Reviewerly/exHarmony huggingface dataset:
Partial Multi-Label Learning with Noisy Label Identification

Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems with the disambiguation strategy, which recovers ground-truth labels from the candidate label set by simply assuming that the noisy labels are generated randomly. In real applications, however, noisy labels are usually caused by some ambiguous contents of the example. Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. The two objectives are formalized in a unified framework with trace norm and l1 norm regularizers. Under the supervision of the observed noise-corrupted label matrix, the multi-label classifier and noisy label identifier are jointly optimized by incorporating the label correlation exploitation and feature-induced noise model. Furthermore, by mapping each bag to a feature vector, we extend PML-NI method into multi-instance multi-label learning by identifying noisy labels based on ambiguous instances. A theoretical analysis of generalization bound and extensive experiments on multiple data sets from various real-world tasks demonstrate the effectiveness of the proposed approach.
Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable of reliably sorting large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.
Text Classification Using Novel Term Weighting Scheme-Based Improved TF-IDF for Internet Media Reports

With the rapid development of the internet technology, a large amount of internet text data can be obtained. The text classification (TC) technology plays a very important role in processing massive text data, but the accuracy of classification is directly affected by the performance of term weighting in TC. Due to the original design of information retrieval (IR), term frequency-inverse document frequency (TF-IDF) is not effective enough for TC, especially for processing text data with unbalanced distributions in internet media reports. Therefore, the variance between the DF value of a particular term and the average of all DFs <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mfenced open="(" close=")" separators="|"><mrow><mover accent="true"><mrow><mi mathvariant="normal">DF</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></mfenced></math> , namely, the document frequency variance (ADF), is proposed to enhance the ability in processing text data with unbalanced distribution. Then, the normal TF-IDF is modified by the proposed ADF for processing unbalanced text collection in four different ways, namely, TF-IADF, TF-IADF+, TF-IADFnorm, and TF-IADF+norm. As a result, an effective model can be established for the TC task of internet media reports. A series of simulations have been carried out to evaluate the performance of the proposed methods. Compared with TF-IDF on state-of-the-art classification algorithms, the effectiveness and feasibility of the proposed methods are confirmed by simulation results.
Dual self-paced multi-view clustering

By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made in past few years, most existing multi-view clustering methods still suffer the following shortcomings: (1) most MVC methods are non-convex and thus are easily stuck into suboptimal local minima; (2) the effectiveness of these methods is sensitive to the existence of noises or outliers; and (3) the qualities of different features and views are usually ignored, which can also influence the clustering result. To address these issues, we propose dual self-paced multi-view clustering (DSMVC) in this paper. Specifically, DSMVC takes advantage of self-paced learning to tackle the non-convex issue. By applying a soft-weighting scheme of self-paced learning for instances, the negative impact caused by noises and outliers can be significantly reduced. Moreover, to alleviate the feature and view quality issues, we develop a novel feature selection approach in a self-paced manner and a weighting term for views. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.
Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their diagnosis as accurate as possible. Deep learning techniques, such as the convolutional neural network (CNN), due to their classification capabilities on learned feature methods and ability of working with complex images, have been widely adopted in CAD systems. The parameters of the network, including the weights of the convolution filters and the weights of the fully connected layers, play a crucial role in the classification accuracy of any CNN model. The back-propagation technique is the most frequently used approach for training the CNN. However, this technique has some disadvantages, such as getting stuck in local minima. In this study, we propose to optimize the weights of the CNN using the genetic algorithm (GA). The work consists of designing a CNN model to facilitate the classification process, training the model using three different optimizers (mini-batch gradient descent, Adam, and GA), and evaluating the model through various experiments on the BreakHis dataset. We show that the CNN model trained through the GA performs as well as the Adam optimizer with a classification accuracy of 85%.
A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images

Object detection (OD) is an important task of computer vision and has been widely used in many fields, including remote sensing (RS). However, the complex scenes, large-scale variation, and dense instances of RS bring huge challenges to OD. To meet these challenges, a novel Nonlocal-aware Pyramid and Multiscale Multitask Refinement Detector (NPMMR-Det) is proposed. Specifically, nonlocal-aware pyramid attention (NP-Attention) is designed for guiding a neural network model to focus more on efficient features and suppress background noise. Then a multiscale refinement feature pyramid network (MSR-FPN) is proposed to fuse the multiscale context features extracted by the NP-Attention guided neural network and adjust the optimal receptive field. In order to use these features more effectively, a multitask refinement head called MTR-Head, with offset sharing and a modulation mechanism, is developed to refine the feature misalignment between the localization task and the classification task. Extensive experiments performed on two public RS data sets demonstrate that the proposed NPMMR-Det achieves competitive performance compared with state-of-the-art methods.
Adaptive discriminant analysis for semi-supervised feature selection

