Reviewerly/exHarmony huggingface dataset:
Proposed approach for breast cancer diagnosis using machine learning

Due to the danger that represents breast cancer in the society and also due to the number of death that causes every year in women's sector, the proposition of solutions that aim to reduce this number of death has become a primordial need. In this paper we present an approach for breast cancer diagnosis using machines learning techniques that can be helpful in breast cancer detection in earlier stage and with the right way without errors to propose the suitable treatment for the patients. We will use machines learning techniques in our approach due to their performance in domain of medicine.
Security Through Blowfish and LSB Bit–Cycling With Mathematical Sequences

Recently, lots of research work and papers are focusing on the combination of steganography and cryptography. Steganography conceals the presence of a secret message. Cryptography scrambles the secret message so that no one can understand it except for the intended receiver. The combination of steganography and cryptography ensures a secure data transmission between the sender side and the receiver side. This paper proposes an RGB image steganography technique using a double-layer steganography and cryptography system that uses Blowfish for the cryptography layer and LSB for the steganography layer. The LSB substitution follows two mathematical sequences which are an arithmetic sequence and the Rudin-Shapiro sequence. The arithmetic sequence determines the pixel to be embedded and the Rudin-Shapiro sequence determines the color channel to be embedded. The steganography and cryptography system is also evaluated using some performance metrics which are the mean square error (MSE), the peak signal to noise ratio (PSNR), the correlation, the image quality index (Q), the entropy, the image histogram, the structural similarity index measurement (SSIM), the R measurement, the normalized cross correlation (NCC) and the image fidelity (IF). In addition, a comparison between the proposed scheme and other proposed work from the literature is presented to show that the proposed scheme has a very good performance that is better than its counterparts from the literature.
Word Difficulty Prediction Using Convolutional Neural Networks

Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. In this paper we explore the use of one of such in predicting the difficulty of words. We treat the problem as a binary classification problem. We train traditional machine learning models and evaluate their performance on the task. Removing dependency on frequency of previously acquired words for measuring difficulty was one of our primary aims. Then we analyze a convolutional neural network based prediction model which operates at the character level and evaluate its efficiency compared to others.
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data

Detecting semantic parts of an object is a challenging task, particularly because it is hard to annotate semantic parts and construct large datasets. In this paper, we present an approach which can learn from a small annotated dataset containing a limited range of viewpoints and generalize to detect semantic parts for a much larger range of viewpoints. The approach is based on our matching algorithm, which is used for finding accurate spatial correspondence between two images and transplanting semantic parts annotated on one image to the other. Images in the training set are matched to synthetic images rendered from a 3D CAD model, following which a clustering algorithm is used to automatically annotate semantic parts of the CAD model. During the testing period, this CAD model can synthesize annotated images under every viewpoint. These synthesized images are matched to images in the testing set to detect semantic parts in novel viewpoints. Our algorithm is simple, intuitive, and contains very few parameters. Experiments show our method outperforms standard deep learning approaches and, in particular, performs much better on novel viewpoints. For facilitating the future research, code is available: https://github.com/ytongbai/SemanticPartDetection.
Robust Image Watermarking Framework Powered by Convolutional Encoder-Decoder Network

The spread of illegal replication of digital image has seriously affected the protection of intellectual property rights. Nowadays, although watermarking is the most popular technology for copyright protection and ownership authentication, most of the current works cannot encounter numerous intentional attacks of watermark destruction. In this research, we introduce a novel framework of blind image watermarking that is able to learn attacking patterns effectively based on a deep convolutional encoder-decoder network. For details, a binary watermark image is hidden into selective wavelet blocks by the mean of an optimal encoding rule, wherein the quality image degradation is minimized over the Mean Square Error metric for a significant image imperceptibility enhancement. Then, the embedding maps, defined as the wavelet coefficient difference values, of various attacks simulated as digital image transformations of the watermarked image are revealed for training the deep learning-based watermark extraction model. Accordingly, the watermark information hidden in an attacked image can be precisely recovered from its embedding map by the trained model. From experimental results, the proposed watermark framework achieves a good performance trade-off between image imperceptibility and watermark robustness through an adjustable embedding strength. In addition, our approach is strongly defeats several state-of-the-art methods in terms of watermark robustness against many critical attacks.
Optimization over degree sequences of graphs

