Reviewerly/exHarmony huggingface dataset:
Multi-modal dataset generation using domain randomization for object detection

Object detectors on autonomous systems often have to contend with dimly-lit environments and harsh weather conditions. RGB images alone typically do not provide enough information. Because of this, autonomous systems have an array of other specialized sensors to observe their surroundings. These sensors can operate asynchronously, have various effective ranges, and create drastically different amounts of data. An autonomous platform must be able to combine the many disparate streams of information in order to leverage all of the available information while creating the most comprehensive model of its environment. In addition to multiple sensors, deep learning-based object detectors typically require swaths of labeled data to achieve good performance. Unfortunately, collecting multimodal, labeled data is exceedingly labor-intensive which necessitates a streamlined approach to data collection. The use of video game graphics engines in the production of images and video has emerged as a relatively cheap and effective way to create new datasets. This helps to close the data gap for computer vision tasks like object detection and segmentation. Another unique aspect of using gaming engines to generate data is the ability to introduce domain randomization which randomizes certain parameters of the game engine and generation scheme in order to improve generalization to real-life data. In this paper, we outline the creation of a multi-modal dataset using domain randomization. Our dataset will focus on the two most popular sensors in autonomous vehicles, LiDAR and RGB cameras. We will perform baseline testing of an object detector using a data-fusion deep learning architecture on both our synthetic dataset and the KITTI dataset for comparison.
Citation Indexing and Indexes

A citation index is a bibliographic database that provides citation links between documents. The first modern citation index was suggested by the researcher Eugene Garfield in 1955 and created by him in 1964, and it represents an important innovation to knowledge organization and information retrieval. This article describes citation indexes in general, considering the modern citation indexes, including Web of Science, Scopus, Google Scholar, Microsoft Academic, Crossref, Dimensions and some special citation indexes and predecessors to the modern citation index like Shepard’s Citations. We present comparative studies of the major ones and survey theoretical problems related to the role of citation indexes as subject access points (SAP), recognizing the implications to knowledge organization and information retrieval. Finally, studies on citation behavior are presented and the influence of citation indexes on knowledge organization, information retrieval and the scientific information ecosystem is recognized.
Webpage Recommendation System Based on the Social Media Semantic Details of the Website

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signals in third-person video that are predictive of key egocentric-specific properties. Incorporating these signals as knowledge distillation losses during pre-training results in models that benefit from both the scale and diversity of third-person video data, as well as representations that capture salient egocentric properties. Our experiments show that our Ego-Exo framework can be seamlessly integrated into standard video models; it outperforms all baselines when fine-tuned for egocentric activity recognition, achieving state-of-the-art results on Charades-Ego and EPIC-Kitchens-100.
Machine Learning Era In Heart Disease Prediction- An Intense Learning Analysis With Big Data

Machine learning and adaptation is a collection of machine learning methods consisting of several stacked layers and using data to explore hierarchical abstractions. As computer power has increased and large data has emerged, deep learning is an appropriate structure for cardiological tasks. The need to optimize medical treatment varies from diagnostic to therapeutic in the absence of a medical Centre. Machine learning systems are previous attempts to imitate medical practitioners in their protocol for solving medical tasks or for producing observations. These systems are known not to be useful as they require extensive design features and domain expertise in order to achieve the new cardio data highly accurate and difficult to map. Overall, with any technical progress, cardiometry and medicine are autonomous and become closer to an automated, detailed learning area. But no complete conceptual basis for in-depth education can be found. A thorough analysis of its internal functional qualities and constraints is required to enable the field to adopt its position on the disease of the heart.In this study, a large number of very complex machine learning concepts integrated into the cardio domain with big data have been studied over a very short time period of time.
College English Mobile Learning Strategies Based on Modern Information Technology

Mobile learning is an emerging product of the rapid development of the Internet. It is a learning behavior that uses information technology concepts to rely on mobile terminals such as smart phones and tablet computers to conduct online self-learning with the help of wireless networks. The mobile learning model of college English can break the time and space limitations of the traditional English classroom teaching model, making the college English teaching model more flexible, convenient and efficient, which will help stimulate students’ curiosity and interest in learning, guide students to learn independently, and solve problems by themselves, cultivate students’ comprehensive ability to use English.
The Limitations of Cross-Site Scripting Vulnerabilities Detection and Removal Techniques

