Reviewerly/exHarmony huggingface dataset:
Location-Based Deep Collaborative Filtering for Quality of Service Prediction in IoT

In the realm of web services, various providers offer many analogous options. The selection of the optimal service from these alternatives relies heavily on Quality of Service (QoS), a metric encompassing non-functional attributes. To enhance QoS assessment for web services, a variety of QoS prediction methods have been proposed. Among these methodologies, collaborative web service QoS prediction emerges as one of the most prominent techniques. Recently, methods based on collaborative filtering (CF) have garnered substantial attention for service recommendations. However, traditional CF predominantly handles low-dimensional and linear interactions between users and services, often grappling with real-world data sparsity challenges. To address these limitations, this article introduces a novel deep CF model for service recommendation. The proposed model, inspired by deep learning and two similarity measures, solves the challenge of traditional collaborative filtering methods and provides a platform for fundamental improvements.
Construction of dynamic detection and analysis system for students' work and employment

Amidst the swift advancement of higher education, universities produce a substantial number of graduates annually. Consequently, the employment landscape for these graduates has captured the attention of educational institutions and the human resources sector. Harnessing the capabilities of the GIS system, and incorporating comprehensive student employment data, company specifics, geographical satellite imagery, national cartographic data, and continuous assimilation of pertinent data, we've formulated a dynamic system to monitor and analyze student employment patterns. This system showcases the employment needs of various regions and academic institutions, gauges talent requirements, and undertakes a thorough review of employment success stories from previous years to predict future trends. The results underline the system's commendable efficacy, achieving set targets and bolstering the operational efficiency of the employment data management team. Boasting a satisfaction rate exceeding 98.12%, the platform fosters enhanced collaboration between academia and industry, making it a prime candidate for widespread adoption and utilization.
A Method for Monitoring the Operation Status of Wind Turbines Based on Cloud Services

This paper studies a monitoring system and monitoring method for the operation status of wind turbines. The system includes: the end side Mechanical floor is used to obtain multiple operation status parameters of each equipment to be monitored of the wind turbine to be monitored, and send the operation status parameters to the edge server at the preset distance of the wind turbine to be monitored, receive fault information and repair the fault information of the equipment to be monitored; The edge server is used to receive multiple operating status parameters sent by the Mechanical floor on the end side, send commands to obtain the monitoring and analysis model corresponding to the device to be monitored, input multiple operating status parameters into the monitoring and analysis model, output the fault information and judge the level, and determine to send the fault information to the Mechanical floor or cloud center platform on the end side; The cloud center platform is used to send multiple trained monitoring and analysis models to the edge server, receive fault information of the equipment to be monitored, and repair and process the fault information of the equipment to be monitored.
SFDoP: A Scalable Fused BFloat16 Dot-Product Architecture for DNN

The BFloat16(BF16) format has emerged as a driving force in Deep Neural Networks(DNNs), owing to its superior energy efficiency and lower memory footprint than traditional formats. Since the BF16 format is mainly used in computation-intensive layers such as the general matrix multi-plication(GEMM) layer, this paper presents SFDoP, a scalable BF16 fused dot-product(DoP) architecture for high-performance computation in DNNs. The SFDoP features a novel fused 4-term DoP unit as a basic unit, which performs 4-term DoP operation in three cycles. More-term DoP units are constructed by extending this basic unit. The extended units incorporate early exponent comparison to mask latency and omit intermediate normalization and rounding to further improve performance. Compared with discrete designs, SFDoP-4 reduces latency by 15.6% for 4-term DoP operation, with greater reductions achieved in the extended units. Compared with existing BF16 designs, SFDoP exhibits improved throughput and energy efficiency, with gains of at least 82.2% and 28.1%, respectively. For GEMM operation of large size, SFDoP achieves better performance in the extended units than the basic unit.
Genetic Algorithm Optimization of Concrete Frame Structure Based on Improved Random Forest

In order to apply the random forest regression algorithm to the structural optimization of concrete frames, this paper uses the relative dynamic elastic modulus as the evaluation index of the concrete frame structure, and uses random forest feature selection based on the original samples to evaluate the importance of influencing factors and select variables. Based on the set of optimal influencing factors, a new method for optimizing the concrete frame structure of random forest based on genetic algorithm is proposed. High-quality decision trees are selected to join the genetic algorithm to reduce the scale of random forest and improve the classification accuracy. And can better improve the genetic algorithm of the concrete frame of random forest. The results show that compared with other models, the random forest model has the highest prediction accuracy and the best fitting effect. The proposed random forest prediction model has broad application prospects in the study of concrete frame structures. The random forest prediction model has the smallest error and the highest accuracy, which verifies the accuracy and reliability of the model. The proposed random forest prediction model provides an effective method for the prediction of concrete frame structures.
Application of Noise Filter Mechanism for T5-Based Text-to-SQL Generation

