Reviewerly/exHarmony huggingface dataset:
CAVE-AR: A VR Authoring System to Interactively Design, Simulate, and Debug Multi-user AR Experiences

Despite advances in augmented reality (AR), the process of creating meaningful experiences with this technology is still extremely challenging. Due to different tracking implementations and hardware constraints, developing AR applications either requires low-level programming skills, or is done through specific authoring tools that largely sacrifice the possibility of customizing the AR experience. Existing development workflows also do not support previewing or simulating the AR experience, requiring a lengthy process of trial and error by which content creators deploy and physically test applications in each iteration. To mitigate these limitations, we propose CAVE-AR, a novel virtual reality system for authoring, simulating and debugging custom augmented reality experiences. Available both as a standalone or a plug-in tool, CAVE-AR is based on the concept of representing in the same global reference system both in AR content and tracking information, mixing geographical information, architectural features, and sensor data to simulate the context of an AR experience. Thanks to its novel abstraction of existing tracking technologies, CAVE-AR operates independently of users' devices, and integrates with existing programming tools to provide maximum flexibility. Our VR application provides designers with ways to create and modify an AR application, even while others are in the midst of using it. CAVE-AR further allows the designer to track how users are behaving, preview what they are currently seeing, and interact with them through several different channels. To illustrate our proposed development workflow and demonstrate the advantages of our authoring system, we introduce two CAVE-AR use cases in which an augmented reality application is created and tested. In particular, we compare the CAVE-AR workflow to traditional development methods and demonstrate the importance of simulation and live application debugging.
effect of speaker sampling in likelihood ratio based forensic voice comparison

Within the field of forensic voice comparison (FVC), there is growing pressure for experts to demonstrate the validity and reliability of the conclusions they reach in casework. One benefit of a fully data-driven approach that utilises databases of speakers to compute numerical likelihood ratios (LRs) is that it is possible to estimate validity and reliability empirically. However, little is known about the stability of LR output as a function of the specific speakers sampled for use in the training, test and reference data sets. The present study addresses this issue using two large sets of formant data: Cantonese sentence final particle /a/ and British English filled pauses UM. Experiments were replicated 100 times varying the 1) training, test and reference speakers, 2) training speakers only, 3) test speakers only, and 4) reference speakers only. The results show that varying the speakers in all three sets has the greatest effect on system stability for both the Cantonese and English variables, with the Cllr varying from 0.60 to 0.97 for /a/ and 0.32 to 1.33 for UM. However, this variability is primarily due to the effects of uncertainty in the test set. Varying only the training speakers has the least effect on system stability for /a/ (Cllr range: 0.76 to 0.88), while varying reference speakers has the smallest effect for UM (Cllr range: 0.40 to 0.54). The results indicate that in LR-based FVC it is important to assess the stability of the system as a function of the samples of speakers used (Cllr range) rather than just reporting a single Cllr value based on one configuration of speakers in each set. The study contributes to the general debate on reporting uncertainty in LR computation.
On the social science of ransomware: Technology, security, and society

Ransomware is a type of malware that either encrypts or steals digital data and demands a financial ransom from the victim in order to release or return them. While criminals have been linking theft and hostage-taking to ransoms for a long time, the frequency and severity of contemporary ransomware, the nature and motivation of the perpetrators who use these methods of attack, and the type of targets victimized by these attacks – from federal governments to city municipalities, and from private companies to private citizens – suggests that ransomware be afforded much greater scholarly attention by social scientists. Ransomware is not solely a computer science problem. It is a security problem that has international, political, intelligence, and diplomatic ramifications. This article provides a detailed description of ransomware tailored to the social sciences. Using seven ransomware case studies, the article breaks down the technology's technical barriers, making ransomware more accessible to public policy and national security debates and analysis.
Commonsense Reasoning Using Theorem Proving and Machine Learning

Commonsense reasoning is a difficult task for a computer to handle. Current algorithms score around 80% on benchmarks. Usually these approaches use machine learning which lacks explainability, however. Therefore, we propose a combination with automated theorem proving here. Automated theorem proving allows us to derive new knowledge in an explainable way, but suffers from the inevitable incompleteness of existing background knowledge. We alleviate this problem by using machine learning. In this paper, we present our approach which uses an automatic theorem prover, large existing ontologies with background knowledge, and machine learning. We present first experimental results and identify an insufficient amount of training data and lack of background knowledge as causes for our system not to stand out much from the baseline.
Extremely Optimized DRLSE Method and Its Application to Image Segmentation

