Reviewerly/exHarmony huggingface dataset:
COMPAQT: Compressed Waveform Memory Architecture for Scalable Qubit Control

On superconducting architectures, the state of a qubit is manipulated by using microwave pulses. Typically, the pulses are stored in the waveform memory and then streamed to the Digital-to-Analog Converter (DAC) to synthesize the gate operations. The waveform memory requires tens of Gigabytes per second of bandwidth to manipulate the qubit. Unfortunately, the required memory bandwidth grows linearly with the number of qubits. As a result, the bandwidth demand limits the number of qubits we can control concurrently. For example, on current RFSoCs-based qubit control platforms, we can control less than 40 qubits. In addition, the high memory bandwidth for cryogenic ASIC controllers designed to operate within a tight power budget translates to significant power dissipation, thus limiting scalability.In this paper, we show that waveforms are highly compressible, and we leverage this property to enable a scalable and efficient microarchitecture COMPAQT - Compressed Waveform Memory Architecture for Qubit Control. Waveform memory is read-only and COMPAQT leverages this to compress waveforms at compile time and store the compressed waveform in the on-chip memory. To generate the pulse, COMPAQT decompresses the waveform at runtime and then streams the decompressed waveform to the DACs. Using the hardware-efficient discrete cosine transform, COMPAQT can achieve, on average, 5x increase in the waveform memory bandwidth, which can enable 5x increase in the total number of qubits controlled in an RFSoC setup. Moreover, COMPAQT microarchitecture for cryogenic CMOS ASIC controllers can result in a 2.5x power reduction over uncompressed baseline. We also propose an adaptive compression scheme to further reduce the power consumed by the decompression engine, enabling up to 4x power reduction. Qubits are sensitive, and even a slight change in the control waveform can increase the gate error rate. We evaluate the impact of COMPAQT on the gate and circuit fidelity using IBM quantum computers. We see less than 0.1% degradation in fidelity when using COMPAQT.
How Does fake review Influence E-Commerce Platform Revenue?

We constructed a tripartite game model that includes consumers, sellers, and platforms to analyze the impact of fake reviews on platform revenue under different market structures. Results indicated that, in a monopoly market when the platform charges sellers transaction fees, on the one side, fake reviews yield more transaction cost for both consumers and sellers, thus generating less revenue for the platform. On the other side, if sellers choose to manufacture more fake reviews, consumers' perceived quality of product will increase in early time and the overall impact of fake reviews on platform revenue may be positive; however, in the long run, fake reviews will damage platform revenue. Furthermore, consumers' increased perceived quality will offset some of the negative impact from transaction costs if sellers choose other enhancement strategies instead of participating in review fraud. Moreover, more fake reviews will yield lower platform revenue when platforms are in a duopoly market. This study provides a theoretical basis for the platform to manage fake reviews.
Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses

We investigate non-contact sensing of temporalis muscle contraction in smart eyeglasses frames to detect eating activity. Our approach is based on infra-red proximity sensors that were integrated into sleek eyeglasses frame temples. The proximity sensors capture distance variations between frame temple and skin at the frontal, hair-free section of the temporal head region. To analyse distance variations during chewing and other activities, we initially perform an in-lab study, where proximity signals and Electromyography (EMG) readings were simultaneously recorded while eating foods with varying texture and hardness. Subsequently, we performed a free-living study with 15 participants wearing integrated, fully functional 3Dprinted eyeglasses frames, including proximity sensors, processing, storage, and battery, for an average recording duration of 8.3hours per participant. We propose a new chewing sequence and eating event detection method to process proximity signals. Free-living retrieval performance ranged between the precision of 0.83 and 0.68, and recall of 0.93 and 0.90, for personalised and general detection models, respectively. We conclude that noncontact proximity-based estimation of chewing sequences and eating integrated into eyeglasses frames is a highly promising tool for automated dietary monitoring. While personalised models can improve performance, already general models can be practically useful to minimise manual food journalling.
Federated Trustworthy AI Architecture for Smart Cities

