Reviewerly/exHarmony huggingface dataset:
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop

The ultimate goal of the Machine Learning (ML) community is to develop algorithms that can automatically learn from data, to extract knowledge and to make decisions without any human intervention. Specifically, automatic Machine Learning (aML) approaches show impressive success, e.g. in speech/image recognition or autonomous drive and smart car industry. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average human efficiency. As human perception is inherently limited to 3D environments, such approaches can discover patterns, e.g. that two objects are similar, in arbitrarily high-dimensional spaces what no human is able to do. Humans can deal simultaneously only with limited amounts of data, whilst “big data” is not only beneficial but necessary for aML. However, in health informatics, there are few data sets; aML approaches often suffer from insufficient training samples. Many problems are computationally hard, e.g. subspace clustering, k-anonymization, or protein folding. Here, interactive machine learning (iML) could be successfully used, as a human-in-the-loop contributes to reduce a huge search space through heuristic selection of suitable samples. This can reduce the complexity of NP-hard problems through the knowledge brought in by a human agent involved into the learning algorithm. A huge motivation for iML is that standard black-box approaches lack transparency, hence do not foster trust and acceptance of ML among end-users. Most of all, rising legal and privacy aspects, e.g. the European General Data Protection Regulations (GDPR) make black-box approaches difficult to use, because they often are not able to explain why a decision has been made, e.g. why two objects are similar. All these reasons motivate the idea to open the black-box to a glass-box. In this paper, we present some experiments to demonstrate the effectiveness of the iML human-in-the-loop model, in particular when using a glass-box instead of a black-box model and thus enabling a human directly to interact with a learning algorithm. We selected the Ant Colony System (ACS) algorithm, and applied it on the Traveling Salesman Problem (TSP). The TSP-problem is a good example, because it is of high relevance for health informatics as for example on protein folding problem, thus of enormous importance for fostering cancer research. Finally, from studies of learning from observation, i.e. of how humans extract so much from so little data, fundamental ML-research also may benefit.
Session Coalgebras: A Coalgebraic View on Session Types and Communication Protocols

Abstract Compositional methods are central to the development and verification of software systems. They allow breaking down large systems into smaller components, while enabling reasoning about the behaviour of the composed system. For concurrent and communicating systems, compositional techniques based on behavioural type systems have received much attention. By abstracting communication protocols as types, these type systems can statically check that programs interact with channels according to a certain protocol, whether the intended messages are exchanged in a certain order. In this paper, we put on our coalgebraic spectacles to investigate session types , a widely studied class of behavioural type systems. We provide a syntax-free description of session-based concurrency as states of coalgebras. As a result, we rediscover type equivalence, duality, and subtyping relations in terms of canonical coinductive presentations. In turn, this coinductive presentation makes it possible to elegantly derive a decidable type system with subtyping for $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus processes, in which the states of a coalgebra will serve as channel protocols. Going full circle, we exhibit a coalgebra structure on an existing session type system, and show that the relations and type system resulting from our coalgebraic perspective agree with the existing ones.
Top-down Discourse Parsing via Sequence Labelling

We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020;Zhang et al., 2020).By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points.We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing.Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.
Effective multiple pedestrian tracking system in video surveillance with monocular stationary camera

Multiple pedestrian tracking in video surveillance is still a pressing challenge, especially under static and dynamic occlusions and target appearance variations. Considering these complex environments in video surveillance, a multiple pedestrian tracking system with special processing procedures is proposed in this paper. In the proposed tracking system, pedestrian candidates are detected on each frame and registered as the tracked targets or associated with existing targets when their situations are suitable. The registered pedestrian targets are tracked frame by frame and terminated when the termination criteria are satisfied. In order to distinguish these target individuals, multi-sample adaptive modeling (MSAM) is proposed, which is used to adapt to a new target’s unpredictable pose variation. Furthermore, static occlusions are annotated for each scene, which may occlude pedestrians in the annotated regions. These occluded targets are modified by the assigned rules and treated differently in the process of target association. Aiming to enhance the effect of target association, each target’s location on the current frame is predicted with information on the previous frames using a Kalman filter. The predicted location is regarded as the center of the search region of the corresponding target. The experimental results show that the proposed tracker achieves the best performance among the five state-of-the-art trackers on three publicly available databases.
Complex Question Answering on knowledge graphs using machine translation and multi-task learning

Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG.In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules.Often these challenges are inter-related and the solutions to them can reinforce each other when handled simultaneously in an end-to-end learning setup.To this end, we propose a multi-task BERT based Neural Machine Translation (NMT) model to address these challenges.Through experimental analysis, we demonstrate the efficacy of our proposed approach on one publicly available and one proprietary dataset.
Adaptive Dropout Method Based on Biological Principles