This paper proposes an extremely optimized distance regularized level set evolution (EO-DRLSE) method for image segmentation applications within the framework of level set method (LSM), which combines various types of local statistical features and adopts an adaptive regularization strategy which specifically serves for zero level set. Firstly, a variable coefficient for the contour length regularization term is designed based on the local normalized entropy, which can adaptively refreshes its value with the change of local disturbance characteristics of the image. Secondly, based on the local fitting means, the constant coefficient of the region term of the original DRLSE model is modified as an adaptive variable with its value changes as the local fitting mean changes. Thirdly, an improved edge stop function is constructed based on the local fitting variances, which enables the evolution process to maintain a considerable evolution speed in the non-target noise interference position without stopping. Obviously, this gives our algorithm a particularly strong noise suppression ability. Fourthly, an additional regularization term that only deals with the zero level curve is added besides the original regularization scheme (the whole level set function (LSF) is regularized). The extended combined regularization strategy further enhances the noise suppression ability and the adaptability to weak edges of the proposed model. Fifthly, two implementation strategies named morphological snakes and significant target detection mechanism are used to ensure the rapidity and automation of the evolution process. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance in terms of accuracy of segmentation results, rapidity of evolution process, robustness against noise, adaptability to weak target edges. In addition, the factors that can have a key impact on segmentation performance are also analyzed in depth.
Learnable Tree Filter for Structure-preserving Feature Transform

Learning discriminative global features plays a vital role in semantic segmentation. And most of the existing methods adopt stacks of local convolutions or non-local blocks to capture long-range context. However, due to the absence of spatial structure preservation, these operators ignore the object details when enlarging receptive fields. In this paper, we propose the learnable tree filter to form a generic tree filtering module that leverages the structural property of minimal spanning tree to model long-range dependencies while preserving the details. Furthermore, we propose a highly efficient linear-time algorithm to reduce resource consumption. Thus, the designed modules can be plugged into existing deep neural networks conveniently. To this end, tree filtering modules are embedded to formulate a unified framework for semantic segmentation. We conduct extensive ablation studies to elaborate on the effectiveness and efficiency of the proposed method. Specifically, it attains better performance with much less overhead compared with the classic PSP block and Non-local operation under the same backbone. Our approach is proved to achieve consistent improvements on several benchmarks without bells-and-whistles. Code and models are available at https://github.com/StevenGrove/TreeFilter-Torch.
Incremental Learning of Fuzzy Decision Trees for Streaming Data Classification

Data stream analysis is growing in popularity in the last years since several application domains require to continuously and quickly analyse data produced by sensors with the aim of, for instance, reacting immediately when problems arise, or detecting new trends.The specificity of these domains imposes strict temporal constraints on machine learning algorithms to be used for mining useful insights.The Hoeffding Decision Tree (HDT) is a well-known classification algorithm for efficient streaming data classification.In this paper, with the aim of improving HDT accuracy and capability of handling noisy data, we exploit the learning procedure proposed in HDT for adapting a recently proposed fuzzy decision tree to cope with streaming data classification problems.We tested the fuzzy approach on a benchmark dataset for the on-line learning of data stream classification models.Results show that, during the on-line learning process, the fuzzy approach outperforms HDT in terms of accuracy.
DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis

This paper proposes novel algorithms for speaker embedding using subjective inter-speaker similarity based on deep neural networks (DNNs). Although conventional DNN-based speaker embedding such as a $d$-vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-speaker similarity and is not necessarily appropriate speaker representation for open speakers whose speech utterances are not included in the training data. We propose two training algorithms for DNN-based speaker embedding model using an inter-speaker similarity matrix obtained by large-scale subjective scoring. One is based on similarity vector embedding and trains the model to predict a vector of the similarity matrix as speaker representation. The other is based on similarity matrix embedding and trains the model to minimize the squared Frobenius norm between the similarity matrix and the Gram matrix of $d$-vectors, i.e., the inter-speaker similarity derived from the $d$-vectors. We crowdsourced the inter-speaker similarity scores of 153 Japanese female speakers, and the experimental results demonstrate that our algorithms learn speaker embedding that is highly correlated with the subjective similarity. We also apply the proposed speaker embedding to multi-speaker modeling in DNN-based speech synthesis and reveal that the proposed similarity vector embedding improves synthetic speech quality for open speakers whose speech utterances are unseen during the training.
An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval

Due to the rapid development of mobile Internet techniques, such as online social networking and location-based services, massive amount of multimedia data with geographical information is generated and uploaded to the Internet. In this paper, we propose a novel type of cross-modal multimedia retrieval, called geo-multimedia cross-modal retrieval, which aims to find a set of geo-multimedia objects according to geographical distance proximity and semantic concept similarity. Previous studies for cross-modal retrieval and spatial keyword search cannot address this problem effectively because they do not consider multimedia data with geo-tags (geo-multimedia). Firstly, we present the definition of kNN geo-multimedia cross-modal query and introduce relevant concepts such as spatial distance and semantic similarity measurement. As the key notion of this work, cross-modal semantic representation space is formulated at the first time. A novel framework for geo-multimedia cross-modal retrieval is proposed, which includes multi-modal feature extraction, cross-modal semantic space mapping, geo-multimedia spatial index and cross-modal semantic similarity measurement. To bridge the semantic gap between different modalities, we also propose a method named cross-modal semantic matching (CoSMat for shot) which contains two important components, i.e., CorrProj and LogsTran, which aims to build a common semantic representation space for cross-modal semantic similarity measurement. In addition, to implement semantic similarity measurement, we employ deep learning based method to learn multi-modal features that contains more high level semantic information. Moreover, a novel hybrid index, GMR-Tree is carefully designed, which combines signatures of semantic representations and R-Tree. An efficient GMR-Tree based kNN search algorithm called $k$ GMCMS is developed. Comprehensive experimental evaluations on real and synthetic datasets clearly demonstrate that our approach outperforms the-state-of-the-art methods.
Eksperimen Naïve Bayes Pada Deteksi Berita Hoax Berbahasa Indonesia

Website and blog are popular as a media to spread news. The validity of an article of news’s can either be valid or fake. A fake article of news is usually called a hoax news article. The purpose of making hoax news is to persuade, manipulate, affect to people to do something that contradicts or prevents the right action. A hoax news usually used threats or misleading information to make them believe things that are not real. This research proposes an experiment using naïve Bayes to detect hoax news in Bahasa Indonesia. In this research, we use our own dataset consisting of a total of 600 valid and hoax articles. We asked three reviewers to conduct manual classification for our dataset. Final tagging was obtained by adopting the maximum score from the three reviewers. In our experiment, we show that naïve Bayes can classify Indonesian online news articles with term frequency feature using the PHP-ML library component’s. We obtained an accuracy is 82.6% with static testing and 68.33% with dynamic testing. We give free access to the dataset so the future research can replicate, comparing the result and make a baseline testing.Keywords : Hoax News Detection, Naïve Bayes Classifier.
Target Detection Method of Underwater Moving Image Based on Optical Flow Characteristics

Bai, Y., 2019. Target detection method of underwater moving image based on optical flow characteristics. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 668–673. Coconut Creek (Florida), ISSN 0749-0208.The detection and recognition ability of underwater moving image target is improved, and an automatic target detection method based on optical flow feature is proposed. The image acquisition model of underwater moving image target detection is constructed, and the local information entropy feature of underwater moving target image is detected by optical flow field scale decomposition method. The contrast feature of the distribution area of the optical flow field of the underwater moving image is extracted, and the information fusion model of the underwater moving target image is constructed according to the statistical value of the inter-frame difference of the underwater moving image. The center position of the candidate target is obtained, and the adaptive feature extraction of the underwater moving image target is carried out by using the correlation fusion method between the pixel neighborhoods, and the automatic detection of the underwater moving image target is realized according to the optical flow feature distribution. The simulation results show that the proposed method has good adaptability, high accuracy and strong anti-optical flow interference ability in underwater moving image target recognition.
A Secure Data Infrastructure for Personal Manufacturing Based on a Novel Key-Less, Byte-Less Encryption Method

We are witnessing the advent of personal manufacturing, where home users and small and medium enterprises manufacture products locally, at the point and time of need.The impressively fast adoption of these technologies indicates this approach to manufacturing can become a key enabler of the real-time economy of the future.In this paper, we contribute a secure and dependable infrastructure and architecture for that new paradigm.Our solution leverages physical limitations of the computational process into a defense strategy that makes distributed file storage and transfer highly secure.The main idea is to replace asymmetric or public-key encryption functions with an unkeyed, collision, second preimage, and preimage resistant cryptographic hash function.Such a cryptosystem does not have an inverse function H -1 .We challenge each block hash against the full hash table to recreate the original message.To illustrate the approach, we describe secured protocols that provide a number of desirable properties during both data storage and streaming.Similar to proof-of-work blockchain consensus algorithms, we parameterized the solution based on the amount of infrastructure available.Experiments show the proposed method can recalculate hashes for a 3-dimensional live matrix of 256 3 at an average of 14 revisions per second, and one revision every 5 minutes for a bigger matrix of 4096 3 .The increase in cloud infrastructure cost is insignificant compared to the level of protection offered.
Examining the day-of-the-week-effect and the-month-of-the-year-effect in cryptocurrency market

