Reviewerly/exHarmony huggingface dataset:
A Novel Approach of Mathematical Theory of Shape and Neuro-Fuzzy Based Diagnostic Analysis of Cervical Cancer

This study aims to detect the abnormal growth of tissue in cervix region for diagnosis of cervical cancer using Pap test of patients. The proposed methodology classifies cervical cancer for pattern recognition either benign or malignant stages using shape and neuro-fuzzy based diagnostic model. In this experiment, firstly the authors segment Pap smear images of cervical cells using fuzzy c-means clustering algorithm and shape theory to classify them according to the presence of abnormality of the cells. Secondly the features extraction process is performed in the part of nucleus and cytoplasm on the squamous and glandular cells and the authors used input variables such as cytoplasm area (CA), cytoplasm circularity (CC), nucleus area (NA), nucleus circularity (NC), nucleus-cytoplasm ratio (NCR), and maximum nucleus brightness (MNB) in fuzzy tools and used fuzzy rules to evaluate the cervical cancer risk status as an output variable. The proposed neuro-fuzzy network system was developed for early detection of cervical cancer. A neural network was trained with 15-Pap image datasets where Levenberg-Marquardt(LM) a feed-forward back-propagation algorithm was used to get the status of the cervical cancer. Out of 15 samples database, 11 data set for training, 2 data set for validation and 2 data set for test were used in the ANN classification system. The presented fuzzy expert system(FES) successfully identified the presence of cervical cancer in the Pap smear images using the extracted features and the use of neuro-fuzzy system(NFS) for the identification of cervical cancer at the early stages and achieve a satisfactory performance with 100% accuracy.
Spatiotemporal Symmetric Convolutional Neural Network for Video Bit-Depth Enhancement

In contrast to the high sensitivity of human eyes and rapid development of modern display devices in terms of dynamic range, mainstream multimedia sources are generally at relatively lower bit depths (BDs). Therefore, BD enhancement (BDE), which attempts to transform low-BD multimedia sources into high-BD sources, is considered of significant research value. Current BDE algorithms are based on images rather than videos. However, for massive numbers of videos, temporal continuity among frames should be considered. Thus, in this paper, we propose a spatiotemporal symmetric BDE network for videos based on an encoder-decoder network. Consecutive frames are input into five subnets in the encoder, where the convolutional filters in the temporal symmetric subnets share the same weights to achieve lower model complexity. In addition, symmetric skip connections are introduced between the symmetric convolutional/deconvolutional layers of the encoder/decoder to pass features and alleviate the gradient diffusion problem. The experimental results show that our model can efficiently eliminate false contours and chroma distortions. The model significantly outperforms state-of-the-art image BDE algorithms and single-frame baseline models in terms of PSNR and SSIM.
Multi-fidelity Bayesian Optimization with Max-value Entropy Search.

Bayesian optimization (BO) is an effective tool for black-box optimization in which objective function evaluation is usually quite expensive. In practice, lower fidelity approximations of the objective function are often available. Recently, multi-fidelity Bayesian optimization (MFBO) has attracted considerable attention because it can dramatically accelerate the optimization process by using those cheaper observations. We propose a novel information theoretic approach to MFBO. Information-based approaches are popular and empirically successful in BO, but existing studies for information-based MFBO are plagued by difficulty for accurately estimating the information gain. Our approach is based on a variant of information-based BO called max-value entropy search (MES), which greatly facilitates evaluation of the information gain in MFBO. In fact, computations of our acquisition function is written analytically except for one dimensional integral and sampling, which can be calculated efficiently and accurately. We demonstrate effectiveness of our approach by using synthetic and benchmark datasets, and further we show a real-world application to materials science data.
The effect of foreign direct investment on financial development

