Reviewerly/exHarmony huggingface dataset:
Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification

Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained increasing attention for its effectiveness on the target domain without manual annotations. Most fine-tuning based UDA person ReID methods focus on encoding global features for pseudo labels generation, neglecting the local feature that can provide for the fine-grained information. To handle this issue, we propose a Learning Feature Fusion (LF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) framework for adaptively learning to fuse global and local features to obtain a more comprehensive fusion feature representation. Specifically, we first pre-train our model within a source domain, then fine-tune the model on unlabeled target domain based on the teacher-student training strategy. The average weighting teacher network is designed to encode global features, while the student network updating at each iteration is responsible for fine-grained local features. By fusing these multi-view features, multi-level clustering is adopted to generate diverse pseudo labels. In particular, a learnable Fusion Module (FM) for giving prominence to fine-grained local information within the global feature is also proposed to avoid obscure learning of multiple pseudo labels. Experiments show that our proposed LF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> framework outperforms the state-of-the-art with 73.5% mAP and 83.7% Rank1 on Market1501 to DukeMTMC-ReID, and achieves 83.2% mAP and 92.8% Rank1 on DukeMTMC-ReID to Market1501. The source code is available at: https://github.com/DJEddyking/LF2.
Joint Learning Method Based On Transformer For Image Retrieval

In order to solve the problem that the supervised information is not effectively used and lack of correlation between image features in image retrieval, a joint learning method based on Transformer (JLMT) for image retrieval is proposed in this paper. This method not only combines Transformer with asymmetric learning strategy to extract image features and generate the correlation between image features, but also combines the classification loss to make full use of the supervised information. For training images, we uses the Transformer to generate hash codes, classification loss and the semantic similarity loss are used to learn the hash function, so that the image hash codes are closer to the real hash codes. For the database (retrieval) images, we use the asymmetric learning strategy to learn the hash codes directly through the hash codes of the training images. Finally, the query images generate hash codes through hash function and search similar images in the database image set according to the Hamming distance. Also, a new loss function for the classification of multi-label datasets is proposed in this paper. Experimental results show that the JLMT has the state-of-the-art performance on public datasets of CIFAR-10, NUS-WIDE and MS-COCO.
A Survey on the Application of Cloud Models in the Reliability of Cloud Environments

In solving the ambiguous phenomena existing in life, qualitative concepts and quantitative values need to be converted to each other, so a qualitative-quantitative uncertainty model is needed. Cloud model is a kind of uncertainty model for qualitative-quantitative conversion proposed by academician Deyi Li. This paper firstly introduces the background and basic definition of cloud model, summarizes the progress of application of cloud model in controller, data mining, genetic algorithm and other fields, and proves that cloud model can play an important role in qualitative-quantitative conversion problems in different fields. Finally, the advantages and application methods of cloud models in the reliability of cloud environment are introduced.
DevTag: A Benchmark for Fingerprinting IoT Devices

Nowadays, various Internet of Things (IoT) devices, such as routers, webcams, and network printers, have been deployed across the Internet. For security and management purposes, it is important to accurately fingerprint IoT devices. In this work, we build a first benchmark called DevTag (IoT Device Tagging) for fingerprinting IoT devices. Specifically, DevTag supports retrieving packet-level features from IoT devices through two different data collections, passive monitoring, and active probing. For detecting IoT devices, DevTag integrates model-based and rule-based fingerprinting methods. For the model-based detection, we reimplemented five typical deep algorithms to infer IoT device classification models. For the rule-based detection, we generated nearly 41 117 rules in a unified format by analyzing several open-source tools. Furthermore, we conducted a systematic analysis to explore the advantages and limitations of those two methods for detecting IoT devices. Our analysis results reveal that the model-based detection has a significant advantage in distinguishing coarse-grained IoT devices (e.g., device type and vendor), while it is not suitable to detect product information as the label amount is massive. The rule-based detection is capable of extracting fine-grained device information with high precision in a short time. However, rules also suffer several inherent problems, such as multiple matching, conflicting, and overlapping issues. Finally, we implemented and distributed a prototype of DevTag working as the first benchmark for detecting IoT devices in the network community.
Fall Detection Method Based on TBM and SVM

