Reviewerly/exHarmony huggingface dataset:
An Interpretable Fuzzy DBN-Based Classifier for Indoor User Movement Prediction in Ambient Assisted Living Applications

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MovementAAL_RSS</i> dataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.
The Inflatable Cat

Research on product experience has a history in investigating the sensory and emotional qualities of interacting with objects. However, this notion has not been fully expanded to the design space of co-designing smart objects. In this paper, we report on findings from a series of co-design workshops where we used the toolkit Loaded Dice in conjunction with a card set that aimed to support participants in reflecting the sensory qualities of domestic smart objects. We synthesize and interpret findings from our study to help illustrate how the workshops supported co-designers in creatively ideating concepts for emotionally valuable smart objects that better connect personal inputs with the output of smart objects. Our work contributes a case example of how a co-design approach that emphasizes situated sensory exploration can be effective in enabling co-designers to ideate concepts of idiosyncratic smart objects that closely relate to the characteristics of their domestic living situations.
Towards explaining recommendations through local surrogate models

The increase in sophistication and complexity of recommendation algorithms has turned them into black boxes where the algorithmic reasoning behind the predictions is hard to understand by users. A popular approach for increasing model interpretability in the machine learning community is the Locally Interpretable Model-agnostic Explanations (LIME), which proposes to learn local interpretable models for explaining single predictions of any model. In this paper, we propose an adaptation of LIME for any recommender system. We evaluate our adaptation of LIME on Factorization Machines, a well-known black-box recommender algorithm trained on MovieLens 20M dataset. We compare our approach to a state-of-the-art model-agnostic method based on association rules and show that our proposed adaptation is a promising alternative since it is comparable in terms of fidelity, i.e., can locally mimic the behavior of a complex recommender, and has the additional advantage of enabling different styles of explanations. Finally, we present a case study for investigating the feasibility and limitations of our proposed adaptation.
Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method

Sensor-based human activity recognition can benefit a variety of applications such as health care, fitness, smart homes, rehabilitation training, and so forth. In this paper, we propose a novel two-layer diversity-enhanced multiclassifier recognition method for single wearable accelerometer-based human activity recognition, which contains data-based and classifier-based diversity enhancement. Firstly, we introduce the kernel Fisher discriminant analysis (KFDA) technique to spatially transform the training samples and enhance the discrimination between activities. In addition, bootstrap resampling is utilized to increase the diversities of the dataset for training the base classifiers in the multiclassifier system. Secondly, a combined diversity measure for selecting the base classifiers with excellent performance and large diversity is proposed to optimize the performance of the multiclassifier system. Lastly, majority voting is utilized to combine the preferred base classifiers. Experiments showed that the data-based diversity enhancement can improve the discriminance of different activity samples and promote the generation of base classifiers with different structures and performances. Compared with random selection and traditional ensemble methods, including Bagging and Adaboost, the proposed method achieved 92.3% accuracy and 90.7% recall, which demonstrates better performance in activity recognition.
ShadowLamp

In this paper we present ShadowLamp, a lamp concept supporting controllable shadow casting for displaying ambient information. The concept uses electrochromic displays to mask light and thereby allow switching of projected shadows. We implemented a prototype in a hexagon frame with six separately controlled LEDs compartmentalized to casts shadows in 60° angles. Alongside the LEDs, each compartment contains an electrochromic display for shadow control. As a use case we fabricated displays for a children's book and used them to change the shadows as the story progress. The displays and LEDs are controlled by a Bluetooth connected Android application.
Automated brain extraction from head CT and CTA images using convex optimization with shape propagation

