Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach Based on Ant Colony and Firefly Algorithm for Traveling Salesman Problem

Parameters have always been the core part of an algorithm, but good parameters are not immutable. They change as the problem changes, so how to set the optimal parameters according to the size of the problem has always been a difficult problem in the field of algorithm. Traditional ant colony algorithm has some problems such as difficulty in determining initial parameters and falling into local optimization. A hybrid ant colony algorithm for searching initial parameters was proposed. The optimal parameter array of the ant colony algorithm is obtained by introducing the firefly algorithm (FA) to optimize the initial parameters. The hybrid ant colony algorithm after parameter optimization to solve the traditional traveling salesman problem (TSP) is used. The results show that the running time and shortest tour path of the hybrid ant colony algorithm with the calculated parameters are shorter. In the last, the number of cities is continuously reduced to repeatedly search the optimal parameters under different difficulty problems. The experimental results have certain reference significance for the follow-up research of the hybrid ant colony algorithm.
Few-Shot Classification With Cross-Feature Fusion

Most existing methods for few-shot image classification treat the support features and query features separately, and the category of a query image is determined based on its similarity to the support images. However, the relationships between the query features and support features are often ignored. In this paper, we propose a strategy that exploits the relationships between query and support features and give more weights to the highly related features. A cross-feature fusion method is further proposed to combine with metric learning to reduce overfitting. Extensive experiments on a wide range of datasets show that our method has achieved advanced results.
Anomaly Detection for Process Industry using Class-Dependent Temperatures Loss

Anomaly detection is essential to the safety of control systems in the process industry. Thus, how to effectively detect anomalies in the presence of operational data has become a crucial issue. Currently, lots of studies utilize machine learning algorithms to detect anomalies in process industrial control systems. But most of these studies ignore the imbalance problem caused by the insufficient number of anomaly samples in the process industry. Therefore, this paper proposes an anomaly detection framework that introduces the Class-Dependent Temperatures (CDT) loss to construct the anomaly detection model. In the CDT loss, a weighting factor is adopted to alleviate data imbalance during the training phase. Our proposed model is evaluated on datasets collected from the Secure Water Treatment (SWaT) and the Water Distribution (WADI). Experimental results show that the method can effectively detect anomalies.
A Blockchain-based Covert Document Communication System Model

The communication method in which both parties could modify certain types of shared resources without attracting the attention of other users is called covert communication. Blockchain is an integrated technology system platform that combines cryptography, mathematics, computer programming, game, and other disciplines. Generally, researchers believe that blockchain, as a distributed storage database that can still guarantee security in an unreliable network environment, has excellent characteristics such as open, decentralized, untrusted, anonymous, tamper prevention, traceability, and contract execution automation. More and more studies have applied it to covert communication technology. To satisfy the requirements of increasing confidentiality of information transmission, enhancing the quantity of data transmitted by covert communication, and decreasing the loss of secret information, this paper proposes a covert document communication model based on blockchain. According to the structure of Monero, the sensitive documents are encrypted by attribute-based encryption, and the communication model that can transmit confidential documents is realized by combining an interplanetary file system. The performance of this scheme is experimentally analyzed, and the concealment and security are proven.
A $2.81\mu \mathrm{W}$, Energy Efficient MFCC Feature Extractor for Keyword-Spotting in 65nm CMOS

Keyword-spotting (KWS) plays a crucial role in human machine interactions involving smart devices. And in most of the KWS systems, Mel Frequency Cepstral Coefficients (MFCC) acts as the feature extractor and consumes an unneglectable amount of computational load and resource. In this paper, to reach a trade-off between accuracy and power consumption, we proposed a modified MFCC algorithm, the computational load of which can be reduced by 82% for multiplications and 66% for additions. Using the Google Speech Command Dataset (GSCD), 96.09% accuracy is achieved for ten keywords. Then, following a hardware/algorithm co-optimized approach, we design an energy efficient hardware architecture for MFCC. Implemented in SMIC 65nm process, our circuit achieves a power consumption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.81 \mu\mathrm{W}$</tex> while operating at 1.0V supply voltage with 16kHz clock, which is 4.5x less than the related work that under the same process technology.
Image Segmentation Based on Tibetan Architecture

