Reviewerly/exHarmony huggingface dataset:
An Unsupervised Online Streaming Feature Selection Algorithm with Density Peak Clustering

Streaming feature selection (SFS), which enters the feature space in the form of flow and plays an important role in processing high dimensional data. Online streaming feature selection (OSFS) is a real-time streaming feature selection method. Most of the existing online streaming feature selection methods are based on the relevance between feature and label, ignoring the lack of label information, which frequently results in the failure of feature selection. In addition, in numerous practical application scenarios like intelligent medical treatment, label information with the high cost of acquiring label information is missing, which poses a great challenge to feature selection. To solve these problems, a novel unsupervised online streaming feature selection algorithm with density peak clustering (UOSFS-DPC) is proposed to reduce feature redundancy by clustering online streaming features. An online unsupervised relevance analysis and online unsupervised redundancy analysis are also adopted to select important feature subsets from the streaming features. Experimental results on six benchmark datasets and compared studies with eight representative SFS algorithms demonstrate that UOSFS-DPC outperforms its peers when label information is unknown.
A Comparative Study of Machine Learning Algorithms for Breast Cancer Classification

Breast cancer remains a global health challenge, demanding effective early detection and classification methods for improved patient outcomes. The integration of machine learning techniques has emerged as a promising approach, aiding healthcare professionals in the complex task of breast cancer diagnosis. This study places a particular focus on the pivotal roles of manual feature selection and outlier handling within the domain of machine learning-based breast cancer classification. Accuracy serves as the primary metric for assessing the performance of the models. Results unveil the substantial impact of the feature selection process, showcasing outstanding accuracy rates. Most notably, Random Forest model demonstrates exceptional proficiency, achieving an impressive 96% accuracy alongside a commendable F1 score of 0.93 after feature selection. Additionally, Logistic Regression, Support Vector Machine, and Decision Tree models exhibit robustness in their capacity to distinguish between breast cancer cases, underlining the instrumental role of the selected features in classification. This study not only advances our understanding of breast cancer classification but also beckons future research endeavors. The exploration of more extensive datasets and alternative machine learning algorithms stands as a promising avenue to further elevate classification performance. Such advancements promise more accurate breast cancer diagnoses, ultimately leading to enhanced outcomes of patient care.
A Visually Meaningful Image Encryption Algorithm with Attention Mechanism and Artificial Bee Colony Optimization

The existing encryption algorithms that generate visually meaningful images usually focus on the recovery of secret information, while neglecting the visual quality degradation of the carrier image, thereby increasing the possibility of being intercepted. This paper proposes a visually meaningful image encryption algorithm based on the attention mechanism and artificial bee colony optimization. It combines the attention mechanism in deep learning and the artificial bee colony optimization in optimization algorithms, aiming to maximize the visual effect of visually meaningful images. Firstly, the plaintext image is compressed and scrambled to obtain the secret information. Then the attention mechanism algorithm is applied to divide the cover image into visually salient regions, and the non-salient regions blocks are prioritized for embedding the secret information. By introducing the artificial bee colony optimization algorithm, the optimal values of noise visibility function (NVF), information entropy, and contrast weight are iteratively obtained. On this basis, select the positions and order of the sub-blocks to be embedded and perform IWT and LSB embedding to obtain the visually meaningful cipher images. Experimental results demonstrate that the proposed scheme effectively improves the quality of visually meaningful images.
Instance Implant-Aided Non-uniformly Cropping for Person Detection in Aerial Images

Human inspection of aerial-based scenarios is a time-consuming, labor-intensive, and error-prone task. However, current aerial-vision-based object detection techniques have not adequately improved the detection performance of persons with sparse and weak features. In this article, a novel approach is proposed to automatically detect persons in aerial images. Specifically, numerous and multiscale instances are first implanted on fresh background images with a copy-paste mechanism to collect rich training data. Then, cropping high-resolution images into irregular sizes with density maps in the training stage. Lastly, hyper-inferencing on the raw and sliced blocks of the image by merging these overlap results to find persons. Experiments were run on the original Heridal datasets against eight approaches of popular object detectors. The metrics of precision, AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</sup> @0.5, and AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</sup> @0.5:0.95 achieved 0.9277 (+2.77%), 0.8012, and 0.547, respectively. Meanwhile, our proposed method is being evaluated on the modified Heridal and a number of publicly available datasets to make a more robust person detector in aerial images.
Face mask detection using video streaming analytics and deep learning for healthcare management system

Video stream analytics is already a well-known topic and is constantly increasing its value these days. In addition, in the context of medical services development and the application of AI technologies to the hospital system. Understanding this, we aim to set a problem by analyzing the entity recognition inside the video in combination with identifying people wearing masks as the topic of the report. This study is of great help in identifying entities in isolation areas that need to wear masks in hospitals. To solve this problem, we uses a combination of three models: object recognition analysis, face identification, and face mask identification to analyze and solve the problem. After a period of research, we completed the Streaming video and identified more than 80 objects with the “person” object that we focused on. We also successfully built a simple HTML application that can display the results as images. This is considered a positive result with facial recognition analysis in real-time video.
PDF-NET: Pitch-adaptive Dynamic Filter Network for Intra-gender Speaker Verification

In this paper, we propose a method to significantly improve the performance of the intra-gender comparison task in a neural speaker verification (SV) system. Recent state-of-the-art neural SV models have a substantial performance decline in the same gender comparison trials, but few studies have comprehensively investigated this problem. To this end, we develop a light-weight pitch-adaptive dynamic filter network (PDF-NET) that generates the convolutional filters conditioned on the input pitch information, from the observation that intra-gender speakers share a similar pitch range. By doing so, speaker embeddings from the same speakers are well clustered with each other even within the same gender class. Experimental results show that our proposed method outperforms the baseline for various SV datasets. In particular, female SV trials on LibriSpeech performed herein achieve a relative improvement of 11.4% in terms of equal error rate with only 1.6k additional parameters.
Adversarial Robustness of Mel Based Speaker Recognition Systems

Convolutional neural networks (CNN) applied to Mel spectrograms now have a dominant presence in the landscape of speaker recognition systems. Correspondingly, it is also important to evaluate their robustness to adversarial attacks that remains not thoroughly explored for end-to-end trained CNNs for speaker recognition. Our work addresses this gap and investigates variations of the iterative Fast Gradient Sign Method (FGSM) to perform adversarial attacks. We observe that a vanilla iterative FGSM can flip the identity of each speaker sample to that of every other speaker in the LibriSpeech dataset. Furthermore, we propose adversarial attacks specific to Mel spectrogram features by (a) limiting the number of pixels attacked, (b) restricting changes to specific frequency bands, (c) restricting changes to particular time duration, and (d) using a substitute model to craft the adversarial sample. Using thorough qualitative and quantitative results, we demonstrate the fragility and non-intuitive nature of the current CNN-based speaker recognition systems, where the predicted speaker identities can be flipped without any perceptible changes in the audio. The samples are available at "https://advdemo.github.io/speech/"
The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling

Are the inputs used by your AI tool correct and up to date? In this paper, we show that the answer to this question: (i) is frequently a “no” in real business contexts, and (ii) has significant implications on the performance of AI tools. In the context of algorithmic labor scheduling, we propose, identify, and study a problem relating to inaccurate employee availability records, which are used by an AI tool to assign employees to shifts that are necessary to meet required service levels. We study this problem using granular data covering multiple retail chains, which contain more than 74 million shifts that are scheduled for more than 290,000 employees in more than 5,900 brick-and-mortar store locations. In our data, we find that employee availability records are often set incorrectly. Specifically, we find that employees who are no longer available to work are scheduled to work by the AI tool, and employees who are available to work have no existing availabilities. We find evidence that such input inaccuracies directly affect the number of overrides as managers rectify these errors, but also have a spillover effect on shifts that are not subject to input inaccuracies. Ultimately, we find that input inaccuracies take up significant managerial time and have a negative effect on the quality of work schedules, which may lead to a decrease in store performance. Overall, our findings suggest that poor AI input quality management could be one explanation behind the well-documented human distrust of algorithms and the lack of observed business gains in their use.
Research Trend Analysis of Multi-Task Behavior Cloning for Robot Manipulation

Large language models have revolutionized various domains of artificial intelligence and robotics, enabling advanced capabilities in manipulation robots. This paper explores the recent trends in the integration of large language models with manipulation robots, behavior cloning, and task planning. We investigate how large language models have been leveraged to enhance the communication and interaction between humans and manipulation robots, enabling intuitive and natural language-based robot control. Additionally, we explore the applications of behavior cloning techniques that leverage large language models to replicate human behaviors and expert demonstrations, enabling robots to learn complex manipulation tasks by imitating and emulating human actions.
Chaos-based Stream Cipher for Embedded Systems

This paper presents a novel lightweight stream cipher for embedded systems based on chaos theory. The proposed cipher exploits the two-dimensional discretized Cat and Standard maps to generate highly effective key streams. Chaotic maps are utilized to construct MixBytes and PerBits layers, ensuring strong cryptographic properties for the resulting cipher. The hardware implementation demonstrates that the proposed diffusion layer is an optimal choice for a stream cipher, as it minimizes resource utilization. Consequently, this study highlights the considerable potential of the proposed chaos-based stream cipher to enhance the security of communication in embedded systems characterized by limited resources.
Class-Agnostic Self-Supervised Learning for Image Angle Classification

The prediction of image angles is a crucial area of study for industrial automation. Despite the various studies conducted in computer vision, predicting image angles has received limited attention. One of the reasons for this lack of focus is that not all objects have clear directionality. For example, an image of a car wheel may have an ambiguous angle, making it infeasible to accurately predict the rotation angle. Therefore, objects with ambiguous angles can introduce noise during the training and testing of rotation angle prediction models. To tackle this issue, we propose a class-agnostic selfsupervised angle prediction learning framework that filters out images containing objects with ambiguous angles based on feature similarity. This approach involves two networks: the directionality categorization network, which identifies and eliminates images of undirectional objects, and the rotation categorization network, which learns from the filtered inputs to improve the accuracy of angle predictions. The experimental results using the STL-10 and CIFAR-100 datasets demonstrate that the proposed framework improves rotation angle classification accuracy without the need for rotation angle labels, which are often difficult to obtain in the literature.
Development of Holonic Autonomous Mobile Robot for Drilling Tasks

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Construction of Automatic Speech Recognition Model that Recognizes Linguistic Information and Verbal/Non-verbal Phenomena

This study addresses an automatic speech recognition (ASR) model that simultaneously recognizes linguistic information and various verbal/non-verbal (VNV) phenomena: fillers, laughter, interrogative ascending tones, end of speech, pronunciation errors, word fragments, etc. Although ASR technology has rapidly advanced in recent years, most ASR systems can only recognize linguistic information and can still not VNV phenomena. Recognition of VNV phenomena is important because speakers often convey their intentions during spontaneous speech using these types of utterances. We achieve VNV recognition by annotating VNV phenomena tags on learning transcribed text when the ASR model is trained. Our experimental results revealed that the accuracy of speech recognition is improved by also recognizing VNV phenomena. The ASR model in which the VNV phenomena tags appeared before the linguistic information in the transcribed text showed the best performance when using data from the Corpus of Everyday Japanese Conversation.
Selecting Suitable Data Input for Deep-Learning Sign-Language Recognition with a Small Dataset

Deep learning-based sign-language recognition usually requires abundant training videos. This research considers generating valid sign-language data for training the recognition models. MediaPipe is used to acquire the hand skeleton from the sign-language video. Then we analyze several hand skeleton adjustment policies with color-weighting strategies and generate hand masks to simulate different hands. Since miss detections of hands may happen due to motion blurring caused by rapid hand movements, we incorporate optical flows to ensure that the hand movement information is retained in each frame. We employ different spatial and temporal data augmentation strategies to simulate varying hand sizes, filming angles, and hand speeds. The experimental results show that the proposed method improves the accuracy of sign-language recognition in the American Sign Language dataset.
IPFed: Identity protected federated learning for user authentication

With the development of laws and regulations related to privacy preservation, it has become difficult to collect personal data to perform machine learning. In this context, federated learning, which is distributed learning without sharing personal data, has been proposed. In this paper, we focus on federated learning for user authentication. We show that it is difficult to achieve both privacy preservation and high accuracy with existing methods. To address these challenges, we propose IPFed which is privacy-preserving federated learning using random projection for class embedding. Furthermore, we prove that IPFed is capable of learning equivalent to the state-of-the-art method. Experiments on face image datasets show that IPFed can protect the privacy of personal data while maintaining the accuracy of the state-of-the-art method.
A Comparison Study of the Compatibility Approaches for SGX Enclaves

Confidential computing technologies, such as that enabled by Intel SGX (Software Guard eXtensions), have been widely deployed in various commercial cloud platforms. Specifically, SGX uses hardware-isolated compartments named enclaves to shield user applications from Operating Systems (OSes) and hypervisors, thus providing confidentiality and integrity guarantees for code and data. However, some crucial problems are not fully analyzed yet, especially for the compatibility with binary applications. This work first delivers an overview of Intel SGX and reviews its five design constraints that may affect compatibility. Subsequently, we revisit three distinct compatibility solutions from the internals and analyze their impact on security, performance, and flexibility. At last, we lay out some fundamental lessons learned from prior SGX studies.
On Detecting and Defending AdvDrop Adversarial Attacks by Image Blurring and Adaptive Noising

