Reviewerly/exHarmony huggingface dataset:
NeuDep: neural binary memory dependence analysis

Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias information is undecidable in theory. The problem aggravates at the binary level due to the presence of compiler optimizations and the absence of symbols and types. Existing approaches either produce significant spurious dependencies due to conservative analysis or scale poorly to complex binaries. We present a new machine-learning-based approach to predict memory dependencies by exploiting the model's learned knowledge about how binary programs execute. Our approach features (i) a self-supervised procedure that pretrains a neural net to reason over binary code and its dynamic value flows through memory addresses, followed by (ii) supervised finetuning to infer the memory dependencies statically. To facilitate efficient learning, we develop dedicated neural architectures to encode the heterogeneous inputs (i.e., code, data values, and memory addresses from traces) with specific modules and fuse them with a composition learning strategy. We implement our approach in NeuDep and evaluate it on 41 popular software projects compiled by 2 compilers, 4 optimizations, and 4 obfuscation passes. We demonstrate that NeuDep is more precise (1.5x) and faster (3.5x) than the current state-of-the-art. Extensive probing studies on security-critical reverse engineering tasks suggest that NeuDep understands memory access patterns, learns function signatures, and is able to match indirect calls. All these tasks either assist or benefit from inferring memory dependencies. Notably, NeuDep also outperforms the current state-of-the-art on these tasks.
Questionable authorship practices across the disciplines: Building a multidisciplinary thesaurus using evolutionary concept analysis

The culture surrounding authorship practices differs from discipline to discipline, with the potential for inconsistent terminology across disciplines to hamper comprehension in interdisciplinary conversations. To address this problem, an interdisciplinary corpus of research literature on the topic of questionable authorship practices was used to create a multidisciplinary thesaurus. This process used Evolutionary Concept Analysis (ECA) as mediated through MAXQDA. Problems of synonymy and polysemy are addressed using ECA which identifies and subsequently analyzes terms used to denote questionable authorship practices as well as their synonyms, relevant uses, attributes, references, antecedents, and consequences. The value is two-fold: first, this addresses the gap in the literature in terms of the identification, analysis, and organization of a set of interdisciplinary terms relating to questionable authorship practices; second, it presents a novel methodological approach to thesaurus construction from a multidisciplinary corpus through using ECA.
Mutual-Supervised Federated Learning and Blockchain-Based IoT Data Sharing

Due to the decentralized, tamper-proof, and auditable properties of blockchain, more and more scholars and researchers are studying the application of blockchain technology in IoT data sharing. Federated learning is an effective way to enable data sharing, but can be compromised by dishonest data owners who may provide malicious models. In addition, dishonest data requesters may also infer private information from model parameters. To solve the above problems, a secure data sharing mechanism based on mutual-supervised federated learning and blockchain, BPCV-FL, is proposed. This mechanism ensures data privacy by adopting gradient descent algorithm with differential privacy protection in local model training and ensures the reliability of shared data through mutual supervision on the blockchain. Experimental results show that the proposed BPCV-FL has high accuracy and security in IoT data sharing.
Using Variational Quantum Algorithm to Solve the LWE Problem

The variational quantum algorithm (VQA) is a hybrid classical-quantum algorithm. It can actually run in an intermediate-scale quantum device where the number of available qubits is too limited to perform quantum error correction, so it is one of the most promising quantum algorithms in the noisy intermediate-scale quantum era. In this paper, two ideas for solving the learning with errors problem (LWE) using VQA are proposed. First, after reducing the LWE problem into the bounded distance decoding problem, the quantum approximation optimization algorithm (QAOA) is introduced to improve classical methods. Second, after the LWE problem is reduced into the unique shortest vector problem, the variational quantum eigensolver (VQE) is used to solve it, and the number of qubits required is calculated in detail. Small-scale experiments are carried out for the two LWE variational quantum algorithms, and the experiments show that VQA improves the quality of the classical solutions.
Auxiliary Graph for Attribute Graph Clustering

Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked nodes but fails to convey relationships between nodes that are not directly linked, limiting the potential for future clustering performance improvement. To solve this issue, we offer the Auxiliary Graph for Attribute Graph Clustering technique (AGAGC). Specifically, we construct an additional graph as a supervisor based on the node attribute. The additional graph can serve as an auxiliary supervisor that aids the present one. To generate a trustworthy auxiliary graph, we offer a noise-filtering approach. Under the supervision of both the pre-defined graph and an auxiliary graph, a more effective clustering model is trained. Additionally, the embeddings of multiple layers are merged to improve the discriminative power of representations. We offer a clustering module for a self-supervisor to make the learned representation more clustering-aware. Finally, our model is trained using a triplet loss. Experiments are done on four available benchmark datasets, and the findings demonstrate that the proposed model outperforms or is comparable to state-of-the-art graph clustering models.
Mixture Complexity and Its Application to Gradual Clustering Change Detection

We consider measuring the number of clusters (cluster size) in the finite mixture models for interpreting their structures. Many existing information criteria have been applied for this issue by regarding it as the same as the number of mixture components (mixture size); however, this may not be valid in the presence of overlaps or weight biases. In this study, we argue that the cluster size should be measured as a continuous value and propose a new criterion called mixture complexity (MC) to formulate it. It is formally defined from the viewpoint of information theory and can be seen as a natural extension of the cluster size considering overlap and weight bias. Subsequently, we apply MC to the issue of gradual clustering change detection. Conventionally, clustering changes have been regarded as abrupt, induced by the changes in the mixture size or cluster size. Meanwhile, we consider the clustering changes to be gradual in terms of MC; it has the benefits of finding the changes earlier and discerning the significant and insignificant changes. We further demonstrate that the MC can be decomposed according to the hierarchical structures of the mixture models; it helps us to analyze the detail of substructures.
Hybrid sentiment analysis with textual and interactive information

In the past decade, various methods have been proposed to address document-level sentiment classification. However, the exploration of user–product interactions has not been sufficiently studied in the literature. In this work, we aim to investigate the effectiveness of exploiting user–product relations, and propose a hybrid semantic and interactive model for the classification task. The novelty of the proposed method is a ranking graph module and a latent matching module, where the former is capable of capturing high-order connectivity among the nodes, while the later is able to preserve semantics of local connectivity during the recursive graph learning processing. These two modules are seamlessly incorporated, enabling the proposed model to learn comprehensive and discriminative representations for the specific classification task. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed model over the state-of-the-art baselines.
Residual-Prototype Generating Network for Generalized Zero-Shot Learning

Conventional zero-shot learning aims to train a classifier on a training set (seen classes) to recognize instances of novel classes (unseen classes) by class-level semantic attributes. In generalized zero-shot learning (GZSL), the classifier needs to recognize both seen and unseen classes, which is a problem of extreme data imbalance. To solve this problem, feature generative methods have been proposed to make up for the lack of unseen classes. Current generative methods use class semantic attributes as the cues for synthetic visual features, which can be considered mapping of the semantic attribute to visual features. However, this mapping cannot effectively transfer knowledge learned from seen classes to unseen classes because the information in the semantic attributes and the information in visual features are asymmetric: semantic attributes contain key category description information, while visual features consist of visual information that cannot be represented by semantics. To this end, we propose a residual-prototype-generating network (RPGN) for GZSL that extracts the residual visual features from original visual features by an encoder–decoder and synthesizes the prototype visual features associated with semantic attributes by a disentangle regressor. Experimental results show that the proposed method achieves competitive results on four GZSL benchmark datasets with significant gains.
Sentence-CROBI: A Simple Cross-Bi-Encoder-Based Neural Network Architecture for Paraphrase Identification

Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a vector given a single sentence and are used in tasks such as textual similarity or information retrieval due to their low computational cost; however, their performance is inferior to that of cross-encoders. In this paper, we present Sentence-CROBI, an architecture that combines cross-encoders and bi-encoders to obtain a global representation of sentence pairs. We evaluated the proposed architecture in the paraphrase identification task using the Microsoft Research Paraphrase Corpus, the Quora Question Pairs dataset, and the PAWS-Wiki dataset. Our model obtains competitive results compared with the state-of-the-art by using model ensembles and a simple model configuration. These results demonstrate that a simple architecture that combines sentence pair and single-sentence representations without using complex pre-training or fine-tuning algorithms is a viable alternative for sentence pair tasks.
A multiple transform based approach for robust and blind image copyright protection

