Reviewerly/exHarmony huggingface dataset:
Charged Particle Tracking via Edge-Classifying Interaction Networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.
GANBOT: a GAN-based framework for social bot detection

Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster content propagation. Thereby, malicious social bots are populating more and more to fool humans with their unrealistic behavior and content. Hence, that's necessary to distinguish these fake social accounts from real ones. Multiple approaches have been investigated in the literature to answer this problem. Statistical machine learning methods are one of them focusing on handcrafted features to represent characteristics of social bots. Although they reached successful results in some cases, they relied on the bot's behavior and failed in the behavioral change patterns of bots. On the other hands, more advanced deep neural network-based methods aim to overcome this limitation. Generative adversarial network (GAN) as new technology from this domain is a semi-supervised method that demonstrates to extract the behavioral pattern of the data. In this work, we use GAN to leak more information of bot samples for state-of-the-art textual bot detection method (Contextual LSTM). Although GAN augments low labeled data, original textual GAN (Sequence Generative Adversarial Net (SeqGAN)) has the known limitation of convergence. In this paper, we invested this limitation and customized the GAN idea in a new framework called GANBOT, in which the generator and classifier connect by an LSTM layer as a shared channel between them. Our experimental results on a bench-marked dataset of Twitter social bot show our proposed framework outperforms the existing contextual LSTM method by increasing bot detection probabilities.
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology

Media reports have accused face recognition of being “biased”, “sexist” and “racist”. There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate. However, there is little published research aimed at identifying the cause of lower accuracy for females. For instance, the 2019 Face Recognition Vendor Test that documents lower female accuracy across a broad range of algorithms and datasets also lists “Analyze cause and effect” under the heading “What we did not do”. We present the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result. Controlling for equal amount of visible face in the test images mitigates the apparent higher false non-match rate for females. Additional analysis shows that makeup-balanced datasets further improves females to achieve lower false non-match rates. Finally, a clustering experiment suggests that images of two different females are inherently more similar than of two different males, potentially accounting for a difference in false match rates.
S2 Transformer for Image Captioning

Transformer-based architectures with grid features represent the state-of-the-art in visual and language reasoning tasks, such as visual question answering and image-text matching. However, directly applying them to image captioning may result in spatial and fine-grained semantic information loss. Their applicability to image captioning is still largely under-explored. Towards this goal, we propose a simple yet effective method, Spatial- and Scale-aware Transformer (S2 Transformer) for image captioning. Specifically, we firstly propose a Spatial-aware Pseudo-supervised (SP) module, which resorts to feature clustering to help preserve spatial information for grid features. Next, to maintain the model size and produce superior results, we build a simple weighted residual connection, named Scale-wise Reinforcement (SR) module, to simultaneously explore both low- and high-level encoded features with rich semantics. Extensive experiments on the MSCOCO benchmark demonstrate that our method achieves new state-of-art performance without bringing excessive parameters compared with the vanilla transformer. The source code is available at https://github.com/zchoi/S2-Transformer
Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10-15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.
The technique of the use of Virtual Learning Environment in the process of organizing the future teachers’ terminological work by specialty

This paper studies the concept related to E-learning and the Virtual Learning Environment (VLE) and their role in organizing future teachers’ terminological work by specialty. It is shown the creation and use of the VLE is a promising approach in qualitative restructuring of future specialists’ vocation training, a suitable complement rather than a complete replacement of traditional learning. The concept of VLE has been disclosed; its structure has been presented as a set of components, such as: the Data-based component, the Communicationbased, the Management-and-Guiding ones, and the virtual environments. Some VLE’s potential contributions to the organization of terminological work of future biology teachers’ throughout a traditional classroom teaching, an independent work, and during the field practices has been considered. The content of professionally oriented e-courses “Botany with Basis of Geobotany” and “Latin. Botany Terminology” has been revealed; the ways of working with online definer (guide), with UkrBIN National Biodiversity Information Network, with mobile apps for determining the plant species, with digital virtual herbarium, with free software have been shown. The content of students’ activity in virtual biological laboratories and during virtual tours into natural environment has been demonstrated. The explanations about the potential of biological societies in social networks in view of students’ terminology work have been given. According to the results of empirical research, the expediency of using VLEs in the study of professional terminology by future biology teachers has been confirmed.
Graph Unlearning

Machine unlearning is a process of removing the impact of some training data from the machine learning (ML) models upon receiving removal requests. While straightforward and legitimate, retraining the ML model from scratch incurs a high computational overhead. To address this issue, a number of approximate algorithms have been proposed in the domain of image and text data, among which SISA is the state-of-the-art solution. It randomly partitions the training set into multiple shards and trains a constituent model for each shard. However, directly applying SISA to the graph data can severely damage the graph structural information, and thereby the resulting ML model utility. In this paper, we propose GraphEraser, a novel machine unlearning framework tailored to graph data. Its contributions include two novel graph partition algorithms and a learning-based aggregation method. We conduct extensive experiments on five real-world graph datasets to illustrate the unlearning efficiency and model utility of GraphEraser. It achieves 2.06$\times$ (small dataset) to 35.94$\times$ (large dataset) unlearning time improvement. On the other hand, GraphEraser achieves up to $62.5\%$ higher F1 score and our proposed learning-based aggregation method achieves up to $112\%$ higher F1 score.\footnote{Our code is available at \url{https://github.com/MinChen00/Graph-Unlearning}.}
Experimental Single-Setting Quantum State Tomography