We consider the problem of finding a subgraph of a given graph minimizing the sum of given functions at vertices evaluated at their subgraph degrees. While the problem is NP-hard already for bipartite graphs when the functions are convex on one side and concave on the other, we have that when all functions are convex, the problem can be solved in polynomial time for any graph. We also provide polynomial time solutions for bipartite graphs with one side fixed for arbitrary functions, and for arbitrary graphs when all but a fixed number of functions are either nondecreasing or nonincreasing. We note that the general factor problem and the (l,u)-factor problem over a graph are special cases of our problem, as well as the intriguing exact matching problem. The complexity of the problem remains widely open, particularly for arbitrary functions over complete graphs.
Factors Affecting e-logistics in Malaysia: The Mediating Role of Trust

Purpose: The purpose of this study is to analyse the factors that influence blockchain users to utilise blockchain in e-Logistic. Also,this study identifies the relationship between supply chain capabilities, E-traceability and E-paymentintegration with blockchain and cryptocurrency. The conceptual framework of this study developed incorporating trust as an installation for a better understanding of the use of blockchain in Malaysia logistic sector. Theory: The unified theory of acceptance and use of technology (UTAUT2) by adding trust as an independent variable. This study aims to study the relationship between the main determinants of UTAUT2 and the factorsto use of blockchain and e-logistic for enhancement oflogistics industry. The conceptual framework of this study developed to merge trust as a synthesis in UTAUT2 for a better understanding ofe-logistic. Methodology: The cross-sectional approach used in this study. Four hundred seventy-eight self-administered questionnaires distributed through simple random sampling technology aimed at blockchain users in Malaysia from 25 to 44 years. Data analysis performed using PLS. The outcome of this research shows that the relationship between e-logistic, blockchain, cryptocurrency and trust have a positive and significant relationship. Significant: This research will significantly contribute to blockchain businesses and merchants in Malaysia.Also, it provides them with useful information about the interaction between the critical determinants of the supply chain capabilities, blockchainand cryptocurrency implementation in the logistics sector in Malaysia. Originality: This is one of the most recent studies that describe the blockchain and cryptocurrency in Malaysia logistics.
Non-linguistic Features for Cyberbullying Detection on a Social Media Platform Using Machine Learning

Cyberbullying on social media platforms has been a severe problem with serious negative consequences. Therefore, a number of researches on automatic detection of cyberbullying using machine learning techniques have been conducted in recent years. While cyberbullying detection has traditionally utilized linguistic features, the cyberbullying on social media does not have only linguistic features. In this paper, a holistic multi-dimensional feature set is developed which takes into account individual-based, social network-based, episode-based and linguistic content-based cyberbullying features. To test performance of the proposed multi-dimensional feature set, we designed and built cyberbullying detection models on the KNIME machine learning platform. Six different machine learning algorithms - Naïve Bayes, Decision Tree, Random Forest, Tree Ensemble, Logistic Regression, and Support Vector Machines - were used in our cyberbullying detection models. Our experimental results demonstrate that applying the proposed multi-dimensional feature set (i.e. the set not limited to the linguistic features) results in an improved cyberbullying detection for all tested machine learning algorithms.
Formal Verification of Smart Contracts Using Interface Automata

Emerging distributed ledger technologies - also known as blockchains - have recently become a hot research topic across a wide variety of industries due to their immutability, availability, security, and more importantly, their ability to execute multi-party agreements or business rules in the form of smart contracts. Smart contracts provide the means to automate and enforce contractual terms between the parties entering the agreement. It is important to guarantee the correctness of such contracts in order to avoid catastrophic events that may require human intervention. This paper presents a method that builds on the interface automata model of computation as a semantic domain to formalize smart contracts. The formal model of computation assigns a precise and unambiguous meaning to smart contracts. Loyalty points are discounts and bonuses provided by companies with the purpose of retaining their loyal customers. The proposed method is evaluated in the context of a loyalty points marketplace. The decentralized, distributed and immutable nature of blockchain provides an appealing platform for the implementation of a loyalty points marketplace, and the formal semantics based on interface automata ensure that violations of the agreement can be detected, and contractual clauses can be enforced by all parties involved in loyalty points transactions.
Evaluation of Most Popular Sentiment Lexicons Coverage on Various Datasets

In this paper we compare three most popular general - purpose lexicon Bing, Afinn and NRC. We omit Loughran on account of its application for financial sentiment analysis. We investigate four various groups of benchmark texts - social media posts, products reviews, songs" lyrics and books. Total count of words is over 2 500 000. This number was than limited to almost 20 thousands after stemming, lemmatization, removing "stop words" and leaving only unique values. As a result, we receive data set composed of literary and universal language which contains lexical, grammatical and stylistic features, accepted and understood by people speaking a specific language. We have gained consistency result between phrases used by Internet users and ones contained in lexicons data between 60 to 75%. The most common part of lexicons and analyzed text has been obtained for books sets, the least for social media post. We also present discussion which lexicon is recommended for various types of dataset taking into account its coverage of used words. Our researchers can be easily reproduced and expanded because we made our source code publicly available.
Keep It Simple

