Reviewerly/exHarmony huggingface dataset:
Optimizing Coverage in Wireless Sensor Networks Using the Cheetah Meta-Heuristic Algorithm

A wireless sensor network (WSN) consists of many sensor nodes that self-organize to form a distributed network system. These networks are mostly installed randomly in cities. To ensure data transmission, reception, and processing in smart city development, many sensor nodes need to be installed throughout the city due to factors causing inadequate coverage of monitoring areas. However, heterogeneous distribution may lead to large losses of resources. To solve this problem, in this research, an optimization model for WSN coverage is proposed based on the Cheetah Optimizer (CO) algorithm. CO is a nature-inspired algorithm inspired by cheetah hunting tactics. Three main strategies used by cheetahs for hunting, searching, sitting and waiting, and attacking are included in the algorithm. In addition, a fourth strategy, leaving prey and returning home, is also added to increase population diversity, convergence performance, and robustness. Different test functions demonstrate that CO outperforms other algorithms significantly in the literature. Moreover, for optimizing WSN coverage, CO produces a higher coverage rate compared to other meta-heuristic algorithms and improved algorithms mentioned in the literature for addressing the WSN coverage optimization problem.
Elderly People Balance Ability Based on Real-Time Training Model

Along with the decline of physical function, elderly people are more likely to fall than ordinary people. This paper established a comprehensive body balance assessment model for the elderly people through machine learning and principal component analysis. Then we use these three characteristics to classify the subjects blindly. In the process of classification, we use K-means clustering analysis method. we use the principal component analysis method to get the principal component factors of the assessment system of individual balance characteristics and the value of risk assessment. Finally reasonable suggestions are put forward for the elderly, such as taking part in more balancing exercises, strengthening muscle exercises and improving body shape.
A Mathematical Foundation for the Spatial Uncertainty of Critical Points in Probabilistic Scalar Fields

Critical points mark locations in the domain where the level-set topology of a scalar function undergoes fundamental changes and thus indicate potentially interesting features in the data. Established methods exist to locate and relate such points in a deterministic setting, but it is less well understood how the concept of critical points can be extended to the analysis of uncertain data. Most methods for this task aim at finding likely locations of critical points or estimate the probability of their occurrence locally but do not indicate if critical points at potentially different locations in different realizations of a stochastic process are manifestations of the same feature, which is required to characterize the spatial uncertainty of critical points. Previous work on relating critical points across different realizations reported challenges for interpreting the resulting spatial distribution of critical points but did not investigate the causes. In this work, we provide a mathematical formulation of the problem of finding critical points with spatial uncertainty and computing their spatial distribution, which leads us to the notion of uncertain critical points. We analyze the theoretical properties of these structures and highlight connections to existing works for special classes of uncertain fields. We derive conditions under which well-interpretable results can be obtained and discuss the implications of those restrictions for the field of visualization. We demonstrate that the discussed limitations are not purely academic but also arise in real-world data.
Enhancing Social Network Security: A Dynamic Approach for Detecting and Mitigating Social Spam

In today’s connected world, social networks have become integral to daily life, but their popularity also exposes them to misuse, such as spam and malicious content. The consequences of social spam go beyond annoyance and can distort discussions and lead to misunderstandings. Therefore, identifying spam-initiating accounts has become an important research concern. This article presents a two-step method that uses specialized data mining techniques tailored for business (Biz) data. The approach dynamically identifies users engaged in spam activities. In the initial phase, a comprehensive user database is created within the Facebook network, identifying potentially anomalous nodes and forming patterns associated with spam behavior. These patterns are used to evaluate new nodes for comparison. When a node is identified as a potential spam source, appropriate actions are taken for effective categorization and management. The effectiveness of our approach is validated through extensive comparative analysis against existing spam detection methods. Empirical results demonstrate a significant improvement in accuracy using our method, highlighting its superiority in detecting and mitigating social spam and addressing a critical challenge in social network security.
Design and implementation of advanced mathematics assisted learning system based on learning analysis technology

In recent years, with the rapid development of the Internet and big data, various industries have been actively integrating information technology. In the field of education, China has also conducted relevant explorations. Currently, the level of informatization in higher education is much higher than that in secondary and early childhood education. Therefore, major universities have always been at the forefront in the construction of auxiliary learning systems. This article adopts the concept and methods of learning analytics technology to design and implement a fully functional personalized auxiliary learning system. Firstly, this system obtains the requirements and positioning data through preliminary research, and then summarizes and summarizes this data to form an overview of the overall framework and functions. Next, based on these elements, the design and implementation of data structures are carried out, including the division of entity items and the creation of database tables, with a focus on describing the process of building the database and the logical relationships between forms, forming the system's global E-R diagram. Finally, based on this data structure, corresponding system development work is carried out. This auxiliary learning system is specifically designed for higher mathematics subjects and offers online question answering and real-time feedback, demonstrating excellent application effectiveness.
Construction of English Assisted Translation Learning System Based on Improved GLR Algorithm

