Reviewerly/exHarmony huggingface dataset:
Fine-Grained Cross-Modal Retrieval for Cultural Items with Focal Attention and Hierarchical Encodings

In this paper, we target the tasks of fine-grained image–text alignment and cross-modal retrieval in the cultural heritage domain as follows: (1) given an image fragment of an artwork, we retrieve the noun phrases that describe it; (2) given a noun phrase artifact attribute, we retrieve the corresponding image fragment it specifies. To this end, we propose a weakly supervised alignment model where the correspondence between the input training visual and textual fragments is not known but their corresponding units that refer to the same artwork are treated as a positive pair. The model exploits the latent alignment between fragments across modalities using attention mechanisms by first projecting them into a shared common semantic space; the model is then trained by increasing the image–text similarity of the positive pair in the common space. During this process, we encode the inputs of our model with hierarchical encodings and remove irrelevant fragments with different indicator functions. We also study techniques to augment the limited training data with synthetic relevant textual fragments and transformed image fragments. The model is later fine-tuned by a limited set of small-scale image–text fragment pairs. We rank the test image fragments and noun phrases by their intermodal similarity in the learned common space. Extensive experiments demonstrate that our proposed models outperform two state-of-the-art methods adapted to fine-grained cross-modal retrieval of cultural items for two benchmark datasets.
Divide-and-Merge the embedding space for cross-modality person search

This study considers the problem of text-based person search, which aims to find the corresponding person of a given text description in an image gallery. Existing methods usually learn a similarity mapping of local parts between image and text, or embed the whole image and text into a unified embedding space. However, the relevance between local and the whole is largely underexplored. In this paper, we design a Divide-and-Merge Embedding (DME) learning framework for text-based person search. DME explicitly 1) models the relations between local parts and global embedding, 2) incorporates local details into global embedding. Specifically, we design a Feature Dividing Network (FDN) to embed the input into K locally guided semantic representations by self-attentive embedding, each representation depicts a local part of the person. Then, we propose a Relevance based Subspace Projection (RSP) method for merging diverse local representations to a compact global embedding. RSP helps the model to obtain discriminative embedding by jointly minimizing the redundancy of local parts and maximizing the relevance between local parts and global embedding. Extensive experimental results on three challenging benchmarks, i.e., CUHK-PEDES, CUB and Flowers datasets, have demonstrated the effectiveness of the proposed method.
Derivative-free optimization adversarial attacks for graph convolutional networks

In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model’s classification of the target nodes, or even cause a degradation of the model’s overall classification performance. In this paper, we first propose a black-box adversarial attack framework based on derivative-free optimization (DFO) to generate graph adversarial examples without using gradient and apply advanced DFO algorithms conveniently. Second, we implement a direct attack algorithm (DFDA) using the Nevergrad library based on the framework. Additionally, we overcome the problem of large search space by redesigning the perturbation vector using constraint size. Finally, we conducted a series of experiments on different datasets and parameters. The results show that DFDA outperforms Nettack in most cases, and it can achieve an average attack success rate of more than 95% on the Cora dataset when perturbing at most eight edges. This demonstrates that our framework can fully exploit the potential of DFO methods in node classification adversarial attacks.
Detecting and Explaining Usability Issues of Consumer Electronic Products

Usability of a consumer electronic product (CEP) is one of the most important factors that the users consider in purchasing a CEP as well as functionality, price, etc. This has led many companies to realize new shapes of user interfaces (UIs) and styles of interaction for CEPs, ranging from modern touchscreens to physical controls and displays of any kind. Even if the general feeling is that such increased interactivity may enhance the overall user experience, the side effect is that often a CEP’s UI provides too many functions that are difficult to learn and use without referring to the user manual, leading to many usability issues. In this paper, we leverage a case study in the CEPs sector to present a novel log-based evaluation technique in the field of Human-Computer Interaction (HCI). Our technique allows us not only to keep track of the user interactions with a CEP’s UI during its daily use, but also to understand what has gone wrong during a user interaction, detecting which user actions have caused usability issues and suggesting explanations for solving them, thus providing a crucial feedback to improve the design of the CEP’s UI next version.
Application Measurement and Software Quality Testing Using Machine Learning Performance Techniques

In the era of technology, we see a vast development in the innovation and Software developmental applications, but the standards of the application are not up to the mark. So, a perfect application can be implemented when it follows certain steps like Performance Measurement, response time, Transaction time, throughput time etc. The correlation helps to find the linearity between the features for analyzing the data. Software quality testing, using machine learning for effective testing will give information about the standards of the application and Image processing to minimize the waiting time in the application. In this paper I am going to introduce all the above-mentioned techniques to develop the perfect application.
Ensemble deep learning to classify specific types of t and i patterns in graphology

Graphology is a method of assessment of human personality from individual's handwriting style. The personality in terms of various attributes like fear, honesty, work habits, social skills etc. can be profiled using Graphology. The personality profiling is based on various features like line spacing, margins, slant, letter size, text density etc. Letter t and i are very important personality trait indicators in graphology. Letter t is an indicator of will in individuals. Letter i is an indicator of social connectivity in individuals. This work proposes a deep learning classifier to classify the specific type of t and i patterns. Convolutional neural network is trained to extract the t and i features. The features extracted from t and i handwriting patterns are classified using the ensemble of Deep learning classifier. The performance of the proposed ensemble classification is tested against different handwritten documents. The proposed solution is able to achieve about 90% classification accuracy
Improved Chinese Short Text Classification Method Based on ERNIE_BiGRU Model

Abstract This paper is aimed at traditional word embedding models and Bidirectional Encoder Representations from Transformers (BERT) that cannot learn text semantic knowledge, as well as convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) unable to distinguish the importance of words, proposing an improved Chinese short text classification method based on ERNIE_BiGRU model. Firstly, learning text knowledge and information through the Enhanced Representation through Knowledge Integration (ERNIE) enhances the model’s semantic representation capabilities. Secondly, considering that CNN can only extract local features of the text while ignoring the semantic relevance between contextual information, and the Bidirectional Gating Recurrent Unit (BiGRU) is simpler, has fewer network parameters and faster calculation speed than the BiLSTM, the combination of CNN and BiGRU enables the model to capture both local phrase-level features and contextual structure information. Finally, according to the importance of features, the attention mechanism is used to assign different weights to improve the classification effect of the model. The experimental results show that the ERNIE_CNN_BiGRU_Attention (ECBA) model used in this paper has achieved good results in the task of Chinese short text classification.
Modeling Image Quality Score Distribution Using Alpha Stable Model

In recent years, image quality is generally described by a mean opinion score (MOS). However, we observe that an image’s quality ratings given by a group of subjects may not follow a Gaussian distribution and the image quality can not be fully described by a MOS. In this paper, we propose to describe the image quality using a parameterized distribution rather than a MOS, and an objective method is also proposed to predict the image quality score distribution (IQSD). Specifically, we selected 100 images from the LIVE database and invited a large group of subjects to evaluate the quality of these images. By analyzing the subjective quality ratings, we find that the IQSD can be well modeled by an alpha stable model and this model can reflect much more information than MOS. Therefore, we propose an algorithm to model the IQSD described by an alpha stable model. Features are extracted from images based on natural scene statistics and support vector regressors are trained to predict the IQSD described by an alpha stable model. We validate the proposed IQSD prediction model on the collected subjective quality ratings. Experimental results verify the effectiveness of the proposed algorithm in modeling the IQSD.
Generating Aesthetic Based Critique For Photographs

The recent surge in deep learning methods across multiple modalities has resulted in an increased interest in image captioning. Most advances in image captioning are still focused on the generation of factual-centric captions, which mainly describe the contents of an image. However, generating captions to provide a meaningful and opinionated critique of photographs is less studied. This paper presents a framework for leveraging aesthetic features encoded from an image aesthetic scorer, to synthesize human-like textual critique via a sequence decoder. Experiments on a large-scale dataset show that the proposed method is capable of producing promising results on relevant metrics relating to semantic diversity and synonymity, with qualitative observations demonstrating likewise. We also suggest the use of Word Mover’s Distance as a semantically intuitive and informative metric for this task.
Telugu News Data Classification Using Machine Learning Approach

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.
Blockchain technology protects diplomas against fraud

At the end of the studies of a higher education institution, a diploma/graduation exam is held, which, if promoted, is followed by the receipt of a study diploma. There are people all over the world who want to get a job, but do not have the slightest form of qualification that can be proven by documents. Due to the evolution of technology, counterfeiting is a means of deception that has been practiced quite a lot lately, although often these counterfeits are not detected in time, many people taking advantage of the lack of vigilance of employers. With the development and application of blockchain technology, issues such as forgery of diplomas and identification of documents issued by unaccredited educational institutions will be resolved quickly and securely. This article aims to demonstrate this by using blockchain technology to remove fraud from the education system. The research shows that any employer or graduate with a non-forged diploma can occupy a job without problems, but at the same time the most important thing is to eliminate fraud that has reached a high level. More and more employers will be interested in dismissing competent employees, although they will have material expenses in the process of verifying and validating the documents attesting the educational development of a person.
Classification of Database Technology Problems on Stack Overflow

Stack Overflow is a question-answer community that provides rich information about computer programming and technology for software developers. Users can ask and answer questions on a wide range of programming topics as well search for problems that other users have faced and find solutions that other users have suggested. From a viewpoint of a technology product owner, Stack Overflow can report various issues that product users have, and this serves as valuable input to the product improvement process. This paper proposes an automated approach to classifying questions that are posted on Stack Overflow with regard to a certain kind of products, i.e. database products in particular. The categories of questions are defined at two levels, i.e problem and subproblem. The problem level includes development, installation, and performance tuning, while the subproblem level consists of design, limitation, and discussion. By cross-combining the two levels, questions can be classified into nine problem-subproblem classes. Natural language processing and text classification are used with several machine learning algorithms, i.e. Naïve Bayes, Decision Tree, Extra Trees, Random Forest, Logistic Regression, Stochastic Gradient Descent, Deep Learning Neural Network, and Convolutional Neural Network. The best classifiers for all classes are used further in a web-based tool that can classify each question by a problem-subproblem tag and also report the number of problems that users of a database product have posted. This information can benefit the owner of a database product in planning product maintenance and evolution.
Incorporating uncertainty in learning to defer algorithms for safe computer-aided diagnosis

Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propose a learning to defer with uncertainty (LDU) algorithm which identifies patients for whom diagnostic uncertainty is high and defers them for evaluation by human experts. LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax (using chest x-rays), and compared with 'learning to defer without uncertainty information' (LD) and 'direct triage by uncertainty' (DT) methods. LDU achieved the same F1 score as LD but deferred considerably fewer patients (e.g. 36% vs. 69% deferral rate for diagnosing pleural effusion with an F1 score of 0.96). Furthermore, even when many patients were assigned the wrong diagnosis with high confidence (e.g. for the diagnosis of any comorbidities) LDU achieved a 17% increase in F1 score, whereas DT was not applicable. Importantly, the weight of the defer loss in LDU can be easily adjusted to obtain the desired trade-off between diagnostic accuracy and deferral rate. In conclusion, LDU can readily augment any existing diagnostic network to reduce the risk of erroneous diagnoses in clinical practice.
Enhancing Organizational Memory Through Virtual Memoryscapes: Does It Work?