Dropout is one of the most widely used methods to avoid overfitting neural networks. However, it rigidly and randomly activates neurons according to a fixed probability, which is not consistent with the activation mode of neurons in the human cerebral cortex. Inspired by gene theory and the activation mechanism of brain neurons, we propose a more intelligent adaptive dropout, in which a variational self-encoder (VAE) overlaps to an existing neural network to regularize its hidden neurons by adaptively setting activities to zero. Through alternating iterative training, the discarding probability of each hidden neuron can be learned according to the weights and thus effectively avoid the shortcomings of the standard dropout method. The experimental results in multiple data sets illustrate that this method can better suppress overfitting in various neural networks than can the standard dropout. Additionally, this adaptive dropout technique can reduce the number of neurons and improve training efficiency.
Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters.
Deep imagination is a close to optimal policy for planning in large decision trees under limited resources

Abstract Many decisions involve choosing an uncertain course of action in deep and wide decision trees, as when we plan to visit an exotic country for vacation. In these cases, exhaustive search for the best sequence of actions is not tractable due to the large number of possibilities and limited time or computational resources available to make the decision. Therefore, planning agents need to balance breadth—considering many actions in the first few tree levels—and depth—considering many levels but few actions in each of them—to allocate optimally their finite search capacity. We provide efficient analytical solutions and numerical analysis to the problem of allocating finite sampling capacity in one shot to infinitely large decision trees, both in the time discounted and undiscounted cases. We find that in general the optimal policy is to allocate few samples per level so that deep levels can be reached, thus favoring depth over breadth search. In contrast, in poor environments and at low capacity, it is best to broadly sample branches at the cost of not sampling deeply, although this policy is marginally better than deep allocations. Our results can provide a theoretical foundation for why human reasoning is pervaded by imagination-based processes.
Iot-Based Secure And Energy Efficient Scheme For Precision Agriculture Using Blockchain And Improved Leach Algorithm

Precision agriculture is a trending technology that helps in improving the agricultural productivity. This technique combines various technologies like Internet of Things (IoT), remote sensing, information technology and wireless sensor networks (WSN). In this research, we propose a new technique for adaptive water scheduling in precision agriculture based on wireless sensors like moisture and temperature sensors. In the proposed framework, the secure data transfer in the cloud is achieved using blockchain technology. Further, energy efficient data transfer is attained by means of Improved LEACH algorithm. The data from the sensors are acquired using PIC micro controller module. The acquired data are then transmitted to the cloud using Raspberry Pi module. This data is then secured and verified using block chain-based IoT technique. In this way, the blockchain technology plays a pivotal role in sharing the agricultural data in the cloud in a reliable and transparent manner. Further, blockchain technology also provides data security in a decentralized manner. The main drawback in wireless sensor-based precision agricultural system is the limitation of battery power. To overcome this drawback, we have implemented Improved LEACH protocol for achieving energy efficiency. In this protocol, a new threshold limit is introduced to increase the level of energy efficiency. The usage of blockchain and ILEACH protocol helps in achieving an intelligent farming system. The proposed technique helped in achieving an increased throughput of 63%. Further, the data from other sensors like pH sensor and humidity sensor were transmitted to the cloud in real-time. The entire sensor data were available to the farmers using IoT-based smart phone module to enable remote monitoring.
Arabic Question Answering Systems: Gap Analysis

Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.
Convolutional neural network-based image denoising for synchronous measurement of temperature and deformation at elevated temperature

Non-contact measurement method at elevated temperatures has been widely studied, which provides an efficient means for evaluating the properties of high-temperature materials. However, such high-temperature environment-induced challenges as strong light radiation and air disturbance may cause a huge quality decrease to the captured images based on the optical systems. As an important application of neural networks, Denoising Convolutional Neural Networks (DNCNN) has shown a considerable performance in conventional image denoising. In this work, an optimized DNCNN method based on the sub-region processing and transfer learning was proposed and applied in the high-temperature measurement. The thermal heating experiment of the SiC material was carried out to validate the applicability and accuracy of the method, while the synchronous measurement of temperature and deformation is realized. Simulation results based on the finite element method (FEM) agree well with the calculated data, indicating the proposed model can improve the measurement accuracy.
An effective and efficient fuzzy approach for managing natural noise in recommender systems

A high-quality recommender system (RS) can effectively alleviate information overload by producing recommendations. The quality of the recommender system not only depends on the recommendation algorithm but also on the quality of collected data. Since users are often affected by environmental and accidental factors during the rating process, natural noise is probably brought into the data of RS by non-malicious users, which will lead to deviations in prediction results. In this paper, we propose a scheme based on fuzzy theory to manage the natural noise in RS. We first classify the ratings into three fuzzy categories with variable boundaries. Then, the fuzzy profiles of users and items are built to detect the natural noise in ratings. Finally, once the noisy ratings are detected, we replace them with the rating threshold values according to the Maximum membership principle. The proposed scheme is tested in two benchmark datasets and experimental results verify that the scheme can significantly improve the recommendation quality and has higher efficiency than the schemes based on re-predication.
Wifi-Based Device-Free Gesture Recognition Through-the-Wall

