Reviewerly/exHarmony huggingface dataset:
Military Chain: Construction of Domain Knowledge Graph of Kill Chain Based on Natural Language Model

With the advent of the Big Data era, the specialized data in the kill chain domain has increased dramatically, and the engine-based method of retrieving information can hardly meet the users' need for more accurate answers. The kill chain domain includes four components: control equipment, sensor equipment, strike equipment (weapon and platform), and evaluator equipment, as well as related data which contain a large amount of valuable information such as the parameter information contained in each component. If these fragmented and confusing data are integrated and effective query methods are established, they can help professionals complete the military kill chain knowledge system. The knowledge system constructed in this paper is based on the Neo4j graph database and the US Command simulation system to establish a target-oriented knowledge map of kill chain, aiming to provide data support for the Q&amp;A system. Secondly, in order to facilitate the query, this paper establishes entity and relationship/attribute mining based on the continuous bag-of-words (CBOW) encoding model, bidirectional long short-term memory–conditional random field (BiLSTM-CRF) named entity model, and bidirectional gated recurrent neural network (BiGRU) intent recognition model for Chinese kill chain question and answer; returns the corresponding entity or attribute values in combination with the knowledge graph triad form; and finally constructs the answer return. The constructed knowledge map of the kill chain contains 2767 items (including sea, land, and air), and the number of parameters involved is 30124. The number of model parameters of the deep learning network is 27.9 M for the Q&amp;A system built this time, and the accuracy rate is 85.5% after 200 simulated queries.
Digital Evolution: blockchain field research

Blockchain is a relatively new technology supported by a decentralized database that received special attention at the research level in the last years due to its fundamental characteristics. Numerous researchers have applied blockchain studies in many fields, but the scope is very large and there is no delimitation of new and emergent trends. The method used to better understand the evolution and impact of blockchain technology was a bibliometric analysis. A search was conducted at digital Elsevier’s database with a single keyword blockchain, and 23383 articles were collected. The study uses cluster analysis method, allowing to construct and visualize bibliometric networks and to build co-occurrences networks. The results indicate that there are positive correlations between countries, that China and the United States are part of the most influential cluster. Indicates also emerging developments in the areas of governance, industry, decision-making processes, management, internet of things, information security, and a new hot topic, energy.
On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR

The technology of 3D recognition is evolving rapidly, enabling unprecedented growth of applications towards human-centric intelligent environments. On top of these applications human segmentation is a key technology towards analyzing and understanding human mobility in those environments. However, existing segmentation techniques rely on deep learning models, which are computationally intensive and data-hungry solutions. This hinders their practical deployment on edge devices in realistic environments. In this paper, we introduce a novel micro-size LiDAR device for understanding human mobility in the surrounding environment. The device is supplied with an on-device lightweight human segmentation technique for the captured 3D point cloud data using density-based clustering. The proposed technique significantly reduces the computational complexity of the clustering algorithm by leveraging the Spatiotemporal relation between consecutive frames. We implemented and evaluated the proposed technique in a real-world environment. The results show that the proposed technique obtains a human segmentation accuracy of 99% with a drastic reduction of the processing time by 66%.
Neural Style Transfer: Reliving art through Artificial Intelligence

Style transfer is an optimizing technique that aims to blend style of input image to content image. Deep neural networks have previously surpassed humans in tasks such as object identification and detection. Deep neural networks, on the contrary, had been lagging behind in generating higher quality creative products until lately. This article introduces deep-learning techniques, which are vital in accomplishing human characteristics and open up a new world of prospects. The system employs a pre-trained CNN so that the styles of the provided image is transferred to the content image to generate high quality stylized image. The designed systems effectiveness is evaluated based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM), it is noticed that the designed method effectively maintains the structural and textural information of the cover image.
Excavating the Potential of Graph Workload on RDMA-based Far Memory Architecture