The objective of the text-to-SQL task is to convert natural language queries into SQL queries However, the presence of extensive text-to-SQL datasets across multiple domains, such as Spider, introduces the challenge of effectively generalizing to unseen data. Existing semantic parsing models have struggled to achieve notable performance improvements on these cross-domain datasets. As a result, recent advancements have focused on leveraging pre-trained language models to address this issue and enhance performance in text-to-SQL tasks. These approaches represent the latest and most promising attempts to tackle the challenges associated with generalization and performance improvement in this field. This paper proposes an approach to evaluate and use the Seq2Seq model providing the encoder with the most pertinent schema items as the input and to generate accurate and valid cross-domain SQL queries using the decoder by understanding the skeleton of the target SQL query. The proposed approach is evaluated using Spider dataset which is a well-known dataset for text-to-sql task and able to get promising results where the Exact Match accuracy and Execution accuracy has been boosted to 72.7% and 80.2% respectively compared to other best related approaches.
Approximating Edit Distance in the Fully Dynamic Model

The edit distance is a fundamental measure of sequence similarity, defined as the minimum number of character insertions, deletions, and substitutions needed to transform one string into the other. Given two strings of length at most n, a simple dynamic programming computes their edit distance exactly in $\mathcal{O}\left(n^{2}\right)$ time, which is also the best possible (up to subpolynomial factors) assuming the Strong Exponential Time Hypothesis (SETH). The last few decades have seen tremendous progress in edit distance approximation, where the runtime has been brought down to subquadratic, to near-linear, and even to sublinear at the cost of approximation. In this paper, we study the dynamic edit distance problem where the strings change dynamically as the characters are substituted, inserted, or deleted over time. Each change may happen at any location of either of the two strings. The goal is to maintain the (exact or approximate) edit distance of such dynamic strings while minimizing the update time. The exact edit distance can be maintained in $\mathcal{O}\left(n \log ^{2} n\right)$ time per update (Charalampopoulos, Kociumaka, Mozes; 2020), which is again tight assuming SETH. Unfortunately, even with the unprecedented progress in edit distance approximation in the static setting, strikingly little is known regarding dynamic edit distance approximation. Utilizing the best near-linear-time (Andoni, Nosatzki; 2020) and sublinear-time (Goldenberg, Kociumaka, Krauthgamer, Saha; 2022) approximation algorithm, an old exact algorithm (Landau and Vishkin; 1988), and a generic dynamic strings implementation (Mehlhorn, Sundar, Uhrig; 1996), it is possible to achieve an $\mathcal{O}\left(n^{c}\right)$-approximation in $n^{0.5-c+o(1)}$ update time for any constant $c \in\left[0, \frac{1}{6}\right]$. Improving upon this trade-off, characterized by the approximation-ratio and update-time product $n^{0.5+o(1)}$, remains wide open. The contribution of this work is a dynamic $n^{o(1)}$-approximation algorithm with amortized expected update time of $n^{o(1)}$. In other words, we bring the approximation-ratio and update-time product down to $n^{o(1)}$, which is also the best possible with the current state of the art in static algorithms. Our solution utilizes an elegant framework of precision sampling trees for edit distance approximation (Andoni, Krauthgamer, Onak; 2010). We show how to dynamically maintain precision sampling trees, which comes with significant nontriviality and can be an independent tool of interest for further development in dynamic string algorithms.
From Grassmannian to Simplicial High-Dimensional Expanders

In this paper, we present a new construction of simplicial complexes of subpolynomial degree with arbitrarily good local spectral expansion. Previously, the only known high-dimensional expanders (HDXs) with arbitrarily good expansion and less than polynomial degree were based on one of two constructions, namely Ramanujan complexes and coset complexes. In contrast, our construction is a Cayley complex over an abelian group, with Cayley generating set given by a Grassmannian HDX.Our construction is in part motivated by a coding-theoretic interpretation of Grassmannian HDXs that we present, which provides a formal connection between Grassmannian HDXs, simplicial HDXs, and LDPC codes. We apply this interpretation to prove a general characterization of the 1-homology groups of our Cayley simplicial complexes. Using this result, we construct simplicial complexes on N vertices with arbitrarily good local expansion for which the dimension of the 1-homology group grows as the squared logarithm of N. No prior constructions in the literature have been shown to achieve as large a 1-homology group.
Optimal PAC Bounds without Uniform Convergence

