Reviewerly/exHarmony huggingface dataset:
On Rank Selection in Non-Negative Matrix Factorization Using Concordance

The choice of the factorization rank of a matrix is critical, e.g., in dimensionality reduction, filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to adjusting the noise, while selecting a rank that is too low results in the oversimplification of the signal. Numerous methods for selecting the factorization rank of a non-negative matrix have been proposed. One of them is the cophenetic correlation coefficient (ccc), widely used in data science to evaluate the number of clusters in a hierarchical clustering. In previous work, it was shown that ccc performs better than other methods for rank selection in non-negative matrix factorization (NMF) when the underlying structure of the matrix consists of orthogonal clusters. In this article, we show that using the ratio of ccc to the approximation error significantly improves the accuracy of the rank selection. We also propose a new criterion, concordance, which, like ccc, benefits from the stochastic nature of NMF; its accuracy is also improved by using its ratio-to-error form. Using real and simulated data, we show that concordance, with a CUSUM-based automatic detection algorithm for its original or ratio-to-error forms, significantly outperforms ccc. It is important to note that the new criterion works for a broader class of matrices, where the underlying clusters are not assumed to be orthogonal.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Models

The implementation of joint source-channel coding (JSCC) schemes using deep learning has accelerated the development of semantic communication research. Existing JSCC schemes based on deep learning (DL) are trained on a fixed signal-to-noise ratio (SNR); however, these trained models are not designed for scenarios in which the SNR is dynamic. Therefore, a novel semantic adaptive model for semantic communication—called joint source-channel coding with adaptive models (AMJSCC)—that has a semantic adaptive model selection (SAMS) module is proposed. The joint source-channel encoding (JSCE) model and the joint source-channel decoding (JSCD) model adapt according to both real-time channel conditions and system available computational power resources. Furthermore, residual networks with different layers are investigated to further improve the accuracy of information recovery. Simulation results demonstrate that our model can achieve higher recovery similarity and is more robust and adaptive to the SNR and communication resources. Meanwhile, compared to the state-of-the-art deep JSCC methods, it reduces storage space and communication resource consumption.
Experimental optimal discrimination of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> states of a qubit with fixed rates of inconclusive outcomes

In a general optimized measurement scheme for discriminating between nonorthogonal quantum states, the error rate is minimized under the constraint of a fixed rate of inconclusive outcomes (FRIO). This so-called optimal FRIO measurement encompasses the standard and well-known minimum-error and optimal unambiguous (or maximum-confidence) discrimination strategies as particular cases. Here, we experimentally demonstrate the optimal FRIO discrimination between $N=2,3,5$, and 7 equally likely symmetric states of a qubit encoded in photonic path modes. Our implementation consists of applying a probabilistic quantum map which increases the distinguishability between the inputs in a controlled way, followed by a minimum-error measurement on the successfully transformed outputs. The results obtained corroborate this two-step approach and, in our experimental scheme, can be straightforwardly extended to higher dimensions. The optimized measurement demonstrated here will be useful for quantum communication scenarios where the error rate and inconclusive rate must be kept below the levels provided by the respective standard strategies.
Optimizing resetting of superconducting qubits

Many quantum algorithms demand a large number of repetitions to obtain reliable statistical results. Thus, at each repetition it is necessary to reset the qubits efficiently and precisely in the shortest possible time, so that quantum computers actually have advantages over classical ones. In this work, we perform a detailed analysis of three different models for information resetting in superconducting qubits. Our experimental setup consists of a main qubit coupled to different auxiliary dissipative systems, which are employed in order to perform the erasure of the information of the main qubit. Our analysis shows that it is not enough to increase the coupling and the dissipation rate associated with the auxiliary systems to decrease the resetting time of the main qubit, a fact that motivates us to find the optimal set of parameters for each studied approach, allowing a significant decrease in the reset time of the three models analyzed.
Dual-Quaternion-Based SLERP MPC Local Controller for Safe Self-Driving of Robotic Wheelchairs

In this work, the motion control of a robotic wheelchair to achieve safe and intelligent movement in an unknown scenario is proposed. The primary objective is to develop a comprehensive framework for a robotic wheelchair that combines a global path planner and a model predictive control (MPC) local controller. The A* algorithm is employed to generate a global path. To ensure safe and directional motion for the wheelchair user, an MPC local controller is implemented taking into account the via points generated by an approach combined with dual quaternions and spherical linear interpolation (SLERP). Dual quaternions are utilized for their simultaneous handling of rotation and translation, while SLERP enables smooth and continuous rotation interpolation by generating intermediate orientations between two specified orientations. The integration of these two methods optimizes navigation performance. The system is built on the Robot Operating System (ROS), with an electric wheelchair equipped with 3D-LiDAR serving as the hardware foundation. The experimental results reveal the effectiveness of the proposed method and demonstrate the ability of the robotic wheelchair to move safely from the initial position to the destination. This work contributes to the development of effective motion control for robotic wheelchairs, focusing on safety and improving the user experience when navigating in unknown environments.
Fine-gained Motion Enhancement for action recognition: Focusing on action-related regions

Fine-grained motion information is crucial to improve the performance of video action recognition. Great strides have been made, with direct subtraction at adjacent original frames or frame features to learn motion representations. However, existing methods lack the ability to focus on the motion details about the action-related regions and to model the video events at multi-scale. To solve the above problems, this paper proposes a Fine-grained Motion Enhancement Network (FMENet), which comprises an Inter-frame Difference Motion Enhancement (IDME) module and a Multi-fiber Multi-level SpatioTemporal (MMST) module. In specific, the IDME module combines inter-frame local difference attention with a foreground encoder to differentiate foreground motion-related and static-related features, in conjunction with inter-frame feature subtraction operation to learn fine-grained motion features. Complementary to the motion features provided by IDME module, the MMST module introduces multi-level receptive fields in both spatial and temporal dimensions to further encode the semantic and appearance information of events. Finally, the IDME and MMST modules are embedded in a standard 2D framework to construct the FMENet with introducing limited computational cost. Extensive experiments on the Mini-Kinetics-200, Something–Something V1, HMDB-51 and UCF-101 datasets have been conducted to demonstrate the superiority of our method over other methods.
A Hybrid Global/Reactive Algorithm for Collision-Free UAV Navigation in 3D Environments with Steady and Moving Obstacles

This paper introduces a practical navigation approach for nonholonomic Unmanned Aerial Vehicles (UAVs) in 3D environment settings with numerous stationary and dynamic obstacles. To achieve the intended outcome, Dynamic Programming (DP) is combined with a reactive control algorithm. The DP allows the UAVs to navigate among known static barriers and obstacles. Additionally, the reactive controller uses data from the onboard sensor to avoid unforeseen obstacles. The proposed strategy is illustrated through computer simulation results. In simulations, the UAV successfully navigates around dynamic obstacles while maintaining its route to the target. These results highlight the ability of our proposed approach to ensure safe and efficient UAV navigation in complex and obstacle-laden environments.
Spoofing Detection for Personal Voice Assistants

Personal Voice Assistants (PVAs) are common acoustic sensing systems that are used as a speech-based controller for critical systems making them vulnerable to speech spoofing attacks. Prior research has focused on the discrimination of genuine and spoofed speech for applications with large population speaker verification and challenges such as ASVspoof have advanced this work over the last few years. In this paper, we consider spoofing detection in a PVA setting where the number of household users is small. We show that when pre-trained models are adapted to household users, spoofing detection is improved. Furthermore, we demonstrate that adaptation is still effective in realistic scenarios where only genuine speech of household users is available but the generation of spoofed speech samples for household users is undesirable.
What do LLMs need to Synthesize Correct Router Configurations?

We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topology, syntax, and semantics. Our strategy, that we call Verified Prompt Programming, is to combine GPT-4 with verifiers, and use localized feedback from the verifier to automatically correct errors. Verification requires a specification and actionable localized feedback to be effective. We show results for two use cases: translating from Cisco to Juniper configurations on a single router, and implementing a no-transit policy on multiple routers. While human input is still required, if we define the leverage as the number of automated prompts to the number of human prompts, our experiments show a leverage of 10X for Juniper translation, and 6X for implementing the no-transit policy, ending with verified configurations.
Adapting Foundation Models for Operator Data Analytics

The complexity of operator networks and myriad of specialized metrics produced by network function providers present a formidable challenge in retrieving and analyzing operator data, a vital component for network operations. This necessitates specialist intervention, which is time-consuming and limits customization. This paper proposes Data Intelligence for Operators Copilot, a natural language interface for retrieval and analytics tasks on operator data, leveraging foundation models. It addresses the challenges posed by operator data through a novel application of semantic search to effectively provide necessary context regarding specialized metrics. The system has outperformed state-of-the-art natural language interfaces for databases, when applied to an operator-specific benchmark dataset of expert-generated representative queries, with 66% execution accuracy.
Weakly Supervised Learning Approach for Implicit Aspect Extraction

Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much attention. This paper proposes a novel weakly supervised method for implicit aspect extraction, which is a task to classify a sentence into a pre-defined implicit aspect category. A dataset labeled with implicit aspects is automatically constructed from unlabeled sentences as follows. First, explicit sentences are obtained by extracting explicit aspects from unlabeled sentences, while sentences that do not contain explicit aspects are preserved as candidates of implicit sentences. Second, clustering is performed to merge the explicit and implicit sentences that share the same aspect. Third, the aspect of the explicit sentence is assigned to the implicit sentences in the same cluster as the implicit aspect label. Then, the BERT model is fine-tuned for implicit aspect extraction using the constructed dataset. The results of the experiments show that our method achieves 82% and 84% accuracy for mobile phone and PC reviews, respectively, which are 20 and 21 percentage points higher than the baseline.
A Deep-Penetrating Geochemical Prospecting Experiment of Mahuagou Gold Deposit in the Core of the Huangling Anticline, Western Hubei, China

The Mahuagou gold deposit is among the most important gold deposits in the core of the Huangling Anticline. However, the geochemical exploration on the surface of the mining area presents challenges due to the thin overburden. This paper focuses on the overburden soil of the Fengxiangshugou (FXS)-Mahuanggou (MHG) section as the research object. It utilizes chemical form analysis of gold, soil halogen survey, and heat-released mercury survey to determine the key deep-penetrating geochemical methods for the mining area. The results indicated that Si and Al components of samples exhibit minimal variation, suggesting that drift loads did not influence the overburden soil. Based on the systematic clustering, As, Sb, Mo, Bi, W, and Hg emerge as ore-body or ore-belt front elements of hydrothermal gold deposits. In the study area, the predominant chemical form of gold in soil is the strong organic bond. Compared to the total amount, strong organic bound gold and heat-released mercury show higher anomaly contrasts, making them crucial indicators of faults, intrusions, and hidden ore bodies. Consequently, chemical form analysis of gold and heat-released mercury surveys can enhance the anomaly contrast, proving beneficial for geochemical prospecting for weak anomalies in this area.
Performance and Applicability of Post-Quantum Digital Signature Algorithms in Resource-Constrained Environments

The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication.
Incorporating Syntactic Cognitive in Multi-granularity Data Augmentation for Chinese Grammatical Error Correction

Chinese grammatical error correction (CGEC) has recently attracted a lot of attention due to its real-world value. The current mainstream approaches are all data-driven, but the following flaws still exist. First, there is less high-quality training data with complexity and a variety of errors, and data-driven approaches frequently fail to significantly increase performance due to the lack of data. Second, the existing data augmentation methods for CGEC mainly focus on word-level augmentation and ignore syntactic-level information. Third, the current data augmentation methods are strongly randomized, and fewer can fit the cognition pattern of students on syntactic errors. In this paper, we propose a novel multi-granularity data augmentation method for CGEC, and construct a syntactic error knowledge base for error type Missing and Redundant Components, and syntactic conversion rules for error type Improper Word Order based on a finely labeled syntactic structure treebank. Additionally, we compile a knowledge base of character and word errors from actual student essays. Then, a data augmentation algorithm incorporating character, word, and syntactic noise is designed to build the training set. Extensive experiments show that the $$F_{0.5}$$ in the test set is 36.77%, which is a 6.2% improvement compared to the best model in the NLPCC Shared Task, proving the validity of our method.
STEM Teachers’ Digital Competence: Different Subjects, Different Proficiencies

STEM—science, technology, engineering, and mathematics—and STEM literacy have emerged as one (of many) concerted efforts aiming to provide the different competences required for future generations to adapt to an evolving world. Despite lacking a comprehensive framework for STEM, this concept has been studied under different perspectives, one of which is the digital competences of teachers. This study focuses on the digital competence and proficiency of 20,935 teachers, distributed across the following subjects: mathematics and natural sciences, physics and chemistry, biology and geology, and mathematics. It uses DigCompEdu as a conceptual framework to describe teachers’ use of digital technologies to teach STEM subjects. The results show that biology and geology teachers achieved significantly higher digital proficiency scores when compared to teachers from the other three subjects. Physics and chemistry teachers also scores significantly higher than mathematics and natural sciences teachers. The results show the existence of significant positive correlations among all competence areas for the four STEM subjects. In conclusion, Portuguese teachers presented different levels of digital competence and perceived themselves as differently prepared for integrating digital technologies. A potential implication of this study is the need for teacher education about digital competences and a focus on producing teachers capable of dealing with STEM in their future classroom teaching and learning.
Towards Analyzing the Efficacy of Multi-task Learning in Hate Speech Detection

Secretary-General António Guterres launched the United Nations Strategy and Plan of Action on Hate Speech in 2019, recognizing the alarming trend of increasing hate speech worldwide. Despite extensive research, benchmark datasets for hate speech detection remain limited in volume and vary in domain and annotation. In this paper, the following research objectives are deliberated (a) performance comparisons between multi-task models against single-task models; (b) performance study of different multi-task models (fully shared, shared-private) for hate speech detection, considering individual dataset as a separate task; (c) what is the effect of using different combinations of available existing datasets in the performance of multi-task settings? A total of six datasets that contain offensive and hate speech on the accounts of race, sex, and religion are considered for the above study. Our analysis suggests that a proper combination of datasets in a multi-task setting can overcome data scarcity and develop a unified framework.
Long Short-Term Planning for Conversational Recommendation Systems

In Conversational Recommendation Systems (CRS), the central question is how the conversational agent can naturally ask for user preferences and provide suitable recommendations. Existing works mainly follow the hierarchical architecture, where a higher policy decides whether to invoke the conversation module (to ask questions) or the recommendation module (to make recommendations). This architecture prevents these two components from fully interacting with each other. In contrast, this paper proposes a novel architecture, the long short-term feedback architecture, to connect these two essential components in CRS. Specifically, the recommendation predicts the long-term recommendation target based on the conversational context and the user history. Driven by the targeted recommendation, the conversational model predicts the next topic or attribute to verify if the user preference matches the target. The balance feedback loop continues until the short-term planner output matches the long-term planner output, that is when the system should make the recommendation.
Epistemic Modality Constructions as Stable Idiolectal Features: A Cross-genre Study of Spanish

Abstract Forensic authorship analysis is based on two assumptions: that every individual has a unique idiolect, and that features characteristic of that idiolect will recur with a relatively stable frequency. Yet, a speaker’s language can change with age, affective states, according to audience, or genre. Thus, studies on authorship analysis should adopt the theory that while some linguistic parameters of an idiolect can remain stable, others can change depending on various circumstances. This investigation, which takes a constructional and functional-based approach to discourse-level phenomena in idiolectal stability, analyzes cross-genre data produced by nine Mexican participants throughout a twelve-year time span. This study contributes to a greater understanding of the linguistic elements that survive genre effects and are potentially useful in both investigative and evidential forensic linguistic work. We provide a detailed description of linguistic features, their specific values, and context-dependent interpretation, keeping in mind the context of expert linguistic testimony, with its preference for methods which “employ linguistically motivated analyses in combination with quantitative tools” (Solan &amp; Tiersma, 2004, p.463). Our findings show that idiolectal style tends to remain stable across genres and communication modes in epistemic modality constructions. Epistemic markers —specifically, markers indicating low commitment by the speaker (e.g., no sé ‘I don’t know’) or expressing indirectness when introducing the illocutionary force (e.g., la verdad [es que] ‘the truth [is that]’)— display idiolectal stability, as these markers seem to be the most effective in terms of allowing speakers to strategically manifest the extent of their knowledge regarding what is said.
Micro-expression Recognition Based on PCB-PCANet+

Micro-expressions (MEs) have the characteristics of small motion amplitude and short duration. How to learn discriminative ME features is a key issue in ME recognition. Motivated by the success of PCB model in person retrieval, this paper proposes a ME recognition method called PCB-PCANet+. Considering that the important information of MEs is mainly concentrated in a few key facial areas like eyebrows and eyes, based on the output of shallow PCANet+, we use a multiple branch LSTM networks to separately learn the local spatio-temporal features for each facial ROI region. In addition, in the stage of multiple branch fusion, we design a feature weighting strategy according to the significances of different facial regions to further improve the performances of ME recognition. The experimental results on the SMIC and CASME II datasets validate the effectiveness of the proposed method.