Summary Digital image sharing and utilization have increasing in a speedy manner at present time. Therefore, copyright/ownership protection for digital images has been an essential requirement in the modern world of digital advancements. This work offers an image watermarking scheme to provide ownership/copyright verification in an effective manner with no conciliation with imperceptibility. The image is first partitioned into small size blocks. During embedding, a multilevel transform domain‐based framework is employed to embed the watermark information into blocks. Additionally, the block selection process is made randomize (key‐based) to offer high security against illegal manipulations/access. Before embedding, the watermark is encrypted using the Arnold transform‐based approach for additional security. The scheme has blind nature, high imperceptibility and it is robust enough to endure a different variety of processing attacks. Experimental results on different images illustrate that the proposed watermarking approach has high imperceptibility, high robustness, decent embedding capacity, and significant security features. The relative comparison with the existing robust watermarking schemes (having the same payload) shows the superiority of the proposed work over existing methods presented in the recent past.
Single node reconstruction of graph signal based on graph fractional Fourier transform

A general scheme to sample signals defined on the nodes of a graph is proposed. In this paper, we study the sampling and recovery of graph signals with successive local aggregations under the graph fractional Fourier transform. In the graph fractional Fourier domain, we show that fractional bandlimited graph signals can be perfectly recovered. Our focus is on reconstructing fractional bandlimited graph signals. This sampling strategy, unlike the graph's subset selection scheme, obtains observations from a single node. We give examples of graph signal sampling to demonstrate that fractional bandlimited graph signals can be perfectly recovered and compare it with graph aggregation sampling.
Research on image hiding algorithm based on deep neural network

Information hiding refers to embedding secret information in an imperceptible way with the help of redundant information in the multimedia carrier without affecting the sensory characteristics and use value of multimedia, and transmitting it in an open channel. Deep learning is a hot research direction in the field of machine learning in recent years. It allows machines to learn the inherent laws and representation levels of sample data, and ultimately enables machines to have the ability to analyze and learn like humans. Based on the baluja algorithm and the ISGAN algorithm, combined with the Inception module in GoogleNet and the relevant knowledge of the residual network in deep learning, this paper designs an image steganography algorithm suitable for hiding various images. It has been proved , the algorithm solves the problem of color distortion of the stego image in the baluja algorithm, reduces the gap between the stego image and the cover image, and improves the security of the stego image.
Improved 2D target detection with YoloV5 based on attention mechanism

For 2D image data target detection, this paper proposes an improved detection algorithm for YoloV5s based on the attention mechanism. By replacing the Focus module in YoloV5s with a CBL module, it facilitates model export. The four attention modules, SELayer, EcaLayer, CBAM and CoordAtt are added to the convolutional network of YoLoV5s. Before the P4 layer, before the P5 layer, before the SPPF layer and before the last layer of the original backbone network, respectively. By experimenting with algorithms that include YoloV5s, It was concluded that among the five algorithms tested for evaluation. The proposed YoloV5s-CoordAtt has the best performance level in terms of accuracy, with a 4.48% improvement compared to the original algorithm.
A multi-object tracking algorithm based on YOLOv5-concise network

As the basis of pedestrian and traffic statistics, multi-object tracking (MOT) is widely used in smart cities and smart shops. Due to the high cost of cloud analysis, MOT algorithm is mainly deployed on the end-side device. However, limited by computing resources and capital cost, it is difficult to deploy large models in end-side device. As tracking based on detection has become the most effective MOT method, a lightweight detection network model YOLOv5- Concise with only 0.302M parameters is proposed for end-side deployment in this paper. Then, the application of knowledge distillation technology based on outputs and feature relation in object detection is studied. By knowledge distillation, the YOLOv5-Concise model's detection precision mAP0.5 is increased by 7.26%, mAP0.5:0.95 is increased by 16%, and the detection speed remained unchanged. Finally, on the camera equipped with T40 chip, the detection speed of the model is measured to 28 FPS, and the accuracy rate of 98.1% is obtained in the pedestrian statistics test.
Design of Accurate Association System of QR Code Information for Cigarette Sorting

This study employs the EIQ analysis approach to quantitatively examine the real order data of the cigarette distribution center in an effort to further increase the efficiency of cigarette sorting and distribution. To further increase the accuracy and efficiency of sorting labor, a sorting system based on QR code information and an accurate identification algorithm is proposed with the use of statistical data and the associated planning scheme. According to the system simulation findings, the sorting efficiency of the system’s sorting line is 18570 pieces per hour. However, by adding packing equipment, the efficiency of the system can be raised by 61.55 percent and can reach 30000 pieces per hour. Additionally, it confirms the viability of the system design. The two-dimensional code’ properties determine which global threshold method the binarization algorithm chooses. The two-dimensional code’ coding principle is investigated, and a two-dimensional code decoding algorithm is developed. The results of the experiment indicate that the decoding algorithm can fulfill these requirements.
Efficient continual learning at the edge with progressive segmented training