Head-mounted displays cause discomfort. This is commonly attributed to conflicting depth cues, most prominently between vergence, which is consistent with object depth, and accommodation, which is adjusted to the near eye displays. It is possible to adjust the camera parameters, specifically interocular distance and vergence angles, for rendering the virtual environment to minimize this conflict. This requires dynamic adjustment of the parameters based on object depth. In an experiment based on a visual search task, we evaluate how dynamic adjustment affects visual comfort compared to fixed camera parameters. We collect objective as well as subjective data. Results show that dynamic adjustment decreases common objective measures of visual comfort such as pupil diameter and blink rate by a statistically significant margin. The subjective evaluation of categories such as fatigue or eye irritation shows a similar trend but was inconclusive. This suggests that rendering with fixed camera parameters is the better choice for head-mounted displays, at least in scenarios similar to the ones used here.
IPCP: Immersive Parallel Coordinates Plots for Engineering Design Processes

Computational engineering design methods and tools are common practice in modern industry. Such approaches are integral in enabling designers to efficiently explore larger and more complex design spaces. However, at the same time, computational engineering design methods tend to dramatically increase the number of candidate solutions that decision-makers must interpret in order to make appropriate choices within a set of solutions. Since all candidate solutions can be represented in digital form together with their assessment criteria, evaluated according to some sort of simulation model, a natural way to explore and understand the complexities of the design problem is to visualize their multidimensional nature. The task now involves the discovery of patterns and trends within the multidimensional design space. In this work, we aim to enhance the design decision-making process by embedding visual analytics into an immersive virtual reality environment. To this end, we present a system called IPCP: immersive parallel coordinates plots. IPCP combines the well-established parallel coordinates visualization technique for high-dimensional data with immersive virtual reality. We propose this approach in order to exploit and discover efficient means to use new technology within a conventional decision-making process. The aim is to provide benefits by enhancing visualizations of 3D geometry and other physical quantities with scientific information. We present the design of this system, which allows the representation and exploration of multidimensional scientific datasets. A qualitative evaluation with two surrogate expert users, knowledgeable in multidimensional data analysis, demonstrate that the system can be used successfully to detect both known and previously unknown patterns in a real-world test dataset, producing an early indicative validation of its suitability for decision support in engineering design processes.
PROPERTIES OF AUTOSEMANTIC WORD NETWORKS IN UKRAINIAN TEXTS

We present results of network analysis of Ukrainian texts. Autosemantic (meaningful) words are considered as network vertices connected with links when belonging to one sentence. Subnetworks corresponding to specific parts of speech (verbs, nouns, adjectives, etc.) are also built. The obtained networks are small-world and scale-free. To make comparisons, random texts with parameters corresponding to real texts are generated using several approaches. Various parameters of networks are calculated, including transitivity, betweenness, degree centralization, mean distance, network diameter, exponents of degree distribution, etc. Comparison of network parameters of real and generated texts shows that borders between them are quite fuzzy.
PRIMA: Subscriber-Driven Interference Mitigation for Cloud Services

Network services, e.g., video streaming services, are increasingly being deployed on public cloud platforms. Such services often employ horizontal scaling where a group of resource instances, e.g., virtual machines (VMs), handle incoming workload. The response time of such services is often affected by interference, i.e., contention among resource instances belonging to multiple cloud subscribers for shared cloud resources. Most commercial cloud platforms do not support built-in mechanisms to detect interference and mitigate its impact. Consequently, subscribers of such platforms, i.e., network service providers, need to deploy their own mechanisms to ensure a specified end user response time target is continuously met even in the face of fluctuations in workload and interference. This paper describes PRIMA, our implementation of such a mechanism. PRIMA uses automated and controlled performance tests to build models that capture the joint impact of workload and interference on the response time of each resource instance employed by a service. It adapts the system to changing workload and interference conditions by using these models at runtime to control the number of instances in the system and the distribution of load among these instances. Unlike existing subscriber-oriented interference mitigation techniques in literature, PRIMA guarantees that a subscriber-specified response time threshold is satisfied at every resource instance assigned to a service. Furthermore, in contrast to these approaches PRIMA can help a subscriber avoid using more instances than necessary by automatically selecting at runtime the least number of instances required for handling the observed workload and interference. We experimentally validate the effectiveness of PRIMA in both private and public cloud environments. Results show that PRIMA outperforms competing approaches proposed by us and others, including those that are commonly used in practice. They also reveal that PRIMA can automatically calibrate its models at runtime to account for any model prediction errors.
Bilattice Logics and Demi-Negation

