Reviewerly/exHarmony huggingface dataset:
Saturated Cost Partitioning for Optimal Classical Planning

Cost partitioning is a method for admissibly combining a set of admissible heuristic estimators by distributing operator costs among the heuristics. Computing an optimal cost partitioning, i.e., the operator cost distribution that maximizes the heuristic value, is often prohibitively expensive to compute. Saturated cost partitioning is an alternative that is much faster to compute and has been shown to yield high-quality heuristics. However, its greedy nature makes it highly susceptible to the order in which the heuristics are considered. We propose a greedy algorithm to generate orders and show how to use hill-climbing search to optimize a given order. Combining both techniques leads to significantly better heuristic estimates than using the best random order that is generated in the same time. Since there is often no single order that gives good guidance on the whole state space, we use the maximum of multiple orders as a heuristic that is significantly better informed than any single-order heuristic, especially when we actively search for a set of diverse orders.
Remote recovery of audio signals from videos of optical speckle patterns: a comparative study of signal recovery algorithms

Optical remote sensors are nowadays ubiquitously used, thanks to unprecedented advances in the last decade in photonics, machine learning and signal processing tools. In this work we study experimentally the remote recovery of audio signals from the silent videos of the movement of optical speckle patterns. This technique can be used even when in between the source and the receiver there is a medium that does not allow for the propagation of sound waves. We use a diode laser to generate a speckle pattern on the membrane of a loudspeaker and a low-cost CCD camera to record the video of the movement of the speckle pattern when the loudspeaker plays an audio signal. We perform a comparative analysis of six signal recovery algorithms. In spite of having different complexity and computational requirements, we find that the algorithms have (except for the simplest one) good performance in terms of the quality of the recovered signal. The best trade-off, in terms of computational costs and performance, is obtained with a new method that we propose, which recovers the signal from the weighted sum of the intensities of all the pixels, where the signs of the weights are determined by selecting a reference pixel and calculating the signs of the cross-correlations of the intensity of the reference pixel and the intensities of the other pixels.
Applying Deep Learning and Wearable Devices for Educational Data Analytics

With the popularity of wearable devices, smart watches containing various sensors have been widely adopted for many healthcare applications. Yet there is rarely any research study on the possible uses of smart watches for learning analytics, particularly for analyzing students' learning activities through the physiological and/or movement data collected on their smart watches. This paper considers a pioneering and sophisticated learning analytics platform using fine-tuned deep learning models to predict students' learning activities based on the real-time data, including their heart rates, calories, three-axis accelerometer and gyroscope data, captured on wearable devices and then uploaded onto a cloud server for thorough analyses. To validate on the actual activities conducted by each student, an intelligent mobile application is developed to push instant notifications for students to report their own activities whenever the change of heart rates are deviated significantly from their normal values. Based on students' heart rates and calories, a long-short term memory (LSTM) model is built to classify students' learning states as active or not with an impressive prediction accuracy of 95% whereas another hybrid model combining both the LSTM and convolutional neural networks attains the highest prediction accuracy of 74% to predict students' specific learning activities as based on their physiological and movement data. The prototype implementation clearly demonstrates the feasibility of the proposed framework for learning analytics. More importantly, this work shed lights on various directions including the integration of noise filters to preprocess the collected data for further investigation.
Application and Comparison of Bayesian Framework Algorithms for Underwater State Estimation

In the maritime environment, bearings-only tracking (BOT) has been holding the attention of researchers for many decades. The purpose of BOT is to estimate the kinematics of the target which is often cited as Target Motion Analysis (TMA). The highly nonlinear nature of the BOT problem requires the use of Bayesian framework algorithms for state estimation. Monte Carlo simulation for the performance evaluation of these algorithms based on the angle on target bow (ATB) is carried out in this paper using Matlab. The scenarios are also designed mathematically to analyse the algorithms using simulator before applying them in real time.
Speaker-independent Spectral Mapping for Speech-to-Singing Conversion

