Reviewerly/exHarmony huggingface dataset:
A New Secure Encryption Scheme Based on Group Factorization Problem

As special types of factorization of finite groups, logarithmic signatures and covers have been used as the main components of cryptographic keys for secret key cryptosystems such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGM$ </tex-math></inline-formula> and public key cryptosystems like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MST_{1}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MST_{2}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MST_{3}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$eMST_{3}$ </tex-math></inline-formula> . In particular, as a natural analogue of integer factorization problem (IFP), group factorization problem (GFP) and its hardness assumption over certain factorization basis, referred as logarithmic signature, play a core role in the security arguments for the family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MST$ </tex-math></inline-formula> cryptosystems. Security is not the unique goal of designing a cryptosystem. Instead, efficiency is also a major issue. In this paper, we design a new secure encryption scheme based on group factorization problem (GFP). Furthermore, we present the security analysis and demonstrate the performance of our scheme. Comparing with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$eMST_{3}$ </tex-math></inline-formula> , our scheme is simplified with more efficiency.
Neural Feature Extraction for Contextual Emotion Detection

This paper describes a new approach for the task of contextual emotion detection.The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based.We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes.The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier.This configuration reached 69.93% in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25%.
Web API Recommendation with Features Ensemble and Learning-to-Rank

In recent years, various methods against service ecosystem have been proposed to address the requirements on recommendation of Web APIs. However, how to effectively combine trivial features of mashups and APIs to improve the recommendation effectiveness remains to be explored. Therefore, we propose a Web API recommendation method using features ensemble and learning-to-rank. Based on available usage data of mashups and Web APIs, textual features, nearest neighbor features, API-specific features, tag features of APIs are extracted to estimate the relevance between the mashup requirement and the candidates of APIs in a regression model, and then a learning-to-rank approach is used to optimize the model. Experimental results show our proposed method is superior to some state-of-the-art methods in the performance of recommendation.
Adaptive Bayesian phase estimation for quantum error correcting codes

Abstract Realisation of experiments even on small and medium-scale quantum computers requires an optimisation of several parameters to achieve high-fidelity operations. As the size of the quantum register increases, the characterisation of quantum states becomes more difficult since the number of parameters to be measured grows as well and finding efficient observables in order to estimate the parameters of the model becomes a crucial task. Here we propose a method relying on application of Bayesian inference that can be used to determine systematic, unknown phase shifts of multi-qubit states. This method offers important advantages as compared to Ramsey-type protocols. First, application of Bayesian inference allows the selection of an adaptive basis for the measurements which yields the optimal amount of information about the phase shifts of the state. Secondly, this method can process the outcomes of different observables at the same time. This leads to a substantial decrease in the resources needed for the estimation of phases, speeding up the state characterisation and optimisation in experimental implementations. The proposed Bayesian inference method can be applied in various physical platforms that are currently used as quantum processors.
From Image to Text in Sentiment Analysis via Regression and Deep Learning

Images and text represent types of content which are used together for conveying user emotions in online social networks.These contents are usually associated with a sentiment category.In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative.The mapping from images to text is performed using a Kernel Ridge Regression model.We considered two types of image features: i) RGB pixel-values features, and ii) features extracted with a deep learning approach.The experimental evaluation was performed on a Twitter data set containing both text and images and the sentiment associated with these.The experimental results show a difference in performance for different sentiment categories, in particular the mapping that we propose performs better for the positive sentiment category in comparison with the neutral and negative ones.Furthermore, the experimental results show that the more complex deep learning features perform better than the RGB pixel-value features for all sentiment categories and for larger training sets.This was so kak sad as a child They fighting over who more useless
Better quality score compression through sequence-based quality smoothing

