Reviewerly/exHarmony huggingface dataset:
Applications of Projected Belief Networks (PBN)

The projected belief network (PBN) is a layered generative network, with tractable likelihood function (LF) that can be trained by gradient ascent as a probability density function (PDF) estimator and classifier. The PBN is derived from a feed-forward neural network (FF-NN) by finding the generative network that implements the probability distribution with maximum entropy (MaxEnt) consistent with the knowledge of the distribution at the output of the FF-NN. The FF-NN, from which the PBN is derived, is a complementary feature extractor that exactly recovers the PBN's hidden variables. This paper presents a multi-layer PBN and a deterministic PBN that are tested using a subset of MNIST data set. When the deterministic PBN is combined with the dual FF-NN, it forms an auto-encoder that achieves much lower reconstruction error on testing data than the equivalent conventional network and functions significantly better as a classifier.
Multiexposure Estimation and Fusion Based on a Sparsity Exposure Dictionary

Multiexposure image fusion (MEF), which is an emerging hot research topic, aims to fuse multiexposure images into a high-quality low dynamic range (LDR) image. In this article, we propose a novel exposure estimation-based MEF method with sparse decomposition and a designed sparsity exposure dictionary (SED). First, in order to represent more elementary information of multiexposure images, SED is trained by underexposed and overexposed images and constructed by the K-means-based algorithm (K-SVD) algorithm. Next, the SED is applied to construct exposure estimation maps by the number of atoms of the image patch, which is obtained by sparse decomposition. When the exposure estimation maps are obtained, a novel fusion framework that uses these maps to fuse multiexposure images is presented. In this framework, we employ the exposure estimation maps and an adaptive guided filter to construct the final fusion decision maps. The two factors of guided filter are adaptively determined without manual interference to obtain an optimal representation effect for multiexposure image sequences with different sizes and number. Finally, a fused image is generated via the pyramid merging method. The proposed fusion method is conducted on a series of multiexposure image sequences and compared to seven popular MEF methods. The experimental results clearly demonstrate that the proposed method outperforms the state-of-the-art methods in terms of subjective visual and quantitative evaluations.
Deep Forest with LRRS Feature for Fine-grained Website Fingerprinting with Encrypted SSL/TLS

With the development of encryption protocol, such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS), the traditional fingerprinting approaches based on packet content and special field are difficult to fingerprint the websites. Therefore, recent research imported machine learning algorithms to deal with this problem, and various features are extracted for the machine learning algorithms. However, previous approaches of fingerprinting encrypted websites are based on HTTP/1.1, which are not applicable to the widely used HTTP/2. In addition, most of the work only fingerprints the home page of each website, but in fact, users also visit other web pages of the website. To solve the feature compatibility problem, we propose to use the local request and response sequence (LRRS) as features. LRRS can represent the patterns of the encrypted Internet traffic not only based on HTTP/1.1 but also based on HTTP/2 using local packet sequences. In order to fingerprint different web pages in the same website, we import Deep Forest to extract fine-grained features. It utilizes a convolution structure to make full use of LRRS sequential features and multi-layer structure to enhance the ability of feature representation. The experimental results show the proposed algorithm has achieved the best overall performance on four datasets. Especially on the bidirectional encrypted traffic dataset with HTTP/2, the proposed approach achieved 55% higher of f1 score than the state-of-the-art method KFP with Random Forest.
DexController : Designing a VR Controller with Grasp-Recognition for Enriching Natural Game Experience

We present DexController, which is a hand-held controller leveraging grasp as an additional modality for virtual reality (VR) game. The pressure-sensitive surface of DexController was designed to recognize two different grasp-poses (i.e. precision grip and power grip) and detect grasp-force. Based on the results of two feasibility tests, a VR defense game was designed in which players could attack each enemy using the proper weapon with a proper level of force. A within-subject comparative study is conducted with a button-based controller which has the same physical form of DexController. The results indicated that DexController enhanced the perceived naturalness of the controller and game enjoyment, with having acceptable physical demand. This study clarifies the empirical effect of utilizing grasp-recognition on VR game controller to enhance interactivity. Also, we provide insight for the integration of VR game elements with the grasping modality of a controller.
A Comparative Evaluation of Techniques for Sharing AR Experiences in Museums