In response to the low power consumption and high accuracy requirements of wearable fall detection technology, a fall detection method based on threshold-based method (TBM) and Support Vector Machine (SVM) was proposed in this paper. TBM is implemented in the designed wearable device, and SVM is implemented in the server. In the TBM, three features corresponding to the weightless, impact and stationary of falls were extracted. They were compared with the preset thresholds to recognize falls and some daily activities. All suspected falls were uploaded to the cloud server, which reduced wireless data transmitted. On the server, 13 kinds of features which can characterize different stages of fall were extracted. Then, the extracted features were standardized. Finally, SVM optimized by the GridSearchCV was used to make the terminal decision on the uploaded suspected falls. The experimental results showed that the specificity of the TBM stage reaches 65.23% when the sensitivity is 100%, which could significantly reduce the uploaded suspected fall events. The accuracy of the fall detection algorithm reaches 97.96%, the sensitivity and specificity reach 98.56% and 97.76%, respectively. Compared with the existing fall detection methods, the method designed in this paper has the advantages of low power consumption and high accuracy.
Learning Soft Estimator of Keypoint Scale and Orientation with Probabilistic Covariant Loss

Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoint scale or orientation, called hard estimators. However, hard estimators are difficult to handle the local patches containing structures of different objects or multiple edges. In this paper, a Soft Self-Supervised Estimator (S3Esti) is proposed to overcome this problem by learning to predict multiple scales and orientations. S3Esti involves three core factors. First, the estimator is constructed to predict the discrete distributions of scales and orientations. The elements with high confidence will be kept as the final scales and orientations. Second, a probabilistic covariant loss is proposed to improve the consistency of the scale and orientation distributions under different transformations. Third, an optimization algorithm is designed to minimize the loss function, whose convergence is proved in theory. When combined with different keypoint extraction models, S3Esti generally improves over 50% accuracy in image matching tasks under significant viewpoint changes. In the 3D reconstruction task, S3Esti decreases more than 10% reprojection error and improves the number of registered images. [code release]
Effectiveness of a Method to Eliminate Fruitless Cycles for Pollard's Rho Method

In this research, the authors focus on an attack on a kind of pairing-friendly curves called the Barreto-Naehring curve. Accelerated attacks involve evaluating the security level concerning the elliptic curve discrete logarithm problem (ECDLP). The authors confirm the effectiveness of a method to eliminate a fruitless cycle in a random-walk path for parallel Pollard's rho method with skew Frobenius mapping for the curve. Though the rho method is known to solve the ECDLP efficiently, a random-walk path sometimes induces the unsolvable cycle, called a fruitless cycle, then the random-walk must restart with yet another starting point. In a previous work, the authors proposed a method to eliminate the fruitless cycle for a random-walk path. In this research, the authors implement a parallel rho method and confirm the effectiveness of the proposed method by several experiments. The results show that the proposed method effectively eliminate the fruitless cycles of length two and three, but increase the fruitless cycles of length four.
Analyzing 2FA Phishing Attacks and Their Prevention Techniques

Two factor authentication (2FA) provides a strong user authentication which increases the security of a vast range of web sites and their services. With its popularity, the 2FA still remains weak to attacks of phishing in which an attacker phishes the user's 2FA as well. In this paper, we analyzed how attacker captures the user 2FA using Man in The Middle (MITM) attack using reverse proxy, review past schemes proposed against this, and the 2FA-PP method by Ulqinaku et al where they introduced a mobile device connectivity to the user's browser that supports direct communication between the two devices through which the authentication will proceed, as a result bypassing the MITM. Finally, we discuss simpler measures the authentication protocols can assume where the integration is minimal and can be adapted easily both on the client and server side.
Complexity of Representations in Deep Learning

Deep neural networks use multiple layers of functions to map an object represented by an input vector progressively to different representations, and with sufficient training, eventually to a single score for each class that is the output of the final decision function. Ideally, in this output space, the objects of different classes achieve maximum separation. Motivated by the need to better understand the inner working of a deep neural network, we analyze the effectiveness of the learned representations in separating the classes from a data complexity perspective. Using a simple complexity measure, a popular benchmarking task, and a well-known architecture design, we show how the data complexity evolves through the network, how it changes during training, and how it is impacted by the network design and the availability of training samples. We discuss the implications of the observations and the potentials for further studies.
GRASP-Based Hybrid Search to Solve the Multi-objective Requirements Selection Problem