Current NGS techniques are becoming exponentially cheaper. As a result, there is an exponential growth of genomic data unfortunately not followed by an exponential growth of storage, leading to the necessity of compression. Most of the entropy of NGS data lies in the quality values associated to each read. Those values are often more diversified than necessary. Because of that, many tools such as Quartz or GeneCodeq, try to change (smooth) quality scores in order to improve compressibility without altering the important information they carry for downstream analysis like SNP calling.We use the FM-Index, a type of compressed suffix array, to reduce the storage requirements of a dictionary of k-mers and an effective smoothing algorithm to maintain high precision for SNP calling pipelines, while reducing quality scores entropy. We present YALFF (Yet Another Lossy Fastq Filter), a tool for quality scores compression by smoothing leading to improved compressibility of FASTQ files. The succinct k-mers dictionary allows YALFF to run on consumer computers with only 5.7 GB of available free RAM. YALFF smoothing algorithm can improve genotyping accuracy while using less resources.https://github.com/yhhshb/yalff.
Working memory and serial order: Evidence against numerical order codes but for item–position associations.

The processing of ordinally organized information is a characteristic of both serial-order working memory and numerical cognition.Serial positions of items presented within a list follow an ordinal organization when stored in working memory, whereas numbers are based on an ordinal structure stored in long-term memory.We tested the hypothesis that long-term numerical ordinal representations support the coding of temporary serial position information in working memory.In Experiment 1, learned word-number associations appeared to have a negative instead of a positive impact on immediate serial recall performance relative to control conditions.Experiments 2 showed that this effect was due to a stronger opportunity for learning associations of words to serial positions in the control lists as compared to the experimental lists.Experiment 3 showed that when controlling for these positional learning effects, there was no reliable effect of learned word-number associations on immediate serial recall performance.This study indicates that numerical codes do not play a major role in coding serial position information in working tasks.At the same time, the robust item-position learning effects demonstrate a contribution of long-term item-position associations to immediate memory for order.
Cervical Histopathology Image Clustering Using Graph Based Unsupervised Learning

In orderLi, Chen to apply theHu, Zhijie important topological information toChen, Hao solve a Cervical Histopathology Image Clustering (CHIC) problem,Xue, Dan a Graph Based Unsupervised Learning (GBUL)Xu, Ning approach is proposed in this paper.Zhang, Yong First, the GBUL method applies color featuresLi, Xiaoyan and k-means clustering for a first-stage "coarse" clustering.Wang, Qian Then, a Skeletonization Based Node Generation (SBNG) approachMa, He is introduced to approximate the distribution of cervical cell nuclei. Thirdly, based on the SBNG nodes, a minimum spanning tree graph is constructed. Next, graph features and additional geometrical features are extracted based on the constructed graph. Finally, the k-means clustering is applied again for the second-stage clustering. In the experiment, a practical cervical histopathology image dataset with ten whole scanned images is tested, obtaining a promising CHIC result and showing a huge potential in the cancer risk prediction field.
Entropy and fractal nature

Existing, the biunivocal correspondents between the fractal nature and the nature discovered by fractals is the source and meeting point from those two aspects which are similar to the thermodynamically philosophical point of view. Sometimes we can begin from the end. We are substantial part of such fractals space nature. The mathematics fractal structures world have been inspired from nature and Euclidian geometry imagined shapes, and now it is coming back to nature serving it. All our analysis are based on several experimental results. The substance of the question regarding entropy and fractals could be analyzed on different ceramics and materials in general. We have reported the results based on consolidation BaTiO3- ceramics by the standard sintering technology, performed with BaTiO3 and different additives (MnCO3, CeO2, Bi2O3, Fe2O3, CaZrO3, Nb2O5, Er2O3, Yt2O3, Ho2O3). Thermodynamic principles are very important. Beside the energy and temperature, the entropy as a measure between the order and disorder (chaos) is very important parameter. In this paper, we establish the relation between the entropy and fractal that opens new frontiers with the goal to understand and establish the order-disorder relation.
FLUSH + PREFETCH: A countermeasure against access-driven cache-based side-channel attacks