Quantum computers solve ever more complex tasks using steadily growing system sizes. Characterizing these quantum systems is vital, yet becoming increasingly challenging. The gold-standard is quantum state tomography (QST), capable of fully reconstructing a quantum state without prior knowledge. Measurement and classical computing costs, however, increase exponentially in the system size - a bottleneck given the scale of existing and near-term quantum devices. Here, we demonstrate a scalable and practical QST approach that uses a single measurement setting, namely symmetric informationally complete (SIC) positive operator-valued measures (POVM). We implement these nonorthogonal measurements on an ion trap device by utilizing more energy levels in each ion - without ancilla qubits. More precisely, we locally map the SIC POVM to orthogonal states embedded in a higher-dimensional system, which we read out using repeated in-sequence detections, providing full tomographic information in every shot. Combining this SIC tomography with the recently developed randomized measurement toolbox ("classical shadows") proves to be a powerful combination. SIC tomography alleviates the need for choosing measurement settings at random ("derandomization"), while classical shadows enable the estimation of arbitrary polynomial functions of the density matrix orders of magnitudes faster than standard methods. The latter enables in-depth entanglement studies, which we experimentally showcase on a 5-qubit absolutely maximally entangled (AME) state. Moreover, the fact that the full tomography information is available in every shot enables online QST in real time. We demonstrate this on an 8-qubit entangled state, as well as for fast state identification. All in all, these features single out SIC-based classical shadow estimation as a highly scalable and convenient tool for quantum state characterization.
From estimation of quantum probabilities to simulation of quantum circuits

Investigating the classical simulability of quantum circuits provides a promising avenue towards understanding the computational power of quantum systems. Whether a class of quantum circuits can be efficiently simulated with a probabilistic classical computer, or is provably hard to simulate, depends quite critically on the precise notion of "classical simulation" and in particular on the required accuracy. We argue that a notion of classical simulation, which we call epsilon-simulation, captures the essence of possessing "equivalent computational power" as the quantum system it simulates: It is statistically impossible to distinguish an agent with access to an epsilon-simulator from one possessing the simulated quantum system. We relate epsilon-simulation to various alternative notions of simulation predominantly focusing on a simulator we call a poly-box. A poly-box outputs 1/poly precision additive estimates of Born probabilities and marginals. This notion of simulation has gained prominence through a number of recent simulability results. Accepting some plausible computational theoretic assumptions, we show that epsilon-simulation is strictly stronger than a poly-box by showing that IQP circuits and unconditioned magic-state injected Clifford circuits are both hard to epsilon-simulate and yet admit a poly-box. In contrast, we also show that these two notions are equivalent under an additional assumption on the sparsity of the output distribution (poly-sparsity).
Hardware-Enhanced Protection for the Runtime Data Security in Embedded Systems

At present, the embedded systems are facing various kinds of attacks, especially for the data stored in the external memories. This paper presents a hardware-enhanced protection method to protect the data integrity and confidentiality at runtime, preventing the data from spoofing attack, splicing attack, replay attack, and some malicious analysis. For the integrity protection, the signature is calculated by the hardware implemented Lhash engine before the data sending off the chip, and the signature of the data block is recalculated and compared with the decrypted one at the load time. For the confidentiality protection, an AES encryption engine is used to generate the key stream, the plain data and the cipher data can translate through a simple XOR operation. The hardware cryptographic engines are optimized to work simultaneously with the memory access operation, which reduces the hardware overhead and the performance overhead. We implement the proposed architecture within OR1200 processor on Xilinx Virtex 5 FPGA platform. The experiment results show that the proposed hardware-enhanced protection method can preserve the integrity and confidentiality of the runtime data in the embedded systems with low power consumption and a marginal area footprint. The performance overhead is less than 2.27% according to the selected benchmarks.
Electronic Learning and its Benefits in Education

The concept of Electronical Learning was emerged which is a method of education in the delivery of information to the learner, depends on the latest technologies of the mathematics, the global network of information and multimedia, the fields of dialogue, discussion and virtual classes. But the field of electronic learning and its solutions will not be successful if there are lack of basic elements of the current traditional education. The latter accomplishes many tasks indirectly or invisible to the passers-by, where the students’ time and attendance of schools is an important thing inculcates educational values indirectly and promotes joint work as a team. A variety of services, such as obtaining a Master’s degree online directly, or awarding technical certificates to programmers, IT professionals and other great features, as they do the necessary procedures and provide the required standards for the introduction of recognized programs for distance study.
Coherent Control of the Rotational Degree of Freedom of a Two-Ion Coulomb Crystal