As the number of smart citizens or users grows, several governments are eager to develop smart cities. All stakeholders, including intelligent users, demand an AI system that is trustworthy. To be considered trustworthy, an AI system must meet seven key criteria: (1) human agency and oversight, (2) robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, nondiscrimination, and fairness, (6) societal and environmental well-being, and (7) accountability. By merging the existing trustworthy AI framework from KServe and Federated Learning with a more reliable model aggregation protocol than earlier studies, we introduced the federated trustworthy Artificial Intelligence (FTAI) architecture. With the integration of FedPSO and AIF360, we updated the FedCS technique. The proposed architecture meets all seven of the essential requirements to a high degree of satisfaction. This paper also includes a detailed explanation of this claim. The scenario demonstrates that the new global model comes with clear measures of fairness metrics, ensuring that the model is devoid of bias.
Long-term Parameters Monitoring of the IDQ Clavis 3 QKD System

Long-term stability is absolutely essential for the real deployment of quantum key distribution systems. The paper focuses on the possibility of long-term measurement of QKD system parameters using the COW protocol. For the measurement, an own developed system for monitoring the basic parameters of the Clavis 3 commercial system from the ID Quantic company was used. Since the stability of the quantum transmission systems can be influenced mainly by environmental influences, such as daylight shining on optical patch cables, the developed system will be used to monitor the stability of parameters in real conditions.
A Feasibility Study for Health and Life-Threatening Conditions Recognition via Sensor Fusion Approach

The paper presents a feasibility study for health and life-threatening conditions such as falls or postural balance abnormalities recognition via sensor fusion approach. As a sensor fusion system we used the Intel RealSense Depth Camera D435 that combines standard video and depth cameras. However, the possibility of recognition of different types of movement patterns especially falls with RGB-D cameras has been studied for more than a decade now, the usage of such methods to detect life and health-threatening conditions which may be manifested by impaired postural balance has not been paid enough attention yet. The proposed approach was validated on an experimental dataset collected for two experimental surroundings with participation of 13 volunteers (9 females and 4 males, age: 19–37 years). Each subject performed 11 different types of motion patterns including 2 patterns specific for life and health threatening situations. We used the Caffe neural network to find key point coordinated and form the feature vector than was used for training models. The proposed classifier for video sensor data classification showed 81 % accuracy and Cohen’s kappa of 78 %, while the usage of sensor fusion approach reduces the number of errors, and increase the classification accuracy and Cohen’s kappa by 7 and 8 %, respectively. Nevertheless, the economic feasibility of such a solution requires an additional assessment.
Processing Particle Data Flows with SmartNICs

Many distributed applications implement complex data flows and need a flexible mechanism for routing data between producers and consumers. Recent advances in programmable network interface cards, or SmartNICs, represent an opportunity to offload data-flow tasks into the network fabric, thereby freeing the hosts to perform other work. System architects in this space face multiple questions about the best way to leverage SmartNICs as processing elements in data flows. In this paper, we advocate the use of Apache Arrow as a foundation for implementing data-flow tasks on SmartNICs. We report on our experiences adapting a partitioning algorithm for particle data to Apache Arrow and measure the on-card processing performance for the BlueField-2 SmartNIC. Our experiments confirm that the BlueField-2's (de)compression hardware can have a significant impact on in-transit workflows where data must be unpacked, processed, and repacked.
GraphBLAS on the Edge: Anonymized High Performance Streaming of Network Traffic

Long range detection is a cornerstone of defense in many operating domains (land, sea, undersea, air, space, ..,). In the cyber domain, long range detection requires the analysis of significant network traffic from a variety of observatories and outposts. Construction of anonymized hypersparse traffic matrices on edge network devices can be a key enabler by providing significant data compression in a rapidly analyzable format that protects privacy. GraphBLAS is ideally suited for both constructing and analyzing anonymized hypersparse traffic matrices. The performance of GraphBLAS on an Accolade Technologies edge network device is demonstrated on a near worse case traffic scenario using a continuous stream of CAIDA Telescope darknet packets. The performance for varying numbers of traffic buffers, threads, and processor cores is explored. Anonymized hypersparse traffic matrices can be constructed at a rate of over 50,000,000 packets per second; exceeding a typical 400 Gigabit network link. This performance demonstrates that anonymized hypersparse traffic matrices are readily computable on edge network devices with minimal compute resources and can be a viable data product for such devices.
Instance Segmentation Graph Neural Network for Detecting Faulty Insulators Using the FLIR SkyRanger R70 UAS