Speech-to-Singing (STS) conversion aims at converting one's reading speech into his/her singing vocal. The prior work was mainly focused on transforming the prosody of speech to singing, however, there exist prominent differences between the spectra of speech and singing, which need to be transformed as well. In this paper, we propose to make use of parallel multi-speaker speak-sing data to develop a speaker-independent spectral mapping model, which is conditioned on i-vector to generate target speaker/singer identity. The model is therefore called speaker conditioned spectral mapping model. The converted singing spectra are then used together with prosody features to synthesize the target singing. We investigate the effectiveness of i-vector based average model adaptation to model the differences between speech and singing spectra for a specific speaker. The proposed model does not require parallel speak-sing data from target speakers during training. The experimental results conducted on NUS-48E and NUS-HLT-SLS database indicate that the proposed approach significantly outperforms the baselines in terms of quality and similarity.
Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Service composition provides an effective means to fulfill users' personalized requirements and complex business applications.With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services.Some efforts are developed to find the near-optimal service combination within an acceptable time range.They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale.Although they solve the scalability problem to some extent, the required computational time is usually high.A promising direction is to integrate these two categories of solutions.However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations.In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm.The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space.MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems.MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations.MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations.MCaBSO is evaluated on two datasets to show effectiveness and efficiency.
Continuous Generation of Quantum Light from a Single Ground-State Atom in an Optical Cavity

We show an optical wave-mixing scheme that generates quantum light by means of a single three-level atom. The atom couples to an optical cavity and two laser fields that together drive a cycling current within the atom. Weak driving in combination with strong atom-cavity coupling induces transitions in a harmonic ladder of dark states, accompanied by single-photon emission via a quantum Zeno effect and suppression of atomic excitation via quantum interference. For strong driving, the system can generate coherent or Schr\"odinger cat-like fields with frequencies distinct from those of the applied lasers.
The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm

With the arrival of Artificial Intelligence (AI), the nature of the firm is changing and economic theory can provide guidance to businesses as well as to politics when formulating adequate strategies for this unknown terrain. By interpreting AI as a new type of agent within the firm, the theory of the firm can serve as a lingua franca to connect computer sciences and social sciences when dealing with the interdisciplinary phenomenon of AI. To achieve this, this paper adopts the perspective of the economic theory of the firm to systematically explore the changes that AI brings to the institution of the firm. In total, five interrelated propositions are discussed that are rooted in the traditional theory but trace the nature of the Artificially Intelligent Firm: AI intensifies the effects of economic rationality on the firm (1). AI introduces a new type of information asymmetry (2). AI can perforate the boundaries of the firm (3). AI can create triangular agency relationships (4) and AI has the potential to remove traditional limits of integration (5).
Measuring Language Distance of Isolated European Languages

Phylogenetics is a sub-field of historical linguistics whose aim is to classify a group of languages by considering their distances within a rooted tree that stands for their historical evolution. A few European languages do not belong to the Indo-European family or are otherwise isolated in the European rooted tree. Although it is not possible to establish phylogenetic links using basic strategies, it is possible to calculate the distances between these isolated languages and the rest using simple corpus-based techniques and natural language processing methods. The objective of this article is to select some isolated languages and measure the distance between them and from the other European languages, so as to shed light on the linguistic distances and proximities of these controversial languages without considering phylogenetic issues. The experiments were carried out with 40 European languages including six languages that are isolated in their corresponding families: Albanian, Armenian, Basque, Georgian, Greek, and Hungarian.
Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

The performance of existing face age progression or regression methods is often limited by the lack of sufficient data to train the model. To deal with this problem, we introduce a novel framework that exploits synthesized images to improve the performance. A conditional generative adversarial network (GAN) is first developed to generate facial images with targeted ages. The semi-supervised GAN, called SS-FaceGAN, is proposed. This approach considers synthesized images with a target age and the face images from the real data so that age and identity features can be explicitly utilized in the objective function of the network. We analyze the performance of our method over previous studies qualitatively and quantitatively. The experimental results show that the SS-FaceGAN model can produce realistic human faces in terms of both identity preservation and age preservation with the quantitative results of a decent face detection rate of 97% and similarity score of 0.30 on average.
Deblurring And Super-Resolution Using Deep Gated Fusion Attention Networks For Face Images

Image deblurring and super-resolution are very important in image processing such as face verification. However, when in the outdoors, we often get blurry and low resolution images. To solve the problem, we propose a deep gated fusion attention network (DGFAN) to generate a high resolution image without blurring artifacts. We extract features from two task-independent structures for deburring and super-resolution to avoid the error propagation in the cascade structure of deblurring and super-resolution. We also add an attention module in our network by using channel-wise and spatial-wise features for better features and propose an edge loss function to make the model focus on facial features like eyes and nose. DGFAN performs favorably against the state-of-arts methods in terms of PSNR and SSIM. Also, using the clear images generated by DGFAN can improve the accuracy on face verification.
Design and Implementation of an EOG-based Mouse Cursor Control for Application in Human-Computer Interaction