Cache-based side-channel attacks (SCAs) are becoming a security threat to the emerging computing platforms. To mitigate these attacks, numerous countermeasures have been proposed. However, these countermeasures require either radical hardware modification or they are incompatible with the performance features like super-page and data de-duplication. This paper presents a countermeasure, called Flush+Prefetch, which obfuscates the memory access behavior of a secure application using independent threads that randomly access the memory belonging to secure application. Unlike existing state-of-the-art countermeasures, Flush+Prefetch works with commodity hardware and it is compatible with existing performance features. As a proof-of-concept, we have studied the effectiveness of Flush+Prefetch by defending the secret key of RSA cryptosystem against a high-resolution cache side-channel attack called Flush+Reload. We have evaluated the confidentiality of RSA decryption process on an Intel Xeon E5-2643 processor by generating 100,000 requests to a web-server sequentially while considering the effect on performance as well. Our experimental results show that the confidentiality of memory accesses by RSA is preserved under Flush+Prefetch countermeasure. Our results show that the performance, in terms of average execution time, is improved by 10.2% for best design case as compared to the system under attack.
Human activity recognition using Deep Learning

Presently now a day's productive human activity acknowledgment stays a difficult zone of research in the field of PC vision.Smarter reconnaissance is the need of this time through which ordinary and unusual exercises can be naturally distinguished utilizing fake information along with PC innovation.In our paperwork, we represented a structural movement acknowledgment in reconnaissance recordings caught upon mechanical frameworks.The constant observation running image stream is first isolated in significant snaps, in which images are chosen utilizing the analysed convolutional neural network-based human saliency highlights.Next, transient highlights of action in the succession of casings are separated by using the convolutional layers of a Flow Net to the CNN model.At long last, a multilayer long transient memory (LSTM) is displayed for adapting large haul arrangements in the worldly optical stream highlights for action acknowledgment.The optical stream is determined, which gives the estimation of development, of A pixel in a succession of edges.Experiments are directed utilizing diverse activity and movement acknowledgment datasets and the outcomes uncover the viability of the proposed technique for action acknowledgment
Region Growing Algorithm Combined With Fast Peak Detection for Segmenting Colloidal Gold Immunochromatographic Strip Images

Accurate segmentation of test line and control line for colloidal gold immunochromatographic strip (GICS) images with image processing algorithms is essential to quantitative analysis of GICS.As common methods for GICS image segmentation, fuzzy c-means (FCM) algorithm and cellular neural network (CNN) algorithm both require presetting initial conditions (specifying initial parameters or training models) and take long running time, due to high calculation cost.Therefore neither is ideal for a point-of-care testing (POCT) device, which has low hardware cost and limited computing power.This paper designs a region growing algorithm combined with fast peak detection (RGFPD) to quickly and self-adaptively segment GICS images.Compared with FCM algorithm and CNN algorithm, the RGFPD algorithm has two obvious advantages.First, as a local algorithm rather than a local one, the region growing algorithm requires low calculation cost, which better suits the POCT device.Second, the fast peak detection algorithm calculates the seed points and growing criterions as initial conditions, realizing self-adaptive segmentation of images.In this paper, RGFPD algorithm is applied to segment GICS images of actual samples, taking FCM algorithm and CNN algorithm as contrast.The results show that RGFPD accurately segment images without presetting initial conditions, with shorter algorithm running time, and performs better in anti-interference.
Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

Sentiment analysis and opinion summarization have become an important research area with the increase of available data on the Web. Since the Internet started containing more and more opinions and reviews for different products, individual users and companies saw the benefits of a priori evaluations based on other users’ experiences; thus, automated analyses centered on customer impressions and experiences emerged as crucial marketing instruments. Our aim is to create a scalable and easily extensible pipeline for building a custom-tailored sentiment analysis model for a specific domain. A corpus of around 200,000 games reviews was extracted, and three state-of-the-art models (i.e., support vector machines, multinomial Naïve-Bayes, and deep neural network) were employed in order to classify the reviews into positive, neutral, and negative. Current results surpass previous experiments based on word counts applied on a similar game reviews dataset, thus arguing for the adequacy of the proposed workflow.
Pancreatic Segmentation via Ringed Residual U-Net

