Reviewerly/exHarmony huggingface dataset:
Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization

Multiple-related tasks can be learned simultaneously by sharing information among tasks to avoid tabula rasa learning and to improve performance in the no transfer case (i.e., when each task learns in isolation). This study investigates multitask learning with conditional neural process (CNP) networks and proposes two multitask learning network models on the basis of CNPs, namely, the one-to-many multitask CNP (OMc-MTCNP) and the many-to-many MTCNP (MMc-MTCNP). Compared with existing multitask models, the proposed models add an extensible correlation learning layer to learn the correlation among tasks. Moreover, the proposed multitask CNP (MTCNP) networks are regarded as surrogate models and applied to a Bayesian optimization framework to replace the Gaussian process (GP) to avoid the complex covariance calculation. The proposed Bayesian optimization framework simultaneously infers multiple tasks by utilizing the possible dependencies among them to share knowledge across tasks. The proposed surrogate models augment the observed dataset with a number of related tasks to estimate model parameters confidently. The experimental studies under several scenarios indicate that the proposed algorithms are competitive in performance compared with GP-, single-task-, and other multitask model-based Bayesian optimization methods.
Extending Patient Education with CLAIRE: An Interactive Virtual Reality and Voice User Interface Application

Patient education aims at strengthening the competence and self-care capabilities of a patient. Through information brochures, movies, and websites or by asking questions, patients learn about diseases and treatment as a basis for their decision-making. However, their motivation to learn is often limited due to the complexity of content or significant barriers for asking specific questions. To address this issue, we developed an interactive smartphone application named CLAIRE. It combines virtual reality (VR), a chatbot and a voice user interface (VUI). In the virtual environment, the user can move freely, interact with objects and talk to the character Claire. She provides information on the respective learning topic, which is in its current implementation information on the donation of personal health data. In a task-oriented user test, 45 participants confirmed that CLAIRE is easy to use and intuitive. Claire's responses were perceived as understandable, linguistically correct and friendly. Participants stated that CLAIRE motivated them to learn about the topic. We conclude that VR with integrated VUI can extend the existing information channels for patient education.
On Improvements of the Human Mental Search Algorithm for Global Optimisation

Population-based metaheuristic algorithms are problem-independent approaches to solve global optimisation problems. The human mental search (HMS) algorithm is a powerful population-based metaheuristic algorithm that has been shown to yield competitive performance for a variety of optimisation problems. HMS comprises three main operators, mental search, grouping, and movement. Mental search explores the neighbourhood of candidate solutions based on a Levy flight distribution to allow for simultaneous exploration and exploitation. Grouping is used to cluster the current population in order to find a promising area in search space, while during movement, candidate solutions move towards the identified promising area. In this paper, we propose an improved HMS algorithm-HMS-IS-OSK - that introduces an adaptive selection of the number of mental processes to improve the exploitation ability of HMS, and a one-step k-means algorithm for grouping to decrease the computational complexity. To evaluate the proposed algorithm, we perform a set of experiments on the CEC 2017 bench-mark functions with dimensionalities of 30, 50, and 100. The obtained results show that HMS-IS-OSK outperforms standard HMS as well as other population-based metaheuristic algorithms including covariance matrix adaptation evolution strategy (CMAES), particle swarm optimisation (PSO), artificial bee colony algorithm (ABC), whale optimisation algorithm (WOA), grey wolf optimiser (GWO), and moth-flame optimisation (MFO).
Adding Intelligent Robots to Business Processes: A Dilemma Analysis of Employees’ Attitudes

Given the advancements in artificial intelligence, organizations are increasingly interested in applying robotics to their business processes. Unlike the many technological implications, we focus on the human side of robotics which remains under-investigated for higher-skilled employees. We particularly consider employee acceptance of intelligent robots with cognitive skills. During 48 interviews, hypothetical dilemmas regarding manual work, full- and semi-automation are discussed by office workers, managers and IT consultants. The results show that employees are positive about intelligent robots. The majority are willing to transfer repetitive tasks as long as humans can control outputs for accountability. However, employees prefer keeping tasks with creativity and human interaction. Many tasks can thus already be replaced by robotics, but more attention is needed for the facilitating role of organizations (e.g., training). The findings affect innovation strategies for implementing intelligent robots with reduced social implications. The idea of a step-by-step plan encourages a gradual adoption.
Smartphone Camera De-identification while Preserving Biometric Utility

