Reviewerly/exHarmony huggingface dataset:
Joint Estimation Algorithm of Target State and Measurement Bias for Doppler Sensor

In recent years, Doppler-only sensors have demonstrated an excellent estimation performance for target tracking. Most of the existing tracking algorithms that utilize Doppler sensors rely on the assumption that sensors capture only measurement errors, disregarding measurement biases. However, in many practical situations, there can be significant measurement biases, which can severely compromise the performance of target state estimation. Recognizing this issue, the study proposes a new joint estimation algorithm that is exclusively reliant on Doppler-only sensors. The proposed algorithm not only provides the estimated results of the target state, but also the measurement bias of the Doppler sensor in use. The approach unfolds in two stages: the first stage is to estimate the target state without considering the measurement bias; the second is to perform bias compensation using the least squares method, and the target state and measurement bias are jointly estimated by linearization of the measurement equation. To validate the efficacy of this method, we analyzed the Cramer-Rao lower bound (CRLB) for measurement bias estimation and designed simulations under both static and moving sensor scenarios to assess its performance. The results indicate that the proposed algorithm can effectively estimate the target state, outperforming Kalman filter (KF) in both the moving and static sensor scenario. The root mean square error (RMSE) of the bias estimation can approach the CRLB.
Enhancing Very High Resolution Satellite Images at 15 cm: A Novel Pipeline with Synthetic Data and Single Image Super Resolution

Employing a two-step pipeline that encompasses an Image-to-Image Translation (I2I) and a Super-Resolution (SR) network, we significantly enhance satellite images with a Ground Sample Distance (GSD) of 30 centimeters (cm) to a superior 15 cm GSD. Our translation network learns from the characteristics of satellite images and replicates these onto aerial images with a GSD of 15 cm, creating a tailored training dataset. The super-resolution network then uses this dataset to train and render enhanced satellite images at a GSD of 15 cm. This innovative approach can provide a cost-effective substitute for commercial high-definition (HD) images, and broadens the usage of high-quality data across various applications.
A Comprehensive Review of the Applications of Blockchain Technology

The academia and industry have thoroughly examined the prospective applications of blockchain technology (BCT), particularly in the domains of banking and payment systems. A contemporary development is the utilization of BCT at the enterprise level, wherein it functions as the foundational framework for ensuring internet security and immutability. Consequently, this has led to the emergence of new application areas such as the Industrial Internet of Things (IIoT) and Industry 4.0 (I4.0). This article provides an overview of IIoT and I4.0, as well as the fundamental properties of BCT. Subsequently, the paper delves into the practical applications of BCT in seven key areas, in commercial contexts. Subsequently, an analysis will be conducted to explore the ten emerging domains where BCT is being applied. This article incorporates academic implementations, startups, and real-world implementations to enhance comprehension.
Development of technological tools in response to primary mental health care needs arising from the emergency caused by COVID-19 in Cauca

The pandemic caused by Covid-19 – Coronavirus forced government authorities to take quick action against the virus, within the framework of these measures, borders, social distancing, confinement, cancellation of face-to-face attendance and implementation of teleworking were interrupted, and effective solutions were sought for the protection of physical health and care of life. However, mental health was neglected, triggering depression, anxiety, different types of violence, suicidal ideation, and action, among other factors. Directly affecting individuals in their environment, emotions, and perceptions. With the understanding of the demands in mental health care, the research team made an interdisciplinary proposal focused on the inhabitants of the department of Cauca, methodologically used a mixed research approach, applying qualitative instruments such as focus groups, debates with experts, discursive analysis; in quantitative, analysis of secondary sources, compilation, and review of reports. As a result, a technological platform was built to favor decision-making by state actors, a virtual learning object, a training process for 210 actors such as technical assistance, installed capacity in the 42 Municipalities of Cauca, a base document for the formulation of public policies in mental health and primary care for 60,000 people through face-to-face visits or teleorientation.
Anonymity-Enhancing Multi-Hop Locks for Monero-Enabled Payment Channel Networks

Payment Channel Networks (PCNs) are innovative second-layer scaling technologies that aim to improve transaction rates, reduce on-chain storage costs, and enable efficient atomic swaps for blockchain-based cryptocurrencies. Despite offering features like relationship anonymity, scriptless script, and cross-chain fairness, current PCNs encounter challenges in achieving identity anonymity and maintaining the fungibility of cryptocurrency units. PayMo, proposed in ESORICS’22, addresses payment anonymity but is limited to Monero, posing difficulties in extending it to a PCN framework. In response, this paper presents a novel Anonymity-Enhancing Multi-Hop Locks (AEMHL) mechanism for Monero-enabled PCNs. The AEMHL mechanism leverages our generic Linkable Ring Adaptor Signature (LRAS) construction and a minimalist PCN framework called anonymous multi-hop locks. This approach effectively combines privacy protection and simplicity while ensuring Monero’s fungibility without the need for specialized scripting support. Security properties, including atomicity, consistency, and anonymity-enhancement, are demonstrated using a universal composability model. Additionally, two optimized LRAS-based schemes are proposed to accommodate multi-hop locks construction in diverse scenarios. Through rigorous security analysis and performance evaluation, we confirm that AEMHL meets essential security objectives and provides efficient and practical solutions for privacy-conscious users within PCNs.
Enhancing Virtual Distillation with Circuit Cutting for Quantum Error Mitigation

