OpenRaiser/Intern-Atlas huggingface dataset:
Controlling Borda Elections by Adding or Deleting either Votes or Candidates: Complete and Top-Truncated Votes
Venue: arXiv cs.CC 2024
Year: 2024

An election is defined as a pair of a set of candidates C=\{c_1,\cdots,c_m\} and a multiset of votes V=\{v_1,\cdots,v_n\}, where each vote is a linear order of the candidates. The Borda election rule is characterized by a vector \langle m-1,m-2,\cdots,0\rangle, which means that the candidate ranked at the i-th position of a vote v receives a score m-i from v, and the candidate receiving the most score from all votes wins the election. Here, we consider the control problems of a Borda election, where the chair of the election attempts to influence the election outcome by adding or deleting either votes or candidates with the intention to make a special candidate win (constructive control) or lose (destructive control) the election. Control problems have been extensively studied for Borda elections from both classical and parameterized complexity viewpoints. We complete the parameterized complexity picture for Borda control problems by showing W[2]-hardness with the number of additions/deletions as parameter for constructive control by deleting votes, adding candidates, or deleting candidates. The hardness result for deleting votes settles an open problem posed by Liu and Zhu. Following the suggestion by Menon and Larson, we also investigate the impact of introducing top-truncated votes, where each voter ranks only t out of the given m candidates, on the classical and parameterized complexity of Borda control problems. Constructive Borda control problems remain NP-hard even with t being a small constant. Moreover, we prove that in the top-truncated case, constructive control by adding/deleting votes problems are FPT with the number \ell of additions/deletions and t as parameters, while for every constant t\geq 2, constructive control by adding/deleting candidates problems are W[2]-hard with respect to \ell.
Leveraging AES Padding: dBs for Nothing and FEC for Free in IoT Systems
Venue: arXiv cs.ET 2024
Year: 2024

The Internet of Things (IoT) represents a significant advancement in digital technology, with its rapidly growing network of interconnected devices. This expansion, however, brings forth critical challenges in data security and reliability, especially under the threat of increasing cyber vulnerabilities. Addressing the security concerns, the Advanced Encryption Standard (AES) is commonly employed for secure encryption in IoT systems. Our study explores an innovative use of AES, by repurposing AES padding bits for error correction and thus introducing a dual-functional method that seamlessly integrates error-correcting capabilities into the standard encryption process. The integration of the state-of-the-art Guessing Random Additive Noise Decoder (GRAND) in the receiver's architecture facilitates the joint decoding and decryption process. This strategic approach not only preserves the existing structure of the transmitter but also significantly enhances communication reliability in noisy environments, achieving a notable over 3 dB gain in Block Error Rate (BLER). Remarkably, this enhanced performance comes with a minimal power overhead at the receiver - less than 15% compared to the traditional decryption-only process, underscoring the efficiency of our hardware design for IoT applications. This paper discusses a comprehensive analysis of our approach, particularly in energy efficiency and system performance, presenting a novel and practical solution for reliable IoT communications.
Exploring Speech Pattern Disorders in Autism using Machine Learning
Venue: arXiv cs.SD 2024
Year: 2024

Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech patterns from examiner-patient dialogues presents significant challenges due to the subtle and diverse manifestations of speech-related symptoms in affected individuals. This study presents a comprehensive approach to identify distinctive speech patterns through the analysis of examiner-patient dialogues. Utilizing a dataset of recorded dialogues, we extracted 40 speech-related features, categorized into frequency, zero-crossing rate, energy, spectral characteristics, Mel Frequency Cepstral Coefficients (MFCCs), and balance. These features encompass various aspects of speech such as intonation, volume, rhythm, and speech rate, reflecting the complex nature of communicative behaviors in ASD. We employed machine learning for both classification and regression tasks to analyze these speech features. The classification model aimed to differentiate between ASD and non-ASD cases, achieving an accuracy of 87.75%. Regression models were developed to predict speech pattern related variables and a composite score from all variables, facilitating a deeper understanding of the speech dynamics associated with ASD. The effectiveness of machine learning in interpreting intricate speech patterns and the high classification accuracy underscore the potential of computational methods in supporting the diagnostic processes for ASD. This approach not only aids in early detection but also contributes to personalized treatment planning by providing insights into the speech and communication profiles of individuals with ASD.
Dynamic Size Counting in the Population Protocol Model
Venue: arXiv cs.DC 2024
Year: 2024