Tibetan-style architecture represents the history and development of the Tibetan people. Since many Tibetan-style buildings have been damaged today, effective division of Tibetan-style buildings has great academic research significance and development prospects. The segmentation technology of Tibetan buildings is the content of artificial intelligence research. Accurate and efficient segmentation helps to provide cheaper data and accurate information for the 3D reconstruction of buildings. In this article, we propose an image segmentation method for Tibetan architecture, which enhances the attention information points of image segmentation through the NAM attention mechanism, and uses the modified residual ASPP to obtain context information, thereby accurately obtaining edge information.
Better Multi-step Time Series Prediction Using Sparse and Deep Echo State Network

Multi-step time series prediction is essential in real-world applications but challenging to obtain accurately due to a fallacy accumulation. Incrementing the required future steps typically results in performance degradation. Data-driven machine learning techniques have the potential to tackle this task but demand significant or special computing powers such as memory and graphics processing units (GPUs). This work demonstrates that Deep Echo State Network (DeepESN) with a sparse configuration can capture multi-step prediction in a comparable or even better performance while demanding lower resources and processing times. Most experimental results documented in the literature examine only one or a few multi-step ahead. Here we report the prediction of up to 250 future steps with better correlation-of-coefficient contrasting to the baseline models. Sparsing the projection of the input signal to each reservoir of the DeepESN can reduce the circumstances of overfitting in time series learning. This finding could lead to utilizing deep learning models with affordable resources and processing times.
Incorporating Multiple Relations into Multi-task Learning for Session-Based Recommendation

Session-based recommendation aims to recommend the next item based on the information of sessions. There are multiple relations between items in sessions, e.g., the similar and sequential relation. How to learn these relations to effectively represent items and sessions is a major technical challenge of session-based recommendation. However, most of the existing session-based recommendation models do not distinguish the relations between items, thus difficult to learn the difference and relevance among relations. In this paper, we propose a model named MML-SR, which incorporates multiple relations into multi-task learning for session-based recommendation. In particular, we divide a session-based recommendation task into a similar relation recommendation task and a sequential relation recommendation task. The expert network layer and gate network layer are proposed to capture the representation of similar relations and sequential relations, then two proposed task towers are used to compute two recommendation results based on the similar relations and sequential relations of items in sessions respectively. We combine both recommendations results to get the final result. Experiments are conducted on three widely-used real-world datasets to evaluate the proposed MML-SR. The results demonstrate that MML-SR outperforms the state-of-the-art session-based recommendation models.
Combining Image Inductive Bias and Self-Attention Mechanism for Accurate Isolated Sign Language Recognition

Isolated sign language recognition has been an important part of breaking down communication bottlenecks for deaf-mute and others. While facing this problem, the purpose of this paper is to classify American isolated sign language video by modeling pose, hands and face keypoints representation. Specifically, this paper introduces a novel framework whose main components are the altered Dense Predictive Coding (DPC) pre-trained model and the Encoder pre-trained model. The DPC model is trained using self-supervised learning to obtain representation of pose and hands keypoints. The Encoder model is trained using supervised learning to obtain representation of face keypoints. Combining the altered DPC model with image inductive biases and the Encoder model with a self-attention mechanism, the final combined model achieves 0.81 on the test set of the ISAL dataset, outperforming the current open-source solution by a significant margin.
A Comparative Study on Grid-Based and Non-grid-based Path Planning Algorithm

Recent years have seen a dramatic uptick in research efforts dedicated to the development of mobile robots. One of the most common research topics in this area involves the path planning of mobile robots. The existing algorithms use the samples to construct a network or a route. There are many methods available for creating samples on the map as well. However, planners need to explore a bigger search space while building a path for the mobile robot because the samples are dispersed around the map. In this study, we examine a gridbased sampling strategy that narrows the search while still allowing us to probe potential avenues of exploration. For this objective, we implement the three most well-known path planning algorithms namely, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{*}$</tex> , Probabilistic Roadmap (PRM), and Rapidly-Exploring Random Tree (RRT). The algorithms are compared using a grid-based path planner and a non-grid-based planner. The observed findings show that the proposed sampling technique is more effective than the previous one.
Embedding Clustering using Lightweight Contrastive Learning For Cross-Modal Classification