Disaggregated architecture brings new opportunities to memory -consuming applications like graph processing. It allows one to outspread memory access pressure from local to far memory, providing an attractive alternative to disk-based processing. Although existing works on general-purpose far mem-ory platforms show great potentials for application expansion, it is unclear how graph processing applications could benefit from disaggregated architecture, and how different optimization methods influence the overall performance. In this paper, we take the first step to analyze the impact of graph processing workload on disaggregated architecture by extending the GridGraph framework on top of the RDMA-based far memory system. We design Fargraph, a far memory coordi-nation strategy for enhancing graph processing workload. Specif-ically, Fargraph reduces the overall data movement through a well-crafted, graph-aware data segment offloading mechanism. In addition, we use optimal data segment splitting and asynchronous data buffering to achieve graph iteration-friendly far memory access. We show that Fargraph achieves near-oracle performance for typical in-local-memory graph processing systems. Fargraph shows up to 8.3 x speedup compared to Fastswap, the state-of-the-art, general-purpose far memory platform.
QoS-awareness of Microservices with Excessive Loads via Inter-Datacenter Scheduling

User-facing applications often experience excessive loads and are shifting towards microservice software architecture. While the local datacenter may not have enough resources to host the excessive loads, a reasonable solution is moving some microservices of the applications to remote datacenters. However, it is nontrivial to identify the appropriate migration decision, as the microservices show different characteristics, and the local datacenter also shows different resource contention situations. We therefore propose ELIS, an inter-datacenter scheduling system that ensures the required Quality-of-Service (QoS) of the microservice application with excessive loads, while minimizing the resource usage of the remote datacenter. ELIS comprises a resource manager and a reward-based microservice migrator. The resource manager finds the near-optimal resource configurations for different microservices to minimize resource usage while ensuring QoS. The microservice migrator migrates some microservices to remote datacenters when local resources cannot afford the excessive loads. Our experimental results show that ELIS ensures the required QoS of user-facing applications at excessive loads. Meanwhile, it reduces overall/remote resource usage by 13.1% and 58.1% on average, respectively.
Social Cybersecurity and Human Behavior

National security in the 21st Century require investments in social cybersecurity that involves basic research into the human interaction between technology and social behavior and beliefs. National security dictates increasing capital spending into appropriate tools for identifying and neutralizing external manipulation of open and free societies. Supplementary policy changes that reflect the technical complexity of the modern information environment while remaining true to national values are also needed. This paper uses an applied and case study research approach to explore the applications of emerging approaches.
Successive Approximation Coding for Distributed Matrix Multiplication

Coded distributed computing was recently introduced to mitigate the effect of stragglers on distributed computing systems. This paper combines ideas of approximate and coded computing to further accelerate computation. We propose successive approximation coding (SAC) techniques that realize a tradeoff between accuracy and speed, allowing the distributed computing system to produce approximations that increase in accuracy over time. If a sufficient number of compute nodes finish their tasks, SAC exactly recovers the desired computation. We theoretically provide design guidelines for our SAC techniques, and numerically show that SAC achieves a better accuracy-speed tradeoff in comparison with previous methods.
Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules

Machine learning plays an indispensable role in modern malware detection; it provides malware researchers with quick and reliable results. On the other hand, the results can be hard to understand as to why a model classified a given file as malicious or benign. This paper focuses on the interpretability of machine learning models' results using decision lists generated by two rule-based classifiers, I-REP and RIPPER. We use the EMBER dataset, which contains features extracted through static analysis from Portable Executable files, to train various machine learning models. We extract decision lists from the machine learning models' results using our implementation of I-REP and RIPPER. By taking into account accuracies, true positive and false positive rates of the decision lists, we reason whether the generated decision lists make a good representation of the results. To comprehend the interpretability of the machine learning models, we define Human Most Understandable Model and Interpretability Entropy. This allows us to measure and compare the interpretability among the models. The most interpretable machine learning model by RIPPER was Gaussian Naïve Bayes. Results show that RIPPER is relatively successful at interpreting other machine learning models; however, it needs some improvements to increase true positive rate.
Pedestrian Detection by Exemplar-Guided Contrastive Learning