Non-Contrast Computer Tomography (NCCT) and CT angiography (CTA) are the most used and widely acceptable imaging modalities in clinical practice for the diagnosis and treatment of acute ischemic stroke (AIS) patients. Brain extraction of CT/CTA images plays an essential role in stroke imaging research. There is no robust automated brain extraction method in the literature that is well established for both NCCT and CTA images. Thus, a validated and automated brain extraction tool for CT imaging would be of great value for both research and clinical practice. The proposed brain extraction method is based on the contour evolution technique, which extracts brain tissues from acquired NCCT and CTA images in a slice-by-slice fashion. Specifically, the proposed approach makes use of a novel propagation framework, which is initialized by a localized slice with the largest brain section in axial views, followed by a geodesic level-set evolution for automatically extracting the brain section in each slice. In particular, the segmented contour propagated from the previous slice is reused to penalize the defined object function for contour evolution to enforce the shape continuity between any two adjacent contours. We show that the defined contour evolution function can be solved iteratively by globally optimal convex optimization. The proposed brain extraction approach is quantitatively evaluated using 40 NCCT and CTA images acquired from 20 AIS patients and drawn from 4 different vendors, compared to manual segmentations using Dice and Jaccard coefficient metrics. The quantitative results show that the proposed segmentation algorithm is consistently accurate for both NCCT and CTA images using Dice metric. The proposed method is further validated on 1736 NCCT and CTA images of 1331 AIS patients acquired from three multi-national multi-centric clinical trials. A visual check performed on these data demonstrates a low failure rate of 0.4% for 1331 NCCT images and a zero-failure rate for 405 CTA images. Both quantitative and qualitative evaluation suggest that the proposed brain extraction approach for NCCT and CTA images can be used for different clinical imaging settings, thus serving to improve current image analysis in the field of neuroimaging.
IoT and Its Impact on the Electronics Market: A Powerful Decision Support System for Helping Customers in Choosing the Best Product

Many companies have observed the significant benefits they can get via using internet. Since then, large companies have been able to develop business transactions with customers at anytime, anywhere, and in relation to anything, so that we now need a more comprehensive concept than the internet. This concept is the Internet of Things (IoT). IoT will influence decision making style in various phases of selling, buying and marketing process. Therefore, every individual and company should know precisely what IoT is, and how and why they should incorporate it in their operations. This motivated us to propose a smart system based on IoT to help companies and marketers make a powerful marketing strategy via utilizing obtained data from IoT devices. Not only this, but the proposed system can also solve the problems which face companies and customers in online shopping. Since there are different types of the same product, and also different criteria for purchasing which can be different between individuals, customers will need a decision support system to recommend them with the best selection. This motivates us to also propose a neutrsophic technique to deal with unclear and conflicting information which exists usually in the purchasing process. Therefore, the smart system and neutrosophic technique is considered as a comprehensive system which links between customers, companies, marketers to achieve satisfaction for each of them.
Quantum Accelerated Computer Architectures

Modern computer applications usually consist of a variety of components that often require quite different computational co-processors. Some examples of such co-processors are TPUs, GPUs or FPGAs. A more recent and promising technology that is being investigated is quantum co-processors. In this paper, we present a modern computer architecture where a quantum co-processor is included as an additional accelerator. In such an environment, the idea is to execute the application on a heterogeneous architecture where the classic processor will execute the host part, but certain components will be mapped, in our case, on the quantum accelerator. To this purpose, we define the distinct layers for the quantum computer architecture where there is a clear boundary between the host program and quantum kernel(s). We also discuss the opportunities and challenges of mapping hybrid algorithms to such a heterogeneous quantum computer architecture.
Fusion of Underwater Image Enhancement and Restoration

Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.
Modeling Students' Attention in the Classroom using Eyetrackers

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student's affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from an eye-tracker can indeed be used to predict a student's attention as a measure of affect over the course of a class. From this research, an accuracy of 77% was achieved using the Extreme Gradient Boosting technique of machine learning. The outcome indicates that eye-gaze can be indeed used as a basis for constructing a predictive model.
Real-Time Drink Trigger Detection in Free-living Conditions Using Inertial Sensors