The principle of Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image. In this work, we design an algorithm that perturbs a face image acquired using a smartphone camera such that (a) sensor-specific details pertaining to the smartphone camera are suppressed (sensor anonymization); (b) the sensor pattern of a different device is incorporated (sensor spoofing); and (c) biometric matching using the perturbed image is not affected (biometric utility). We employ a simple approach utilizing Discrete Cosine Transform to achieve the aforementioned objectives. Experiments conducted on the MICHE-I and OULU-NPU datasets, which contain periocular and facial data acquired using 12 smartphone cameras, demonstrate the efficacy of the proposed de-identification algorithm on three different PRNU-based sensor identification schemes. This work has application in sensor forensics and personal privacy.
How Music AI Is Useful: <i>Engagements with Composers, Performers and Audiences</i>

Abstract Critical but often overlooked research questions in artificial intelligence applied to music involve the impact of the results for music. How and to what extent does such research contribute to the domain of music? How are the resulting models useful for music practitioners? This article describes work arising from research engaging with composers, musicians and audiences to address such questions: two websites that make their AI models accessible to a wide audience and a professionally recorded album released to expert reviewers to gauge the plausibility of AI-generated material. The authors describe the use of their models as tools for cocreation. Evaluating AI research and music models in such ways illuminates their impact on music-making.
Wearable magnetic field sensing for finger tracking

Obtaining a signal useful for continuous pointing input is still an open problem for wearables. While magnetic field sensing is one promising approach, there are significant limitations. Our key contribution in this work is a simulation of a system that tracks a magnet in 3D while also accounting for the ambient magnetic field. The simulated sensor data is processed and the position and rotation is determined by using magnetic field equations, a particle filter and a kinematic model of the hand.
Implications of dissemination strategies on the security of distributed ledgers

This paper describes a simulation study on security attacks over Distributed Ledger Technologies (DLTs). We specifically focus on attacks at the underlying peer-to-peer layer of these systems, that is in charge of disseminating messages containing data and transaction to be spread among all participants. In particular, we consider the Sybil attack, according to which a malicious node creates many Sybils that drop messages coming from a specific attacked node, or even all messages from honest nodes. Our study shows that the selection of the specific dissemination protocol, as well as the amount of connections each peer has, have an influence on the resistance to this attack.
A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles.
Modeling misconceptions in knowledge space theory

Building intelligent tutoring systems (ITSs) that are aware of the students’ misconceptions has been one of the ambitions for many of the approaches to computerized assessment of knowledge and learning. In the present article we extend knowledge space theory (KST) to mistakes and misconceptions. The proposed approach completes and extends the work initiated by J. Lukas (1997, Modellierung von Fehlkonzepten in einer algebraischen Wissensstruktur [Modeling misconceptions in an algebraic knowledge structure], Kognitionswissenschaft, 6(4), 196–204) on the application of information systems in KST for modeling misconceptions. The approach is divided into a deterministic and a probabilistic part. The notion of a “polytomous skill map” (PSM) represents the cornerstone of the deterministic part. Properties of the PSM are established that assure a consistent link between item responses on the one hand and the (correct or buggy) cognitive rules applied by an individual on the other hand. A weaker notion of information system (named a “cognitive rule system”) is proposed for representing two types of dependencies among cognitive rules: precedence and compatibility. The probabilistic part consists of an extension of the basic local independence model to more than two response alternatives. This model can be used for knowledge diagnoses, as well as for empirically testing the deterministic assumptions from a PSM. An empirical application of this probabilistic model to the responses of 331 university students is illustrated and discussed using two different PSMs.
ML-MDLText: An efficient and lightweight multilabel text classifier with incremental learning