Cryptocurrency market is an attractive field for researchers in finance nowadays. One topic that can be studied is related to the existence of anomalies in the cryptocurrency market. This research was conducted to examine whether the cryptocurrency market, especially on Bitcoin and Litecoin, has day-of-the-week and month-of-the-year effects. The Bitcoin and Litecoin were used as objects because they were a cryptocurrency with a large market capitalization. The data used were monthly cryptocurrency returns for examining the month-of-the-year-effect and daily returns for examining the day-of-the-week-effect from 2014-2018. GARCH (1,1) analysis was done to see these effects on the cryptocurrency market. The results indicate that the phenomena of day-of-the-week and month-of-the-year effect existed in the cryptocurrency market. Therefore, the cryptocurrency market was not an efficient market. The pattern in the Bitcoin and Litecoin could later be utilized by investors. The investors should buy Bitcoin at the end of January and they should sell them at the end of February. While, for the investors who traded daily, can trade Bitcoin in Monday, Wednesday and Thursday because in these days, the Bitcoin have the potential to generate daily profits. JEL Classification: G14, G19 DOI: https://doi.org/10.26905/jkdp.v23i3.3005
Intelligence Augmented Reality Tutoring System for Mathematics Teaching and Learning

This study developed an interactive Intelligence Augmented Reality Tutoring System (IARTS) for mathematics teaching and learning. IARTS consists of learning aid, virtual tutor, and guiding mechanism for mathematics problem solving. One hundred thirty-seven eighth grade students from four classes of a southern Taiwan junior high school participated in this study. Two classes were set as the experimental group with 67 students using the IARTS. Two classes were the control group with 70 students using multimedia material with a QR code that linked to a YouTube channel. A set of questionnaires was administered to evaluate students’ learning motivation and system acceptance. Also, a semi-structured focus group interview of both students and teachers was conducted. The results showed that learning motivation and system acceptance of the experimental group were not significantly better than the control group. The theoretical implications of using augmented reality for teaching and learning are discussed and practical implications are proposed.
Feature Selection for Text Classification Using Mutual Information

The feature selection can be defined as the selection of the best subset to represent the data set, that is, the removal of unnecessary data that does not affect the result. The efficiency and accuracy of the system can be increased by decreasing the size and the feature selection in classification applications. In this study, text classification was applied by using “20 news group” data published by Reuters news agency. The pre-processed news data were converted into vectors using the Doc2Vec method and a data set was created. This data set is classified by the Maximum Entropy Classification method. Afterwards, a subset of data sets was created by using the Mutual Information Method for the feature selection. Reclassification was performed with the resulting data set and the results were compared according to the performance rates. While the success of the system with 600 features was (0.9285) before the feature selection, (0.9285), then, the performance rates of the 200, 100, 50, 20 models were obtained as (0.9454, 0.9426, 0.9407, 0.9123), respectively. When the results were examined, the success of the 50-featured model was higher than the 600-featured model initially created.
Antecedents of Dubai Revisit Intention: The Role of Destination Service Quality and Tourist Satisfaction

In this study, the researchers have proposed, developed and tested a novel model which highlighted the effect of the Destination Service Quality (DSQ) dimensions (like local transport, accommodation, cleanliness, hospitality, different activities, airport, language communication services) on the tourist intention to revisit, via the mediating role played by tourist satisfaction, in UAE’s tourism industry. For this purpose, the researchers distributed 700 questionnaires amongst the international tourists visiting Dubai. 565 of the 700 (i.e., 80.71%) of the valid tourist questionnaires were returned. Results indicated that the study constructs were very reliable and displayed the convergent and discriminated validity. Thereafter, the researcher carried out the SEM for testing all research hypotheses. The findings have supported all the study’s hypotheses (direct and indirect relationships). The destination marketers, planners and managers must investigate all dimensions for developing better marketing and operational strategies. Finally, theoretical and managerial implications were discussed.