One of the most important and recurring issues that the development of a software product faces is the requirements selection problem. Addressing this issue is especially crucial if agile methodologies are used. The requirements selection problem, also called Next Release Problem (NRP), seeks to choose a subset of requirements which will be implemented in the next increment of the product. They must maximize clients satisfaction and minimize the cost or effort of implementation. This is a combinatorial optimization problem studied in the area of Search-Based Software Engineering. In this work, the performance of a basic genetic algorithm and a widely used multi-objective genetic algorithm (NSGA-II) have been compared against a multi-objective version of a randomized greedy algorithm (GRASP). The results obtained show that, while NSGA-II is frequently used to solve this problem, faster algorithms, such as GRASP, can return solutions of similar or even better quality using the proper configurations and search techniques. The repository with the code and analysis used in this study is made available to those interested via GitHub.
Exploring clustering in SOMA

During the developing phase of the new evolutionary algorithm (EA) or the analysis, several techniques or measurements are used to capture the inner dynamic of an algorithm. Besides the usual ones, for example, convergence graphs, population diversity, or complex networks, the scientists may also use clustering. Clustering analysis may naturally be used to analyze the grouping of individuals in swarm-based algorithms. This paper examines the possibilities of the clustering analysis for the Self-Organizing Migrating Algorithm with CLustering-aided migration (SOMA-CL). The algorithm is described in detail, together with several cluster analyses which can be used to investigate the behavior of the algorithm.
Deep Active Learning with Range Feedback for Facial Age Estimation

Deep learning has achieved unprecedented break-throughs in machine learning research. However, the paucity of labeled training data, together with the human effort and time associated with obtaining a large amount of labeled data, poses significant challenges in training a reliable deep learning model. Active Learning (AL) algorithms automatically select the exemplar instances from large amounts of unlabeled data, and are instrumental in reducing the human annotation effort in inducing a machine learning model. However, in certain applications providing the exact label to queried unlabeled instances may be challenging even for human annotators. Vision based facial age estimation is one such application where it is difficult to estimate the exact age of a person merely from a face image; it maybe easier, and more practical, to provide other forms of annotation such as the best estimated lower and upper bounds on the age of the person within a given span. In this paper, we propose DALRange, a novel deep active learning framework, where annotators merely need to provide an estimated range on the label of an unlabeled sample, rather than the exact label. We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the network. To the best of our knowledge, this is the first research effort to develop an active learning algorithm to train a deep neural network, which poses only range label queries to the oracles. Our extensive empirical studies on human-annotated data corroborate the practical usefulness of our framework in applications where providing the exact labels to queried samples can be challenging.
Dentawyanjana Character Segmentation Using K-Means Clustering CLAHE Adaptive Thresholding Based

Each country has its own historical heritage, one of which is letters/characters. Indonesia has 12 regional characters, and this must be preserved. One of them is the Dentawyanjana character or usually called the Javanese character/carakan. This study aims to segment the Dentawyanjana character using K-means clustering CLAHE adaptive thresholding based. This study consisted of 3 experiments, and each experiment implemented a different hybrid method. The first method is preprocessing which is converting the RGB image to grayscale, then converting it from grayscale to a binary image. After this process, segmentation is carried out using K-means and moment methods. The second method is preprocessed using a grayscale process, then converting it from grayscale to a binary image using adaptive. The results of the adaptive threshold process are clustered using K-means and segmentation is done using the moment method. The third method used begins with standardizing the dimensions to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$800\times 1200$</tex> pixels which are then preprocessed as in the first and second methods converting the RGB image to grayscale. Then the enhancement is done using CLAHE, and the result of this process is continued to be implemented with an adaptive threshold. The segmentation process is clustered using K-means and moment methods. The results show that the accuracy of the first, second, and third methods are 68.23%, 94.18%, and 95.52%. While the resulting AUC values are 0.798, 0.962, and 0.975.
Unsupervised Video-Based Action Recognition With Imagining Motion and Perceiving Appearance

Video-based action recognition is a challenging task, which demands carefully considering the temporal property of videos in addition to the appearance attributes. Particularly, the temporal domain of raw videos usually contains significantly more redundant or irrelevant information than still images. For that, this paper proposes an unsupervised video-based action recognition approach with imagining motion and perceiving appearance, called IMPA, by comprehensively learning the spatio-temporal characteristics inherited in videos, with a particular emphasis on the moving object for action recognition. Specifically, a self-supervised Motion Extracting Block (MEB) is designed to extract the principal motion features by focusing on the large movement of the moving object, based on the observation that humans can infer complete motion trajectories from partial moving objects. To further take the indispensable appearance attribute in videos into account, an unsupervised Appearance Learning Block (ALB) is developed to perceive the static appearance, thus in combination with the MEB to recognize actions. Extensive validation experiments and ablation studies on multiple datasets demonstrate that our proposed IMPA approach obtains superior performance and surpasses other classical and state-of-the-art unsupervised action recognition methods.
MPDFF: Multi-source Pedestrian detection based on Feature Fusion