The aim of this paper is to analyze the relationship between foreign direct investments and financial development in Bahrain. The estimation financial development effects was performed for the period 1978 to 2015, which covers the political conflicts that recently happened in Middle East area (Arab Spring). On the other hand, the paper sought to examine the causality relationship between foreign direct investments and financial development. The study empirically investigates the short and long run equilibrium relationship between the variables by applied co-integration and Autoregressive Distributed Lags Approach (ARDL). The Granger causality test was employed to capture causality relationship. The obtained results show that there is a significant positive relationship between FDI and financial development in short and long run, while, a significant negative relationship between Arab Spring and financial development. However, the results also revealed bidirectional causality relationship between FDI and financial development.
The Challenge of Negotiation in the Game of Diplomacy

The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.
Reservoir Computing Approaches Applied to Energy Management in Industry

Echo-State Neural Networks represent a very efficient solution for modelling of dynamic systems, thanks to their particular structure, which allows faithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of industrial processes. The developed models are part of a complex system for optimizing the exploitation of process off-gases in an integrated steelwork. Two models are presented and discussed, where both shallow Echo-State Neural Networks and Deep Echo State Neural networks are applied. The achieved results are presented and discussed, by comparing advantages and drawbacks of both approaches.
Performance of a convolutional neural network in screening liquid based cervical cytology smears

Cervical cancer is the second most common cancer in women. The liquid based cervical cytology (LBCC) is a useful tool of choice for screening cervical cancer.To train a convolutional neural network (CNN) to identify abnormal foci from LBCC smears.We have chosen retrospective study design from archived smears of patients undergoing screening from cervical cancer by LBCC smears.2816 images, each of 256 × 256 pixels, were prepared from microphotographs of these LBCC smears, which included 816 "abnormal" foci (low grade or high grade squamous intraepithelial lesion) and 2000 'normal' foci (benign epithelial cells and reactive changes). The images were split into three sets, Training, Testing, and Evaluation. A convolutional neural network (CNN) was developed with the python programming language. The CNN was trained with the Training dataset; performance was assayed concurrently with the Testing dataset. Two CNN models were developed, after 20 and 10 epochs of training, respectively. The models were then run on the Evaluation dataset.A contingency table was prepared from the original image labels and the labels predicted by the CNN.Combined assessment of both models yielded a sensitivity of 95.63% in detecting abnormal foci, with 79.85% specificity. The negative predictive value was high (99.19%), suggesting potential utility in screening. False positives due to overlapping cells, neutrophils, and debris was the principal difficulty met during evaluation.The CNN shows promise as a screening tool; however, for its use in confirmatory diagnosis, further training with a more diverse dataset will be required.
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the $\beta$-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at this https URL.
Optical logic operation via plasmon-exciton interconversion in 2D semiconductors

Abstract Nanophotonic devices manipulating light for high-speed computing are a counterpart of speed-limited electronic circuits. Although plasmonic circuits are a promising platform for subwavelength miniaturization, the logic-operation principle is still limited to mimicking those of photonic waveguides using phase shifts, polarization, interference, and resonance. Meanwhile, reconfigurable interconversion between exciton and plasmon engender emerging applications like exciton transistors and multiplexers, exciton amplifiers, chiral valleytronics, and nonlinear excitonics. Here, we propose optical logic principles realized by exciton-plasmon interconversion in Ag-nanowires (NW) overlapped on transition metal dichalcogenides (TMDs) monolayers. Excitons generated from TMDs couple to the Ag-NW plasmons, eventually collected as output signals at the Ag-NW end. Using two lasers, we demonstrate AND gate by modulating single excitons in Ag-NW on MoS 2 and a half-adder by modulating dual excitons in lateral WSe 2 and WS 2 . Moreover, a 4-to-2 binary encoder is realized in partially overlapped MoSe 2 and MoS 2 using four-terminal laser inputs. Our results represent great advances in communication processing for optical photonics integrable with subwavelength architectures.
Mixed linear and non-linear recursive types