We demonstrate the preparation and coherent control of the angular momentum state of a two-ion crystal. The ions are prepared with an average angular momentum of 7850ℏ freely rotating at 100 kHz in a circularly symmetric potential, allowing us to address rotational sidebands. By coherently exciting these motional sidebands, we create superpositions of states separated by up to four angular momentum quanta. Ramsey experiments show the expected dephasing of the superposition which is dependent on the number of quanta separating the states. These results demonstrate coherent control of a collective motional state described as a quantum rotor in trapped ions. Moreover, our Letter offers an expansion of the utility of trapped ions for quantum simulation, interferometry, and sensing.
Effective Entailment Checking for Separation Logic with Inductive Definitions

Symbolic-Heap Separation logic is a popular formalism for automated reasoning about heap-manipulating programs, which allows the user to give customized data structure definitions. In this paper, we give a new decidability proof for the separation logic fragment of Iosif, Rogalewicz and Simacek. We circumvent the reduction to MSO from their proof and provide a direct model-theoretic construction with elementary complexity. We implemented our approach in the Harrsh analyzer and evaluate its effectiveness. In particular, we show that Harrsh can decide the entailment problem for data structure definitions for which no previous decision procedures have been implemented.
Universal Graphs and Good for Games Automata: New Tools for Infinite Duration Games

Abstract In this paper, we give a self contained presentation of a recent breakthrough in the theory of infinite duration games: the existence of a quasipolynomial time algorithm for solving parity games. We introduce for this purpose two new notions: good for small games automata and universal graphs. The first object, good for small games automata, induces a generic algorithm for solving games by reduction to safety games. We show that it is in a strong sense equivalent to the second object, universal graphs, which is a combinatorial notion easier to reason with. Our equivalence result is very generic in that it holds for all existential memoryless winning conditions, not only for parity conditions.
Service Reliability in an HC: Considering From the Perspective of Scheduling With Load-Dependent Machine Reliability

Considering the fact that a server is more likely to fail if it is highly loaded, in this paper, we involve load impacts on service reliability in a heterogeneous cluster, where servers have load-dependent reliabilities and jobs have resource and execution interval demands. Specifically, each server is specified by a resource capacity and a workload limitation, i.e., the server is expected to perform reliably if its workload is less than the workload limitation. There is also a set of jobs needing to be executed on these servers. Each job is associated with a resource demand and an execution interval, i.e., the time interval that the job is planned to be executed. Our goal is to find a reliable schedule ofjobs to servers such that all servers' workload limitations are satisfied. The problem is proved to be strongly NP-complete, which implies that there is not even a pseudo-polynomial time algorithm. Hence, we try our best to find a reliable schedule. To solve the problem, we define a stereogram (specifically defined as a bipartite stereogram) and develop a bipartite stereogram matching-based scheduling method (BSMSM), which combines the matching of our constructed bipartite stereogram with reliable scheduling. Some theoretical results are also derived in this paper. We perform extensive random experiments and the results show that BSMSM can find reliable schedules, i.e., the workloads on all servers can be maintained to be less than or equal to the servers' workload limitations, to a large extent.
Constrained adversarial loss for generative adversarial network‐based faithful image restoration

Generative adversarial networks (GAN) have been successfully used in many image restoration tasks, including image denoising, super‐resolution, and compression artifact reduction. By fully exploiting its characteristics, state‐of‐the‐art image restoration techniques can be used to generate images with photorealistic details. However, there are many applications that require faithful rather than visually appealing image reconstruction, such as medical imaging, surveillance, and video coding. We found that previous GAN‐training methods that used a loss function in the form of a weighted sum of fidelity and adversarial loss fails to reduce fidelity loss. This results in non‐negligible degradation of the objective image quality, including peak signal‐to‐noise ratio. Our approach is to alternate between fidelity and adversarial loss in a way that the minimization of adversarial loss does not deteriorate the fidelity. Experimental results on compression‐artifact reduction and super‐resolution tasks show that the proposed method can perform faithful and photorealistic image restoration.
Big Data Opportunities: System Health Monitoring and Management

The concept of a system, generally defined as an organized set of detailed methods, procedures, and routines that are created to carry out a specific activity or solve a specific problem, has been successfully applied to many domains, ranging from mechanical systems to public health. System health monitoring and management (SHMM) refers to the framework of continuous surveillance, analysis, and interpretation of relevant data for system maintenance, management, and strategic planning. This framework is essential to ensure that an entire system is stable and under control. A fundamental problem in SHMM is the optimal use of correlated active and passive data in tasks including prediction and forecasting, monitoring and surveillance, fault detection and diagnostics, engineering management, and supply chain management. In this paper, we provide a new perspective on SHMM in a big data environment, discuss its relationship with other disciplines, and present several of its applications to complex systems.