Typical methods for pedestrian detection focus on either tackling mutual occlusions between crowded pedestrians, or dealing with the various scales of pedestrians. Detecting pedestrians with substantial appearance diversities such as different pedestrian silhouettes, different viewpoints or different dressing, remains a crucial challenge. Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized. To facilitate the efficiency and effectiveness of contrastive learning, we construct an exemplar dictionary with representative pedestrian appearances as prior knowledge to construct effective contrastive training pairs and thus guide contrastive learning. Besides, the constructed exemplar dictionary is further leveraged to evaluate the quality of pedestrian proposals during inference by measuring the semantic distance between the proposal and the exemplar dictionary. Extensive experiments on both daytime and nighttime pedestrian detection validate the effectiveness of the proposed method.
Multiple Attention Mechanism Graph Convolution HAR Model Based on Coordination Theory

Human action recognition (HAR) is the foundation of human behavior comprehension. It is of great significance and can be used in many real-world applications. From the point of view of human kinematics, the coordination of limbs is an important intrinsic factor of motion and contains a great deal of information. In addition, for different movements, the HAR algorithm provides important, multifaceted attention to each joint. Based on the above analysis, this paper proposes a HAR algorithm, which adopts two attention modules that work together to extract the coordination characteristics in the process of motion, and strengthens the attention of the model to the more important joints in the process of moving. Experimental data shows these two modules can improve the recognition accuracy of the model on the public HAR dataset (NTU-RGB + D, Kinetics-Skeleton).
Siamese visual tracking combining granular level multi-scale features and global information

Despite the great success achieved in visual tracking, it is still hard for most trackers to address scenes with targets subject to large-scale changes and similar objects. The capacity of existing methods is first insufficient to efficiently extract multi-scale features. Then, convolutional neural networks focus primarily on local characteristics while easily ignoring global characteristics, which is essential for visual tracking. Furthermore, the recently popular tracking methods based on Siamese-like networks can perform the image matching of two branches through simple cross-correlation operations, and cannot effectively establish their connection. An improved Siamese tracking network, called GSiamMS, is proposed to address these challenges via the integration of Res2Net blocks and transformer modules. Within this network, a feature extraction module based on Res2Net blocks is constructed to obtain multi-scale information from the granular level without relying on multi-layer outputs. Then, the cross-attention mechanism is utilized to learn the connection between template features and search features while the self-attention mechanism focusing on the global information establishes long-range dependencies between the object and the background. Finally, numerous experiments on visual tracking benchmarks including TrackingNet, GOT-10k, LaSOT, NFS, UAV123, and TNL2K are implemented to verify that the developed method running at 38fps achieves the superior performance compared with several state-of-the-art methods.
PRA-TPE: Perfectly Recoverable Approximate Thumbnail-Preserving Image Encryption

With the popularity of cloud servers, an increasing number of people are willing to store their images in the cloud due to many conveniences such as online browsing and managing images. On the other hand, this inevitably causes users' concerns about image privacy leakage. Many image encryption schemes are proposed to prevent privacy leakage, while most of them focus only on privacy protection and ignore the usability of encrypted images. For this purpose, Marohn et al. (2017) designed two approximate thumbnail-preserving encryption (TPE) schemes to balance image privacy and usability. However, the decrypted image in these two schemes are only perceptually close to the original one and the original image cannot be perfectly recovered. To this end, we design a perfectly recoverable approximate TPE scheme in this paper, which combines reversibledata hiding (RDH) with encryption schemes. The thumbnails of the original and processed images are similar to balance image privacy and usability well. Meanwhile, the reversibility of RDH and encryption schemes is utilized to ensure the perfect recoverability in the proposed scheme. Experiments show that the proposed approximate TPE scheme is no longer limited to balancing usability and privacy but attains perfect recovery.
Entanglement diagnostics for efficient VQA optimization