Museums are constantly searching for new ways to increase engagement with their exhibits, from electronic guides to modern digital technologies such as special-purpose tablets, smartphones, and virtual and augmented reality (AR). For AR exhibits in particular, promoting shared experience and group cohesion is not straightforward. In this work, we investigate scenarios in which not everyone is using a head-worn display (HWD), either because there aren't enough available or simply because someone might feel uncomfortable using it. We propose two sharing techniques for AR experiences and evaluate them in a long term in-the-wild study: Over-the-Shoulder AR, which renders a real-time virtual representation of the augmented reality content on a large secondary display; Semantic Linking, which displays contextual information about the virtual content on the same large display. We also introduce a complementary technique: Indicator Rings, which display the locations of the HWD user's objects-of-focus. We observed that participants in the Over-the-Shoulder AR and Semantic Linking conditions stayed together and exhibited more verbal exchanges than participants in a Baseline condition, which could indicate that they were more engaged. Self-reported measures indicated an increase in pair communication and increased comprehension of the virtual content for participants without the HWD. Participants without the HWD also displayed a greater understanding of the location of virtual elements with support from the Indicator Rings, and used them as a tool to guide the HWD user through the virtual content. We discuss design implications for interactive augmented reality exhibits and possible applications outside the cultural heritage scenario.
Knowledge-driven mineral prospectivity modelling in areas with glacial overburden: porphyry Cu exploration in Quesnellia, British Columbia, Canada

Modern mineral exploration involves making discoveries in geological environments where detecting deposits is increasingly difficult. This study presents an integrated, knowledge-driven prospectivity mapping approach to exploration in the glacial till-covered region of the Quesnel Terrane in British Columbia, Canada. The Quesnel Terrane hosts base and precious metal deposits; however, a ∼20,000 km2 area consisting of variably thick glacial overburden truncates surface exposure, likely masking buried deposits. Through a combined fuzzy logic and index overlay approach, geophysical, structural, and geochemical criteria were integrated into a prospectivity model. Three concealed porphyry Cu targets were identified in the till-covered region and were validated by statistical analysis of lake and stream sediment geochemistry. The process of integrating various geoscientific disciplines and identification of targets has provided valuable insight for future exploration in covered regions. Moreover, the adopted approach leverages open-source public datasets, highlighting advantages of utilising such datasets for regional-scale exploration.
Geometric Matrix Completion With Deep Conditional Random Fields

The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially recommender systems. The existing matrix completion models generally follow either a memory- or a model-based approach, whereas geometric matrix completion (GMC) models combine the best from both approaches. Existing deep-learning-based geometric models yield good performance, but, in order to operate, they require a fixed structure graph capturing the relationships among the users and items. This graph is typically constructed by evaluating a pre-defined similarity metric on the available observations or by using side information, e.g., user profiles. In contrast, Markov-random-fields-based models do not require a fixed structure graph but rely on handcrafted features to make predictions. When no side information is available and the number of available observations becomes very low, existing solutions are pushed to their limits. In this article, we propose a GMC approach that addresses these challenges. We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posteriori (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem. The proposed model simultaneously learns the similarities among matrix entries, computes the CRF potentials, and solves the inference problem. Its training is performed in an end-to-end manner, with a method to supervise the learning of entry similarities. Comprehensive experiments demonstrate the superior performance of the proposed model compared to various state-of-the-art models on popular benchmark data sets and underline its superior capacity to deal with highly incomplete matrices.
Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics

Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content.In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents.The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.
Hybrid Octree-Plane Point Cloud Geometry Coding

Point clouds provide an efficient way of representing 3D data for applications such as virtual or augmented reality, free-viewpoint video, and gaming. However, this rich, realistic and immersive representation demands a very high amount of data and thus efficient point cloud coding solutions are increasingly needed. A promising way to represent point clouds is through octree partitioning, which provides good compression performance with low complexity, as well as level-of-detail scalability. However, other data representations may offer additional benefits to the octree data structure. The goal of this paper is to propose a point cloud geometry coding solution that leverages the adaptive octree partitioning, and enhances it with a novel coding mode. This mode exploits a plane representation for leaf nodes at different layers of the octree. This corresponds to a hybrid solution where two point cloud representation models are combined. The proposed approach can outperform octree based solutions that are currently considered for standardization and it also provides significant compression gains against a static octree solution (average BD-rate gains of 35% are reported).
Including the Voice of Care Recipients in Community Health Feedback Loops in Rural Kenya