The population protocol model describes collections of distributed agents that interact in pairs to solve a common task. We consider a dynamic variant of this prominent model, where we assume that an adversary may change the population size at an arbitrary point in time. In this model we tackle the problem of counting the population size: in the dynamic size counting problem the goal is to design an algorithm that computes an approximation of $\log n$. This estimate can be used to turn static, non-uniform population protocols, i.e., protocols that depend on the population size $n$, into dynamic and loosely-stabilizing protocols. Our contributions in this paper are three-fold. Starting from an arbitrary initial configuration, we first prove that the agents converge quickly to a valid configuration where each agent has a constant-factor approximation of $\log n$, and once the agents reach such a valid configuration, they stay in it for a polynomial number of time steps. Second, we show how to use our protocol to define a uniform and loosely-stabilizing phase clock for the population protocol model. Finally, we support our theoretical findings by empirical simulations that show that our protocols work well in practice.
Hybrid Convolutional Neural Networks with Reliability Guarantee
Venue: arXiv cs.AI 2024
Year: 2024

Making AI safe and dependable requires the generation of dependable models and dependable execution of those models. We propose redundant execution as a well-known technique that can be used to ensure reliable execution of the AI model. This generic technique will extend the application scope of AI-accelerators that do not feature well-documented safety or dependability properties. Typical redundancy techniques incur at least double or triple the computational expense of the original. We adopt a co-design approach, integrating reliable model execution with non-reliable execution, focusing that additional computational expense only where it is strictly necessary. We describe the design, implementation and some preliminary results of a hybrid CNN.
The Potential and Implications of Generative AI on HCI Education
Venue: arXiv cs.HC 2024
Year: 2024

Generative AI (GAI) is impacting teaching and learning directly or indirectly across a range of subjects and disciplines. As educators, we need to understand the potential and limitations of AI in HCI education and ensure our graduating HCI students are aware of the potential and limitations of AI in HCI. In this paper, we report on the main pedagogical insights gained from the inclusion of generative AI into a 10 week undergraduate module. We designed the module to encourage student experimentation with GAI models as part of the design brief requirement and planned practical sessions and discussions. Our insights are based on replies to a survey sent out to the students after completing the module. Our key findings, for HCI educators, report on the use of AI as a persona for developing project ideas and creating resources for design, and AI as a mirror for reflecting students' understanding of key concepts and ideas and highlighting knowledge gaps. We also discuss potential pitfalls that should be considered and the need to assess students' literacies and assumptions of GAIs as pedagogical tools. Finally, we put forward the case for educators to take the opportunities GAI presents as an educational tool and be experimental, creative, and courageous in their practice. We end with a discussion of our findings in relation to the TPACK framework in HCI.
Systematic Use of Random Self-Reducibility against Physical Attacks
Venue: arXiv cs.CR 2024
Year: 2024

This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. Our approach is at the operation level, is not algorithm-specific, and thus, can be applied for protecting a wide range of algorithms. The countermeasure is empirically evaluated against attacks over operations like modular exponentiation, modular multiplication, polynomial multiplication, and number theoretic transforms. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems. The countermeasure reduced the power side-channel leakage by two orders of magnitude, to an acceptably secure level in TVLA analysis. For fault injection, the countermeasure reduces the number of faults to 95.4% in average.
Anomaly Detection in Certificate Transparency Logs
Venue: arXiv cs.CR 2024
Year: 2024

We propose an anomaly detection technique for X.509 certificates utilizing Isolation Forest. This method can be beneficial when compliance testing with X.509 linters proves unsatisfactory, and we seek to identify anomalies beyond standards compliance. The technique is validated on a sample of certificates from Certificate Transparency logs.
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation
Venue: arXiv cs.CV 2024
Year: 2024

Diffusion models are a powerful generative framework, but come with expensive inference. Existing acceleration methods often compromise image quality or fail under complex conditioning when operating in an extremely low-step regime. In this work, we propose a novel distillation framework tailored to enable high-fidelity, diverse sample generation using just one to three steps. Our approach comprises three key components: (i) Backward Distillation, which mitigates training-inference discrepancies by calibrating the student on its own backward trajectory; (ii) Shifted Reconstruction Loss that dynamically adapts knowledge transfer based on the current time step; and (iii) Noise Correction, an inference-time technique that enhances sample quality by addressing singularities in noise prediction. Through extensive experiments, we demonstrate that our method outperforms existing competitors in quantitative metrics and human evaluations. Remarkably, it achieves performance comparable to the teacher model using only three denoising steps, enabling efficient high-quality generation.
An LSTM-Based Chord Generation System Using Chroma Histogram Representations
Venue: arXiv cs.SD 2024
Year: 2024