Amidst the backdrop of a globally interconnected world, English has emerged as the lingua franca, seeing its usage surge exponentially. Consequently, the demand for machine translation technologies has skyrocketed, indicating a promising trajectory for future advancements in this realm. Traditional models for intelligent recognition in English translation have exhibited several shortcomings. These include misidentification of English translations, inaccuracies in rectifying learners' pronunciation errors, and unintentionally reinforcing incorrect pronunciations, which ultimately hinder the learning process. Within the realm of machine translation, the pivotal role is played by smart phrase recognition. This tech innovation ensures appropriate tone selection for translation examples and precise synchronization of parallel corpora. By harnessing intelligent phrase recognition, one can substantially mitigate grammatical ambiguities. Mastering English requires a comprehensive grasp of its various facets, with pronunciation standing out as a crucial component. For those aspiring to utilize English in routine conversational contexts, an emphasis on rectifying and honing pronunciation is indispensable. This manuscript delves into an English support translation learning system built upon the enhanced GLR algorithm. This approach paves the way for a revolutionary blueprint for English learning platforms.
Execute on Clear (EoC): Enhancing Security for Unsafe Speculative Instructions by Precise Identification and Safe Execution

Speculative execution attacks exploit incorrect speculation to execute malicious instructions and leak data via microarchitectural covert channels. Existing mitigations focus on restricting transmission-related instructions related to covert channels. In this paper, we propose the Execute on Clear (EoC), which offers an efficient defense strategy against covert channels in speculative execution attacks. EoC employs a two-stage identification method, which precisely identifies malicious transmission-related instructions by considering both the insecure data dependency and the status of microarchitecture components exploited by the attack. Moreover, with identification results, EoC guarantees the safe execution of transmission-related instructions, preventing unnecessary blocking. By reducing the misidentification and the blocked execution of such instructions, EoC avoids unnecessary maintenance operations and reduces performance overheads. We evaluate EoC on SPEC2006 and PARSEC3.0 workloads, revealing a performance overhead of merely 0.98% and 3.29% in the Spectre and Futuristic defense models, respectively. Notably, EoC exhibits lower performance overhead in comparison to existing methods.
RealArch: A Real-Time Scheduler for Mapping Multi-Tenant DNNs on Multi-Core Accelerators

Nowadays, the significance of multi-tenant deep neural networks (DNNs) has grown exponentially, particularly for cloud providers who execute multiple DNN models on one server to fulfill the users’ requirements while reducing the computational overhead. To satisfy the heavy computation requirement of multi-tenant DNNs, a feasible approach is to establish a multi-core accelerator housing multiple sub-accelerators. Although many researchers have achieved a certain success by designing either offline schedulers for heterogeneous accelerators or real-time schedulers for homogeneous accelerators, they fail to schedule multi-tenant DNNs to both homogeneous and heterogeneous multi-core accelerators in real time given the large search space and restricted overhead constraint.In this paper, we propose RealArch, a novel real-time scheduler that efficiently schedules multi-tenant DNNs to both homogeneous and heterogeneous multi-core accelerators in real-time. The key idea of RealArch is to quickly find the minimal latency of mapping multi-tenant DNNs to sub-accelerators, considering the occupation of DRAM, sub-accelerators, and buffers. To support the key idea, we first establish lightweight estimation models for multiple sub-accelerators to evaluate the Data Movement (DM) and Execution (EX) time when mapping a layer to them. Then, we design a real-time scheduling algorithm to compute and select the mapping solution with minimal latency. Finally, we build a low-cost hardware scheduler to perform the estimation models and the real-time scheduling algorithm. Extensive experiment results verify that RealArch can exceed the baseline Round Robin scheduling algorithm and two state-of-the-art schedulers AI-MT and MAGMA with acceptable hardware overhead.
Design of Intelligent Positioning Device for Group Exercises Based on Bluetooth Communication Technology

Based on the problems that arise in the training of large group exercises, this paper proposes a device for the positioning of players on the training field, which can not only guide the positioning of players, but also indicate the direction of movements. In order to realize the function of the device, this paper proposes a technical solution based on Bluetooth IoT, and gives the schematic diagram of the circuit composition and the circuit diagram of the key parts.
FlexGM: An Adaptive Runtime System to Accelerate Graph Matching Networks on GPUs

GMNs (Graph Matching Networks) exploit recently developed GNNs (Graph Neural Networks) to analyze the similarity between two graphs. They are increasingly deployed in many application domains due to their improved inference accuracy. A GMN consists of two stages, i.e., node-embedding and node-matching stages. The node-matching stage matches node features from two graphs for similarity, which accounts for over 90% of the total execution time. However, it is challenging to accelerate GMNs on GPUs due to their diverse computing patterns for different graph inputs. For large graphs, the overhead comes mainly from the high computation overhead, which increases quadratically to the size of the graphs; for small graphs, the overhead comes from the low parallelism and resource utilization.In this paper, we propose FlexGM, a flexible runtime, to adaptively accelerate GMNs on GPUs. For large graphs, we exploit the massive computation redundancy in GMNs and develop a low-overhead deduplication module to mitigate the high computation overhead. For small graphs, we develop a unified matching module to optimize GPU hardware resource usage. An adaptive module manager is then developed to judiciously select beneficial optimization strategies. Experimental results show that the FlexGM system achieves 2.5× (up to 7.6 ×) average speedup over existing methods.
Consumers’ Intention to Use of the Retail Service Robots: A Humanoid Perspective

This study focused on explaining the influence of retail service robots’ (RSR) rapport and social presence on consumer engagement and intention to use in retail. Based on extensive literature review, this study formulated three hypotheses to support the arguments. Quantitative methodology with a survey strategy was undertaken, which had an effective sample size of 300 young consumers. Furthermore, analysis was carried out using Smart partial least squares (PLS)-structural equation modelling. The study finds a significant direct and positive impact of the RSRs’ rapport and social presence on consumer engagement and intention to use in retail. The findings shed the light on improving retail service quality with humanoid based applications.
Transcend Adversarial Examples: Diversified Adversarial Attacks to Test Deep Learning Model

Existing optimized adversarial attacks rely on the ability to search for perturbation within l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> norm while keeping maximized loss for highly non-convex loss functions. Random initialization perturbation and the steepest gradient direction strategy are efficient techniques to prevent falling into local optima but compromise the capability of diversity exploration. Therefore, we introduce the Diversity-Driven Adversarial Attack (DAA), which incorporates Output Diversity Strategy (ODS) and diverse initialization gradient direction into the optimized adversarial attack algorithm, aiming to refine the inherent properties of the adversarial examples (AEs). More specifically, we design a diversity-promoting regularizer to penalize the insignificant distance between initialization gradient directions based on the version of ODS. Extensive experiments demonstrate that DAA can efficiently improve existing coverage criteria without sacrificing the performance of attack success rate, which implies that DAA can implicitly explore more internal model logic of DL model.
Speech Command Recognition Using Self-Supervised ASR in Tamil

ASR technology has made significant advancements, approaching performance levels comparable to humans. However, developing ASR models for all languages, especially Low Resource Languages (LRLs), presents challenges due to limited resources. Recent studies on intent classification in LRI.s employ transfer karning techniques, leveraging state-of-the-art English ASR models to improve performance in these domains. Additionally, the emerging trend of self-supervised learning has proven advantageous in developing sophisticated ASR models, requiring less labeled data to achieve high performance. In our research, we utiliued a self-supervised ASR model to classify intent in an LRI. specifically the Tamil language. We compared two methods for the same ASR framework: a fine-tuning approach and a transfer learning approach with a limited amount of labeled data in Tamil. Our findings indicate that the fine-tuning method outperforms the transfer learning technique. Morewer, the model exhibited a noteworthy increase in accuracy compared to the established phoneme-based speech intent classification methodology in Tamil. This study represents a significant step forward in enhancing speech recognition capabilities for LRL.s.
Properly learning decision trees with queries is NP-hard

We prove that it is NP-hard to properly PAC learn decision trees with queries, resolving a longstanding open problem in learning theory (Bshouty 1993; Guijarro–Lavín–Raghavan 1999; Mehta–Raghavan 2002; Feldman 2016). While there has been a long line of work, dating back to (Pitt–Valiant 1988), establishing the hardness of properly learning decision trees from random examples, the more challenging setting of query learners necessitates different techniques and there were no previous lower bounds. En route to our main result, we simplify and strengthen the best known lower bounds for a different problem of Decision Tree Minimization (Zantema—Bodlaender 2000; Sieling 2003).On a technical level, we introduce the notion of hardness distillation, which we study for decision tree complexity but can be considered for any complexity measure: for a function that requires large decision trees, we give a general method for identifying a small set of inputs that is responsible for its complexity. Our technique even rules out query learners that are allowed constant error. This contrasts with existing lower bounds for the setting of random examples which only hold for inverse-polynomial error.Our result, taken together with a recent almost-polynomial time query algorithm for properly learning decision trees under the uniform distribution (Blanc–Lange–Qiao–Tan 2022), demonstrates the dramatic impact of distributional assumptions on the problem.
Research and Practice of Enterprise Education Mode in Universities Based on Data Mining

At present, enterprise education has become a strategic measure to build an innovative country in China, an important direction of tertiary education reform and talent cultivation, and an inherent requirement to push tertiary education to a higher level. It plays an important role to conduct in-depth research on the assessment of innovation ability, to help students reasonably assess their innovation ability, and to provide data support for enterprise education. Educators can find out the value of enterprise education by obtaining a large amount of data and analyzing and researching it. Based on this, this article puts forward a data classification method of innovative entrepreneurship in universities based on data mining (DM), in order to build an intelligent assessment model of enterprise education. After many iterations, the error of this method is obviously better than that of the comparison algorithm in data classification analysis. The error is reduced by 33.22%, the recall rate reaches 95.89%, and it is improved by 15.88% compared with the comparison algorithm. Compared with the traditional data classification mode, the data classification mode designed in this article innovatively demarcates the data processing area under the complex processing environment, and with the assistance of DM, it improves the overall data classification processing effect.
When Does Adaptivity Help for Quantum State Learning?

We consider the classic question of state tomography: given copies of an unknown quantum state $\rho \in \mathbb{C}^{d \times d}$, output $\widehat{\rho}$ which is close to $\rho$ in some sense, e.g. trace distance or fidelity. When one is allowed to make coherent measurements entangled across all copies, $\Theta\left(d^{2} / \varepsilon^{2}\right)$ copies are necessary and sufficient to get trace distance $\varepsilon$ [18], [29]. Unfortunately, the protocols achieving this rate incur large quantum memory overheads that preclude implementation on near-term devices. On the other hand, the best known protocol using incoherent (single-copy) measurements uses $O\left(d^{3} / \varepsilon^{2}\right)$ copies [24], and multiple papers have posed it as an open question to understand whether or not this rate is tight [6], [18]. In this work, we fully resolve this question, by showing that any protocol using incoherent measurements, even if they are chosen adaptively, requires $\Omega\left(d^{3} / \varepsilon^{2}\right)$ copies, matching the upper bound of [24]. We do so by a new proof technique which directly bounds the “tilt” of the posterior distribution after measurements, which yields a surprisingly short proof of our lower bound, and which we believe may be of independent interest. While this implies that adaptivity does not help for tomography with respect to trace distance, we show that it actually does help for tomography with respect to infidelity. We give an adaptive algorithm that outputs a state which is $\gamma$-close in infidelity to $\rho$ using only $\widetilde{O}\left(d^{3} / \gamma\right)$ copies, which is optimal for incoherent measurements. In contrast, it is known [18] that any nonadaptive algorithm requires $\Omega\left(d^{3} / \gamma^{2}\right)$ copies. While it is folklore that in 2 dimensions, one can achieve a scaling of $O(1 / \gamma)$, to the best of our knowledge, our algorithm is the first to achieve the optimal rate in all dimensions.
HiSEP-Q: A Highly Scalable and Efficient Quantum Control Processor for Superconducting Qubits

Quantum computing promises an effective way to solve targeted problems that are classically intractable. Among them, quantum computers built with superconducting qubits are considered one of the most advanced technologies, but they suffer from short coherence times. This can get exaggerated when they are controlled directly by general-purpose host machines, which in turn leads to the loss of quantum information. To mitigate this, we need quantum control processors (QCPs) positioned between quantum processing units (QPUs) and host machines to reduce latencies. However, existing QCPs are built on top of designs with no or inefficient scalability, requiring a large number of instructions when scaling to more qubits. In addition, interactions between current QCPs and host machines require frequent data transmissions and offline computations to obtain final results from hundreds of repeated executions, which limits the performance of quantum computers.In this paper, we propose a QCP — called HiSEP-Q — featuring a novel quantum instruction set architecture (QISA) and its microarchitecture implementation. For efficient control, we utilize mixed-type addressing modes and mixed-length instructions in HiSEP-Q, which provides an efficient way to concurrently address more than 100 qubits. Further, for efficient read-out and analysis, we develop a novel onboard accumulation and sorting unit, which eliminates the data transmission of raw data between the QCPs and host machines and enables real-time result processing. Compared to the state-of-the-art, our proposed QISA achieves at least 62% and 28% improvements in encoding efficiency with real and synthetic quantum circuits, respectively. We also validate the microarchitecture on a field-programmable gate array (FPGA), which exhibits low power and resource consumption, even as the number of qubits scales to 100. Both hardware and ISA evaluations demonstrate that HiSEP-Q features high scalability and efficiency toward the number of controlled qubits.