players got more scared. Although some of the issues of this preliminary study remain to be investigated further, it opens a new direction that utilizes body motion measurements for enhancing the user experience for the survival horror game genre.
Optimization of Binary Tournament and Reciprocal Exchange in the Genetic Algorithm for Flexible Scheduling Problem

Scheduling is one of the most challenging computing Scheduling is a reasonably tricky problem if it has to be prosecuted in a short time. It will be even more inconvenient if the scheduled arrangement is uncertain, with many choices requiring more complicated decisions. This problem often occurs among teachers at Malang's MTS Nurul Huda school. The teaching scheduling model is one example of scheduling problems frequently encountered in recent years at MTS Nurul Huda Malang. The solution is complex, and the best solution can only be obtained by trying all the possibilities. This study uses a genetic algorithm that can provide solutions to complex problems in a rationally adequate time to apply to flexible problems in teaching. This algorithm uses binary tournament optimization in search and reciprocal exchange in mutations. The results of this optimization test get a crossover rate of 0.4 and a mutation rate of 0.4, so this optimization is the most effective and efficient in determining flexible scheduling for teachers. The result of the testing of applications is that the average accuracy n is 73.33%.
Hierarchical Associative Encoding and Decoding for Bottom-Up Human Pose Estimation

Bottom-up human pose estimation decouples computational complexity from the number of people but requires additional operations to match the detected keypoints to each human instance. Existing approaches treat all keypoints equally while ignoring the relationships among keypoints, which in turn limit the performance ceilings. In this work, we propose a hierarchical associative encoding and decoding framework for bottom-up human pose estimation by introducing additional prior knowledge. Specifically, in addition to keypoint-level and instance-level associations, we further divide keypoints into groups and explore group-level associations. This way, prior knowledge is incorporated to determine the keypoint groups for better associative encoding. To deal with complex poses, we introduce a focal pulling loss to focus more on the hard-to-associate keypoints. Moreover, instead of using a pre-defined order for keypoint grouping, we propose a progressive associative decoding method to dynamically determine the order of keypoints for grouping, which helps reduce isolated keypoints. Experimental results on the MS-COCO, CrowdPose and MPII datasets show superior performance of our proposed associative encoding and decoding algorithms. More importantly, we prove, through validation, that hierarchical associative encoding and decoding can be used as a plug-n-play module for performance improvement regardless of backbone architecture. Our source code and pretrained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ducongju/HAE</uri> .
Privacy Preserving Federated Learning Using CKKS Homomorphic Encryption

With the rapid development of distributed machine learning and Internet of things, tons of distributed data created by devices are used for model training and what comes along is the concern of security and privacy. Traditional method of distributed machine learning asks devices to upload their raw data to a server, which may cause the privacy leakage. Federated learning mitigates this problem by sharing each devices’ model parameters only. However, it still has the risk of privacy leakage due to the weak security of model parameters. In this paper, we propose a scheme called privacy enhanced federated averaging (PE-FedAvg) to enhance the security of model parameters. By the way, our scheme achieves the same training effect as Fedavg do at the cost of extra but acceptable time and has better performances on communication and computation cost compared with Paillier based federated averaging. The scheme uses the CKKS homomorphic encryption to encrypt the model parameters, provided by detailed scheme design and security analysis. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted in two real-life datasets, and shows the advantages on aspects of communication and computation. Finally, we discuss the feasibility of deployment on IoT devices.
Bug tracking process smells in practice

Software teams use bug tracking (BT) tools to report and manage bugs. Each record in a bug tracking system (BTS) is a reporting entity consisting of several information fields. The contents of the reports are similar across different tracking tools, though not the same. The variation in the workflow between teams prevents defining an ideal process of running BTS. Nevertheless, there are best practices reported both in white and gray literature. Developer teams may not adopt the best practices in their BT process. This study investigates the non-compliance of developers with best practices, so-called smells, in the BT process. We mine bug reports of four projects in the BTS of JetBrains, a software company, to observe the prevalence of BT smells in an industrial setting. Also, we survey developers to see (1) if they recognize the smells, (2) their perception of the severity of the smells, and (3) the potential benefits of a BT process smell detection tool. We found that (1) smells occur, and their detection requires a solid understanding of the BT practices of the projects, (2) smell severity perception varies across smell types, and (3) developers considered that a smell detection tool would be useful for six out of the 12 smell categories.
OSSGAN: Open-Set Semi-Supervised Image Generation

