Reviewerly/exHarmony huggingface dataset:
An evolutionary framework for machine learning applied to medical data

Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.
Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance

Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.
Fuzzy cognitive mapping: An old tool with new uses in nursing research

Abstract Aims Describe the implementation and uses of fuzzy cognitive mapping (FCM) as a constructive method for meeting the unique and rapidly evolving needs of nursing inquiry and practice. Design Discussion paper. Data sources Drawing on published scholarship of cognitive mapping from the fields of ecological management, information technology, economics, organizational behaviour and health development, we consider how FCM can contribute to contemporary challenges and aspirations of nursing research. Implications for nursing Fuzzy cognitive mapping can generate theory, describe knowledge systems in comparable terms and inform questionnaire design and dialogue. It can help build participant–researcher partnerships, elevate marginalized voices and facilitate intercultural dialogue. As a relatively culturally safe and foundational approach in participatory research, we suggest that FCM should be used in settings of transcultural nursing, patient engagement, person‐ and family‐centred care and research with marginalized populations. FCM is amenable to rigorous analysis and simultaneously allows for greater participation of stakeholders. Conclusion In highly complex healthcare contexts, FCM can act as a common language for defining challenges and articulating solutions identified within the nursing discipline. Impact There is a need to reconcile diverse sources of knowledge to meeting the needs of nursing inquiry. FCM can generate theory, describe knowledge systems, facilitate dialogue and support questionnaire design. In its capacity to engage multiple perspectives in defining problems and identifying solutions, FCM can contribute to advancing nursing research and practice.
Leakage-Resilient Secret Sharing Against Colluding Parties

In this work, we consider the natural goal of designing secret sharing schemes that ensure security against an adversary who may learn some “leaked'' information about all the shares. We say that a secret sharing scheme is p-party leakage-resilient, if the secret remains statistically hidden even after a computationally unbounded adversary learns a bounded amount of leakage, where each bit of leakage adaptively and jointly depends on the shares of an adaptively chosen subset of p parties. Existing multi-party secret sharing schemes (Dziembowski and Pietrzak FOCS 07), (Goyal and Kumar STOC 18) and (Benhamouda, Degwekar, Ishai and Rabin CRYPTO 18) have focused on handling non-adaptive and individual leakage for (limited special cases of) threshold secret sharing schemes. (1) We give an unconditional compiler that transforms any secret sharing scheme on n parties into a p-party leakage-resilient one for p upto O(log n). This yields the first multi-party secret sharing schemes that are secure against adaptive or joint leakage. (2) As a natural extension, we initiate the study of leakage-resilient non-malleable secret sharing. We empower the adversary to adaptively leak from each of the shares and then use the leakage to tamper with all of them arbitrarily and independently. Leveraging our p-party leakage-resilient schemes, we compile any secret sharing scheme into a non-malleable one ensuring that any such tampering either preserves the secret or completely `destroys' it. This improves upon the non-malleable secret sharing scheme of (Goyal and Kumar CRYPTO 18) where no leakage was permitted. Leakage-resilient non-malleable codes can be seen as 2-out-of-2 schemes satisfying our guarantee and have already found many applications in cryptography. (3) Our constructions rely on a clean connection we draw to communication complexity in the well-studied number-on-forehead (NOF) model and rely on functions that have strong communication-complexity lower bounds in the NOF model (in a black-box way). We get efficient p-party leakage-resilient schemes for p upto O(log n) as our share sizes have exponential dependence on p. We observe that improving this exponential dependence, even for simultaneous, non-adaptive leakage, will lead to progress on longstanding open problems in complexity theory.
Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks

Internet of things (IoT) is a very important research area, having many applications such as smart cities, intelligent transportation system, tracing, and smart homes. The underlying technology for IoT are wireless sensor networks (WSN). The selection of cluster head (CH) is significant as a part of the WSN's optimization in the context of energy consumption. In WSNs, the nodes operate on a very limited energy source, therefore, the routing protocols designed must meet the optimal utilization of energy consumption in such networks. Evolutionary games can be designed to meet this aspect by providing an adequately efficient CH selection mechanism. In such types of mechanisms, the network nodes are considered intelligent and independent to select their own strategies. However, the existing mechanisms do not consider a combination of many possible parameters associated with the smart nodes in WSNs, such as remaining energy, selfishness, hop-level, density, and degree of connectivity. In our work, we designed an evolutionary game-based approach for CH selection, combined with some vital parameters associated with sensor nodes and the entire networks. The nodes are assumed to be smart, therefore, the aspect of being selfish is also addressed in this work. The simulation results indicate that our work performs much better than typical evolutionary game-based approaches.
A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.
A Learning-Based Data Placement Framework for Low Latency in Data Center Networks