AdvDrop, a recently emerged adversarial attack method, generates adversarial samples by dropping information from the clean images. Compared with the traditional adversarial attack based on adding perturbation, AdvDrop is more robust against current de-noising based defense techniques, which poses a great threat to the security of DNNs. To defend AdvDrop, this paper proposes an image blurring detection method to detect AdvDrop adversarial samples and also proposes a defense method based on adaptive noising combined with fast and flexible denoising convolution neural network (FFDNet) filtering to restore the correct class of adversarial samples. Image blurring is first conducted to add a mask to the spectrum of the input image in the frequency domain. Thanks to the robustness of the DNN, the blurring operation has no significant impact on the accuracy of the clean samples but prevents the attackers from extracting useful image features. As a result, the adversarial samples can be detected by analyzing the consistency of the predicted classification output by DNN. In the defense method, we introduce adaptive noising to enhance the high-frequency information in each image. Combined with FFDNet filtering, this strategy increases the diversity of local changes in the image while preserving the overall image structure. As a result, DNN can focus more on essential image features rather than those created by the attacker. We evaluated our detection and defense methods on Cifar-10 and ImageNet datasets for some DNN models, and the results surpassed the current state-of-the-art methods..
An Analysis of Personalized Speech Recognition System Development for the Deaf and Hard-of-Hearing

Deaf or hard-of-hearing (DHH) speakers typically have atypical speech caused by deafness. With the growing support of speech-based devices and software applications, more work needs to be done to make these devices inclusive to everyone. To do so, we analyze the use of openly-available automatic speech recognition (ASR) tools with a DHH Japanese speaker dataset. As these out-of-the-box ASR models typically do not perform well on DHH speech, we provide a thorough analysis of creating personalized ASR systems. We collected a large DHH speaker dataset of four speakers totaling around 28.05 hours and thoroughly analyzed the performance of different training frameworks by varying the training data sizes. Our findings show that 1000 utterances (or 1-2 hours) from a target speaker can already significantly improve the model performance with minimal amount of work needed, thus we recommend researchers to collect at least 1000 utterances to make an efficient personalized ASR system. In cases where 1000 utterances is difficult to collect, we also discover significant improvements in using previously proposed data augmentation techniques such as intermediate fine-tuning when only 200 utterances are available.
Efficient Constraint-Aware Neural Architecture Search for Object Detection

We propose an efficient Neural Architecture Search (NAS) method, named Zero-DNAS, for object detection tasks capable of discovering a suitable architecture under given memory and FLOPs constraints. NAS aims to explore the search space automatically to discover the best-performing network architectures for a given task. However, NAS is resource-consuming and usually requires hundreds of hours of GPU computations to discover a neural network architecture with good performance. Especially for more extensive and complex computer vision tasks such as object detection, the computing time and memory when conducting NAS would increase dramatically. Practically, the conventional sampling-based NAS methods do not guarantee the best possible solutions, whereas differentiable methods require substantial memory resources, making it challenging to apply in macro search space settings. In comparison, we propose a differentiable NAS paradigm with zero-cost proxy metrics and aims to determine the architecture within the constraints of memory and FLOPS. The experiments on object detection datasets show that our proposed algorithm can discover more accurate and faster architectures in a heavy macro search space in less than 2 NVIDIA 2080TI GPU hours.
Adopting Neural Translation Model in Data Generation for Inverse Text Normalization

This research proposes a novel data generation pipeline for the Inverse Text Normalization task with a focus on improving the data alignment technique. The pipeline adopts Automatic Speech Recognition (ASR) and Neural Word Alignment techniques to facilitate the needs. The pipeline is a three-stage process from data crawling and spoken transcript generation to data alignment. Firstly, audio recordings and their corresponding written-form transcripts were collected in a semiautomatic manner. Subsequently, the audio recordings are sent to an ASR system to obtain spoken transcripts. Lastly, a cutting-edge word alignment technique is utilized to pair words between written and spoken transcripts to produce a word-level alignment ITN dataset. The proposed data alignment technique achieves at least 3.3% improvement in decimal word transformation compared to the representative baselines.
Identifying the Style of Chatting

Group chat brings great convenience to people’s online social interaction. When identifying users in online social networks, individual speaking style of the member in group chat particularly plays an important role. In this paper, we propose a novel and efficient group chat hashing framework termed Group Chat Style Hashing(GCSH), which is the first work to utilize personal hashes in group chats to identify the speakers. GCSH combines a supervised VAE and a discriminator to extract accurate identity-related features and applies a new sample aggregation hash algorithm to generate an exclusive identity style hash for each one in the group chat. Our model fuses more context information to directly get the representative hashes, achieving an end-to-end identification model and sharply reducing the matching cost compared with previous text hashing methods. We conduct extensive experiments to prove the effectiveness and the generalization ability of our method across datasets.