In statistical learning theory, determining the sample complexity of realizable binary classification for VC classes was a long-standing open problem. The results of Simon [1] and Hanneke [2] established sharp upper bounds in this setting. However, the reliance of their argument on the uniform convergence principle limits its applicability to more general learning settings such as multiclass classification. In this paper, we address this issue by providing optimal high probability risk bounds through a framework that surpasses the limitations of uniform convergence arguments.Our framework converts the leave-one-out error of permutation invariant predictors into high probability risk bounds. As an application, by adapting the one-inclusion graph algorithm of Haussler, Littlestone, and Warmuth [3], we propose an algorithm that achieves an optimal PAC bound for binary classification. Specifically, our result shows that certain aggregations of one-inclusion graph algorithms are optimal, addressing a variant of a classic question posed by Warmuth [4].We further instantiate our framework in three settings where uniform convergence is provably suboptimal. For multiclass classification, we prove an optimal risk bound that scales with the one-inclusion hypergraph density of the class, addressing the suboptimality of the analysis of Daniely and Shalev-Shwartz [5]. For partial hypothesis classification, we determine the optimal sample complexity bound, resolving a question posed by Alon, Hanneke, Holzman, and Moran [6]. For realizable bounded regression with absolute loss, we derive an optimal risk bound that relies on a modified version of the scale-sensitive dimension, refining the results of Bartlett and Long [7]. Our rates surpass standard uniform convergence-based results due to the smaller complexity measure in our risk bound.
Bounding the Quantum Value of Compiled Nonlocal Games: From CHSH to BQP Verification

We present a step towards the goal of producing a general cryptographic ’compilation’ procedure which can translate any entangled nonlocal game into a single-prover interactive protocol while preserving quantum completeness and soundness, using cryptography to simulate the separation between the provers. A candidate for such a procedure was introduced by Kalai et al. (STOC ’23), who defined a black-box cryptographic compilation procedure that applies to any nonlocal game and showed that it preserves classical value. In this work, we make progress towards a full understanding of the quantum value of the single-prover protocols that result from applying the Kalai et al. compilation procedure to entangled games. For the special case of CHSH, we prove that the Tsirelson bound holds under the compilation procedure introduced by Kalai et al., and we also recover a strong version of the ’rigidity’ property that makes CHSH so useful. As an application, we give a single-prover cryptographically sound classical verification protocol for BQP, and we prove its soundness using our CHSH rigidity analysis. Our protocol replicates the functionality of Mahadev’s protocol (FOCS ’18) but with two advantages: (1) the protocol is conceptually intuitive and requires fewer bespoke ingredients, and the soundness analysis is simpler and directly follows the analysis of the nonlocal case, and (2) the soundness analysis does not explicitly use the assumption of a TCF or an adaptive hardcore bit, and only requires QFHE as a black box (though currently the only known constructions of QFHE use TCFs).
Advisor-Verifier-Prover Games and the Hardness of Information Theoretic Cryptography

A major open problem in information-theoretic cryptography is to obtain a super-polynomial lower bound for the communication complexity of basic cryptographic tasks. This question is wide open even for very powerful non-interactive primitives such as private information retrieval (or locally-decodable codes), general secret sharing schemes, conditional disclosure of secrets, and fully-decomposable randomized encoding (or garbling schemes). In fact, for all these primitives we do not even have super-linear lower bounds. Furthermore, it is unknown how to relate these questions to each other or to other complexity-theoretic questions.In this note, we relate all these questions to the classical topic of query/space trade-offs, lifted to the setting of interactive proof systems. Specifically, we consider the following Advisor-Verifier-Prover (AVP) game: First, a function f is given to the advisor who computes an advice a. Next, an input x is given to the verifier and to the prover who claims that $f(x) \quad =1.$ The verifier should check this claim via a single round of interaction based on the private advice a and without having any additional information on f. We focus on the case where the prover is laconic and communicates only a constant number of bits, and, mostly restrict the attention to the simplest, purely information-theoretic setting, where all parties are allowed to be computationally unbounded. The goal is to minimize the total communication complexity which is dominated by the length of the advice plus the length of the verifier’s query.As our main result, we show that a super-polynomial lower bound for AVPs implies a super-polynomial lower bound for a wide range of information-theoretic cryptographic tasks. In particular, we present a communication-efficient transformation from any of the above primitives into an AVP protocol. Interestingly, each primitive induces some additional property over the resulting protocol. Thus AVP games form a new common yardstick that highlights the differences between all the above primitives.Equipped with this view, we revisit the existing (somewhat weak) lower bounds for the above primitives, and show that many of these lower bounds can be unified by proving a single counting-based lower bound on the communication of AVPs, whereas some techniques are inherently limited to specific domains. The latter is shown by proving the first polynomial separations between the complexity of secret-sharing schemes and conditional disclosure of secrets and between the complexity of randomized encodings and conditional disclosure of secrets.
Polynomial-Time Pseudodeterministic Construction of Primes