Pedestrian detection from UAV images is vital for many fields. Given that visible images are susceptible to bad illumination, thermal images with the ability to characterize the temperature of an object can provide auxiliary information. It is useful to fuse the visible and thermal images to improve the pedestrian detection performance. Unfortunately, studies on pedestrian detection with UAV visible-thermal image pairs are still rare. Therefore, we propose a method for Multi-source Pedestrian Detection based on Feature Fusion (MPDFF). With the registered visible and thermal image pairs as the input, MPDFF can achieve better characterization of pedestrians by concatenating the features from the two images. MPDFF performs much better than the methods that use only single-source images, which demonstrates that visible and thermal images are complementary in pedestrian detection.
Suggestion mining from online reviews using temporal convolutional network

Business and brand owners are using social media networks to provide and deliver various services to their clients and collect information about their products from customers. Customers give their opinions as well as ideas for the improvement of the products on the review platforms and portals. Suggestion Mining is a technique of automatic extraction of these innovative ideas or suggestions from online source data. In this paper, we proposed TCN architecture for suggestion mining from online reviews. The TCN uses causal and dilated convolutional layers to process sequential or temporal data and captures long-term dependencies. TCN architecture on the dataset of SemEval-2019 subtask A is experimented. The dataset is highly imbalanced and to overcome this problem, the ensemble oversampling technique to balance the dataset is applied. TCN is also experimented with the attention mechanism. Our proposed model outperforms the existing works by achieving an F1 score of 82.0 %.
Informative Communication of Robot Plans

When a robot is asked to verbalize its plan it can do it in many ways. For example, a seemingly natural strategy is incremental, where the robot verbalizes its planned actions in plan order. However, an important aspect of this type of strategy is that it misses considerations on what is effectively informative to communicate, because not considering what the user knows prior to explanations. In this paper we propose a verbalization strategy to communicate robot plans informatively, by measuring the information gain that verbalizations have against a second-order theory of mind of the user capturing his prior knowledge on the robot. As shown in our experiments, this strategy allows to understand the robot’s goal much quicker than by using strategies such as increasing or decreasing plan order. In addition, following our formulation we hint to what is informative and why when a robot communicates its plan.
TensorFI+: A Scalable Fault Injection Framework for Modern Deep Learning Neural Networks

Deep Neural Networks (DNNs) are widely deployed in various applications such as autonomous vehicles, healthcare, space applications. TensorFlow is the most popular framework for developing DNN models. After the release of TensorFlow 2, a software-level fault injector named TensorFI is developed for TensorFlow 2 models, which is limited to inject faults only in sequential models. However, most popular DNN models today are non-sequential. In this paper, we are the first to propose TensorFI+, an extension to TensorFI to support for non-sequential models so that developers can assess resiliency of any DNN model developed with TensorFlow 2. For the evaluation, we conduct a large-scale fault injection experiment on 30 sequential and non-sequential models with three popularly used classification datasets. We observe that our tool can inject faults in any layer for any sequential or non-sequential DNN model, and fault-injected inference incurs only 7.62 x overhead compared to fault-free inference.
A construction Method of Knowledge Graph for Electric Power Field

he electric power business is characterized by numerous types, wide audiences, and a high level of expertise, so we need to develop intelligent customer service for the electric power field based on the domain-specific knowledge graph. In this paper, a knowledge graph was constructed for the electric power business according to the research on the intelligent dialogue system for the electric power field, based on the electric power expertise and the daily service Q&A scenarios of Anhui Electric Power Service Hall. We proposed a construction method that consists of the knowledge modeling method for building a knowledge graph for the electric power business, knowledge extraction method based on machine learning, triplet knowledge representation method, and knowledge storage method based on a graph database. Multiple entity and relation extraction methods were compared through experiments. The BERT-BI-LSTM-CRF and BERT-BI-LSTM-ATT methods that perform the best in experiments were selected for knowledge extraction, and 4,109 entities and 1,587 triplets were extracted.