This paper proposes a system for chord generation to monophonic symbolic melodies using an LSTM-based model trained on chroma histogram representations of chords. Chroma representations promise more harmonically rich generation than chord label-based approaches, whilst maintaining a small number of dimensions in the dataset. This system is shown to be suitable for limited real-time use. While it does not meet the state-of-the-art for coherent long-term generation, it does show diatonic generation with cadential chord relationships. The need for further study into chroma histograms as an extracted feature in chord generation tasks is highlighted.
Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance
Venue: arXiv cs.HC 2024
Year: 2024

This study investigates the metacognitive capabilities of Large Language Models relative to human metacognition in the context of the International Coaching Federation ICF mimicking exam, a situational judgment test related to coaching competencies. Using a mixed method approach, we assessed the metacognitive performance, including sensitivity, accuracy in probabilistic predictions, and bias, of human participants and five advanced LLMs (GPT-4, Claude-3-Opus 3, Mistral Large, Llama 3, and Gemini 1.5 Pro). The results indicate that LLMs outperformed humans across all metacognitive metrics, particularly in terms of reduced overconfidence, compared to humans. However, both LLMs and humans showed less adaptability in ambiguous scenarios, adhering closely to predefined decision frameworks. The study suggests that Generative AI can effectively engage in human-like metacognitive processing without conscious awareness. Implications of the study are discussed in relation to development of AI simulators that scaffold cognitive and metacognitive aspects of mastering coaching competencies. More broadly, implications of these results are discussed in relation to development of metacognitive modules that lead towards more autonomous and intuitive AI systems.
Certifying Euclidean Sections and Finding Planted Sparse Vectors Beyond the $\sqrt{n}$ Dimension Threshold
Venue: arXiv cs.DS 2024
Year: 2024

We consider the task of certifying that a random $d$-dimensional subspace $X$ in $\mathbb{R}^n$ is well-spread - every vector $x \in X$ satisfies $c\sqrt{n} \|x\|_2 \leq \|x\|_1 \leq \sqrt{n}\|x\|_2$. In a seminal work, Barak et. al. showed a polynomial-time certification algorithm when $d \leq O(\sqrt{n})$. On the other hand, when $d \gg \sqrt{n}$, the certification task is information-theoretically possible but there is evidence that it is computationally hard [MW21,Cd22], a phenomenon known as the information-computation gap. In this paper, we give subexponential-time certification algorithms in the $d \gg \sqrt{n}$ regime. Our algorithm runs in time $\exp(\widetilde{O}(n^{\varepsilon}))$ when $d \leq \widetilde{O}(n^{(1+\varepsilon)/2})$, establishing a smooth trade-off between runtime and the dimension. Our techniques naturally extend to the related planted problem, where the task is to recover a sparse vector planted in a random subspace. Our algorithm achieves the same runtime and dimension trade-off for this task.
Channel Capacity of Near-Field Line-of-Sight Multiuser Communications
Venue: arXiv cs.IT 2024
Year: 2024

The channel capacity of near-field (NF) communications is characterized by considering three types of line-of-sight multiuser channels: i) multiple access channel (MAC), ii) broadcast channel (BC), and iii) multicast channel (MC). For NF MAC and BC, closed-form expressions are derived for the sum-rate capacity as well as the capacity region under a two-user scenario. These results are further extended to scenarios with an arbitrary number of users. For NF MC, closed-form expressions are derived for the two-user channel capacity and the capacity upper bound with more users. Further insights are gleaned by exploring special cases, including scenarios with infinitely large array apertures, co-directional users, and linear arrays. For comparison, the MAC and BC sum-rates achieved by typical linear combiners and precoders are also analyzed. Theoretical and numerical results are presented and compared with far-field communications to demonstrate that: i) the NF capacity of these three channels converges to finite values rather than growing unboundedly as the number of array elements increases; ii) the capacity of the MAC and BC with co-directional users can be improved by using the additional range dimensions in NF channels to reduce inter-user interference (IUI); and iii) the MC capacity benefits less from the NF effect compared to the MAC and BC, as multicasting is less sensitive to IUI.
Adversary-Guided Motion Retargeting for Skeleton Anonymization
Venue: arXiv cs.CV 2024
Year: 2024

Skeleton-based motion visualization is a rising field in computer vision, especially in the case of virtual reality (VR). With further advancements in human-pose estimation and skeleton extracting sensors, more and more applications that utilize skeleton data have come about. These skeletons may appear to be anonymous but they contain embedded personally identifiable information (PII). In this paper we present a new anonymization technique that is based on motion retargeting, utilizing adversary classifiers to further remove PII embedded in the skeleton. Motion retargeting is effective in anonymization as it transfers the movement of the user onto the a dummy skeleton. In doing so, any PII linked to the skeleton will be based on the dummy skeleton instead of the user we are protecting. We propose a Privacy-centric Deep Motion Retargeting model (PMR) which aims to further clear the retargeted skeleton of PII through adversarial learning. In our experiments, PMR achieves motion retargeting utility performance on par with state of the art models while also reducing the performance of privacy attacks.
Towards Invariant Time Series Forecasting in Smart Cities
Venue: arXiv cs.LG 2024
Year: 2024