Single-label text classification has been extensively studied in the last decades, and usually, more attention has been given to offline learning scenarios, where all of the training data is available in advance. However, real-world text classification problems often involve multilabel instances and have dynamic textual patterns that can change frequently. In this context, the methods must predict a subset of target labels rather than a single one, and ideally should be able to update their model incrementally to be scalable and adaptable to changes in data patterns using limited time and memory. In this study, we present a text classification method based on the minimum description length principle that can be applied to multilabel classification without requiring the transformation of the classification problem. It also takes advantage of dependency information among labels and naturally supports online learning. We evaluated its performance using fifteen datasets from different application domains and compared it with traditional benchmark classifiers, considering three online learning scenarios. Even without requiring problem transformation tricks, the results obtained by the proposed method were very competitive with existing state-of-the-art online learning methods and those that transform multilabel problems into several single-label ones.
Skeletal scintigraphy image enhancement based neutrosophic sets and salp swarm algorithm

Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions is first converted into an optimization problem. The SSA algorithm is used to find the best improvement for each image separately, and then the neutrosophic algorithm is used to find similarity score to each image with adaptive weight coefficients obtained by the SSA algorithm. The proposed algorithm is applied to an Egyptian medical dataset collected from Menoufia University Hospital and it is a no-reference image. The experiments are done using 3 different resolutions 512*512, 256*256, and 128*128 and compared with Gamma Correction, the NS algorithm and the local enhance algorithm. The results demonstrate that the proposed algorithm achieves superior performance in almost criteria fitness function, entropy, eumber of edges, nNaturalness image quality Evaluator, sharpness, sharpness index, and contrast-distorted images using contrast enhancement. The results showed the idea of integration between the falsity membership of the neutrosophic set and the Salp swarm algorithm can be used to Skeletal Scintigraphy enhancement. This paper proved that it can depend on falsity membership of the neutrosophic set in the Image Enhancement field.
Energy-Efficient Machine Learning Accelerator for Binary Neural Networks

Binary neural network (BNN) has shown great potential to be implemented with power efficiency and high throughput. Compared with its counterpart, the convolutional neural network (CNN), BNN is trained with binary constrained weights and activations, which are more suitable for edge devices with less computing and storage resource requirements. In this paper, we introduce the BNN characteristics, basic operations and the binarized-network optimization methods. Then we summarize several accelerator designs for BNN hardware implementation by using three mainstream structures, i.e., ReRAM-based crossbar, FPGA and ASIC. Based on the BNN characteristics and hardware custom designs, all these methods achieve massively parallelized computations and highly pipelined data flow to enhance its latency and throughput performance. In addition, the intermediate data with the binary format are stored and processed on chip by constructing the computing-in-memory (CIM) architecture to reduce the off-chip communication costs, including power and latency.
Ranked Archive Differential Evolution with Selective Pressure for CEC 2020 Numerical Optimization

The single-objective numerical optimization is an important research field due to variety of real world applications. One of the most promising classes of numerical optimization algorithms is Differential Evolution. This paper proposes a new algorithm called RASP-SHADE to solve the CEC 2020 Bound Constrained Single Objective Optimization benchmark problems. The developed algorithm is based on the L-SHADE with Distance-based success history adaptation, incudes parameter adaptations of the jSO algorithm, and introduces several novelties. The ranking of population and archive according to fitness introduces the selective pressure, resulting in a new mutation strategy. A new archive update rule is applied with replacing only worst points and the parameters sampling scheme is changed. The experiments show that RASP-SHADE modifications result in significant improvements when compared to other state-of-the-art algorithms.
Towards Programmable All-Digital True Random Number Generator

Random number generator (RNG) is a core component in many applications such as scientific research, testing and diagnosis, gaming, and cryptosystems (e.g., obfuscation, encryption, and authentication). Although, there are various RNG designs targeting specific application goals such as low-power, high-throughput, stronger security guarantees, a universal programmable RNG design has remained elusive. Indeed, it is a challenge to have only one RNG unit in a system with multiple compute modules with different randomness requirements. In this work, we aim to provide a practical solution to this design challenge by proposing a multi-purpose true random number generator (TRNG), which can be configured in real time to generate random sequences with different requirements. Such a programmable TRNG is able to supply random bits to multiple modules with different demands. The proposed TRNG is a highly convenient multi-purpose hardware primitive that can be deployed in many designs as it provides a tunable physical entropy source and a dynamic cost-performance trade-off.
Voting-mechanism based ensemble constraint handling technique for real-world single-objective constrained optimization