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.
Finding decision jumps in text classification

Text classification is one of the key problems in natural language processing (NLP), and in early years, it was usually accomplished by feature-based machine learning models. Recently, the deep neural network has become a powerful learning machine, making it possible to work with text itself as raw input for the classification problems. However, existing neural networks are typically end-to-end and lack explicit interpretation of the prediction. In this paper, we propose Jumper, a novel framework that models text classification as a sequential decision process. Generally, Jumper is a neural system that scans a piece of text sequentially and makes classification decisions at the time it wishes, which is inspired by the cognitive process of human text reading. In our framework, both the classification result and when to make the classification are part of the decision process, controlled by a policy network and trained with reinforcement learning. Experimental results of real-world applications demonstrate the following properties of a properly trained Jumper: (1) it tends to make decisions whenever the evidence is enough, therefore reducing total text reading by 30–40% and often finding the key rationale of the prediction; and (2) it achieves classification accuracy better than or comparable to state-of-the-art models in several benchmark and industrial datasets. We further conduct a simulation experiment with mock data, which confirms that Jumper is able to make a decision at the theoretically optimal decision position.
Breast Cancer Diagnosis from Histopathological Image based on Deep Learning

Breast cancer is the most common cancer worldwide with high death rate especially for women, early diagnosis can increase the survival opportunity with correct treatment in the hospital. The Computer-Assisted Diagnosis (CAD) system is of vital significant for improving the diagnostic accuracy as the diagnosis process is tedious and the result may be different between pathologists. In this paper, we proceed research on breast cancer histopathological images classification based on deep Convolutional Neural Network (CNN). The approach proposed in this work utilize CNN to extract features of histopathological images and classify the images into begin tumors and malignant tumors by softmax function. Eliminate overfitting phenomenon by data augmentation and fine tune technology while improving the performance of networks by cross validation training strategy. The experiments in this paper were conducted on BreaKHis database [4] available to scientific study from 2014 and the results proved the algorithm's advantage on accuracy.
Forensic science. The importance of identity in theory and practice

There is growing consensus that there is a crisis in forensic science at the global scale. Whilst restricted resources are clearly part of the root causes of the crisis, a contested identity of forensic science is also a significant factor. A consensus is needed on the identity of forensic science that encompasses what forensic science ‘is’, and critically, what it is ‘for’. A consistent and cogent identity that is developed collaboratively and accepted across the entire justice system is critical for establishing the different attributes of the crisis and being able to articulate effective solutions. The degree to which forensic science is considered to be a coherent, interdisciplinary yet unified discipline will determine how forensic science develops, the challenges it is able to address, and how successful it will be in overcoming the current crisis.
Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements, and propagation paths is unknown. In order to effectively utilize multipath measurements from one target to improve detection and tracking performance, a tracker has to handle high-dimensional estimation of latent variables including target existence state, target kinematic state, and multipath data association. Based on variational Bayesian inference, we propose a novel joint detection and tracking algorithm that incorporates multipath data association, target detection, and target state estimation in a unified Bayesian framework. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for reducing the performance deterioration caused by the coupling between estimation errors and identification errors. Loopy belief propagation is exploited to approximately calculate the probability of multipath data association, saving the computational cost significantly. Simulation results of over-the-horizon radar multitarget tracking show that the proposed algorithm outperforms multihypothesis multipath track fusion and multidetection (hypothesis-oriented) multiple hypothesis tracker, especially under low signal-to-noise ratio circumstance.
Deep in the Dark - Deep Learning-Based Malware Traffic Detection Without Expert Knowledge