Abstract There is an increasing need for continual learning in dynamic systems at the edge, such as self-driving vehicles, surveillance drones, and robotic systems. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference, within a limited power budget. Different from previous continual learning algorithms with dynamic structures, this work focuses on a single network and model segmentation to mitigate catastrophic forgetting problem. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and a secondary group to be saved (not pruned) for future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of progressive segmented training (PST) successfully incorporates multiple tasks and achieves state-of-the-art accuracy in the single-head evaluation on the CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning and thus, enabling efficient continual learning at the edge. On Intel Stratix-10 MX FPGA, we further demonstrate the efficiency of PST with representative CNNs trained on CIFAR-10.
Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device

In this paper, a novel approach for abnormal gait and tremor detection using a smart walking cane is introduced. Periodic muscle movement associated with Parkinson’s disease, such as arm shaking, vibrating arm, trembling fingers, rhythmic wrist movements, normal and abnormal walking pattern, was learned and classified with linear discriminant analysis. Although detecting symptoms related to disease with walking sticks might look trivial at first, throughout history, a cane or walking stick has been used as an assistive device to aid in ambulating, especially in the elderly and disabled, so embedding smart devices (that can learn ambulating pattern and detect anomalies associated with it) in the cane will help in early detection of diseases and facilitate early intervention. This approach is non-intrusive, and privacy issues being experienced in visual models do not arise, as users do not need to wear any special bracelet or wrist monitoring, and they only need to pick up the cane when they wish to move. The simplicity and efficient usage of a technique for detecting ambulatory anomalies is also demonstrated in this research. We extracted step counts, fall data and other valuable features from the cane, and detected anomalies by using isolation forest and one-class support vector machine (SVM) methods. Falls were detected easily and naturally with the cane, which had different alert modes (a soft alert when the cane lost equilibrium and was picked up within 15 s, and a strong alert otherwise). Intervention systems are proposed to forestall and limit the possibility of a type 2 error.
Speeding up deep neural architecture search for wearable activity recognition with early prediction of converged performance

Neural architecture search (NAS) has the potential to uncover more performant networks for human activity recognition from wearable sensor data. However, a naive evaluation of the search space is computationally expensive. We introduce neural regression methods for predicting the converged performance of a deep neural network (DNN) using validation performance in early epochs and topological and computational statistics. Our approach shows a significant improvement in predicting converged testing performance over a naive approach taking the ranking of the DNNs at an early epoch as an indication of their ranking on convergence. We apply this to the optimization of the convolutional feature extractor of an LSTM recurrent network using NAS with deep Q-learning, optimizing the kernel size, number of kernels, number of layers, and the connections between layers, allowing for arbitrary skip connections and dimensionality reduction with pooling layers. We find architectures which achieve up to 4% better F1 score on the recognition of gestures in the Opportunity dataset than our implementation of DeepConvLSTM and 0.8% better F1 score than our implementation of state-of-the-art model Attend and Discriminate, while reducing the search time by more than 90% over a random search. This opens the way to rapidly search for well-performing dataset-specific architectures. We describe the computational implementation of the system (software frameworks, computing resources) to enable replication of this work. Finally, we lay out several future research directions for NAS which the community may pursue to address ongoing challenges in human activity recognition, such as optimizing architectures to minimize power, minimize sensor usage, or minimize training data needs.
Minimal length multi-segment clothoid return paths for vehicles with turn rate constraints

Continuous curvature recovery paths are needed to accurately return a fixed wing autonomous vehicle with turn rate constraints back to a missions path in the correct direction after collision avoidance. Clothoid paths where curvature is linearly dependent to arc length can be used to make multi-segment splines with continuous curvature, but require optimization to ensure that the path is of minimal length while meeting curvature and sharpness limits. The present work considers the problem of returning a fixed wing aircraft back to its original path facing the correct direction after a leaving it during collision avoidance by presenting a method of optimizing a three segment clothoid spline to be of minimal length while meeting fixed wing turn rate constraints and targeting a path function. The impact of this work is enabling accurate path recovery after collision avoidance with minimal length paths that minimize the time spent off a missions planned route, giving better control over time of arrival for the planned route and more time to complete mission objectives.