Community health programs in low-resource settings (like rural Kenya) aim to provide essential health services to vulnerable populations. However, to date, there has been limited research that explores the design of mechanisms that enable care recipients to provide feedback regarding their satisfaction with the services they receive. Such feedback has the potential to increase the motivation of community health workers (CHWs), enhance training procedures, detect fraudulent behavior, and inform key performance indicators for health programs. Our paper explores the design and deployment of a USSD-based system that allows anyone who possesses a basic mobile phone to provide feedback regarding the health services and quality of care they received from a CHW or during a hospital visit. Our system was designed through iterative fieldwork in rural Kenya that engaged with multiple stakeholder groups, including care recipients, CHWs, and high-level decision makers. After designing and testing the system, we deployed it for seven weeks in Siaya, Kenya, collecting both quantitative system usage data and qualitative data from six focus groups with 42 participants. Findings from our deployment show that 168 care recipients engaged with the system, submitting 495 reports via USSD. We discuss the broader factors impacting deployment, including the feasibility of USSD, actionability of feedback, scalability, and sustainability. Taken together, our findings suggest that USSD is a promising approach for enabling care recipients to submit feedback in a way that balances privacy, equity, and sustainability.
Video classification and retrieval through spatio-temporal Radon features

The rise in the availability of video content for access via the Internet and the medium of television has resulted in the development of automatic search procedures to retrieve the desired video. Searches can be simplified and hastened by employing automatic classification of videos. This paper proposes a descriptor called the Spatio-Temporal Histogram of Radon Projections (STHRP) for representing the temporal pattern of the contents of a video and demonstrates its application to video classification and retrieval. The first step in STHRP pattern computation is to represent any video as Three Orthogonal Planes (TOPs), i.e., XY, XT and YT, signifying the spatial and temporal contents. Frames corresponding to each plane are partitioned into overlapping blocks. Radon projections are obtained over these blocks at different orientations, resulting in weighted transform coefficients that are normalized and grouped into bins. Linear Discriminant Analysis (LDA) is performed over these coefficients of the TOPs to arrive at a compact description of STHRP pattern. Compared to existing classification and retrieval approaches, the proposed descriptor is highly robust to translation, rotation and illumination variations in videos. To evaluate the capabilities of the invariant STHRP pattern, we analyse the performance by conducting experiments on the UCF-101, HMDB51, 10contexts and TRECVID data sets for classification and retrieval using a bagged tree model. Experimental evaluation of video classification reveals that STHRP pattern can achieve classification rates of 96.15%, 71.7%, 93.24% and 97.3% for the UCF-101, HMDB51,10contexts and TRECVID 2005 data sets respectively. We conducted retrieval experiments on the TRECVID 2005, JHMDB and 10contexts data sets and the results revealed that STHRP pattern is able to provide the videos relevant to the user's query in minimal time (0.05s) with a good precision rate.
Machine learning based diagnosis strategy for refrigerant charge amount malfunction of variable refrigerant flow system

Malfunctions would occur in a variable refrigerant flow (VRF) system after years of operation or inappropriate maintenance, thus causing unnecessary energy waste and even occupant discomfort. This study presents a machine learning based malfunction diagnosis strategy that combines the recursive feature elimination algorithm (RFE) and the classification algorithms for the typical malfunctions of VRF system. RFE based on Random Forest (RF) model firstly serves as the feature selection process to evaluate variables importance, thus acquiring the key variables related to malfunction. Then five kinds of machine learning classification models are trained using the chosen key variables to diagnosis refrigerant leakage malfunction. By comparison, the AdaBoost.M1 (ABM) model shows the most desirable performance on the all nine malfunction severity levels. The results show that the RFR-RF based feature selection method can select the most six critical variables and the ABM model established based on the six variables achieves admirable diagnostic accuracy and AUC value for faults corresponding to nine severity levels.
Lattice-Based Key-Aggregate (Searchable) Encryption in Cloud Storage

In cloud storage, selectively sharing encrypted data is becoming increasingly important. One key design challenge is the management of encryption keys. Traditionally, a large quantity of encryption keys have to be managed by the data owner, and an equally large number of keyword trapdoors must be sent to the cloud for the purpose of searching over the shared data, which are cumbersome in terms of secure communication and management. Recently, key-aggregate (searchable) encryption schemes have been introduced to alleviate the problem. However, they were only designed under the Bilinear Diffie-Hellman Exponent assumption in the prior works. Lattice-based key-aggregate (searchable) encryption schemes are valuable, because they have security against quantum computing attacks, average-case to worse-case equivalence as well as simplicity and potential efficiency. Here we propose a key-aggregate encryption scheme and a key-aggregate searchable encryption scheme which are both based on a lattice problem (i.e., the Learning with Errors problem). Some key techniques are employed during the construction of the schemes. A basis delegation algorithm is designed to generate the aggregate key without increasing the lattice dimension. The encryption algorithms of the two schemes are trickily devised to make the encrypted files decryptable or searchable. To overcome the problem of general matrix multiplication failing to satisfy commutative law, a hash function is designed by using diagonalizable matrices to make the encrypted file decryptable and the trapdoor adjustable. We present the schemes’ correctness proof, formal security analysis as well as performance analysis, which confirm that they are provably secure and practically efficient. To the best of our knowledge, the former is the first lattice-based key-aggregate encryption scheme and the latter is the first lattice-based key-aggregate searchable encryption scheme. We also demonstrate their application to cloud storage for searchable group data sharing by combining the two schemes.
Multilevel and Multiscale Network for Single-Image Super-Resolution

