Reviewerly/exHarmony huggingface dataset:
A large-scale image database for benchmarking mobile camera quality and NR-IQA algorithms

Camera-oriented quality assessment (CQA) differs from traditional image quality assessment (IQA) in that “distorted” images are straight out of real devices instead of various types and levels of artificial operations. However, despite its value for both customers and manufacturers, academic and industrial fields, there are few CQA databases created years ago. To reflect recent mobile imaging advancements, we present a new massive Phone Camera Benchmarking database (PCB2021) in this paper. In PCB2021, 40 modern phone units featuring photography are simultaneously compared in 182 scenes for a total of 7280 images, which can be classified into six categories (sub-datasets) based on different focal lengths and user cases: main camera (107), ultra-wide (20), 2×, 3×, 5× zoom lenses (26, 8, 10) and night-mode (11). The shooting process begins from 7:00 am to 11:00 pm and lasts more than a month. In the subjective study, to overcome the high-resolution-induced overall quality evaluation difficulty, five image attributes: exposure/contrast, color, sharpness, graininess, artifacts are assessed separately on each dataset. To reduce ranking complexity for large-scale cameras, a dynamic anchor ruler method is proposed to obtain quality orders efficiently. With the constructed PCB2021, we further evaluate 15 mainstream no-reference (NR) IQA algorithms. The finding is that for zoomed images, sharpness metrics can achieve Spearman correlation coefficients above 0.8, while for the subtle main camera and night-mode images, performances of all fifteen algorithms drop down quickly, i.e. 0.1-0.2 for the former and ∼0.5 for the latter. The entire database, expert rankings and algorithm performance reports will be freely available on request.
On acceptance conditions in abstract argumentation frameworks

Dealing with controversial information is an important issue in several application contexts. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. In abstract argumentation frameworks, each argument can be associated with an acceptance condition that may be either implicit (e.g., Dung’s framework where they are encoded in the attack relation) or explicit (e.g., Dialectical Framework where propositional formulae are associated with arguments/statements). Explicit acceptance conditions allow for expressing reasoning tasks in a more natural and compact way. However, in some cases, current argumentation frameworks allowing explicit conditions do not permit to express in a compact and intuitive way some general acceptance conditions, such as those that could be expressed by first-order logic formulae. In this paper, we propose an argumentation framework where arguments’ acceptance conditions allow for checking general properties also concerning sets of arguments/statements by exploiting aggregate functions (e.g., is the number of nearby agents greater than 5?). Notably, though providing such versatile and easily understandable acceptance conditions, the complexity of credulous and skeptical reasoning does not increase w.r.t. that for Dung’s framework under the well-known non-deterministic semantics, i.e., preferred, stable, and least-undefined (a.k.a. semi-stable) semantics.
Total variation distance privacy: Accurately measuring inference attacks and improving utility

Differential privacy (DP) is a general approach to defend against inference attacks, but hard to balance the privacy-utility trade-off for some complex data analysis tasks. To improve the utility of data analysis, a weaker privacy definition with a more accurate estimate of privacy risk may be a straightforward and effective solution. Total variation distance (TVD) privacy is an appropriate tool for this issue, but it has not been adequately studied. In this paper, we systematically study TVD privacy and explore its applications. We provide a comprehensive theoretical analysis of TVD privacy and demonstrate its advantage in measuring privacy risks with the example of membership inference attacks. Our work indicates that TVD privacy is a helpful tool in estimating privacy risks and has the potential to be widely used as a general privacy definition.
Cuckoo Algorithm Based on Global Feedback

This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a "re-fly" mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as "re-fly." To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution.
Block-Chain Abnormal Transaction Detection Method Based on Dynamic Graph Representation