As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the noise features. In this paper, we propose a novel adaptive discriminant analysis for semi-supervised feature selection, namely, SADA. Instead of computing a similarity matrix first, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative process. Moreover. we introduce the ℓ2,p norm to control the sparsity of S by adjusting p. Experimental results show that S will become sparser with the decrease of p. The experimental results for synthetic datasets and nine benchmark datasets demonstrate the superiority of SADA, in comparison with 6 semi-supervised feature selection methods.
Novel local feature extraction for age invariant face recognition

Age variation is a major problem in the area of face recognition under uncontrolled environments such as pose variation, lighting effects, expression etc. Most of the works of this area have used discriminative feature descriptors. These discriminative feature descriptors are based on their fixed encoding which considers pixels of different radial widths for feature extraction and ignores some radii pixels which hold important discriminative information for age variation. Therefore, consideration of all the pixels of the local region is necessary for important feature extraction in the case of age invariant face recognition. This paper introduces a novel local feature descriptor to find difference pattern and dual directional relation pattern for age invariant face recognition. The proposed descriptor is applied over the preprocessed face images and its parts-periocular region i.e. left and right eye, mouth and nose region of a face image. The proposed difference pattern and dual directional relation pattern descriptors extract the texture features on the local region of a specified dimension. Chi-square metric has been used for finding the similarity between probe and gallery images. Evaluation of the proposed feature descriptor has been performed on two standard challenging datasets FGNET and MORPH for age invariant face recognition. The proposed descriptor performed well and outperformed to the existing age invariant face recognition state-of-the-art methods on FGNET dataset and also performed well on MORPH dataset.
Significant Microsegment Transformants Encoding Method to Increase the Availability of Video Information Resource

The presence of an imbalance caused by an insufficient level of performance of modern and promising infocommunication technologies with respect to the information intensity of bit streams is shown. It is substantiated that a decrease in the level of imbalance is organized as a result of an increase in the efficiency of information processing technologies. It is substantiated that at the moment the JPEG platform is the basic concept for the construction of compression representation technologies. Accordingly, it is proposed to organize the further development of methods for processing video resources using individual components of the JPEG platform in the direction of increasing the integrity of information while ensuring the required level of its availability. The development of the basic concept of processing segmented video frames using the core of the JPEG technology is presented, which is based on the following aspects: conditions are created for differential processing of video frame segments, taking into account their previous identification, depending on their importance from the position of maintaining the desired level of integrity of video resources; provides compatibility of segment identification processes and their processing using the JPEG platform. It is shown that the created video resource encoding methods based on the developed concept of processing significant segments provide a reduction in the information delivery time under the conditions of the required level of its integrity, i.e. an increase in the level of information security is achieved.
Hot air and corporate sociotechnical imaginaries: Performing and translating digital futures in the Danish tech scene

This article analyzes the role of hype in performing and translating corporate sociotechnical imaginaries of digital technologies, into the context of Danish society. Drawing on ethnographic fieldwork of technology events in Denmark, this article proposes “hot air” as a concept to describe how hype for the future performs these imaginaries. This article describes the overlapping sociotechnical imaginaries that dominate these events and the performative effects of hype and its critique, in articulating and translating them. This article makes an empirical and conceptual contribution to the study of sociotechnical imaginaries, in particular, their socio-material performance, the role of corporations in articulating them, and how hype is central to their translation.
Deep learning based algorithm (ConvLSTM) for Copy Move Forgery Detection

Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms.
Crowd emotion evaluation based on fuzzy inference of arousal and valence

Crowd behavior analysis is an important research topic in the field of video surveillance and public safety management. Crowd emotion has a strong relation with the crowd behavior. However, it is very challenging to predict crowd emotion using conventional emotion clues of humans from the video surveillance data, e.g. facial expression or body gesture. To tackle this challenge, this paper presents a crowd emotion evaluation method using fuzzy inference according to the arousal-valence model of the crowd movement. Specifically, the enthalpy, magnitude variance, confusion index and crowd density are extracted to describe crowd emotion. The enthalpy value and magnitude variance are taken as the input of the fuzzy inference system of arousal. And the confusion index and crowd density are used as the input of the fuzzy system of valence. The arousal value and valence value are the output respectively. Through establishing the relationship between arousal, valence and crowd features, the fuzzy rules are constructed to infer the emotion in the crowd scene. Experimental results show that the proposed method can effectively evaluate the arousal and valence in crowd emotion.
BCAS: A blockchain-based ciphertext-policy attribute-based encryption scheme for cloud data security sharing