We describe a type system with mixed linear and non-linear recursive types called LNL-FPC (the linear/non-linear fixpoint calculus). The type system supports linear typing which enhances the safety properties of programs, but also supports non-linear typing as well which makes the type system more convenient for programming. Just like in FPC, we show that LNL-FPC supports type-level recursion which in turn induces term-level recursion. We also provide sound and computationally adequate categorical models for LNL-FPC which describe the categorical structure of the substructural operations of Intuitionistic Linear Logic at all non-linear types, including the recursive ones. In order to do so, we describe a new technique for solving recursive domain equations within the category CPO by constructing the solutions over pre-embeddings. The type system also enjoys implicit weakening and contraction rules which we are able to model by identifying the canonical comonoid structure of all non-linear types. We also show that the requirements of our abstract model are reasonable by constructing a large class of concrete models that have found applications not only in classical functional programming, but also in emerging programming paradigms that incorporate linear types, such as quantum programming and circuit description programming languages.
Related-Key Boomerang Attacks on GIFT with Automated Trail Search Including BCT Effect

In Eurocrypt 2018, Cid et al. proposed a novel notion called the boomerang connectivity table, which formalised the switch property in the middle round of boomerang distinguishers in a unified approach. In this paper, we present a generic model of the boomerang connectivity table with automatic search technique for the first time, and search for (related-key) boomerang distinguishers directly by combining with the search of (related-key) differential characteristics. With the technique, we are able to find 19-round related-key boomerang distinguishers in the lightweight block cipher Gift-64 and Gift-128. Interestingly, a transition that is not predictable by the conventional switches is realised in a boomerang distinguisher predicted by the boomerang connectivity table. In addition, we experimentally extend the 19-round distinguisher by one more round. A 23-round key-recovery attack is presented on Gift-64 based on the distinguisher, which covers more rounds than previous known results in the single-key setting. Although the designers of Gift do not claim related-key security, bit positions of the key addition and 16-bit rotations were chosen to optimize the related-key differential bound. Indeed, the designers evaluated related-key differential attacks. This is the first work to present better related-key attacks than the simple related-key differential attack.
User needs assessment for research data services in a research university

This study assesses the needs of researchers for data-related assistance and investigates their research data management behavior. A survey was conducted, and 186 valid responses were collected from faculty, researchers, and graduate students across different disciplines at a research university. The services for which researchers perceive the greatest need include assistance with quantitative analysis and data visualization. Overall, the need for data-related assistance is relatively higher among health scientists, while humanities researchers demonstrate the lowest need. This study also investigated the data formats used, data documentation and storage practices, and data-sharing behavior of researchers. We found that researchers rarely use metadata standards, but rely more on a standard file-naming scheme. As to data sharing, respondents are likely to share their data personally upon request or as supplementary materials to journal publications. The findings of this study will be useful for planning user-centered research data services in academic libraries.
A New Framework for Evaluating Straggler Detection Mechanisms in MapReduce

Big Data systems (e.g., Google MapReduce, Apache Hadoop, Apache Spark) rely increasingly on speculative execution to mask slow tasks, also known as stragglers, because a job’s execution time is dominated by the slowest task instance. Big Data systems typically identify stragglers and speculatively run copies of those tasks with the expectation that a copy may complete faster to shorten job execution times. There is a rich body of recent results on straggler mitigation in MapReduce. However, the majority of these do not consider the problem of accurately detecting stragglers. Instead, they adopt a particular straggler detection approach and then study its effectiveness in terms of performance, e.g., reduction in job completion time or higher efficiency, e.g., high resource utilization. In this article, we consider a complete framework for straggler detection and mitigation. We start with a set of metrics that can be used to characterize and detect stragglers including Precision, Recall, Detection Latency, Undetected Time, and Fake Positive. We then develop an architectural model by which these metrics can be linked to measures of performance including execution time and system energy overheads. We further conduct a series of experiments to demonstrate which metrics and approaches are more effective in detecting stragglers and are also predictive of effectiveness in terms of performance and energy efficiencies. For example, our results indicate that the default Hadoop straggler detector could be made more effective. In a certain case, Precision is low and only 55% of those detected are actual stragglers and the Recall, i.e., percent of actual detected stragglers, is also relatively low at 56%. For the same case, the hierarchical approach (i.e., a green-driven detector based on the default one) achieves a Precision of 99% and a Recall of 29%. This increase in Precision can be translated to achieve lower execution time and energy consumption, and thus higher performance and energy efficiency; compared to the default Hadoop mechanism, the energy consumption is reduced by almost 31%. These results demonstrate how our framework can offer useful insights and be applied in practical settings to characterize and design new straggler detection mechanisms for MapReduce systems.
Selective applicative functors

