Reviewerly/exHarmony huggingface dataset:
Intel Alder Lake CPU Architectures

Alder Lake cores, system-on-a-chip, and hybrid architecture were designed to meet the challenging requirements for various traditional, emerging, and real-world computational tasks at extensive power performance points. Alder Lake introduces a revolutionary hybrid architecture with a whole new performance core and efficient core. The revolutionary thread director built into Alder Lake hardware and firmware guides the operating system in scheduling the right task to the right core type. Alder Lake hybrid architecture with Intel thread director delivers the highest power-performance dynamic range and computational density.
Defining Security Requirements With the Common Criteria: Applications, Adoptions, and Challenges

Advances of emerging Information and Communications Technology (ICT) technologies push the boundaries of what is possible and open up new markets for innovative ICT products and services. The adoption of ICT products and systems with security properties depends on consumers' confidence and markets' trust in the security functionalities and whether the assurance measures applied to these products meet the inherent security requirements. Such confidence and trust are primarily gained through the rigorous development of security requirements, validation criteria, evaluation, and certification. Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC) is an international standard (ISO/IEC 15408) for cyber security certification. In this paper, we conduct a systematic review of the CC standards and its adoptions. Adoption barriers of the CC are also investigated based on the analysis of current trends in security evaluation. Specifically, we share the experiences and lessons gained through the recent Development of Australian Cyber Criteria Assessment (DACCA) project that promotes the CC among stakeholders in ICT security products related to specification, development, evaluation, certification and approval, procurement, and deployment. Best practices on developing Protection Profiles, recommendations, and future directions for trusted cybersecurity advancement are presented.
Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment

Breast cancer is a common neoplasm among women. The cure of the disease depends on early identification and treatment of the tumor to avoid advanced stages such as metastasis of the initial lesion. Recently, studies have shown that computational methods could help specialists in this process and even provide advantages over the traditional analysis method. Thus, this work proposes a tumor cell detection method based on an ensemble of convolutional neural networks (CNN) trained with normalized images using different color adjustment techniques. Analyses of eight different color spaces and their channels, and two color normalization methods were performed to reduce the effects caused by color variation of histopathological images on the generalization of predictive models. The proposed approach evaluated six CNN architectures with color adjustment methods to define which ones improve and preserve important characteristics for the classification task. The ensemble that constitutes the proposed method comprises three models of the VGG-19 architecture trained in images generated through from the color space HSV, from the color channel RED of the RGB images, and RGB images normalized with Reinhard method, respectively. This approach was evaluated using a public image database containing 327,680 histopathological images extracted from breast tissue. The method achieved promising results, with an accuracy of 0.9193 and an AUC of 0.9772. These results demonstrate that a combination of color adjustment techniques produces better results than applying the techniques individually.
An Infrared Image Detection Method of Substation Equipment Combining Iresgroup Structure and CenterNet

The infrared image diagnostic technology of substation equipment is often interfered by its complex background, and cannot reflect the effective temperature information of the equipment, which is not conducive to the realization of subsequent fault diagnosis. In order to solve this problem, an improved CenterNet is proposed to improve the recognition effect of substation equipment under complex backgrounds. First, a richer substation infrared image dataset is established. Second, the Iresgroup structure is proposed as a backbone to further improve the feature learning ability of CenterNet. In the Iresgroup structure, an improved projection shortcut and Iresnet structure are improved to control the number of ReLUs on the main path, which can further improve the detection accuracy of the model, with higher robustness. In addition, the group conv structure is used to further improve the model, so that the model maintains the same amount of parameters while strengthening its feature learning ability. At last, The hyperparameters of centerNet have been redesigned to further improve the accuracy of the proposed method. The results show that compared with the several native backbones of the CenterNet and several mainstream detection models, this method has a significant improvement of 2% to 4% on mAP, and the improvement of single-type objects is as high as 7.4%. It can be seen that this method has achieved excellent detection results and can provide key technical support for fault detection of substation equipment.
Lightweight Hardware Implementation of Binary Ring-LWE PQC Accelerator