The advent of cryptocurrency introduced by Bitcoin ignited an explosion of technological and entrepreneurial interest in payment processing. The user scale of Bitcoin is dynamic, and the participating identities are anonymous, which will lead to more hidden, sophisticated and intelligent money laundering crimes. Therefore, in order to realize intelligent anti-money laundering, it is necessary to accurately detect abnormal transactions. Recently, graph representation learning has shown strong advantages in the field of machine learning, and the current blockchain anomaly detection models based on graph representation learning are mainly designed for static graphs, however, real-world graphs evolve over time. Based on this, this paper proposes a block-chain abnormal transaction detection model DynAEGCN based on dynamic graph representation learning. This model uses the autoencoder as the framework. Firstly, the encoder uses the graph convolutional neural networks to gather neighborhood information to obtain low-dimensional feature vectors. Then, considering the dynamics of graphs, the GRU network is used to evolve the graph model itself over time. Finally, the decoder reconstructs the adjacency matrix and compares it with the real graph to construct the loss. Extensive experiments on the Bitcoin transaction dataset for edge classification tasks against financial crimes show that DynAEGCN model has better performance compared with related approaches.
Perfect quantum state transfer on Cayley graphs over dicyclic groups

Perfect state transfer has attracted much interest recently due to its significant applications in quantum information processing and cryptography. In this paper, we investigate perfect state transfer on Cayley graphs over dicyclic groups. Utilizing the representations and characters of dicyclic groups T4n, some necessary and sufficient conditions for Cayley graphs over dicyclic groups admitting perfect state transfer are provided. By those results, some concrete constructions are presented.
Spatial-invariant convolutional neural network for photographic composition prediction and automatic correction

“Composition” determines the vividness of the image and its narrative power. Current research on image aesthetics implicitly considers simple composition rules, but no reliable composition classification and image optimization method explicitly considers composition rules. The existing composition classification models are not suitable for snapshots. We propose a composition classification model based on spatial-invariant convolutional neural networks (RSTN) with translation invariance and rotation invariance. It enhances the generalization of the model for snapshots or skewed images. Ultimately, the accuracy of the RSTN model improved by 3% over the Baseline to 90.8762%, and the rotation consistency improved by 16.015%. Furthermore, we classify images into three categories based on their sensitivity to editing: skew-sensitive, translation-sensitive, and non-space-sensitive. We design a set of composition optimization strategies for each composition that can effectively adjust the composition to beautify the image.
Influence of context on users’ views about explanations for decision-tree predictions

We consider the influence of two types of contextual information, background information available to users and users’ goals, on users’ views and preferences regarding textual explanations generated for the outcomes predicted by Decision Trees (DTs). To investigate the influence of background information, we generate contrastive explanations that address potential conflicts between aspects of DT predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. To investigate the influence of users’ goals, we employ an interactive setting where given a goal and an initial explanation for a predicted outcome, users select follow-up questions, and assess the explanations that answer these questions. Here, we offer algorithms to generate explanations that address six types of follow-up questions. The main result from both user studies is that explanations which have a contrastive aspect about a predicted class are generally preferred by users. In addition, the results from the first study indicate that these explanations are deemed especially valuable when users expectations differ from predicted outcomes; and the results from the second study indicate that contrastive explanations which describe how to change a predicted outcome are particularly well regarded in terms of helping users achieve this goal, and they are also popular in terms of helping users achieve other goals.
Perspective of HCI and UX from Academics, Industry and Community in Indonesia

HCI and UX have been taught, practiced, and supported uniquely in Indonesia. While the efforts to develop the fields of HCI and UX have been made a long way back, the awareness of the community about the importance of these topics just started in the last decade, and the practice still keeps evolving. This paper will describe a couple of highlights of HCI and UX education, professional practice, and communities in Indonesia.
How HCI can Inform the Design of Human Language Technologies

In this paper, we present how practices in human-computer interaction can be used to inform the design of human language technologies. We begin with a description of human language technologies, paying particular attention to the collaborative nature of dialogue systems that necessitates a strong interaction between man and machine. We then share our insights from our application of principles and practices of human computer interaction in the design and evaluation of these conversational interfaces.
Semantic Similarity Comparison Between Production Line Failures for Predictive Maintenance