It is the most important and challenging problem to share the data safely in cloud computing. Some so-called trusted third parties may also infringe users’ data privacy. It is an urgent problem for data owners to share data safely with the designated users rather than the third party or other users. Traditional encryption schemes utilize different keys to produce multiple encrypted copies of the same data for users. It is no longer applicable for cloud data sharing security. Attribute-based encryption can solve above problems, but it needs to rely on trusted third parties to protect the users’ privacy. In this article, in order to address the above problems, we propose a blockchain-based ciphertext-policy attribute-based encryption scheme for cloud data secure sharing without relying on any trusted third parties. Blockchain-based ciphertext-policy attribute-based encryption scheme can protect the rights and security of data owner. Compared with existing cloud security schemes, the proposed scheme has more advantages in terms of the six aspects: (1) data owners have the authority to decide who can decrypt the data; (2) the operations of users are retained permanently, and all records are tamper-proof; (3) our proposed scheme has the characteristic of “one-to-many” encryption, and data is encrypted only once; (4) our scheme does not rely on any trusted third party; (5) in terms of the discrete logarithm problem and decisional q parallel-bilinear Diffie–Hellman exponent problem, we prove that our proposed scheme is secure; and (6) experiment shows that our proposed scheme is more efficient than the comparative scheme.
Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach. The source codes and models have been made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/SMCP</uri> .
WITHDRAWN: Algorithm for movie recommendation system using collaborative filtering

Recommender systems are information filtering system that predicts the rating for users and items, basically from big data to recommend their likes. Movie recommendation systems provide a mechanism to assist users in classifying users with similar interests. Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to eliminate it through a singular value decomposition collaborative filtering approach applied to a web-based movie recommendation system. This paper proposed a movie recommendation system whose primary objective is to suggest a recommender list through singular value decomposition collaborative filtering and cosine similarity. We enhance the model with a factorization form, which greatly reduces the model's number of parameters with a controlled complexity. This paper proposed a movie recommendation system whose primary objective is to suggest a recommender list through singular value decomposition collaborative filtering and cosine similarity. The present work improves these approaches by taking the movies' content information into account during the item similarity calculations. The proposed approach recommends the top n recommendation list of movies to users on user's interest preferences that were not already rated. Graphically shows the percentage of already viewed movies by user and movies recommended to User.
SPLASH: Learnable activation functions for improving accuracy and adversarial robustness

We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: (1) continuous; (2) grounded (f(0)=0); (3) use symmetric hinges; and (4) their hinges are placed at fixed locations which are derived from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance across three datasets (MNIST, CIFAR-10, and CIFAR-100) and four architectures (LeNet5, All-CNN, ResNet-20, and Network-in-Network). Furthermore, we show that SPLASH units significantly increase the robustness of deep neural networks to adversarial attacks. Our experiments on both black-box and white-box adversarial attacks show that commonly-used architectures, namely LeNet5, All-CNN, Network-in-Network, and ResNet-20, can be up to 31% more robust to adversarial attacks by simply using SPLASH units instead of ReLUs. Finally, we show the benefits of using SPLASH activation functions in bigger architectures designed for non-trivial datasets such as ImageNet.
Natural Language Processing (NLP) based Text Summarization - A Survey

The size of data on the Internet has risen in an exponential manner over the past decade. Thus, the need for a solution emerges, that transforms this vast raw information into useful information which a human brain can understand. One such common technique in research that helps in dealing with enormous data is text summarization. Automatic summarization is a renowned approach which is used to reduce a document to its main ideas. It operates by preserving substantial information by creating a shortened version of the text. Text Summarization is categorized into Extractive and Abstractive methods. Extractive methods of summarization minimize the burden of summarization by choosing from the actual text a subset of sentences that are relevant. Although there are a ton of methods, researchers specializing in Natural Language Processing (NLP) are particularly drawn to extractive methods. Based on linguistic and statistical characteristics, the implications of sentences are calculated. A study of extractive and abstract methods for summarizing texts has been made in this paper. This paper also analyses above mentioned methods which yields a less repetitive and a more concentrated summary.