This paper explores the use of Unmanned Aerial System (UAS) for detecting faulty insulators using a Graph Neural Network called Instance Segmentation Graph Neural Networks (IS-GNN). Research regarding the problem of faulty insulator detection on power distribution networks remains an open research area, due to the significant role insulators play to maintain quality inspection in transmission of power lines. Most existing methods detect insulators by rectangular bounding box but do not perform segmentation down to the instance pixel-pixel level. In this paper, a novel Instance Segmentation Graph Neural Network (IS-GNN) method for detecting faulty insulators is proposed. Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations used for tracking. Both inter and infra-frame information is effectively propagated and shared via graph updates and all the sub problems (i.e. detection, segmentation and tracking) are jointly optimized in an unified framework. Existing methods either rely only on single-frame information for the detection and segmentation sub problems or handle tracking as a separate post-processing step, which limit their capability to fully leverage and share useful spatial temporal information for all the sub problems. This IS-GNN model is trained on a custom data set and deployed on to a UAS for evaluating the model in real-time.
Self-attentive Semantic Segmentation Model Based On Generative Adversarial Network

To address the problems that existing recognition networks rely on a large amount of labeled data and the perceptual field is limited to the local area of convolution, and the lack of understanding of contextual information, this paper proposes a self-attentive semantic segmentation method based on generative adversarial networks. The method is based on generating adversarial networks, constructing semantic segmentation networks and discriminators, where the semantic segmentation network uses Resnet101 as the backbone to connect the spatial pyramid pooling module of PSPNet and adopts the cross-attention method in order to overcome the problem of too many parameters of classical attention models. The model was simulated in the publicly available PASCAL VOC 2012 dataset, and the results showed that the MIoU of the model reached 73.1%, 74.4%, and 75.1% for 1/8, 1/4, and 1/2 with labels, respectively, in the first semi-supervised experiments without improving the segmentation network compared to the control group, which were 3.6%, 2.3%, and 1.3% higher, respectively. The MIoU value reached 75.4% after improving the segmentation network, which proved the superiority and effectiveness of this model.
A K-means Optimized by Improved Grey Wolf Algorithm Anomaly Detection method for Wireless Sensor Networks

Anomaly detection in wireless sensor networks is crucial for the implementation of tasks such as fault diagnosis, intrusion detection, and event monitoring. Under existing research, a large number of anomaly detection algorithms use supervised or semi-supervised algorithms to solve a specific application scenario problem. However, in wireless sensor network multi-scenario applications, the feature definition for anomaly data is not available a priori. In this paper, an unsupervised algorithm based on K-means is proposed to solve this problem. Since the effect of K-means algorithm is sensitive to the number of clusters and the selection of initial points, it is easy to fall into local optimum. To enhance the reliability of the algorithm, the gray wolf algorithm is used to find the original cluster centers, and then the clustering results are compared with the weighted neighboring clusters to determine whether the edge points or edge clusters are abnormal data. The experimental results show that the accuracy, recall and F1 value of the algorithm are significantly improved compared with K-means algorithm and other algorithms optimizing K-means under different wireless sensor network data sets.
Toolset of “Turkic Morpheme” Portal for Creation of Electronic Corpora of Turkic Languages in a Unified Conceptual Space

Sphere of electronic corpora creation as a way of preservation and development of natural languages as well as a resource base for developers of NLP technologies and language researchers experience a rapid increase in number and volume of electronic corpora for many languages, including Turkic languages. However, a lot of Turkic languages has no corpus due to their developers having problems with implementation and supporting the operation of such large, technically demanding resources. This paper presents the toolset for creation of Turkic corpora within the “Turkic Morpheme” web-portal, the multilingual resource with language-independent grammatical, syntactic and semantic level models and language-dependent data for Turkic language family. Use of this toolset will help to solve corpus annotation and NLP processing unification problem and enrich the portal resources by creating a unified conceptual space of Turkic electronic corpora.
Application of System Diversity for IncreasingSecurity and Reliability of Distributed Systems

This article quantifies the application of system diversity to increase the reliability and security of communications in geographically distributed computer systems. Based on the multidimensional stress strength analysis and the classical theory, new models have been proposed, and new analytical dependencies have been derived, which can be used to quantify the increase in reliability and security of distributed systems.
Watermarking for Large-Scale Video Distribution through Out-of-the-Loop Frame Replacement

Forensic watermarking is used in large-scale video-distribution applications to track digital pirates after they illegally leak videos. Existing methods either have a high embedding complexity or have troubles providing imperceptibility and robustness. Therefore, this paper proposes a novel watermarking method that has a low embedding complexity, as well as sufficient imperceptibility and robustness. The main novelty is that frames are replaced out of the loop, which has a very low complexity. This is inspired by A/B watermarking, yet applied on frame level rather than video-segment level. The resulting drift-error artifacts are used for robust watermark detection. Additionally, a faster watermark detection method is proposed at the cost of a decrease in embedding capacity. We show that the drift errors caused by frame replacement are imperceptible, and that our method has a negligible impact on the video bitrate. Moreover, we demonstrate a high level of robustness. These interesting properties come at the cost of a non-blind detection with a relatively high complexity, that is partially solved by the proposed fast detection method. Additionally, a high detection complexity is not necessarily a problem since detection occurs infrequently and recent speed-up solutions exist. In conclusion, the proposed method is a novel and scalable solution that enables secure large-scale video distribution.
FCNN Model for Diagnosis and Analysis of Symmetric Key Cryptosystem

An important part of a cryptosystem is a cryptographic algorithm, which protects unauthorized attackers from obtaining private and sensitive data. This study is a research project on identifying cryptographic algorithms using deep learning techniques and categorizing cryptographic algorithms based on feature extraction. The research involves employing block cipher modes called electronic codebook with the encryption algorithms Blowfish and advanced encryption standard (AES), where the data will be encrypted using the same key and a different key. The model has been developed by changing the structure and parameters of the proposed model and the training rate of the data. This model will build several dense FCNN of n layers on regular fully connected neural networks. Its construction will consist of five hidden layers, with each layer consisting of 128 neurons and hidden layers activation Relu except for the output layer, which consists of two classifiers and the SoftMax activation function. FCNN is better able to classify big data. It is also more efficient in use, reducing complexity, with the ability to store training data. First, the fully connected neural network (FCNN) model was used to evaluate the categorization of the models. Then, all models, even the encryption forms, were evaluated utilizing true positive measurements for satisfactory classification of the identified encryption method and false positive measurements for incorrect classification. The effectiveness of the model was then calculated using the precision value, recall, loss, accuracy range, and F1-Score metrics using a confusion matrix. The FCNN model parameters will be changed to more effectively identify the encryption algorithm. In the proposed method, when using the same key, the accuracy was 81%, and when using a different key, the accuracy was 49%. The FCNN model’s adjusted weights and learning will be based on large data to define and assess encryption algorithms more effectively and efficiently
A Self-Adaptive Multiple Exposure Image Fusion Method for Highly Reflective Surface Measurements

Fringe projection profilometry (FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring objects with highly reflective surfaces or high dynamic range surfaces remains challenging when using FPP. A number of multiple exposure image fusion methods have been proposed and successfully improved measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by practical experiences or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness with regard to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two areas of improvement relating to adaptively optimizing the initial exposure and the exposure sequence. First, by introducing the theory of information entropy, combined with an analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the basis of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are provided. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method via the measurement of objects with differences in surface reflectivity under different ambient lighting conditions.
Lattice-Based Verifiably Encrypted Signature Scheme without Gaussian Sampling for Privacy Protection in Blockchain

Before the transaction data in the blockchain is successfully linked, its signature must be publicly verified by a large number of nodes in the blockchain, which is also one of the ways to leak transaction information. To alleviate the contradiction between the public verifiability of signatures and the protection of transaction privacy, we introduce a verifiably encrypted signature scheme into the blockchain. A verifiably encrypted signature scheme contains two parts of signature information: the encrypted signature is used for public verification, and the ordinary signature is used for internal verification. To reach this goal even better, we design a new lattice-based verifiably encrypted signature scheme, which separates the parameter settings of the signer and the adjudicator, and replaces the Gaussian sampling algorithm with a small range of uniform random sampling, achieving better efficiency and security.
Privacy-Preserving Contrastive Explanations with Local Foil Trees

We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the quality of these explanations can be upheld whilst ensuring the privacy of both the training data and the model itself.