Virtual distillation is a technique that aims to mitigate errors in noisy quantum computers. It works by preparing multiple copies of a noisy quantum state, bridging them through a circuit, and conducting measurements. As the number of copies increases, this process allows for the estimation of the expectation value with respect to a state that approaches the ideal pure state rapidly. However, virtual distillation faces a challenge in realistic scenarios: preparing multiple copies of a quantum state and bridging them through a circuit in a noisy quantum computer will significantly increase the circuit size and introduce excessive noise, which will degrade the performance of virtual distillation. To overcome this challenge, we propose an error mitigation strategy that uses circuit-cutting technology to cut the entire circuit into fragments. With this approach, the fragments responsible for generating the noisy quantum state can be executed on a noisy quantum device, while the remaining fragments are efficiently simulated on a noiseless classical simulator. By running each fragment circuit separately on quantum and classical devices and recombining their results, we can reduce the noise accumulation and enhance the effectiveness of the virtual distillation technique. Our strategy has good scalability in terms of both runtime and computational resources. We demonstrate our strategy’s effectiveness through noisy simulation and experiments on a real quantum device.
MixRec: Orchestrating Concurrent Recommendation Model Training on CPU-GPU platform

The development of deep learning recommendation models (DLRM) and recommendation systems has significantly improved the precision of information matching. Due to distinct computation, data access, and memory usage characteristics of recommendation models, they may suffer from low resource utilization on prevalent heterogeneous CPU-GPU hardware platforms. Existing concurrent training solutions cannot be directly applied to DLRM due to various factors, such as insufficient fine-grained memory management and the lack of collaborative CPU-GPU scheduling. In this paper, we introduce MixRec, a scheduling framework that addresses these challenges by pro-viding an efficient job management and scheduling mechanism for DLRM training jobs on heterogeneous CPU-GPU platforms. To facilitate training co-location, we first estimate the peak memory consumption of each job. Additionally, we track and collect resource utilization for DLRM training jobs. Based on the information of resource usage, a batched job dispatcher with dynamic resource-complementary scheduling policy is proposed to co-locate DLRM training jobs on CPU-GPU platform. Experimental results demonstrate that our implementation achieved up to 4.42× higher throughput and 3.97× higher resource utilization for training jobs involving various recommendation models.
FM-P2L: An Algorithm Hardware Co-design of Fixed-Point MSBs with Power-of-2 LSBs in CNN Accelerators

Convolutional neural networks (CNNs) are a focal point for advancing the field of artificial intelligence, enabling significant advances in image and speech recognition, natural language processing, and other complex tasks. However, due to the high requirements of memory storage and computational resources, implementing CNNs can be challenging.To alleviate these deficiencies, this work presents a novel algorithm hardware co-design centered on a new number format, fixed-point MSBs with power-of-2 LSBs (FM-P2L), which can significantly reduce the requirement of computational resources of the CNN accelerators by trading negligible accuracy loss. First, we propose the novel FM-P2L number format that uses fixed-point to represent MSBs and power-of-2 to represent LSBs, which can reduce the computation complexity of multiplications. The optimal bitwidths of MSBs and LSBs are determined from our proposed algorithm with the capability to preserve accuracy. Second, we propose a novel multiplier that best matches FM-P2L with CNN accelerators, which can significantly reduce the area and power of the computational units. Finally, to evaluate the benefits of FM-P2L, we compared FM-P2L with fixed-point and low-bitwidith floating-point on a weight-stationary based systolic array accelerator with vector-vector multiplication PEs, which is adopted by Google TPU and NVDLA, in TSMC 40nm technology. Our evaluation results demonstrate that FM-P2L can achieve up to 44% computing power and 50% area reduction compared with fixed-point and up to 55% computing power and 65% area reduction compared with floating-point, while maintaining negligible inference accuracy loss on the state-of-the-art CNN models.
Research on the Construction Algorithm of Chinese Language Teaching System Based on Fuzzy Clustering