Device-free (passive) gesture recognition offers an enormous potential to simplify Human-Computer Interaction (HCI) in future smart environments. WIFI-based gesture recognition approaches have attained acclaim amongst others due to the omnipresence, privacy-preservation, and broad coverage of WIFI. However, there is no universal solution built on off-the-shelf devices that can accommodate an expandable set of gestures in a through-the-wall setting. In this work, we propose such a gesture recognition system that can recover information about the actual trajectory of the hand movement allowing an expandable set of gestures. Further, we leverage the rich multipath in a through-the-wall setting to develop a statistical model for the channel variations induced by a hand gesture. This model is used to derive a correspondence between the relative distance moved by the hand and the Time Reversal Resonating Strength (TRRS) decay. Based on this relation and the geometry of the gesture shape, we design feature extraction modules to enable gesture classification. We built a prototype of the proposed system on off-the-shelf WIFI devices and achieved a classification accuracy of 87% on a set of 6 uppercase English alphabets.
Snowflakes: A Prototyping Tool for Computational Jewelry

Smart-jewelry design has many layers such as comfort, ergonomics, fashionability, interactivity and functionality that create a complex design process, making the form exploration challenging. Various wearable prototyping tools were developed to overcome this challenge; however, they are usually textile-based and do not target smart jewelry design. To bridge this gap, we developed Snowflakes that differentiates from existing tools by 1) allowing designers to explore different jewelry forms, 2) incorporating external materials such as leather, 3) creating form factors that fit body parts with flexible connectors. In this paper, we explain the design process of Snowflakes which is inspired by 7 design parameters (limbs, materials, grip, fastener, decoration, placement, form) extracted through the examination of non-smart jewelry. We also demonstrate three reimplementations and design concepts implemented with Snowflakes. Our exploration with Snowflakes contributes to the wearable community in terms of smart-jewelry visual expressions, interaction modalities, and merger of traditional and computational materials.
Hierarchical Classification of Very Small Objects: Application to the Detection of Arthropod Species

Automated image analysis and deep learning tools such as object detection models are being used increasingly by biologists. However, biological datasets often have constraints that are challenging for the use of deep learning. Classes are often imbalanced, similar, or too few for robust learning. In this paper we present a robust method relying on hierarchical classification to perform very small object detection. We illustrate our results on a custom dataset featuring 22 classes of arthropods used to study biodiversity. This dataset shows several constraints that are frequent when using deep learning on biological data with a high class imbalance, some classes learned on only a few training examples and a high similarity between classes. We propose to first perform detection at a super-class level, before performing a detailed classification at a class level. We compare the obtained results with our proposed method to a global detector, trained without hierarchical classification. Our method succeeds in obtaining a mAP of 75 %, while the global detector only achieves a mAP of 48 %. Moreover, our method shows high precision even on classes with the less train examples. Confusions between classes with our method are fewer and are of a lesser impact. We are able achieve a more robust object classification with the use of our proposed method. This method can also enable better control on the model’s output which can be particularly valuable when handling ecological, biological or medical data for example.
Image deblurring via enhanced local maximum intensity prior

The enhanced local maximum intensity prior is proposed in this paper. We find that the intermediate latent image restoration can be enhanced by joining the local maximum intensity with the local maximum gradient, and the quality of the recovered kernel is hereby improved. Our method is based on the fact that the maximum value of local patch pixels and gradients will decrease with the blurring process, which has been proved mathematically and experimentally. This joint prior has been named enhanced local maximum intensity prior. Based on this prior, a new energy function is established and the image deblurring is successfully carried out by alternating optimization strategy. Extensive experiments have been conducted, which shows that our method is more effective compared with the state-of-the-art methods.
Multi-layer optical Fourier neural network based on the convolution theorem

To take full advantage of the application of neural networks to optical systems, we design an optical neural network based on the principle of free-space optical convolution. In this article, considering the need for a high-power light source to excite the nonlinearity of an optical material, we describe how to reduce the power consumption of the system by quantifying the output of each layer after the softmax operation as an 8-bit value and loading these values into amplitude-only spatial light modulators (SLMs). In addition, we describe how to load the matrix with positive and negative values in the amplitude-only SLM by utilizing Fourier properties of the odd-order square matrix. We apply our six-layer optical network to the classification of Mixed National Institute of Standards and Technology database (MNIST) and Fashion-MNIST and find that the accuracy reaches 92.51% and 80.67%, respectively. Finally, we consider the error analysis, power consumption, and response time of our framework.
A grammar compressor for collections of reads with applications to the construction of the BWT

We describe a grammar for DNA sequencing reads from which we can compute the BWT directly. Our motivation is to perform in succinct space genomic analyses that require complex string queries not yet supported by repetition-based self-indexes. Our approach is to store the set of reads as a grammar, but when required, compute its BWT to carry out the analysis by using self-indexes. Our experiments in real data showed that the space reduction we achieve with our compressor is competitive with LZ-based methods and better than entropy-based approaches. Compared to other popular grammars, in this kind of data, we achieve, on average, 12% extra compression and require less working space and time.