Abstract Human Computer Interaction (HCI) has turned into an emerging technology due to the advancement in the field artificial intelligence and biomedical engineering. Acquiring different bio-signals such as Electro-oculography (EOG), Electromyography (EMG) and Electroencephalography (EEG) to control external machine or computer is the essence of HCI technology. In this research, we attempt to extract the EOG signal from different ways of eye movements and process it for HCI application. By utilizing Arduino, EOG data can be transmitted to computer and those signal characteristics is analysed through MATLAB. We have designed and implemented hardware and interfaced it with software to control a computer mouse cursor only by eye movement. Certain classification module like Support Vector machine (SVM) and Multilayer Perceptron (MLP) are used to classify different EOG data generated from different eye movement. According to the eye position, cursor automatically moves in that specific direction and PyAutoGUI module is used for this task. Results after experimentations with different subjects to control mouse cursor in real-time show that the average classification accuracy can reach up to 93% across all directions.
Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. If NE processing tools are increasingly being used in the context of historical documents, performance values are below the ones on contemporary data and are hardly comparable. In this context, this paper introduces the CLEF 2020 Evaluation Lab HIPE (Identifying Historical People, Places and other Entities) on named entity recognition and linking on diachronic historical newspaper material in French, German and English. Our objective is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents in order to support scholarship on digital cultural heritage collections.
An Effective Implementation of Face Recognition Using Deep Convolutional Network

Human Face Recognition for forensic investigations and e-governance is widely adopted so that the specific face points can be trained and further investigations can be done. In this approach, the key points of human face with the dynamic features are extracted and trained in the deep neural network model so that the intrinsic aspects of the human face can be realized and further can be used for the criminal investigation or social analytics based applications. In this research manuscript, the usage of deep learning based convolutional network is integrated for the human face analytics and recognition for diversified applications. It is done to have the cavernous evaluation patterns in multiple domains for the knowledge discovery and predictive features of the human face identification domain.
Hybrid Data Encryption Algorithm for Secure Medical Data Transmission in Cloud Environment

At present times, healthcare data stored in cloud is considered as a highly sensitive record, which should be hidden towards unauthorized accesses to protect the information about the patient. Hence, security related to cloud based medical data transmission receives significant attention among researchers and academicians. This study presents a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is presented by the integration of 2D discrete wavelet transform technique with a proposed hybrid encryption scheme. The presented hybrid encryption scheme is derived by the integration of Blowfish and Two fish encryption algorithms. The presented model begins with the encryption of secrecy data and then concealed the outcome by the use of outcome in a cover image and 2D-DWT-1L or 2D-DWT-2L. The color images are utilized as cover images for concealing various text sizes. The outcome of the projected technique is tested against different benchmark images and the results are ensured by the use of different performance measures.
A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.
Human Activity Recognition Based on Accelerometer Vibrations Using Artificial Neural Network

In recent years, activity recognition gained popularity in the field of wearable applications. One field that benefitted from the development of activity recognition is medical health and monitoring. In the Philippines, FNRI stated that Non-Communicable Diseases (NCDs) caused 67% of the total death in the country. As such, companies started producing wearable devices that can measure burned calories based on the inputted exercise of the user which can help especially the health conscious. Although diet and exercise cannot be considered as cure, it can be an assistance and prevention. In this paper, the researchers aimed to recognize the activity of the user using a wrist worn device. The device is composed of a raspberry pi and accelerometer. In order to recognize the activity, the vibration signals from the accelerometer were analyse and processed based on neural network analysis which was used to predict the activity done by the user. Once the activity is predicted it will compute the corresponding burned calories per activity. The researchers conducted the experiment composed of 50 persons for training and 30 persons for testing of the prediction model wherein the subjects were asked to perform an exercise within 20 seconds equivalent to 1 trial. The subjects performed each activity good for 60 seconds which is equivalent to 3 trials for each activity, 12 trials in total for 1 person. The test resulted with a 91.11% accuracy based on a 360-trial experiment.
Fall Detection using Motion History Image and Shape Deformation

Computer vision takes more roles in daily life both at work and home. Especially, in healthcare area, it assists and relieves the burden of caregivers at either nursing homes or home environments. This paper presents a fall event detection applying computer image processing techniques as an assistant tool for a caregiver to look after the elderly. The system works as a video surveillance that detects activities of an elderly one and notifies a caregiver at certain events. Our system classifies activity into two categories: (1) a normal daily activity such as walking, sitting, and running, (2) an abnormal activity such as falling. The fall detection separates into two steps: first, the system applies a Motion History Image (MHI) to quantify human motion for a moment in terms of its percentage. Next, it analyzes human shape deformation after detecting a large motion (MHI >65%). Shape deformation consists of a ratio and speed of change in shape. The experimental results demonstrate that our system can accurately achieve fall detection by 95.16%.
Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System

The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity.