Reviewerly/exHarmony huggingface dataset:
New Cryptcodes for Burst Channels

RCBQ are cryptcodes proposed in 2007. After that, several papers for performances of these codes for transmission through a binary-symmetric and Gaussian channels, have been published. Also, for improving the performances of these codes several new algorithms have been defined. In this paper we proposed a new modification of existing cryptcodes obtaining new cryptcodes suitable for transmission in burst channels. For generating burst errors we use the model of Gilbert-Elliott burst channel. Experimental results for bit-error and packet-error probabilities obtained for different channel and code parameters are presented. Also, we made comparison of the results obtained with the old and the new algorithms for RCBQ.
Requirements Monitoring and Diagnosis for Improving Adaptive E-Learning Systems Design

Aim/Purpose: In this paper, we highlight the need to monitor and diagnose adaptive e-learning systems requirements at runtime to develop a better understanding of their behavior during learning activities and improve their design. Our focus is to reveal which learning requirements the adaptive system is satisfying while still evolving and to provide specific recommendations regarding what actions should be taken and which relevant features are needed to help meet the specified learning requirements. Background: Adaptive e-learning systems research has long focused on user modeling and social learning to personalize each learner experience, while fewer instruments are reported to assess the quality of the solutions provided by such adaptive systems and to investigate their design problems. The design problems may emerge due to ever-evolving requirements being statically specified at design stages and to the changing environments that can be difficult to control and observe. The combination of some or all of these factors can lead to a definition of inconsistent or insufficient adaptation rules, which in turn may prevent these systems from providing appropriate resources to learners even if the needed ones have been accounted for within the knowledge space. Methodology: An empirical study has been performed to check and validate the behavior of a real-world adaptive e-learning system under four stated requirements. The study used a novel monitoring and diagnosing tool that reads the collected data from the system and checks its behavior against constraints that are derived automatically from the requirements specification. Contribution: The results provide statistical insights and highlight some issues related to requirements compliance at runtime, which helped us detect unforeseen instructional design issues. Recommendations for Practitioners: The study suggests that diagnosing requirements compliance at runtime can be an essential means to increase the confidence about their adaptive e-learning systems capabilities at runtime. Recommendation for Researchers: The study suggests that further research for developing specific indicators related to requirements compliance is needed in the field of adaptive e-learning systems. Future Research: Future work includes the study of possible improvement of our diagnostic tool using probabilistic reasoning.
HeadREST: A Specification Language for RESTful APIs

Representational State Transfer (REST), an architectural style providing an abstract model of the web, is by far the most popular platform to build web applications. Developing such applications require well-documented interfaces. However, and despite important initiatives such as the Open API Specification, the support for interface description is currently quite limited, focusing essentially on simple syntactic aspects. In this paper we present HeadREST, a dependently-typed language that allows describing semantic aspects of interfaces in a style reminiscent of Hoare triples.
Classification of Leucocytes for Leukaemia Detection

The blood sample images consist of Red Blood Cells, Platelets, White Blood Cells, and Plasma. White Blood Cells are classified into different types and their sub type based on its features. The white blood cells are further classified as granulocytes, Monocytes and lymphocytes. Leukaemia is a type of blood cancer which causes severe damage to human body if it is left untreated. The counting and classification of blood cells allows in evaluating and diagnosing of vast number of diseases. These diseases can be detected and treated at early stage Analysing of white blood cells (WBCs) allows for detecting acute lymphoblastic leukaemia (ALL). The morphological analysis of blood cells is performed manually by skilled operators. However, this method has many drawbacks such as slow analysis, non-standard accuracy, and dependencies depend on the skills of the operator. This paper presents an automated method for identifying WBC and classifies those using microscopic images. The other approaches identify the leucocytes first and then the other components but the proposed system isolate the whole leucocytes and then separates the cytoplasm and nucleus. This approach is necessary to analyse each cell component and extract its features. From each cell component, various features like shape, colour and texture, are extracted using a new approach for background pixel removal. The feature set obtained was used to train different classification models to determine which one is most suitable for the detection of acute and chronic leukaemia.
Asymptotic Network Independence in Distributed Optimization for Machine Learning