We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set. Unlike the existing semi-supervised image generation task, where unlabeled data only contain closed-set samples, our task is more general and lowers the data collection cost in practice by allowing open-set samples to appear. Thanks to entropy regularization, the classifier that is trained on labeled data is able to quantify sample-wise importance to the training of cGAN as confidence, allowing us to use all samples in un-labeled data. We design OSSGAN, which provides decision clues to the discriminator on the basis of whether an unlabeled image belongs to one or none of the classes of interest, smoothly integrating labeled and unlabeled data during training. The results of experiments on Tiny ImageNet and ImageNet show notable improvements over supervised Big-GAN and semi-supervised methods. Our code is available at https://github.com/raven38/OSSGAN.
YFormer: A New Transformer Architecture for Video-Query Based Video Moment Retrieval

Video-query based video moment retrieval (VQ-VMR) aims to localize the segment in a long reference video that semantically corresponds to a short query video. This task faces the problem of matching long and short videos feature, which requires extracting dependencies of long-term sequences. To address this problem, we developed a new transformer architecture, termed YFormer, for VQ-VMR task. Specific to this work, a Spatio-temporal Feature Extractor based on self-attention is proposed to build fine-grained semantic embedding for each frame, and a Semantic Relevance Matcher based on cross-attention is proposed to extract cross-correlation between the query and reference videos. The token-based prediction head and pool-based head are developed to localize the start and end boundaries of the result. These prediction heads for video moment localization facilitate a complete end-to-end retrieval process. We reorganize the videos in ActivityNet dataset to build a video moment retrieval dataset and conduct extensive experiments on this benchmark. Our model achieves favorable performance compared to those state-of-the-art methods.
Experimental Characterization of Google’s Sycamore Quantum AI on an IBM’s Quantum Computer

The development of high-fidelity programmable processors, that can execute quantum algorithms in an exponentially large computational space, is a fundamental challenge. Towards achieving a quantum supremacy regime in [Nature 574, 505–510 (2019)]; A programmable superconducting transmon-qubits processor –known as Google’s Sycamore quantum AI– is introduced to create quan- tum states on 53 qubits, synonymous with a computational state-space of dimension 2 53 ≈ 9 × 10 15 . In this article, we use a different state-of-the-art quantum computing artificial architecture to com- pare error measurements and operation times in Google’s Sycamore quantum processor. We give a complete review of Google’s Sycamore quantum AI, with an evaluation of its performance and capabilities. We also provide a description of the IBM’s Superconducting quantum computer that employed in our experimental realizations. We then present an experimental characterization of the universal gate set used in Google’s Sycamore quantum processer, based on an IBM quantum computer. Furthermore, for characterization and verification of quantum states, quantum process tomography (QPT) experiments are constructed and executed in three different environments (noisy, noiseless and on a real quantum machine), using both simulators and the IBM Quantum Experience real quantum processors. Comparable fidelities from both theoretical expectations and experimental setups are introduced to explain implementation’s accuracy and measurements error.
BoosterNet: Improving Domain Generalization of Deep Neural Nets using Culpability-Ranked Features

Deep learning (DL) models trained to minimize empirical risk on a single domain often fail to generalize when applied to other domains. Model failures due to poor generalizability are quite common in practice and may prove quite perilous in mission-critical applications, e.g., diagnostic imaging where real-world data often exhibits pronounced variability. Such limitations have led to increased interest in domain generalization (DG) approaches that improve the ability of models learned from a single or multiple source domains to generalize to out-of-distribution (OOD) test domains. In this work, we propose BoosterNet, a lean add-on network that can be simply appended to any arbitrary core network to improve its generalization capability without requiring any changes in its architecture or training procedure. Specifically, using a novel measure of feature culpability, BoosterNet is trained episodically on the most and least culpable data features extracted from critical units in the core network based on their contribution towards class-specific prediction errors, which have shown to improve generalization. At inference time, corresponding test image features are extracted from the closest class-specific units, determined by smart gating via a Siamese network, and fed to BoosterNet for improved generalization. We evaluate the performance of BoosterNet within two very different classification problems, digits and skin lesions, and demonstrate a marked improvement in model generalization to OOD test domains compared to SOTA.
Information Security Culture Concept towards Information Security Compliance: A Comparison between IT and Non-IT Professionals

This paper examines the factors determining a positive Information Security Culture (ISC) concept and the influence of ISC towards ISP compliance intention (INT) between IT and non-IT professionals in Malaysian public universities. Partial least square structural equation modelling, using PLS MGA, is used to assess the measurement and structural models, and to compare the results between the two groups. Results indicate all factors have significant contribution towards ISC in both groups, with two out of seven ISC factors have significant differences. This study has revealed that although both groups have the same ISC factors, IT and non-IT professionals have significant difference in terms of believe that Top Management Commitment and Information Security Knowledge are required for implementing apositive ISC. In addition, there is a significant difference between these two groups in terms of the influence of ISC towards ISP compliance intention. ISC has less influence towards INT for Non-IT professionals compared to IT professionals within the same ISC. These empirical findings would benefit in formulating better security strategies by providing appropriate efforts for different groups of employees in the organizations. This study also provides a total cyber security solution for improving information security culture and employees’ compliance towards Information Security Policy.
Privacy Preserving Outsourced k Nearest Neighbors Classification: Comprehensive Study