We consider information spreading measures in randomly initialized variational quantum circuits and introduce entanglement diagnostics for efficient variational quantum/classical computations. We establish a robust connection between entanglement measures and optimization accuracy by solving two eigensolver problems for Ising Hamiltonians with nearest-neighbor and long-range spin interactions. As the circuit depth affects the average entanglement of random circuit states, the entanglement diagnostics can identify a high-performing depth range for optimization tasks encoded in local Hamiltonians. We argue, based on an eigensolver problem for the Sachdev-Ye-Kitaev model, that entanglement alone is insufficient as a diagnostic to the approximation of volume-law entangled target states and that a large number of circuit parameters is needed for such an optimization task.
Gray-code fringe order jump error self-correction based on shifted phase encoding for phase measuring profilometry

Temporal phase unwrapping based on Gray-code method (TPU-GC) has prospective application in three-dimensional (3D) shape measurement due to its high-speed measurement and high-efficiency code. Currently, it is still an essential task to remove jump error that may be caused by edge ambiguity of Gray-code pattern or uneven reflectivity of the measured object. So, a Gray-code fringe order jump error self-correction method based on shifted phase encoding (FOSC-SP) is proposed. By changing the calculating sequences of N-step phase-shifting patterns, a series of wrapped phases with a shifted phase of 2π/N can be obtained to identify the 2π discontinuities uniquely. The fringe order error caused by the misalignment between the edges of fringe order and the 2π discontinuities can be located and then corrected accurately. Subsequently, the fringe order caused by uneven reflectivity can be identified by the difference map of wrapped phase and the primary unwrapped phase without edge fringe order error, and then eliminated by a statistical method. Experimental results demonstrate the feasibility and validity of the proposed method. It performs good anti-noise capability and robustness to correct the above two kinds of jump errors in TPU-GC and it is also a generalized correct method that can be used to others TPU, such as multi-frequency and phase-coded methods.
Progressive representation recalibration for lightweight super-resolution

Recently, the lightweight single-image super-resolution (SISR) task has received increasing attention due to the computational complexities and sizes of convolutional neural network (CNN)-based SISR models and the explosive demand in applications on resource-limited edge devices. Current algorithms reduce the number of layers and channels in CNNs to obtain lightweight models for this task. However, these algorithms may reduce the representation ability of the learned features due to information loss, inevitably leading to poor performance. In this work, we propose the progressive representation recalibration network (PRRN), a new lightweight SISR network to learn complete and representative feature representations. Specifically, a progressive representation recalibration block (PRRB) is developed to extract useful features from pixel and channel spaces in a two-stage approach. In the first stage, PRRB utilizes pixel and channel information to explore important feature regions. In the second stage, channel attention is further used to adjust the distribution of important feature channels. In addition, current channel attention mechanisms utilize nonlinear operations that may lead to information loss. In contrast, we design a shallow channel attention (SCA) mechanism that can learn the importance of each channel in a simpler yet more efficient way. Extensive experiments demonstrate the superiority of the proposed PRRN.
DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.
TC3KD: Knowledge distillation via teacher-student cooperative curriculum customization

Knowledge distillation aims to improve the performance of a lightweight student network by transferring some knowledge from a large-scale teacher network. Most existing knowledge distillation methods follow the traditional training strategy which feeds the sequence of mini-batches sampled randomly from the training set. Inspired by curriculum learning, we propose a novel knowledge distillation method via teacher-student cooperative curriculum customization. Specifically, a weighted ensemble of teacher and snapshot student is designed to measure the difficulty of samples. Dynamically update the ensemble weights and the snapshot student in the difficulty measurer that customizes appropriate curricula to guide the student network in different training stages. A “fetch and remove in balance” training scheduler is adopted to maintain the training stability and reduce the ranking cost. Extensive experiments on CIFAR-100, CINIC-10 and ImageNet validate the effectiveness of our method. As an independent training strategy of distillation, the proposed teacher-student cooperative curriculum customization paradigm also can be combined with the mainstream knowledge distillation approaches to improve their performance.