Despite the importance of maintaining an adequate hydration status, water intake is frequently neglected due to the fast pace of people's lives. For the elderly, poor water intake can be even more concerning, not only due to the damaging impact of dehydration, but also since seniors' hydration regulation mechanisms tend to be less efficient. This work focuses on the recognition of the pre-drinking hand-to-mouth movement (a drink trigger) with two main objectives: predict the occurrence of drinking events in real-time and free-living conditions, and assess the potential of using this method to trigger an external component for estimating the amount of fluid intake. This shall contribute towards the efficiency of more robust multimodal approaches addressing the problem of water intake monitoring. The system, based on a single inertial measurement unit placed on the forearm, is unobtrusive, user-independent, and lightweight enough for real-time mobile processing. Drinking events outside meal periods were detected with an F-score of 97% in an offline validation with data from 12 users, and 85% in a real-time free-living validation with five other subjects, using a random forest classifier. Our results also reveal that the algorithm first detects the hand-to-mouth movement 0.70 s before the occurrence of the actual sip of the drink, proving that this approach can have further applications and enable more robust and complete fluid intake monitoring solutions.
Rademacher dropout: An adaptive dropout for deep neural network via optimizing generalization gap

Dropout plays an important role in improving the generalization ability in deep learning. However, the empirical and fixed choice of dropout rates in traditional dropout strategies may increase the generalization gap, which is counter to one of the principle aims of dropout. To handle this problem, in this paper, we propose a novel dropout method. By the theoretical analysis of Dropout Rademacher Complexity, we first prove that the generalization gap of a deep model is bounded by a constraint function related to dropout rates. Meanwhile, we derive a closed form solution via optimizing the constraint function, which is a distribution estimation of dropout rates. Based on the closed form solution, a lightweight complexity algorithm called Rademacher Dropout (RadDropout) is presented to achieve the adaptive adjustment of dropout rates. Moreover, as a verification of the effectiveness of our proposed method, the extensive experimental results on benchmark datasets show that RadDropout achieves improvement of both convergence rate and prediction accuracy.
Stability-Based Generalization Analysis of Distributed Learning Algorithms for Big Data

As one of the efficient approaches to deal with big data, divide-and-conquer distributed algorithms, such as the distributed kernel regression, bootstrap, structured perception training algorithms, and so on, are proposed and broadly used in learning systems. Some learning theories have been built to analyze the feasibility, approximation, and convergence bounds of these distributed learning algorithms. However, less work has been studied on the stability of these distributed learning algorithms. In this paper, we discuss the generalization bounds of distributed learning algorithms from the view of algorithmic stability. First, we introduce a definition of uniform distributed stability for distributed algorithms and study the distributed algorithms' generalization risk bounds. Then, we analyze the stability properties and generalization risk bounds of a kind of regularization-based distributed algorithms. Two generalization distributed risks obtained show that the generalization distributed risk bounds for the difference between their generalization distributed and empirical distributed/leave-one-computer-out risks are closely related to the size of samples n and the amount of working computers m as O(m/n1/2) . Furthermore, the results in this paper indicate that, for a good generalization regularized distributed kernel algorithm, the regularization parameter λ should be adjusted with the change of the term m/n1/2 . These theoretic discoveries provide the useful guidance when deploying the distributed algorithms on practical big data platforms. We explore our theoretic analyses through two simulation experiments. Finally, we discuss some problems about the sufficient amount of working computers, nonequivalence, and generalization for distributed learning. We show that the rules for the computation on one single computer may not always hold for distributed learning.
Efficient Certificateless Signcryption in the Standard Model: Revisiting Luo and Wan’s Scheme from Wireless Personal Communications (2018)