Significant innovation has been made in the development of public-key cryptography that is able to withstand quantum attacks, known as post-quantum cryptography (PQC). This paper focuses on the development of an efficient PQC hardware implementation. Specifically, an implementation of the binary Ring-learning-with-errors (BRLWE)-based encryption scheme, a promising lightweight PQC suitable for resource-constrained applications, is proposed. The paper first develops the mathematical formulation to present the proposed algorithmic process. The corresponding hardware accelerators are then described in detail. Finally, comparisons with previous implementations are provided to demonstrate the superior performance of the proposed design. For instance, the proposed low-complexity accelerator has 34.7% less area-delay product (ADP) than the state-of-the-art design for <inline-formula><tex-math notation="LaTeX">$n=256$</tex-math></inline-formula> in the field-programmable gate array (FPGA) platform. Apart from the efficiency of the hardware architectures, the proposed design also has a complete input/output processing setup, and thus is feasible for emerging lightweight applications.
Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery

Broad learning system (BLS) is widely used in intelligent fault diagnosis (IFD) since its high computation efficiency and incremental learning ability. However, its applicability is limited to the single-task learning scenario in which only one diagnosis task needs to be learned. Generally, a machinery system contains multiple critical components that need to be diagnosed. The fault data of different components will be collected at different times for model training. It is essentially a task-incremental learning scenario in which the diagnosis model needs to learn a series of diagnosis task for different components at different times. Existing BLS methods cannot meet this requirement due to the catastrophic forgetting issue. Therefore, this paper proposes a task-incremental broad learning system (TiBLS) for multi-component IFD. The TiBLS is developed as a multi-head configuration with a series of BLS blocks to learn different diagnosis tasks sequentially. Then, the catastrophic forgetting is prevented via parameter isolation. Finally, the structure-incremental learning ability is developed for the TiBLS to enhance the diagnosis performance of each task without retraining. In this way, the TiBLS will gain more and more functions to diagnose different components over time. The experiment validation is implemented on a simulated machinery system including three critical components. The diagnosis accuracies of the three tasks are 94.75%, 93.02%, and 92.00%, respectively. The training times of the three tasks are 22.2 s, 34.5 s, and 7.6 s, respectively. The satisfying results demonstrate that the TiBLS is an effective and efficient method for multi-component IFD.
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data

The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognized to be three key ingredients for deep learning. Most of the recent theoretical studies for deep learning focus on the necessity and advantages of depth and structures of neural networks. In this article, we aim at rigorous verification of the importance of massive data in embodying the outperformance of deep learning. In particular, we prove that the massiveness of data is necessary for realizing the spatial sparseness, and deep nets are crucial tools to make full use of massive data in such an application. All these findings present the reasons why deep learning achieves great success in the era of big data though deep nets and numerous network structures have been proposed at least 20 years ago.
A Spatiotemporal Graph Neural Network for session-based recommendation

Session-based recommendation is a challenging task dedicating to predict the user’s next item of interest based on a given sequence of anonymous behaviors. Most existing methods aim to model items’ temporal and spatial relationships, including recurrent neural network-based and graph neural network-based approaches. However, they have stagnated, expressed inadequacy, and rarely considered user behavior patterns hidden behind items that were common in the click process. In this paper, we propose a S patiotemporal G raph N eural N etwork (SGNN) model for the session-based recommendation, which can effectively simulate the users’ behavior patterns from a spatiotemporal perspective to determine the next click. The proposed SGNN model mainly consists of two modules: a S patiotemporal S ession G raph (SSG) module and a P reference-aware A ttentio N (PAN) module. In the SSG module, to simulate the possible behavior patterns of users, we model all session sequences as directed and weighted sequences. Then, these session sequences are integrated into a spatial graph, followed by an auto-regressive moving average convolution filter, thus availably assessing the time-decay of user preference. In the PAN module, considering that each item is not equally important to the user’s next click on the item, first of all, we divide the session into global behavioral interest and current interest, where the global behavioral interests fully capture the overall preferences in the session. Furthermore, we use the last clicked item in the session as the current interest to enhance the prediction of the next click behavior. Extensive experiments on two public datasets show that the proposed SGNN model consistently outperforms the comparative methods interns of accuracy and performance. • Simulate users’ behavior patterns in the session without destroying the click order • Highlight the critical preferences of users during the simulate process. • Use gated fusion functions to combine global behavioral interest and current interest. • Auto-regressive moving average convolution filter evaluates the user preferences. • Use graph neural networks to obtain richer potential information.
MMatch: Semi-Supervised Discriminative Representation Learning for Multi-View Classification

Semi-supervised multi-view learning has been an important research topic due to its capability to exploit complementary information from unlabeled multi-view data. This work proposes MMatch, a new semi-supervised discriminative representation learning method for multi-view classification. Unlike existing multi-view representation learning methods that seldom consider the negative impact caused by particular views with unclear classification structures (weak discriminative views). MMatch jointly learns view-specific representations and class probabilities of training data. The representations concatenated to integrate multiple views’ information to form a global representation. Moreover, MMatch performs the smoothness constraint on the class probabilities of the global representation to improve pseudo labels, whereas the pseudo labels regularize the structure of view-specific representations. A discriminative global representation is mined with the training process, and the negative impact of weak discriminative views is overcome. Besides, MMatch learns consistent classification while preserving diverse information from multiple views. Experiments on several multi-view datasets demonstrate the effectiveness of MMatch.
An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.
Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Leveraging on-site renewable sources like solar and wind provides ample opportunities on developing environmental friendly and energy-efficient data centers. Due to intermittent nature of renewable energy and fluctuating grid energy price, advanced data centers often deploy distributed UPS systems with high efficiency, scalability, and reliability. In this study, we investigate the problem of energy management for data centers with renewable resources and energy storages. We propose an interface-backbone layer framework designed unified energy efficiency management system called REDUX, which properly allocate fluctuating renewable energy, UPS battery energy storage or grid power with dynamic price aiming to minimize the long-term electricity bill for the data center. With prediction of renewable energy supply, categorization of grid power price level and energy storage in the UPS devices, REDUX orchestrates workload distribution with heuristic algorithms which act as renewable energy smoothing, UPS device control, and high level control strategies, and make back-fills or defer decisions for the non-urgent jobs. Compared with the existing strategies, REDUX provides a comprehensive view for the energy efficiency data center and demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.
Conceptual Compression via Deep Structure and Texture Synthesis

Existing compression methods typically focus on the removal of signal-level redundancies, while the potential and versatility of decomposing visual data into compact conceptual components still lack further study. To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks. In particular, we propose to compress images by a dual-layered model consisting of two complementary visual features: 1) structure layer represented by structural maps and 2) texture layer characterized by low-dimensional deep representations. At the encoder side, the structural maps and texture representations are individually extracted and compressed, generating the compact, interpretable, inter-operable bitstreams. During the decoding stage, a hierarchical fusion GAN (HF-GAN) is proposed to learn the synthesis paradigm where the textures are rendered into the decoded structural maps, leading to high-quality reconstruction with remarkable visual realism. Extensive experiments on diverse images have demonstrated the superiority of our framework with lower bitrates, higher reconstruction quality, and increased versatility towards visual analysis and content manipulation tasks.
A transfer learning-based particle swarm optimization algorithm for travelling salesman problem

Abstract To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search for optimal solutions from zero initial information without taking advantage of the historical information of solving similar problems. This paper studies a transfer learning-based particle swarm optimization (PSO) algorithm, where the optimal information of historical problems is used to guide the swarm to find optimal paths quickly. To begin with, all cities in the new and historical TSP problems are clustered into multiple city subsets, respectively, and a city topology matching strategy based on geometric similarity is proposed to match each new city subset to a historical city subset. Then, on the basis of the above-matched results, a hierarchical generation strategy of the feasible path (HGT) is proposed to initialize the swarm to improve the performance of PSO. Moreover, a problem-specific update strategy, i.e. the particle update strategy with adaptive crossover and clustering-guided mutation, is introduced to enhance the search capability of the proposed algorithm. Finally, the proposed algorithm is applied to 20 typical TSP problems and compared with 12 state-of-the-art algorithms. Experimental results show that the transfer learning mechanism can accelerate the search efficiency of PSO and make the proposed algorithm achieve better optimal paths.
Soft Exemplar Highlighting for Cross-View Image-Based Geo-Localization

The goal of ground-to-aerial image geo-localization is to determine the location of a ground query image by matching it against a reference database consisting of aerial/satellite images. This task is highly challenging due to the large appearance difference caused by extreme changes in viewpoint and orientation. In this work, we show that the training difficulty is an important cue that can be leveraged to improve metric learning on cross-view images. More specifically, we propose a new Soft Exemplar Highlighting (SEH) loss to achieve online soft selection of exemplars. Adaptive weights are generated for exemplars by measuring their associated training difficulty using distance rectified logistic regression. These weights are then constrained to remove simple exemplars from training and truncate the large weights of extremely hard exemplars to escape from the trap with a local optimal solution. We further use the proposed SEH loss to train two mainstream convolutional neural networks for ground-to-aerial image-based geo-localization. Experimental results on two benchmark cross-view image datasets demonstrate that the proposed method achieves significant improvements in feature discriminativeness and outperforms the state-of-the-art image-based geo-localization methods.
CyBERT: Contextualized Embeddings for the Cybersecurity Domain

We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is providing the security community with an initial fine-tuned BERT model that can perform a variety of cybersecurity-specific downstream tasks with high accuracy and efficient use of resources. We create a cybersecurity corpus from open-source unstructured and semi-unstructured Cyber Threat Intelligence (CTI) data and use it to fine-tune a base BERT model with Masked Language Modeling (MLM) to recognize specialized cybersecurity entities. We evaluate the model using various downstream tasks that can benefit modern Security Operations Centers (SOCs). The fine-tuned CyBERT model outperforms the base BERT model in the domain-specific MLM evaluation. We also provide use-cases of CyBERT application in cybersecurity based downstream tasks.
Deep Siamese Network With Motion Fitting for Object Tracking in Satellite Videos

With the advancement in remote sensing satellite technology, object tracking in satellite videos has become an emerging research field. However, due to small object size, little appearance features, and poor distinguishability between targets and the background, traditional trackers with handcraft visual features achieve poor results in satellite videos. Deep neural networks have shown powerful potential for object tracking in ordinary videos but remain developing in satellite videos. In this letter, a Siamese network and a motion regression network are adopted to form a two-stream deep neural network (SRN) for satellite object tracking, which simultaneously utilizes appearance and motion features. Besides, a trajectory fitting motion (TFM) model based on history trajectories is also employed to further alleviate model drift. Comprehensive experiments demonstrate that the proposed method performs favorably compared with the state-of-the-art tracking methods.
Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields

How to reduce radiation dose while preserving the image quality as when using standard dose is an important topic in the computed tomography (CT) imaging domain because the quality of low-dose CT (LDCT) images is often strongly affected by noise and artifacts. Recently, there has been considerable interest in using deep learning as a post-processing step to improve the quality of reconstructed LDCT images. This paper provides, first, an overview of learning-based LDCT image denoising methods from patch-based early learning methods to state-of-the-art CNN-based ones and, then, a novel CNN-based method is presented. In the proposed method, preprocessing and post-processing techniques are integrated into a dilated convolutional neural network to extend receptive fields. Hence, large distance pixels in input images will participate in enriching feature maps of the learned model, leading to effective denoising. Experimental results showed that the proposed method is light, while its denoising effectiveness is competitive with well-known CNN-based models.