In recent years, deep convolutional neural networks (CNNs) have achieved great success in Single Image Super-Resolution (SISR). Most existing networks for Super-Resolution (SR) concentrate on wider or deeper network designs, leading to neglect of the feature correlations of intermediate layers. In this letter, a novel Multilevel and Multiscale Network for SISR (M2SR) is presented. The proposed network framework consists of four parts, including the feature extraction network, the cascade residual U-shaped blocks, the channel-wise attention U-shaped block and the fusion reconstruction network. Specially, the residual U-shaped blocks are designed to extract different scales of features, which are stacked to better refine the multifeatures. Then, to fully exploit the different levels of features, a channel-wise attention U-shaped block (At-U) is proposed to adjust the feature weights, which can adaptively enhance the feature expression and correlation learning. Finally, a fusion reconstruction network is constructed to fuse the different scales of the enhanced features to achieve the reconstructed result. Quantitative and qualitative evaluations of four public datasets show that the proposed method can achieve better performance compared with the state-of-the-art SR methods.
Open Vision System for Low-Cost Robotics Education

Vision devices are currently one of the most widely used sensory elements in robots: commercial autonomous cars and vacuum cleaners, for example, have cameras. These vision devices can provide a great amount of information about robot surroundings. However, platforms for robotics education usually lack such devices, mainly because of the computing limitations of low cost processors. New educational platforms using Raspberry Pi are able to overcome this limitation while keeping costs low, but extracting information from the raw images is complex for children. This paper presents an open source vision system that simplifies the use of cameras in robotics education. It includes functions for the visual detection of complex objects and a visual memory that computes obstacle distances beyond the small field of view of regular cameras. The system was experimentally validated using the PiCam camera mounted on a pan unit on a Raspberry Pi-based robot. The performance and accuracy of the proposed vision system was studied and then used to solve two visual educational exercises: safe visual navigation with obstacle avoidance and person-following behavior.
FaceDrive: Facial Expression Driven Operation to Control Virtual Supernumerary Robotic Arms

Supernumerary Robotic Arms (SRAs) can make physical activities easier, but require cooperation with the operator. To improve cooperation, we predict the operator's intentions by using his/her Facial Expressions (FEs). We propose to map FEs to SRAs commands (e.g. grab, release). To measure FEs, we used a optical sensor-based approach (here inside a HMD), the sensors data are fed to a SVM classifying them in FEs. The SRAs can then carry out commands by predicting the operator's FEs (and arguably, the operator's intention). We made a Virtual reality Environment (VE) with SRAs and synchronizable avatar to investigate the most suitable mapping between FEs and SRAs. In SIGGRAPH Asia 2019, the user can manipulate virtual SRAs using his/her FEs.
DBSCAN algorithm: twitter text clustering of trend topic pilkada pekanbaru

Abstract Social media is one of the most common sources used to communicate, such as Twitter. Every tweet on Twitter contains data such as text which when collected can be processed into information. Data processed from Twitter tweet will create a trend which can be used for information such as in education, economics, politics, etc. This then created the concept of text mining. Text mining techniques are needed to find an interesting pattern in search of trends based on Twitter text with topics related to Pilkada Pekanbaru 2017. This research is intended to cluster Twitter text data using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. This research was conducted with several experiments using different Eps and MinPts parameters for 2,184 text data which has been through several stages, such as cleaning, duplication removal, pre-processing like stemming and stopwords. Based on the highest average of Silhouette Index, Eps 0.1 and MinPts 10 with SI = 0.413 were chosen as paramaters, thus forming 31 clusters. According to the frequency of word occurrences in the cluster, the highest are “kpu”, followed by “firdaus”, “kota”, “pasang”, and “ayat”. As can be seen that the candidate pairs most often appear on cluster results are Firdaus-Ayat, and based on the results of Pilkada 2017, Firdaus-Ayat was chosen as Mayor and Vice Mayor of Pekanbaru.