We provide a discussion of several recent results which have overcome a key barrier in distributed optimization for machine learning. Our focus is the so-called network independence property, which is achieved whenever a distributed method executed over a network of $n$ nodes achieves comparable performance to a centralized method with the same computational power as the entire network. We explain this property through an example involving of training ML models and sketch a short mathematical analysis.
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation

In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly. Therefore, it is necessary to exploit the label correlations to enhance the accuracy of the MIML classification. This paper presents the metric learning-based MIML-kNN (MI(ML)2kNN) method, which is composed of three parts. First, the label Laplacian matrix can be learned to obtain the label correlations by minimizing the label manifold regularizer. Then, based on label correlations, a novel objective function for the MIML is proposed where Mahalanobis distances between positively correlated labels and bags are minimized. Moreover, this objective function can be optimized by employing the Rayleigh-Ritz theorem and gradient descent (GD) alternately. Finally, the average Hausdorff distances of bag-bag pairs and bag-label pairs are calculated to construct the MIML-kNN classifier. Multiple classification experiments on three image and text benchmarks show the practicability and validity of our proposed method by comparing with the baseline methods.
On equivalence of algorithm's implementations

When a new optimization algorithm is proposed, it is compared with state-of-the-art methods. That comparison is made using implementations of the algorithms, but names and versions of the implementations are usually not revealed. This paper compares five implementations of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) taken from a trusted source. The comparisons were performed using the Comparing Continuous Optimizers (COCO) platform. The results show that all examined implementations produce a different outcome. The variation of the results stems from differences in the auxiliary codes of the implementations and from implementing an algorithm which is still under development. It is therefore important to use an appropriate implementation for experiments. Using a weak implementation can lead to the wrong conclusions.
Event-based Visual Gesture Recognition with Background Suppression running on a smart-phone

Gesture recognition is an area of research that is expanding as it would allow facilitated interaction with modern handheld devices for the naive user. Currently, smart-phones and tablets are not commonly used by some groups such as the visually-impaired and the elderly, because they require fine motor control and a good visual accuracy. Gesture recognition plays a key role in having these user categories to interact with IT devices.
A Simple and Mighty Arrowhead Detection Technique of Bangla Sign Language Characters with CNN

Sign Language is argued as the first Language for hearing impaired people. It is the most physical and obvious way for the deaf and dumb people who have speech and hearing problems to convey themselves and general people. So, an interpreter is wanted whereas a general people needs to communicate with a deaf and dumb person. In respect to Bangladesh, 2.4 million people uses sign language but the works are extremely few for Bangladeshi Sign Language (BdSL). In this paper, we attempt to represent a BdSL recognition model which are constructed using of 50 sets of hand sign images. Bangla Sign alphabets are identified by resolving its shape and assimilating its structures that abstract each sign. In proposed model, we used multi-layered Convolutional Neural Network (CNN). CNNs are able to automate the method of structure formulation. Finally the model gained 92% accuracy on our dataset.
Observational Learning by Reinforcement Learning

Observational learning is a type of learning that occurs as a function of observing, retaining and possibly imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the effect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a 'teacher' (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent's behaviour. The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour.
Evaluation of named entity coreference

In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name. We evaluate state-of-the-art coreference resolution systems for the task of resolving all mentions to named entities. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name to an entity and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she–the student–no name). We introduce new metrics for evaluating named entity coreference that address these discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. We are, however, able to confirm that the state-of-the art system according to traditional evaluations also performs vastly better than other systems on the named entity coreference task.
Data-based, synthesis-driven: Setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, tosolve general questions in a field. Computation ecology has the potential totransform the way ecologists think about the integration of data and models. Asthe practice is gaining prominence as a way to conduct ecological research, itis important to reflect on what its agenda could be, and how it fits within thebroader landscape of ecological research. In this contribution, we suggest areasin which empirical ecologists, modellers, and the emerging community ofcomputational ecologists could engage in a constructive dialogue to build on oneanother's expertise; specifically, about the need to make predictions frommodels actionable, about the best standards to represent ecological data, andabout the proper ways to credit data collection and data reuse. We discuss howtraining can be amended to improve computational literacy.
Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran)

The aim of this work was to delineate the prospects of podiform-type chromite by staged factor analysis and geochemical mineralization prospectivity index in Balvard area, SE Iran. The stream sediment data and fault density were used as the exploration features for prospectivity modeling in the studied area. In this regard, two continuous fuzzified evidence layers were generated and integrated using fuzzy operator. Then fractal modeling was used for defuzzification of the prospectivity model obtained. Furthermore, the prediction-area plot was used for evaluation of the predictive ability of the generated target areas. The results obtained showed that using the prospectivity model, 82% of mineral occurrences was predicted in 18% of the studied area. In addition, the target areas were correlated with the geological particulars including ultrabasic and serpentinization rocks, the host rocks of the podiform-type chromite deposit type.
Classification with unknown class-conditional label noise on non-compact feature spaces

We investigate the problem of classification in the presence of unknown class-conditional label noise in which the labels observed by the learner have been corrupted with some unknown class dependent probability. In order to obtain finite sample rates, previous approaches to classification with unknown class-conditional label noise have required that the regression function is close to its extrema on sets of large measure. We shall consider this problem in the setting of non-compact metric spaces, where the regression function need not attain its extrema. In this setting we determine the minimax optimal learning rates (up to logarithmic factors). The rate displays interesting threshold behaviour: When the regression function approaches its extrema at a sufficient rate, the optimal learning rates are of the same order as those obtained in the label-noise free setting. If the regression function approaches its extrema more gradually then classification performance necessarily degrades. In addition, we present an adaptive algorithm which attains these rates without prior knowledge of either the distributional parameters or the local density. This identifies for the first time a scenario in which finite sample rates are achievable in the label noise setting, but they differ from the optimal rates without label noise.
PBAR: Parallelized Brain Storm Optimization for Association Rule Mining

The brain storm optimization (BSO) algorithm is a new and promising swarm intelligence paradigm, based on the emerging intelligence of the human brain storming process. However, BSO is ineffective to deal with the large data sets because its clustering and idea updating operations are computationally expensive. Aiming at this issue, we propose a parallelized brain storm optimizer based on Spark framework called PLBSO. The basic idea is to parallelize the complex operations of population clustering and idea updating in BSO so as to reduce the computation cost. Especially, the generation process of new ideas is modified to make BSO more suitable for parallelism. In addition, a new parallelized algorithm called PBAR based on PLBSO is proposed for association rule mining. The performance of PLBSO is evaluated over serialized BSO on complex multimodal benchmarks. Results show that, compared with serialized BSO, PLBSO can acquire a 350% speedup approximately while keeping similar accuracy. Finally, PBAR is adopted to resolve the association rule mining problem on a transactional dataset taken from IBM SPSS modeler. The encouraging results prove the validity of our algorithms.
HackPPL: a universal probabilistic programming language

HackPPL is a probabilistic programming language (PPL) built within the Hack programming language. Its universal inference engine allows developers to perform inference across a diverse set of models expressible in arbitrary Hack code. Through language-level extensions and direct integration with developer tools, HackPPL aims to bridge the gap between domain-specific and embedded PPLs. This paper overviews the design and implementation choices for the HackPPL toolchain and presents findings by applying it to a representative problem faced by social media companies.
Deep Neighbor Embedding for Evaluation of Large Portfolios of Variable Annuities

Variable annuities are very profitable financial products that pose unique challenges in risk prediction. Metamodeling techniques are popular due to the significant saving in computation time. However, the current metamodeling techniques still have a low valuation accuracy. One key difficulty is the selection of a small number of contracts that optimally represent the whole portfolio. In this paper, we propose a novel and highly effective method for selecting representative contracts. At the center of this method is a deep neighbor embedding that supports robust clustering of the contracts in a portfolio. The embedding is a low-dimensional representation that respects similarities among contracts in both contract-specific features and their historical performance, achieved through abstract representation in a deep neural network. Empirical results show that the proposed model achieves significant improvement in valuation accuracy, often 10 times or more accurate compared with the popular Kriging-based model.
Effects of Some Operation Parameters on the Performance of a Reservoir Computing System Based on a Delay Feedback Semiconductor Laser With Information Injection by Current Modulation

Via a Santa Fe time series prediction task, the performance of a reservoir computing (RC) system based on a delay feedback semiconductor laser (SL) under current modulation is experimentally investigated and evaluated in detail. Experimental results show that some key operation parameters including the modulation index, the feedback ratio and the SL bias current, seriously affect the prediction performance of this RC system. By optimizing parameter settings, the degradation of the RC performance with the increase of the SL bias current can be efficiently alleviated, and the RC system can still realize a good performance with a NMSE smaller than 0.1 even for the SL biased at a high level of three times threshold current Ith. This research is an improvement to a previously experimental report in which such RC system can achieve a good performance only under a SL bias level near I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> .
Object landmark discovery through unsupervised adaptation

This paper proposes a method to ease the unsupervised learning of object landmark detectors. Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category. Contrary to previous works, we do however assume that a landmark detector, which has already learned a structured representation for a given object category in a fully supervised manner, is available. Under this setting, our main idea boils down to adapting the given pre-trained network to the target object categories in a fully unsupervised manner. To this end, our method uses the pre-trained network as a core which remains frozen and does not get updated during training, and learns, in an unsupervised manner, only a projection matrix to perform the adaptation to the target categories. By building upon an existing structured representation learned in a supervised manner, the optimization problem solved by our method is much more constrained with significantly less parameters to learn which seems to be important for the case of unsupervised learning. We show that our method surpasses fully unsupervised techniques trained from scratch as well as a strong baseline based on fine-tuning, and produces state-of-the-art results on several datasets. Code can be found at tiny.cc/GitHub-Unsupervised
Progressive Augmentation of GANs

Training of Generative Adversarial Networks (GANs) is notoriously fragile, requiring to maintain a careful balance between the generator and the discriminator in order to perform well. To mitigate this issue we introduce a new regularization technique - progressive augmentation of GANs (PA-GAN). The key idea is to gradually increase the task difficulty of the discriminator by progressively augmenting its input or feature space, thus enabling continuous learning of the generator. We show that the proposed progressive augmentation preserves the original GAN objective, does not compromise the discriminator's optimality and encourages a healthy competition between the generator and discriminator, leading to the better-performing generator. We experimentally demonstrate the effectiveness of PA-GAN across different architectures and on multiple benchmarks for the image synthesis task, on average achieving 3 point improvement of the FID score.
Trajectory Planning for Automated Vehicles in Overtaking Scenarios

Overtaking is a challenging task in the field of autonomous driving, especially on roads with an opposite lane and oncoming vehicles. Since trajectory planning is repeated cyclic it is highly important to trigger the maneuver only if it is guaranteed that collision-free trajectories that satisfy kinematic constraints exist at each planning step. The goal of this paper is to present an algorithm for planning overtaking trajectories on large temporal horizons in real-time. The main idea is as follows: once overtaking is desired by the behavior module an initial trajectory is simulated using a path tracking control algorithm for lane changing combined with a classical PI-controller for approaching the target speed. The controllers are parametrized in a way that the simulated trajectory will satisfy kinematic constraints. If no collisions are detected a corridor containing the simulated trajectory is created to state constraints for a subsequent optimal control problem to relax the trajectory and smooth it to be comfortable to the vehicle passengers.