A randomized algorithm for a search problem is pseudodeterministic if it produces a fixed canonical solution to the search problem with high probability. In their seminal work on the topic, Gat and Goldwasser [1] posed as their main open problem whether prime numbers can be pseudodeterministically constructed in polynomial time. We provide a positive solution to this question in the infinitely-often regime. In more detail, we give an unconditional polynomial-time randomized algorithm B such that, for infinitely many values of $n, B\left(1^{n}\right)$ outputs a canonical n-bit prime $p_{n}$ with high probability. More generally, we prove that for every dense property Q of strings that can be decided in polynomial time, there is an infinitely-often pseudodeterministic polynomial-time construction of strings satisfying Q. This improves upon a subexponential-time construction of Oliveira and Santhanam [2]. Our construction uses several new ideas, including a novel bootstrapping technique for pseudodeterministic constructions, and a quantitative optimization of the uniform hardness-randomness framework of Chen and Tell [3], using a variant of the Shaltiel-Umans generator [4].
CNN Inference Accelerators with Adjustable Feature Map Compression Ratios

Recently, an increasing interest has been in developing a convolution neural network (CNN) with adjustable configurations, enabling instant adaption to different resource constraints during inference. The trained CNN in run-time can switch to different modes to achieve a certain accuracy-energy trade-off point, similar to DVFS (dynamic voltage and frequency scaling) and turbo boost, which are widely adopted in CPUs. In this paper, we propose strategies to enable CNN inference accelerators to have an adjustable feature map compression ratio, making them tunable regarding their external memory access amount. We resort to the mature JPEG technique to compress those intermediate feature maps. The critical challenge is to support such adjustable compression ratios using one single CNN instead of multiple CNNs corresponding to multiple ratios. In response, we propose compression-aware joint-training and switchable batch normalization.We use ResNet18, ResNet50, and MobileNetV2 on ImageNet to demonstrate our design, achieve inference-time compression ratio adjustability, and reduce external memory access bandwidth requirements. The result shows that our proposed strategies can maintain the Top-1 accuracy and reduce external memory access by at most 22.7× ∼ 28.3× only using a single CNN model with sets of BN parameters corresponding to multiple compression ratios.
User Profiling Based Proactive Interaction Manager for Adaptive Human-robot Interaction

Proactive interaction is a highly researched topic in the robotics arena. By using its proactive ability, a robot can decide intelligently in specific situations, taking particular attributes into account, to offer a certain service to the user. We propose a proactive interaction manager to suggest suitable reading suggestions to the user. User profiling is used as a tool to identify the nature and preferences of each user. A user’s profile is based on multiple attributes that determine whether a user chooses a particular genre for reading. This is developed as a strategy to identify the preferences of users. Since the user’s preferences may change over time, an algorithm to adaptively provide reading suggestions to the user considering their changes in preferences is also proposed. We conducted two experiments to evaluate the performance of the proposed proactive interaction manager. The results of the experiments show that the proposed approach is satisfactory for a domestic service robot based application.
ChessEye: An Integrated Framework for Accurate and Efficient Chessboard Reconstruction

This research paper presents a novel and generalizable approach for precisely detecting and identifying the configuration of pieces on both 2D and 3D chessboard images with different chess sets and varying background contexts. It makes a significant milestone in the digitalization of the chess world by enabling the recreation of physical chess boards on computer screens using a single image. It also provides a framework for real-time tracking and visualization of live chess games using video frames obtained directly from the camera. The novelty lies in the methodology that achieves remarkable accuracy through four key steps: ❨1❩ identifying the corner points of the chessboard, ❨2❩ detecting the chess pieces, ❨3❩ localizing the pieces within the chessboard, and ❨4❩ evaluating the position with the best possible variations. The introduction of the Fisher Linear Discriminant Analysis-based dynamic thresholding technique contributes to the perfect $ 100\%$ accuracy in distinguishing between the white and black chess pieces. The entire algorithm undergoes a thorough experimentation and evaluation process, confirming the effectiveness and versatility of the proposed approach.