The logic of distributive bilattices, an offshoot of Belnap and Dunn’s four-valued logic, is presented as an appropriate logic for deductive reasoning in situations where (a) sentences can be accepted and rejected at the same time, and (b) the semantic value of a sentence α is given, depending on the dominant connective in α, in terms of mutually independent acceptance and rejection conditions. Taking our cue from some writings by Lloyd Humberstone, we expand this logic by a demi-negation connective, whose behaviour is intermediate between affirmation and negation. The resulting logic is introduced semantically and axiomatised using methods from abstract algebraic logic.
Improved Low-Rank Filtering of MR Spectroscopic Imaging Data With Pre-Learnt Subspace and Spatial Constraints

Objective: To investigate the use of pre-learnt subspace and spatial constraints for denoising magnetic resonance spectroscopic imaging (MRSI) data. Method: We exploit the partial separability or subspace structures of high-dimensional MRSI data for denoising. More specifically, we incorporate a subspace model with pre-learnt spectral basis into the low-rank approximation (LORA) method. Spectral basis is determined based on empirical prior distributions of the spectral parameters variations learnt from auxiliary training data; spatial priors are also incorporated as is done in LORA to further improve denoising performance. Results: The effects of the explicit subspace and spatial constraints in reducing estimation bias and variance have been analyzed using Cramér-Rao Lower bound analysis, Monte-Carlo study, and experimental study. Conclusion: The denoising effectiveness of LORA can be significantly improved by incorporating pre-learnt spectral basis and spatial priors into LORA. Significance: This study provides an effective method for denoising MRSI data along with comprehensive analyses of its performance. The proposed method is expected to be useful for a wide range of studies using MRSI.
Deep Representation Calibrated Bayesian Neural Network for Semantically Explainable Face Inpainting and Editing

Image inpainting seeks to fill in corrupted areas with pixels that have a similar texture and content with its surroundings. For high-structured data, e.g., human face, some recent works can achieve quite realistic results. However, almost all existing methods learned a determined mapping from a corrupted input to the final result, yet ignored the potential multiple plausible solutions of the same input. Furthermore, they have not explored the underlying connections between those plausible solutions and semantic conditions. In this work, we propose a novel deep representation calibrated Bayesian neural network (DRCBNN) for semantically explainable face inpainting and editing. By leveraging the advantages that Bayesian decision theory deals with uncertainty, the proposed framework exploits deep representation into Bayesian decision theory and derive a deep representation calibrated evidence lower bound (ELBO). In comparison with traditional ELBO in BNN, the newly calibrated ELBO is a more task-specific loss function. After optimizing the newly calibrated ELBO, it allows to inference desired inpainting outputs in accordance with specific semantics. Finally, experiments demonstrated that our method can produce multiple semantics-aware inpainting outputs and outperforms the state-of-the-arts.
Information-seeking in multilingual digital libraries

Purpose Less attention has been paid to users’ interactions and behavior in studying multilingual search. Although digital library researchers have yet to assess user interaction and behavior in multilingual search, they have concurred that there is a need for user studies that document the extent to which information retrieval systems meet multilingual users’ needs and expectations. The paper aims to discuss these issues. Design/methodology/approach This study is composed of five individual cases. The case study participants were Saudi students enrolled either at a large state university or Historically Black College and University located in the same community. Research questions are, what do Saudi Digital Library (SDL) users experience when searching within the SDL in Arabic and English? And what strategies do they use if they fail to find resources? Data collected for this study were via a qualitative method called video-stimulated recall. Findings In the Arabic search tasks, participants realized that finding resources is not easy. Participants expressed their concerns about the lack of relevance and accuracy of results returned by the search system, indicating weak trust and confidence in the search system. Whereas in the English search task, participants felt more satisfied and confident in their ability to trust the results returned from the search system. Participants expressed their satisfaction in the search experience as it provided them with accurate and varying resources. The participants faced difficulties finding Arabic resources than English resources in the SDL. Originality/value This study is considered one of the earliest works in studying the information-seeking behavior of multilingual digital libraries in the Arabic language. The value of this study arises as being the first study to investigate and report the information-seeking behavior of SDL users.