Cloud computing is now an integral infrastructure for individuals and companies to store data and provide services that leverage this data. Thus, it is a routine for data owners to outsource their data and data operations to a public cloud that serves certain mining services. In order to preserve privacy, the valuable outsourced data has to be encrypted before outsourcing to the public cloud. Because of this, it is now required that classification is performed using the encrypted sensitive data in an un-trusted outsourced environment, like cloud, and at the same time maintain its privacy. Since the k nearest neighbor (kNN) classification techniques are considered to be a basic module for many common data mining tasks, establishing an efficient and privacy preserving kNN approach will form a concrete platform for other fundamental data analysis operations. Due to its significance in different mining applications, many solutions have been proposed to solve the outsourced kNN classification over encrypted data problem. In this paper, we present a comprehensive study of such solutions. As per our study, most of these solutions either struggle to completely achieve the privacy requirements of outsourced classification or they induce heavy computational cost which makes them impractical for use in real-world applications. In this context, we compare the existing state-of-the-art solutions in terms of performance with standard and scaled datasets. It is observed that most of the efficient solutions for the outsourced kNN classification problem collapse as the dataset is scaled both attribute-wise and size-wise and their performance reduces significantly. We have attempted to identify such significant issues in this research area and address them through our future subsequent research work.
Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning

Exemplar-free incremental learning is extremely challenging due to inaccessibility of data from old tasks. In this paper, we attempt to exploit the knowledge encoded in a previously trained classification model to handle the catas-trophic forgetting problem in continual learning. Specifically, we introduce a so-called knowledge delegator, which is capable of transferring knowledge from the trained model to a randomly re-initialized new model by generating informative samples. Given the previous model only, the delegator is effectively learned using a self-distillation mechanism in a data-free manner. The knowledge extracted by the delegator is then utilized to maintain the performance of the model on old tasks in incremental learning. This simple incremental learning framework surpasses existing exemplar-free methods by a large margin on four widely used class incremental benchmarks, namely CIFAR-100, ImageNet-Subset, Caltech-101 and Flowers-102. Notably, we achieve comparable performance to some exemplar-based methods without accessing any exemplars.
A Privacy-Preserving Comparison Protocol

The <i>multiparty comparison</i> allows to compare two integers <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$y$</tex-math></inline-formula> blindly, where a set of players hold the shares of the elements <inline-formula><tex-math notation="LaTeX">$x and y, x, y \in \mathbb {F}_{p}$</tex-math></inline-formula> , a prime field. The existing <i>multiparty comparison</i> protocols execute in constant rounds, but the number of multiplications depends on the size of the prime <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> , i.e., the communication complexity will be high for large prime <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> . In this paper, we present a <i>multiparty comparison</i> protocol with constant rounds in which the number of multiplications depends on the number of players rather than the prime <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> itself. This <i>multiparty comparison</i> protocol is further extended to design a <i>multiparty equality-test</i> protocol. An equality-test protocol computes the equality of shares in constant rounds and its number of multiplications depends on the number of players. Our proposed protocols <i>multiparty comparison</i> and equality-test are unconditionally secure against the active and passive attacks and have <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> communication complexity, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of players. We also present an efficient technique for <i>fault detection</i> that can verify the correctness of various protocols.
ACE: A Coarse-to-Fine Learning Framework for Reliable Representation Learning Against Label Noise

The prosperity of deep neural networks in various computer vision applications heavily depends on large-scale high-quality annotated datasets. However, inaccurate annotations are often inevitable when building hand-labeled datasets, which can lead to imperfectly supervision problems. Most existing methods to address these problems mainly use noisy labels to learn latent representations, which may be corrupted when the noise rate is high, thus limiting the predictions for clean test data set. In this paper, we propose a flexible coarse-to-fine representation learning framework to improve the quality of latent representations by exploiting the reliable geometry information from raw features. This framework includes: (i) an anchored representation learner, which is a coarse mechanism for learning reasonable feature-similarity to avoid feature corruption, (ii) a confident representation learner, which is used to learn confident data pairs, and (iii) an exploratory representation learner, which is proposed for learning from feasible unconfident pairs and performing label refurbishment. Comprehensive experiments have demonstrated the effectiveness of our proposed method, especially when the noise level is high and the task is relatively difficult.