With the ever-growing occurrence of networking attacks, robust network security systems are essential to prevent and mitigate their harming effects.In recent years, machine learning-based systems have gain popularity for network security applications, usually considering the application of shallow models, where a set of expert handcrafted features are needed to pre-process the data before training.The main problem with this approach is that handcrafted features can fail to perform well given different kinds of scenarios and problems.Deep Learning models can solve this kind of issues using their ability to learn feature representations from input raw or basic, non-processed data.In this paper we explore the power of deep learning models on the specific problem of detection and classification of malware network traffic, using different representations for the input data.As a major advantage as compared to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as the input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones.Our results suggest that deep learning models can better capture the underlying statistics of malicious traffic as compared to classical, shallow-like models, even while operating in the dark, i.e., without any sort of expert handcrafted inputs.
Auto-correction of English to Bengali Transliteration System using Levenshtein Distance

The automated transliteration process is a function or software application that checks words against a computerized corpus to ensure that they are correct. A transliteration system is required either during the typing of text or when a user does not know the correct spelling of a word. The main objective of this research is to develop a system that is much better to check the spelling of the transliterated word by calculating Levenshtein distance. To make the mechanism more efficient and accurate unigram method has been implemented. Several techniques have been integrated for data collection to make the system more reliable and flexible. Almost twenty thousand words are included to create a data lexicon for this research work. This system is able to deal with signed or unsigned numeric values and float numbers with 78.13% accuracy.
Investigating Agile Practices in Software Startups

Software development practices have smoothly shifted from traditional software development to new approaches that fit better to the real and unpredictable world. Agile practices might help practitioners respond quickly to customer change requests and deliver a working software on-schedule. Software startups are companies that develop innovative and software-intensive products and services in a dynamic and fast-growing market. This study aims to investigate the use of agile practices in software startups. We conducted 14 in-depth semi-structured interviews with the CEO and CTO from early-stage software startups. The results indicate that DevOps, Fundamentals, Design and Extreme Programming are the most used agile practice areas. Our results open up an opportunity to improve software engineering practices in early-stage software startups.
Virtual Grasping Feedback and Virtual Hand Ownership

In this work, we investigate the influence of different visualizations on a manipulation task in virtual reality (VR). Without the haptic feedback of the real world, grasping in VR might result in intersections with virtual objects. As people are highly sensitive when it comes to perceiving collisions, it might look more appealing to avoid intersections and visualize non-colliding hand motions. However, correcting the position of the hand or fingers results in a visual-proprioceptive discrepancy and must be used with caution. Furthermore, the lack of haptic feedback in the virtual world might result in slower actions as a user might not know exactly when a grasp has occurred. This reduced performance could be remediated with adequate visual feedback.
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning

Automatic pancreas segmentation in radiology imagesRadiology image, e.g., computed tomography (CT), and magnetic resonance imaging (MRI) Magnetic Resonance Imaging (MRI) , is frequently required by computer-aided screeningScreening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal organAbdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networksConvolutional Neural Networks (CNNs) (CNN) have demonstrated promising performance on accurate segmentation of pancreas. However, the CNN-based method often suffers from segmentation discontinuity for reasons such as noisy image quality and blurry pancreatic boundary. In this chapter, we first discuss the CNN configurations and training objectives that lead to the state-of-the-art performance on pancreas segmentationPancreas segmentation. We then present a recurrent neural network (RNN) Recurrent Neural Network (RNN) to address the problem of segmentation spatial inconsistency across adjacent image slices. The RNNRecurrent Neural Network (RNN) takes outputs of the CNN and refines the segmentation by improving the shape smoothness.
A Sentiment Classification in Bengali and Machine Translated English Corpus

The resource constraints in many languages have made the multi-lingual sentiment analysis approach a viable alternative for sentiment classification. Although a good amount of research has been conducted using a multi-lingual approach in languages like Chinese, Italian, Romanian, etc. very limited research has been done in Bengali. This paper presents a bilingual approach to sentiment analysis by comparing machine translated Bengali corpus to its original form. We apply multiple machine learning algorithms: Logistic Regression (LR), Ridge Regression (RR), Support Vector Machine (SVM), Random Forest (RF), Extra Randomized Trees (ET) and Long Short-Term Memory (LSTM) to a collection of Bengali corpus and corresponding machine translated English version. The results suggest that using machine translation improves classifiers performance in both datasets. Moreover, the results show that the unigram model performs better than higher-order n-gram model in both datasets due to linguistic variations and presence of misspelled words results from complex typing system of Bengali language; sparseness and noise in the machine translated data, and because of small datasets.