Computer-aided diagnosis (CAD) has a wide range of clinical applications, and medical image segmentation is essential in CAD. In medical image segmentation, due to the high anatomical variability of the pancreas and the weak contrast of the environment, the segmentation of the pancreas has always been the most challenging task. This study proposes a novel segmentation method for pancreatic segmentation. On the basis of complete convolution, an attentional mechanism is added to enhance the information exchange between downsampling and upsampling. By using the ring residual module, the proposed segmentation method can yield satisfactory results via deep convolution and can consolidate the characteristics of traditional deep learning networks. At the same time, compared to previous methods that used the dice coefficient (DICE) as a loss function, this study proposes a new loss function in the proposed method. This new loss function focuses not only on the area coincidence degree but also on the focus shape similarity. In the present work, ten-fold cross-validation computed tomography (CT) data (82 samples) from the NIH public pancreas dataset was conducted. The average dice similarity coefficient (DSC) of the results is 88.32±2.84, which is higher than the most advanced available methods and corresponds to higher robustness. Therefore, in practical applications, these methods can be used to provide more reliable auxiliary diagnostic data in the application of clinical medicine.
Let’s FACE it. Finnish Poetry Generation with Aesthetics and Framing

We present a creative poem generator for the morphologically rich Finnish language.Our method falls into the master-apprentice paradigm, where a computationally creative genetic algorithm teaches a BRNN model to generate poetry.We model several parts of poetic aesthetics in the fitness function of the genetic algorithm, such as sonic features, semantic coherence, imagery and metaphor.Furthermore, we justify the creativity of our method based on the FACE theory on computational creativity and take additional care in evaluating our system by automatic metrics for concepts together with human evaluation for aesthetics, framing and expressions.
Movienet: a movie multilayer network model using visual and textual semantic cues

Abstract Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the modelsdeveloped so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters interactions, we can obtain a broad picture of the narration’s content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. . However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, weperform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.
Customer Journey Construction of the Indonesian Open-Education Resources Platform

Indonesia Open-Educational Resources (IOER) need an approach to design a massive open online course (MOOC) interaction to accommodate users’ needs and suggestions. The purpose of this research is to implement customer journey as an alternative approach for developing a MOOC’s interaction design. An online questionnaire, requirement gathering, prototyping, and contextual interview were used to support this research. As a result, there are three phases of customer journey consisting of planning, learning, and completion. In addition, a MOOC interaction design prototype was produced to depict the result.
Corpora of social media in minority Uralic languages

This paper presents an ongoing project aimed at creation of corpora for minority Uralic languages that contain texts posted on social media.Corpora for Udmurt and Erzya are fully functional; Moksha and Komi-Zyrian are expected to become available in late 2018; Komi-Permyak and Meadow and Hill Mari will be ready in 2019.The paper has a twofold focus.First, I describe the pipeline used to develop the corpora.Second, I explore the linguistic properties of the corpora and how they could be used in certain types of linguistic research.Apart from being generally "noisier" than edited texts in any language (e.g. in terms of higher number of out-of-vocabulary items), social media texts in these languages present additional challenges compared to similar corpora of major languages.One of them is language identification, which is impeded by frequent code switching and borrowing instances.Another is identification of sources, which cannot be performed by entirely automatic crawling.Both problems require some degree of manual intervention.Nevertheless, the resulting corpora are worth the effort.First, the language of the texts is close to the spoken register.This contrasts to most newspapers and fiction, which tend to use partially artificial standardized varieties.Second, a lot of dialectal variation is observed in these corpora, which makes them suitable for dialectological research.Finally, the social media corpora are comparable in size to the collections of other texts available in the digital form for these languages.This makes them a valuable addition to the existing resources for these languages.