Applicative functors and monads have conquered the world of functional programming by providing general and powerful ways of describing effectful computations using pure functions. Applicative functors provide a way to compose independent effects that cannot depend on values produced by earlier computations, and all of which are declared statically. Monads extend the applicative interface by making it possible to compose dependent effects, where the value computed by one effect determines all subsequent effects, dynamically. This paper introduces an intermediate abstraction called selective applicative functors that requires all effects to be declared statically, but provides a way to select which of the effects to execute dynamically. We demonstrate applications of the new abstraction on several examples, including two industrial case studies.
Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from materials science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.
Understanding Deep Neural Networks through Input Uncertainties

Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are point estimates and use a sensitivity analysis of these estimates to interpret the model. Using lightweight probabilistic networks we show how including prediction uncertainties in the sensitivity analysis leads to: (i) more robust and generalizable models; and (ii) a new approach for model interpretation through uncertainty decomposition. In particular, we introduce a new regularization that takes both the mean and variance of a prediction into account and demonstrate that the resulting networks provide improved generalization to unseen data. Furthermore, we propose a new technique to explain prediction uncertainties through uncertainties in the input domain, thus providing new ways to validate and interpret deep learning models.
A comparison of text classification methods using different stemming techniques

In the retrieval of information, two factors have an important impact on the performance of systems: the extract features and the matching process. In this work, we compare three well-known stemming techniques: Lovins stemmer, iterated Lovins and snowball stemmer. Concerning the classification phase, we compare, experimentally, six methods: BNET, NBMU, CNB, RF, SLogicF, and SVM. Basing on this comparison, we propose a new retrieval system by calling the voting method, as a matching tool, to improve the performance of the classical systems. In this paper, we use the TF-IDF algorithm to extract features. The envisaged systems are tested on two databases: BBCNEWS and BBCSPORT. The systems based on Lovins stemmers and on the voting technique give the best results. In fact, for the first databases, the best accuracy observed is for the system Lovins + Vote with a recognition rate of 97%. Concerning the second database, the system snowball +Vote gives us 99% as recognition rate.
Modeling Tracks and Controller for Servosila Engineer Robot

Modeling is an important part of solving any problem in robotics. Models let our opportunity to make mistakes that could break real robot and learn on these mistakes causing no damage to the robot. This paper presents a creation of model for crawler robot “Servosila Engineer.” In this model, tracks were replaced by big count of small wheels to get construction physically close to the track but with simple realization for simulation. To test efficiency of model was created three groups of controllers: for ground tracks wheels, flippers and flippers tracks wheels. This model takes into account mistakes made creating previous version and has opportunity to be used in teleoperation mode. To create controller, we tried some base types of controllers represented by Gazebo plugin. Created model and controller are integrated with Gazebo simulation system and ROS framework and will be used in our future researches.
Applicability of Blockchain Technology in Scheduling Resources Within Distributed Manufacturing

Collaborative production networks are becoming a common setting in modern times. Issues of how and where data from IoT devices is stored and how communication between entities is conducted are not fully resolved. Decentralized approaches provide opportunities compared with centralized approaches in this context, especially in terms of trust and data security. One technology that has emerged recently is the blockchain technology. Although the potential benefits are well known, literature on blockchain in supply chain management and manufacturing is scarce. This paper presents a concept for the use of blockchain technology in distributed manufacturing. By utilizing smart contracts, manufacturing processes are executed on shared resources in distributed collaborative production networks. Benefits and risks of the proposed methodology are identified. The paper is completed by a case study with several companies operating in distributed manufacturing.