Deep learning models excel at computer vision tasks, but they require a lot of memory and computation power. Thermal image classification has gained popularity due to the robustness of this modality in the face of adverse weather and lighting conditions. Images from both visible and/or thermal modalities, on the other hand, must frequently be processed in memory constrained environments, such as embedded edge applications. We propose a novel cross-modal constrastive learning model with a novel objective function that learns and averages mappings from both visible and thermal modalities into a shared embedding space for cross-modal data clustering and classification. A lightweight architecture is used to generate embedding from images in both modalities, and the proposed algorithm yields cross-modal centroids that can be efficiently used for clustering and classification in either modality. We demonstrate the model's accuracy by running experiments on a dataset that contains both thermal and visible counterparts of the same objects.
ALRS: An Attention Loss Function based on Row-Sparsity for Incremental Learning

Incremental learning has received significant attention, but the problem of catastrophic forgetting remains a major challenge for existing approaches. This issue hinders models from accumulating knowledge over long stretches. To address this problem, we propose a new approach called Attention Loss function based on Row-Sparsity (ALRS) that mines significant patches by simultaneously learning patch weights and logits (class vectors) using the same parameters. We integrate the attention mechanism with the novel loss function to avoid catastrophic forgetting. This innovative approach enables the model to conflate the newly introduced classes with the existing ones, without the need to store any data or models from the previous steps' base classes. To assess its efficacy, we incorporate ALRS into the distillation loss for validation and conduct a thorough evaluation of the approach's performance on three datasets: CIFAR-100, Caltech-101, and CUBS-200-2011. Compared to LWM, which also does not store data, our method achieves an average improvement of more than 8 percentage points with an absolute advantage on CIFAR-100. Additionally, on Caltech-101 and CUBS-200-2011, our new approach provides comparable accuracy to baseline.
Audio Watermarking Scheme for Mobile Communication Based on Psychoacoustic Analysis

In this paper, a novel audio authentication algorithm for mobile phone communication is proposed. Nowadays more and more agreements are reached over the mobile phones. Unlike the traditional paper agreements, whose integrity and authenticity can be guaranteed by the signatures, the digital contracts made over phones can be either faked or edited. Consequently, how to prove the authenticity and the integrity is very critical. The proposed scheme can fulfil the above tasks by inserting authentication information, the watermark, into sub bands based on psychoacoustic analysis. According to the embedded watermark, the identity of the voice, as well as the integrity of the audio recording can be verified. Experimental results demonstrate the effectiveness of the algorithm.
Efficiently Identifying Binary Similarity Based on Deep Hashing and Contrastive Learning

Binary similarity is to identify the semantic similarities of two or more binary code snippets. In recent years, deep learning-based methods have shown promising results. They formalize code similarity as the nearest neighbor retrieval task, and the overall workflow can be divided into two stages: 1) feeding the code snippets into the embedding model to get the corresponding high-dimensional vectors as fingerprints (i.e., constructing the codebase). 2) using the codebase for nearest neighbor retrieval to get the top-k results. Most existing studies only focus on the first stage (more specifically, the embedding model) while ignoring the overhead of the retrieval stage. In real-world scenarios, the codebase could be quite large and contain massive embeddings, which keeps the precise nearest neighbor retrieval prohibitive expensive. To mitigate the issue above, this paper proposes a novel approach, dubbed BinCH, which can efficiently perform code search without sacrificing accuracy.
A Voice Disguise Communication System Based on Real-Time Voice Conversion

A Voice disguise communication (VDC) system is a system that disguises the voice in transmission and restores it at the receiving end. VDC systems have great application prospects in the field of confidential communication. The use of voice conversion (VC) can enhance the quality of voice disguise and reduce the recognition rate of eavesdroppers or automatic speaker verification (ASV) systems as a way to hide the identity of the speaker. There are three main constraints in applying VC to voice disguise at present: first, the real-time VC is poor and cannot meet the needs of real-time communication; second, the quality of voice disguise is not high, which is easily recognized by eavesdroppers or ASV systems; third, the corpus used for training is large, which poses certain difficulties and costs for implementation. Based on the above problems this research proposed a real-time VC method trained by a small corpus, and used it to design and implement a VDC system, and verified the effectiveness through experiments.
Research on Recommendation Methods Based on Data Augmentation in a Heterogeneous View

In recent years, many domains contain different object and relationship types, forming Heterogeneous Information Networks (HINs). These HINs include richer semantic and structural information than homogeneous information networks. In this paper, a comprehensive survey of data mining and applications using HINs is presented. Specifically, we first review the current achievements in HIN research and outline the research trends of HINs in data mining. Next, we explore the applications of HINs in various fields, such as finance and education. Finally, through a series of experiments, we explore research hotspots and the development of HINs in educational applications and make recommendations for future areas of study in HIN applications. The experimental results demonstrate the effectiveness of HIN based techniques in real-world data mining tasks, particularly in educational applications such as course recommendation.
Resnet for Classification of Leukocyte: Application and Optimization