With the introduction of Industry 4.0 into our lives and the creation of smart factories, predictive maintenance has become even more important. Predictive maintenance systems are often used in the manufacturing industry. On the other hand, text analysis and Natural Language Processing (NLP) techniques are gaining a lot of attention by both research and industry due to their ability to combine natural languages and industrial solutions. There is a great increase in the number of studies on NLP in the literature. Even though there are studies in the field of NLP in predictive maintenance systems, no studies were found on Turkish NLP for predictive maintenance. This study focuses on the similarity analysis of failure texts that can be used in the predictive maintenance system we developed for VESTEL, one of the leading consumer electronics manufacturers in Turkey. In the manufacturing industry, operators record descriptions of failure that occur on production lines as short texts. However, these descriptions are not often used in predictive maintenance work. In this study, semantic text similarities between fault definitions in the production line were compared using traditional word representations, modern word representations and Transformer models. Levenshtein, Jaccard, Pearson, and Cosine scales were used as similarity measures and the effectiveness of these measures were compared. Experimental data including failure texts were obtained from a consumer electronics manufacturer in Turkey. When the experimental results are examined, it is seen that the Jaccard similarity metric is not successful in grouping semantic similarities according to the other three similarity measures. In addition, Multilingual Universal Sentence Encoder (MUSE), Language-agnostic BERT Sentence Embedding (LAbSE), Bag of Words (BoW) and Term Frequency - Inverse Document Frequency (TF-IDF) outperform FastText and Language-Agnostic Sentence Representations (LASER) models in semantic discovery of error identification in embedding methods. Briefly to conclude, Pearson and Cosine are more effective at finding similar failure texts; MUSE, LAbSE, BoW and TF-IDF methods are more successful at representing the failure text.
Analysis of Tweets with Emoticons for Sentiment Detection Using Classification Techniques

Social networking services allow users to communicate with their friends and exchange ideas, photos, and videos that delineate their feelings. Sen- timents are emotions that express a person's attitude, feelings, and worldview. This raises the possibility of analyzing individual moods and emotions in social network data in order to learn more about people's inclinations and perspectives when communicating online. Sentiment Analysis is the computational study of opinions, assessments, attitudes, subjectivity, and viewpoints represented in text. The emotive appraisal of a condition is a general evaluation of that condition that may be positive or negative depending on physical or mental reactions. In this paper, we attempt to evaluate tweets that contain both text and emoticons in order to determine whether they are positive or negative. This study looked at XGBoost, LinearSVC, Logistic Regression, and BernoulliNB algorithms, with XGBoost providing the highest accuracy of 87.841%. The paper's key contribu- tion is the use of the XGBoost model for tweets that include emoticons, which also produced the greatest accuracy.
Text Classification Using Correlation Based Feature Selection on Multi-layer ELM Feature Space

Text data available on the Web are generally unstructured. Text classification, a machine learning technique, has proven to be a great alternative to structure textual data in a cost-effective, faster, and scalable manner. This study examines the feature space of Multilayer ELM (ML-ELM) for the classification of text data with the help of a novel feature selection technique termed as Correlation-based Feature Selection (CRFS). Experimental results show that the feature space of ML-ELM is better for text classification compared to the traditional vector space.
The Implementation of community-based Android application to accommodate public aspiration in economic activities in Indonesia

The purpose of this study is to see how optimizing the use of community-based smartphone apps to accommodate the community's aspirations in economic activities in Karawang. The Department of Communications and Informatics of Karawang manages the process. The theory used in this study uses the theory of George C. Edwards III, in which there are four policy implementation variables. The results showed that the socialization carried out by the Communication and Informatics Office of Karawang to the community had been carried out according to the procedure but was not maximized evenly. This was because the resources had not been maximized in responding to incoming reports, and the complete support facilities were not yet available to manage smartphone apps. So, it can be concluded that the Department of Communication and Information Karawang needs a solution to solve this problem.
Using global feedback to induce learning of gist of abnormality in mammograms

Abstract Extraction of global structural regularities provides general ‘gist’ of our everyday visual environment as it does the gist of abnormality for medical experts reviewing medical images. We investigated whether naïve observers could learn this gist of medical abnormality. Fifteen participants completed nine adaptive training sessions viewing four categories of unilateral mammograms: normal, obvious-abnormal, subtle-abnormal, and global signals of abnormality (mammograms with no visible lesions but from breasts contralateral to or years prior to the development of cancer) and receiving only categorical feedback. Performance was tested pre-training, post-training, and after a week’s retention on 200 mammograms viewed for 500 ms without feedback. Performance measured as d’ was modulated by mammogram category, with the highest performance for mammograms with visible lesions. Post-training, twelve observed showed increased d’ for all mammogram categories but a subset of nine, labelled learners also showed a positive correlation of d’ across training. Critically, learners learned to detect abnormality in mammograms with only the global signals, but improvements were poorly retained. A state-of-the-art breast cancer classifier detected mammograms with lesions but struggled to detect cancer in mammograms with the global signal of abnormality. The gist of abnormality can be learned through perceptual/incidental learning in mammograms both with and without visible lesions, subject to individual differences. Poor retention suggests perceptual tuning to gist needs maintenance, converging with findings that radiologists’ gist performance correlates with the number of cases reviewed per year, not years of experience. The human visual system can tune itself to complex global signals not easily captured by current deep neural networks.
RICE: An Optimizing Curry Compiler