Constraint handling techniques are of great significance in efficiently solving constrained optimization problems. This paper proposes a novel ensemble framework for constraint handling techniques based on voting-mechanism, in which four popular constraint handling techniques are included. Each of the constituent constraint handling techniques votes for the solutions at each generation based on its own rules. Solutions getting more votes are regarded as promising individuals and survive to the next generation. This ensemble framework based on voting-mechanism reflects the collective wisdom in decision-making of human beings. In addition, a differential evolution (DE) variant is designed as the search engine, in which four search strategies are combined to generate new individuals and maintain the balance between diversity and convergence of the population. The proposed algorithm has been tested on 57 real world single-objective constraint optimization problems and 7 problems are selected using variable reduction strategy (VRS). The experiment shows that the proposed algorithm achieves competitive performance, indicating that the voting-mechanism ensemble constraint handling technique combine DE algorithm together can effectively deal with constrained optimization problems.
VibroSense

Smart homes of the future are envisioned to have the ability to recognize many types of home activities such as running a washing machine, flushing the toilet, and using a microwave. In this paper, we present a new sensing technology, VibroSense, which is able to recognize 18 different types of activities throughout a house by observing structural vibration patterns on a wall or ceiling using a laser Doppler vibrometer. The received vibration data is processed and sent to a deep neural network which is trained to distinguish between 18 activities. We conducted a system evaluation, where we collected data of 18 home activities in 5 different houses for 2 days in each house. The results demonstrated that our system can recognize 18 home activities with an average accuracy of up to 96.6%. After re-setup of the device on the second day, the average recognition accuracy decreased to 89.4%. We also conducted follow-up experiments, where we evaluated VibroSense under various scenarios to simulate real-world conditions. These included simulating online recognition, differentiating between specific stages of a device's activity, and testing the effects of shifting the laser's position during re-setup. Based on these results, we discuss the opportunities and challenges of applying VibroSense in real-world applications.
Effects of position of real-time translation on AR glasses

Augmented reality (AR) provides users with contextually relevant multimedia content by overlaying it on real-world objects. However, overlaying virtual content on real-world objects can cause occlusion. Especially for learning use cases, the occlusion might result in missing real-world information important for learning gain. Therefore, it is important to understand how virtual content should be positioned relative to the related real-world information without negatively affecting the learning experience. Thus, we conducted a study with 12 participants using AR glasses to investigate the position of virtual content using a vocabulary learning task. Participants learned foreign words shown in the surrounding while viewing translations using AR glasses as an overlay, on the right or below the foreign word. We found that showing virtual translations on top of foreign words significantly decreases comprehension and increase users' task load. Insights from our study inform the design of applications for AR glasses supporting vocabulary learning.
High Dimensional Feature Selection Method of Dual Gbest Based on PSO

Particle swarm optimization (PSO) has a bright future in feature selection (FS). However, with the increase of data set dimension, the search space becomes larger, PSO is easy to fall into local optima and brings a lot of time and space overhead. It is still a big challenge to apply PSO to thousands of feature data sets. In this paper, the features are reordered to ensure that the shorter particles can get better classification performance. We propose a subset length constraint mechanism to reduce the number of selected features of particles gradually, so as to introduce particles into smaller and more effective space and jump out of local optima. Although the short running time of filter FS is more suitable for high-dimensional feature selection, the classification accuracy of filter FS is generally lower than that of wrapper FS. In order to achieve better classification performance in a short time, we propose a dual global optimal (Gbest) updating model. PSO has been modified. Compared with six algorithms on six high-dimensional data sets. The results show that the new dual Gbest update mechanism based on length restriction mechanism has higher efficiency and accuracy.