Hitherto, the classification of blood cell subtypes constitutes a typical method for diagnosing various diseases, infections, and inflammations, therefore the application of effective cell classification methods is essential. The boom in machine learning has made it possible to use computers to identify cell types. Specifically, we adopt the Resnet50 framework and propose methods of data augmentation, image mask generation and ensemble learning to optimize the test results. Finally, our model achieves 88.76% accuracy on the Blood Cell Images dataset with high robustness. We put forward some possible directions for future model improvements.
Sample Selection Method for Concept Drift

The concept drift problem makes it impossible to achieve stable predictions in a non-stationary environment. This is because the occurrence of concept drift causes the old samples to become noisy, resulting in the knowledge learned by the model no longer being applicable in the new environment. Existing methods have difficulty in identifying the region where the drift occurs, and thus cannot select the appropriate samples for learning. In this paper, we propose a dynamic sample selection method that first uses a micro cluster-based clustering algorithm to partition the feature space into regions, and then performs sample denoising in the region where the new samples are located, while reusing the removed samples with high weights. The denoising effect of the algorithm was verified on four synthetic datasets containing different drift types, and the validity of the results after sample reduction was tested on real datasets. The experimental results show that the method can effectively remove the drift noise caused by the concept drift, and can inductively summarize the more representative sample points for model learning, which improves the performance of the prediction model on streaming data.
Knowledge Aware Embedding for Distantly Supervised Relation Extraction

Relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a distantly supervised relation extraction method based on knowledge aware embedding to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework, which uses entity type and relation alias information in the knowledge base to improve relation extraction. Firstly, use an entity-aware word embedding method is used to fuse the entity type information to highlight the essence of entities for this task, and then capturing rich contextual dependencies with bidirectional gated recurrent units, for noisy data, improved intrabag attention using relational alias information to generate match relation embedding, while using interbag attention aggregation bag representation for final relationship classification. The model achieves the F1 score of 57.2 on the NYT dataset, which is 1.7 higher than the state-of-the-art baselines. The experimental results verified the effectiveness of the model in improving the accuracy of relation extraction.
A Feature Structure Based Interpretability Evaluation Approach for Deep Learning

The shortcomings of deep learning in interpretability make it difficult to trust such complex black-box models in high-value decision problems. Nowadays, there is still no breakthrough in the research of deep learning interpretability, and people can not see the full picture inside the model. Meanwhile, there is no reliable and universal standard to evaluate the interpretability of deep learning model. Therefore, a deep learning interpretability evaluation method based on the feature structure of deep learning is proposed. Firstly, the trustworthiness evaluation is performed to confirm the robustness of the model with the help of Layer-wise relevance propagation. On this basis, the interpretability of the feature structure is measured based on the relevance between features and outputs. Experiments show that this method can effectively compare the interpretability of models.
Unsupervised Dual Modality Prompt Learning for Facial Expression Recognition

A method of facial expression recognition using a vision language model is proposed. Recently vision-language models for example CLIP (Contrastive Language-Image Pre-training) models developed by OpenAI have achieved exceptional results on a variety of image recognition and retrieval tasks, exhibiting strong zero-shot performance. Transferable representations can be adapted through prompt tuning to a variety of downstream tasks. From the general knowledge stored in a pre-trained model, prompt tuning attempts to extract useful information for downstream tasks. In order to avoid time-consuming prompt engineering, recent works use a small amount of labeled data for adapting vision language models to downstream image recognition problems. However, requiring target datasets to be labeled may restrict their scalability. Moreover, we also note that adapting prompt learning techniques in only one branch of CLIP (vision or language) is suboptimal because it won't allow for the dynamic adjustment of both representation spaces on a downstream task. In this paper, we evaluated the performance of the CLIP model as a zero-shot face recognizer and proposed an Unsupervised Dual Modality Prompt Learning framework for Facial Expression Recognition. Our model tunes the prompts through learning text and visual prompts simultaneously to improve alignment between the linguistic and visual representations when labels are not provided for the target dataset. The experimental results on CK+, JAFFE, RAF-DB, and FER2013 datasets showed that our proposed method performs better compared with CLIP Zero-Shot and other unsupervised prompt-based learning methods for facial expression recognition tasks.