This paper introduces the RICE compiler, an optimizing compiler for the functional logic language Curry, written entirely in Curry. We show the advantages of using Curry as an implementation language by creating a robust optimizer that is easy to extend. Furthermore, we show the benefits of optimizing Curry programs directly, as opposed to translating to another high level language. When we compiled programs in RICE, they ran anywhere from 3 to 1000 times faster than programs compiled using the current state of the art compilers.
Decision Support System for Industry Machine Maintenance Using Weight Product (WP) Method

Machine maintenance management is one of the important aspects in every industry. Implementation of machine maintenance management requires the management of various types of data such as machine age, machine condition and so on. Data management with the help of a decision support system will help improve the effectiveness and efficiency of machine maintenance management. In making the right and correct decisions, it is necessary to have decision support. Without machine maintenance management, it can harm the industry because the machine maintenance schedule cannot be determined optimally, and the costs incurred by repair, maintenance and production cessation activities cannot be determined directly. Therefore, the industry needs a decision support system that is able to improve the efficiency of its machine maintenance management. This research focuses on the design and development of a machine maintenance decision support system application using the weighted product (WP) method. Weighted product (WP) method is one of the multi criteria decision making methods used to determine the final decision by using several criteria. This method can provide a reliable methodology that helps to organize the most optimized result in the presence of many different opportunities, parameters, and constraints. Research method used in this study is the Web Engineering Method. This system is developed by using PHP programming language and MySQL as a database. This system is expected to increase the effectiveness and efficiency of machine maintenance management.
Some features of censorship policy during the Crimean War of 1853–1856

The article is devoted to the analysis of new sources found in the National Archives of the Republic of Tatarstan in Kazan, which clarify the picture of the censorship policy of the tsarist government during the war years. The study was carried out based on the modernization concept of Russian history, in which the printed word appears as one of the main engines of modernization, as well as on the methods of intellectual history, in which book publishing and the press are studied in close connection with issues of censorship policy. The modern historiography of the Crimean (Eastern) War continues to be enriched with new works devoted to Russian public life and connected with the history of the press and censorship. А rich literature is devoted to the history of Russian periodicals and journalism, but not to the censorship policy of the Russian Empire. The article considers a number of circulars of the Ministry of Public Education, which was the leading state body in the implementation of the censorship policy, but in war conditions it carefully took into account the requirements of the Military Censorship Committee under the General Staff. The emperor actively intervened in censorship issues, especially in publicizing literary works in the press. For example, the newspaper Samarskiye Gubernskiye Vedomosti shows, that the provincial press reprinted notes and reports about the war from the capital news that had already passed the censorship check. Despite the selection of information, these reports did not hide information about the losses of Russian troops
LBP-Based CAD System Designs for Breast Tumor Characterization

For diagnosing breast abnormalities, mammography and ultrasound are widely used due to the ease of use and low cost. Mammography uses ionizing radiations which are unsuitable for pregnant women; hence, ultrasound is preferred for examination. Ultrasound images are marred by speckle noise, resulting in masking of important diagnostic information, which makes it challenging for radiologists to differentiate between breast abnormalities. For breast tumor characterization, most of the studies have used statistical, signal processing or transform domain-based methods for texture quantification. However, use of local binary pattern (LBP) texture features in combination with morphological features has not been reported. The present work proposes an efficient CAD system for the characterization of breast ultrasound images based on LBP texture features and morphological features. The results illustrate that the CAD system based on the ANFC-LH algorithm yields optimal performance for breast tumor characterization.