In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep neural networks has significantly improved forecasting performance. However, a notable challenge lies in the ability of these models to generalize well to out-of-distribution (OOD) time series data. The inherent spatial heterogeneity and domain shifts across urban environments create hurdles that prevent models from adapting and performing effectively in new urban environments. To tackle this problem, we propose a solution to derive invariant representations for more robust predictions under different urban environments instead of relying on spurious correlation across urban environments for better generalizability. Through extensive experiments on both synthetic and real-world data, we demonstrate that our proposed method outperforms traditional time series forecasting models when tackling domain shifts in changing urban environments. The effectiveness and robustness of our method can be extended to diverse fields including climate modeling, urban planning, and smart city resource management.
Dynamic Posture Manipulation During Tumbling for Closed-Loop Heading Angle Control
Venue: arXiv cs.RO 2024
Year: 2024

Passive tumbling uses natural forces like gravity for efficient travel. But without an active means of control, passive tumblers must rely entirely on external forces. Northeastern University's COBRA is a snake robot that can morph into a ring, which employs passive tumbling to traverse down slopes. However, due to its articulated joints, it is also capable of dynamically altering its posture to manipulate the dynamics of the tumbling locomotion for active steering. This paper presents a modelling and control strategy based on collocation optimization for real-time steering of COBRA's tumbling locomotion. We validate our approach using Matlab simulations.
AFEN: Respiratory Disease Classification using Ensemble Learning
Venue: arXiv cs.SD 2024
Year: 2024

We present AFEN (Audio Feature Ensemble Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion to perform state-of-the-art audio classification for a range of respiratory diseases. We use a meticulously selected mix of audio features which provide the salient attributes of the data and allow for accurate classification. The extracted features are then used as an input to two separate model classifiers 1) a multi-feature CNN classifier and 2) an XGBoost Classifier. The outputs of the two models are then fused with the use of soft voting. Thus, by exploiting ensemble learning, we achieve increased robustness and accuracy. We evaluate the performance of the model on a database of 920 respiratory sounds, which undergoes data augmentation techniques to increase the diversity of the data and generalizability of the model. We empirically verify that AFEN sets a new state-of-the-art using Precision and Recall as metrics, while decreasing training time by 60%.
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2024
Year: 2024

Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on improving the performance of the target domain by fine-tuning domain-specific models (trained on source domains) based on the target domain dataset. Few works propose continual learning tasks for ABSA, which aim to learn the target domain's ability while maintaining the history domains' abilities. In this paper, we propose a Large Language Model-based Continual Learning (\texttt{LLM-CL}) model for ABSA. First, we design a domain knowledge decoupling module to learn a domain-invariant adapter and separate domain-variant adapters dependently with an orthogonal constraint. Then, we introduce a domain knowledge warmup strategy to align the representation between domain-invariant and domain-variant knowledge. In the test phase, we index the corresponding domain-variant knowledge via domain positioning to not require each sample's domain ID. Extensive experiments over 19 datasets indicate that our \texttt{LLM-CL} model obtains new state-of-the-art performance.
Research on the Tender Leaf Identification and Mechanically Perceptible Plucking Finger for High-quality Green Tea
Venue: arXiv cs.RO 2024
Year: 2024

BACKGROUND: Intelligent identification and precise plucking are the keys to intelligent tea harvesting robots, which are of increasing significance nowadays. Aiming at plucking tender leaves for high-quality green tea producing, in this paper, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed. RESULTS: Based on segmentation algorithm and color features, the tender leaf identification algorithm shows an average identification accuracy of over 92.8%. The mechanically perceptible plucking finger plucks tender leaves in a way that a human hand does so as to remain high quality of tea products. Though finite element analysis, we determine the ideal size of grippers and the location of strain gauge attachment on a gripper to enable the employment of feedback control of desired gripping force. Revealed from our experiments, the success rate of tender leaf plucking reaches 92.5%, demonstrating the effectiveness of our design. CONCLUSION: The results show that the tender leaf identification algorithm and the mechanically perceptible plucking finger are effective for tender leaves identification and plucking, providing a foundation for the development of an intelligent tender leaf plucking robot.