Enhancing cognitive memory through virtual reality represents an issue, that has never been investigated in organizational settings. Here, we compared a virtual memoryscape (treatment) – an immersive virtual environment used by subjects as a shared memory tool based on spatial navigation – with respect to the traditional individual-specific mnemonic tool based on the “method of loci” (control). A memory task characterized by high ecological validity was administered to 82 subjects employed by large banking group. Memory recall was measured, for both groups, immediately after the task (Phase 1) and one week later (Phase 2). Results show that (i) in Phase 1, the method of loci was more efficient in terms of recalling information than the to the virtual memoryscape; (ii) in Phase 2, there was no difference. Compared to the method of loci, the virtual memoryscape presents the advantages – relevant for organizations – of being collective, controllable, dynamic, and non-manipulable.
Chinese clinical entity recognition based on pre-trained models

In the Chinese clinical domain, there are problems such as blurred word boundaries, multiple meanings of one word, insufficient access to character information and a large number of unregistered words, making it more difficult to recognize named entities for this domain. In view of this, this paper proposes a glyph-based enhanced information model, using convolutional neural network and ALBERT to pre-train the language model to obtain the enhanced character information vector and also introduces an attention mechanism on the BiLSTM-CNN-CRF structure to solve the problem that the traditional model extracts features ignoring the importance and location information of words. Finally, experiments are conducted on the CCKS2018 dataset, and the results show that the model outperforms other commonly used models in Chinese clinical recognition.
Research on English Vocabulary Classification Based on Computer Deep Learning

Abstract With the popularity of the Internet, the analysis and classification of text data has become a research hotspot in recent years, and text classification is one of them. In this paper, cbow algorithm is used to train the word vector based on deep learning, and fast text is used for text classification experiment.
Dirt(y) media: Dirt in ecological media art practices

While we may imagine that the unblemished surfaces of our shiny devices are free from the grime of the everyday – flawless and pure – we do not have to dig far below the surface to find that these technologies are anything but untainted and dirt-free. This realisation is at the heart of a shift in international media art towards materials based media. While there have been numerous books and papers describing a materials based media within the notion of ‘media ecologies’, there is more to be learnt from media art, an area that both critiques media and information, and which develops practices that engage in issues around the materiality of media technologies. This article will specifically address dirt through media ecologies by investigating how media artists have made their work literally dirty. The first section of the article critically addresses the notion of media as being material, as opposed to the imagined as pristine, pure and immaterial, and being so, is able to become dirty. The second section looks to a pre-history of dirt(y) media found in the work of artists Milan Knížák and Christian Marclay. The remainder of the article discusses a shift in the approach to contemporary media art that turns away from the digital studio towards a resolutely analogue and physical practice.
Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

Purpose With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images. Design/methodology/approach This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images. Findings Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al. , neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al. , respectively, in terms of accuracy. Originality/value The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.
Chaotic Pseudo Random Number Generators: A Case Study on Replication Study Challenges

Chaotic Pseudo Random Number Generators have been seen as a promising candidate for secure random number generation. Using the logistic map as state transition function, we perform number generation experiments that illustrate the challenges when trying to do a replication study. Those challenges range from uncertainties about the rounding mode in arithmetic hardware over chosen number representations for variables to compiler or programmer decisions on evaluation order for arithmetic expressions. We find that different decisions lead to different streams with different security properties, where we focus on period length, but descriptions in articles often are not detailed enough to deduce all decisions unambiguously. Similar problems might, to some extent, appear in other types of replication studies for security applications. Therefore we propose recommendations for descriptions of numerical experiments on security applications to avoid the above challenges.
Concealing–restoring system for physical layer data: Based on stochastic filtering theory

We propose a nonlinear concealing–restoring system (CRS) set on the data link layer of the open-systems-interconnection (OSI) reference model to secure the data on physical layer. CRS conceals those data by disturbing them with some random noises, and restores the data from the concealed ones to the original ones by the noise elimination based on a proper stochastic filtering theory.