Abstract Certificateless public key cryptography (CL-PKC) promises a practical resolution in establishing practical schemes, since it addresses two fundamental issues, namely the necessity of requiring certificate managements in traditional public key infrastructure (PKI) and the key escrow problem in identity-based (ID-based) setting concurrently. Signcryption is an important primitive that provides the goals of both encryption and signature schemes as it is more efficient than encrypting and signing messages consecutively. Since the concept of certificateless signcryption (CL-SC) scheme was put forth by Barbosa and Farshim in 2008, many schemes have been proposed where most of them are provable in the random oracle model (ROM) and only a few number of them are provable in the standard model. Very recently, Luo and Wan (Wireless Personal Communication, 2018) proposed a very efficient CL-SC scheme in the standard model. Furthermore, they claimed that their scheme is not only more efficient than the previously proposed schemes in the standard model, but also it is the only scheme which benefits from known session-specific temporary information security (KSSTIS). Therefore, this scheme would indeed be very practical. The contributions of this paper are 2-fold. First, in contrast to the claim made by Luo and Wan, we show that unfortunately Luo and Wan made a significant error in the construction of their proposed scheme. While their main intention is indeed interesting and useful, the failure of their construction has indeed left a gap in the research literature. Hence, the second contribution of this paper is to fill this gap by proposing a CL-SC scheme with KSSTIS, which is provably secure in the standard model.
Integrated UAV-Based Real-Time Mapping for Security Applications

Security applications such as management of natural disasters and man-made incidents crucially depend on the rapid availability of a situation picture of the affected area. UAV-based remote sensing systems may constitute an essential tool for capturing aerial imagery in such scenarios. While several commercial UAV solutions already provide acquisition of high quality photos or real-time video transmission via radio link, generating instant high-resolution aerial maps is still an open challenge. For this purpose, the article presents a real-time processing tool chain, enabling generation of interactive aerial maps during flight. Key element of this tool chain is the combination of the Terrain Aware Image Clipping (TAC) algorithm and 12-bit JPEG compression. As a result, the data size of a common scenery can be reduced to approximately 0.4% of the original size, while preserving full geometric and radiometric resolution. Particular attention was paid to minimize computational costs to reduce hardware requirements. The full workflow was demonstrated using the DLR Modular Airborne Camera System (MACS) operated on a conventional aircraft. In combination with a commercial radio link, the latency between image acquisition and visualization in the ground station was about 2 s. In addition, the integration of a miniaturized version of the camera system into a small fixed-wing UAV is presented. It is shown that the described workflow is efficient enough to instantly generate image maps even on small UAV hardware. Using a radio link, these maps can be broadcasted to on-site operation centers and are immediately available to the end-users.
Criminal networks in a digitised world: on the nexus of borderless opportunities and local embeddedness

This article presents the results of empirical analyses of the use of information technology (IT) by organized crime groups. In particular, it explores how the use of IT affects the processes of origin and growth of criminal networks. The empirical data presented in this article consist of 30 large scale criminal investigations into organized crime, including traditional organized crime, traditional organized crime in which IT is an innovative element, low tech cybercrimes and high tech cybercrimes. Networks involved in cybercrimes or traditional crimes with an innovative IT element can be characterized as a mixture of old school criminals that have a long criminal career, and a limited number of technically skilled members. Furthermore, almost all cases have a local dimension. Also the cybercrime cases. Dutch sellers of drugs on online marketplace, for example, mainly work for customers in the Netherlands and surrounding countries.
A statistical descriptor for texture images based on the box counting fractal dimension

This work proposes a method for supervised classification of grayscale texture images using the numerical computation of the box counting fractal dimension. Each pixel is mapped onto a point in a three-dimensional cloud, where the normalized gray level of each pixel is the third coordinate, and we analyze the distribution of points inside a mesh of boxes. Information at different resolutions are captured by varying the size of each box in the mesh. The texture descriptors are provided by a measure of organization of the points in the mesh, which is the entropy, and other statistical measures of this distribution, namely, mean, deviation and energy. We also propose a mathematical analysis of the model, which is accomplished here by employing techniques from Statistics and Combinatorics, quantifying the relation between the distribution of points and attributes classically associated to textures such as homogeneity and scale dependence. The proposed descriptors are applied to the classification of three well-known texture databases for benchmark purposes. In a comparison with other texture descriptors in the literature, the proposal demonstrated to be competitive, confirming the potential of a combination of box counting fractal dimension and statistics.