With the rapid development of the Internet in China, a large amount of Chinese text information emerges on the Internet. In the face of massive Chinese text information, how to quickly find the needed information is an urgent problem. In order to realize the clustering analysis of Chinese online learning users' learning behaviors, a clustering analysis of Chinese language learning users' learning behaviors based on HIS-FCM is proposed, aiming at the fact that FCM clustering results are easily influenced by the selection of its initial clustering center. Text clustering is an important research topic in the field of text mining and information retrieval, and Chinese automatic word segmentation, feature selection and similarity calculation are the key technologies of Chinese text clustering research. Experiments show that the improved algorithm can effectively improve the accuracy of text clustering, especially when the text set is small. The fuzzy clustering method is used to classify and analyze teachers' classroom teaching, select the appropriate similarity coefficient, establish the fuzzy similarity matrix, and classify with the direct clustering method. It can not realize the Ranking Ranking in our traditional sense, but it well realizes the combination of quantitative evaluation and qualitative evaluation, realizes the diagnostic function of evaluation, and provides the basis for information feedback, Give full play to the guiding and incentive functions of evaluation.
Simulation Calculation of National Compulsory Education Information Infrastructure Construction Based on Data Mining Algorithm

With the maturity of computer technology, the popularization and application of database and Internet technology in various fields such as finance, production, marketing, education and so on, the accumulation of data by human beings is also surging. School informatization construction has developed from digital campus stage to smart campus stage, and the era of school big data has arrived. The informatization infrastructure construction in the stage of national compulsory education includes five dimensions: computer room area (square meters), number of computers (including teaching computers and tablets), network multimedia classrooms, number of schools with campus network and number of schools with Internet access. According to the statistics of the above five dimensions, combined with the data mining algorithm, this paper makes statistics and evaluation on the construction of school information infrastructure in the national compulsory education stage in the past five years (2015-2019). It is found that the total number of five infrastructure projects is increasing. The gap between urban and rural areas in compulsory education information infrastructure construction is narrowing, and the Internet access rate of urban and rural primary and secondary schools is relatively balanced. In the process of education system innovation, a detailed analysis of the operation of big data and the integration of education system will help to promote the overall effect of education and fully meet the diversified needs of education development.
Clinical Nursing Strategies for Pulmonary Disease Patients Based on Random Forest Algorithm

Lung disease is a chronic, consumptive and infectious disease, which seriously endangers human health. Taking chronic obstructive pulmonary disease (COPD) as the research object, the Random Forest Algorithm (RFA) was proposed in this paper. The clinical nursing(CN) strategies for patients with pulmonary disease based on SFA were studied and analyzed; The CN strategies of different lung diseases were discussed, and then the CN strategies of lung disease patients under the RFA were analyzed; Finally, the RFA is tested. The results show that the accuracy of lung CT images is as high as 99.09% by using this algorithm. It is of great significance for CN of lung diseases to verify the effectiveness and accuracy of this research algorithm.
Research on Intelligent Assessment Algorithm of English Teaching Diagnosis System Based on SVM

In light of the intricate fluctuations in CET quality, a CET quality evaluation framework using SVM has been developed to achieve highly accurate results. The integration of SVM theory into the CET assessment model has garnered significant interest. Experience indicates that this diagnostic test, a teaching reform initiative, addresses long-standing challenges in foreign language instruction, such as isolated teaching practices in specialized courses. It significantly enhances the overall quality of foreign language instruction. An index to measure CET quality has been devised, and through principal component analysis, the assessment criteria for CET quality have been refined. The CET quality rating is then determined using SVM. The outcomes from the diagnostic test serve as a crucial reference for assessing and comprehending the grasp, expression challenges, and root causes of difficulties for online English learners. The CET quality evaluation framework can precisely determine CET quality levels, offering invaluable insights to enhance CET's quality.
Research on Chinese Entity Linking Based on Multi-Granularity Text Representation and Graph Neural Network

Considering the problems of Chinese entity linking in named entity recognition, entity disambiguation, and feature dimension reduction, this paper proposes a Chinese entity linking based on multi-granularity text representation and graph neural network. First, a text representation is constructed at multiple granularities according to the BERT model to obtain character-level, word-level, sentence-level, and segment-level information about the entity candidates. Second, a feature dimension reduction strategy based on principal component analysis is proposed to eliminate the ambiguity between entity candidates. Third, based on the topological features of the graph neural network, a graph neural network model is constructed taking into account the entity context and knowledge-based influences that describe the relationships between entities in more detail. Finally, a Chinese entity linking system is built based on multi-granularity text representation and graph neural network algorithm. Simulation and experiments prove the effectiveness of the entity linking proposed in this paper.
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators

Spatio-Temporal Convolutional Neural Networks (ST-CNN) allow extending CNN capabilities from image processing to consecutive temporal-pattern recognition. Generally, state-of-the-art (SotA) ST-CNNs inflate the feature maps and weights from well-known CNN backbones to represent the additional time dimension. However, edge computing applications would suffer tremendously from such large computation/memory overhead. Fortunately, the overlapping nature of ST-CNN enables various optimizations, such as the dilated causal convolution structure and Depth-First (DF) layer fusion to reuse the computation between time steps and CNN sliding windows, respectively. Yet, no hardware-aware approach has been proposed that jointly explores the optimal strategy from a scheduling as well as a hardware point of view.To this end, we present ACCO, an automated optimizer that explores efficient Causal CNN transformation and DF scheduling for ST-CNNs on edge hardware accelerators. By cost-modeling the computation and data movement on the accelerator architecture, ACCO automatically selects the best scheduling strategy for the given hardware-algorithm target. Compared to the fixed dilated causal structure, ST-CNNs with ACCO reach an ~8.4× better Energy-Delay-Product. Meanwhile, ACCO improves ~20% in layer-fusion optimals compared to the SotA DF exploration toolchain. When jointly optimizing ST-CNN on the temporal and spatial dimension, ACCO’s scheduling outcomes are on average 19× faster and 37× more energy-efficient than spatial DF schemes.
Gilbert and Varshamov Meet Johnson: List-Decoding Explicit Nearly-Optimal Binary Codes

We give an efficient algorithm for list-decoding the binary code by Ta-Shma (STOC 2017) to the Johnson Bound. Ta-Shma’s code has distance $\frac{1-\varepsilon}{2}$ and rate $\Omega\left(\varepsilon^{2+o(1)}\right)$ and thus it almost achieves the Gilbert-Varshamov bound. Johnson bound states that such codes are combinatorially list decodable upto $\frac{1-\rho}{2}-$ fraction of errors as long as $\rho \geq \sqrt{\varepsilon}$. We give a polynomial time decoding algorithm that nearly achieves this bound. Thus our result implies the only known binary code that simultaneously nearly achieves both the Gilbert-Varshamov and the Johnson bounds. Our decoding algorithm is based on semidefinite programming hierarchies and includes a new rounding step which might be of independent interest.
Streaming Euclidean k-median and k-means with o(log n) Space

We consider the classic Euclidean k-median and k-means objective on data streams, where the goal is to provide a $(1+\varepsilon)$-approximation to the optimal k-median or k-means solution, while using as little memory as possible. Over the last 20 years, clustering in data streams has received a tremendous amount of attention and has been the test-bed for a large variety of new techniques, including coresets, the merge-and-reduce framework, bicriteria approximation, sensitivity sampling, and so on. Despite this intense effort to obtain smaller sketches for these problems, all known techniques require storing at least $\Omega(\log (n \Delta))$ words of memory, where n is size of the input and $\Delta$ is the aspect ratio. A natural question is if one can beat this logarithmic dependence on n and $\Delta$. In this paper, we break this barrier by first giving an insertion-only streaming algorithm that achieves a $(1+\varepsilon)$-approximation to the more general $(k, z)$-clustering problem, using $\tilde{\mathcal{O}}\left(\frac{d k}{\varepsilon^{2}}\right) \cdot\left(2^{z \log z}\right) \cdot \min \left(\frac{1}{\varepsilon^{z}}, k\right) \cdot \operatorname{poly}(\log \log (n \Delta))$ words of memory. Our techniques can also be used to achieve two-pass algorithms for k-median and k-means clustering on dynamic streams using $\tilde{\mathcal{O}}\left(\frac{1}{\varepsilon^{2}}\right) \cdot \operatorname{poly}(d, k, \log \log (n \Delta))$ words of memory.
The Price of Explainability for Clustering

Given a set of points in d-dimensional space, an explainable clustering is one where the clusters are specified by a tree of axis-aligned threshold cuts. Dasgupta et al. (ICML 2020) posed the question of the price of explainability: the worst-case ratio between the cost of the best explainable clusterings to that of the best clusterings.We show that the price of explainability for k medians is at most $1+H_{k-1}$; in fact, we show that the popular Random Thresholds algorithm has exactly this price of explainability, matching the known lower bound constructions. We complement our tight analysis of this particular algorithm by constructing instances where the price of explainability (using any algorithm) is at least $(1-o(1)) \ln k$, showing that our result is best possible, up to lower-order terms. We also improve the price of explainability for the k-means problem to $O(k \ln \ln k)$ from the previous $O(k \ln k)$, considerably closing the gap to the lower bounds of $\Omega(k)$. Finally, we study the algorithmic question of finding the best explainable clustering: We show that explainable k medians and k-means cannot be approximated better than $O(\ln k)$, under standard complexity-theoretic conjectures. This essentially settles the approximability of explainable k-medians and leaves open the intriguing possibility to get significantly better approximation algorithms for k-means than its price of explainability.