Web applications have become very important tools in our daily activities as we use them to share and get information, conduct businesses, and interact with family and friends on social media through the Internet. Despite their importance, web applications are plagued with many security vulnerabilities that enable hackers to attack them and compromise user information and privacy. Cross-site scripting vulnerabilities are a type of injection vulnerabilities existing in web applications. They can lead to attacks in web applications due to the lack of proper validation of input data in the affected web pages of an application. Many approaches and techniques have been proposed to mitigate this type of vulnerabilities. However, these solutions have some limitations and cross-site scripting vulnerabilities still remain as a major security problem for web applications. This paper explores and presents the existing techniques for detecting and for removing cross-site scripting vulnerabilities in web application. It gives an overview of cross-site scripting as a security issue in web application and its different types. The advantages as well as the limitations of each techniques are highlighted and discussed. Based on the limitations, some possible future research directions are identified, and recommendations are given as reference for researchers interested in this topic.
Heterogeneous architecture for reversible watermarking system for medical images using Integer transform based Reverse Contrast Mapping

Algorithms in multimedia security including machine learning and deep learning find its way into more and more application field resulting in strict power and area constraints. This challenges traditional homogeneous computing concepts and drives the development of new, heterogeneous architectures. One way to attain a balance of high data throughput and flexibility is to combine soft-core FPGA accelerators with CPUs as hosts. In this paper, in order to achieve higher performance, low latency, unlimited re-configurability and most important very high energy efficiency, we have proposed a hybrid architecture using CPU and FPGA. These components are then dynamically configured to form complex algorithms directly in hardware by implementing reversible watermarking of medical images. We are proposing such reversible watermarking using Integer transform based Reversible Contrast Mapping (RCM) algorithm. We got improvement in results with respect to Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM), when compare to the implementation in proposed architecture and simulation results of different image formats like jpeg, png and DICOM.
TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction

We propose TalkNet, a non-autoregressive convolutional neural model for speech synthesis with explicit pitch and duration prediction. The model consists of three feed-forward convolutional networks. The first network predicts grapheme durations. An input text is expanded by repeating each symbol according to the predicted duration. The second network predicts pitch value for every mel frame. The third network generates a mel-spectrogram from the expanded text conditioned on predicted pitch. All networks are based on 1D depth-wise separable convolutional architecture. The explicit duration prediction eliminates word skipping and repeating. The quality of the generated speech nearly matches the best auto-regressive models - TalkNet trained on the LJSpeech dataset got MOS 4.08. The model has only 13.2M parameters, almost 2x less than the present state-of-the-art text-to-speech models. The non-autoregressive architecture allows for fast training and inference. The small model size and fast inference make the TalkNet an attractive candidate for embedded speech synthesis.
Evaluasi Tata Kelola TI Menggunakan Framework COBIT 5 Studi Kasus STMIK Primakara

STMIK Primakara is a Technopreneurship campus who have a vision to produce IT Scholar with entrepreneurial mentality. However, to achieve that, STMIK Primakara need to manage the IT Government to support the academics and non-academics environment around the students until graduates are controlled by department of IT Development and Implementation (PPTI). The researcher will evaluates the capability level of IT Governance in STMIK Primakara and provide recommendations for the gap between the performance in domain process APO03, APO04, and BAI01 using quantitative and qualitative methods. Moreover, the researcher will use survey, interview, and document studies for the data required. The results of this research shows the average of capability level in domain process APO03, APO04, and BAI01 are partially achieved. Therefore, to fill the gap between STMIK Primakara current capability level and expected capability level, recommendation have been given by the researcher to create an improvement regarding to STMIK Primakara IT Governance.
Towards Automated Psychotherapy via Language Modeling

In this experiment, a model was devised, trained, and evaluated to automate psychotherapist/client text conversations through state-of-the-art, Seq2Seq Transformer-based Natural Language Generation (NLG) systems. Through training upon a mix of the Cornell Movie Dialogue Corpus for language understanding and an open-source, anonymized, and public licensed psychotherapeutic dataset, the model achieved statistically significant performance in published, standardized qualitative benchmarks against human validation data — meeting or exceeding human-written response performance in 59.7% and 67.1% of the test set two independent test methods respectively. Although the model cannot replace the work of psychotherapy, its ability to synthesize human-appearing utterances for the majority of the test set serves as a promising step towards communizing and easing tensions at the psychotherapeutic point-of-care.
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction

Contrastive learning has been used to learn a high-quality representation of the image in computer vision. However, contrastive learning is not widely utilized in natural language processing due to the lack of a general method of data augmentation for text data. In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction. The key knob of our framework is a unique contrastive pre-training step tailored for the relation extraction tasks by seamlessly integrating linguistic knowledge into the data augmentation. Furthermore, we investigate how large-scale data constructed from the external knowledge bases can enhance the generality of contrastive pre-training of BERT. The experimental results on three relation extraction benchmark datasets demonstrate that our method can improve the BERT model representation and achieve state-of-the-art performance. In addition, we explore the interpretability of models by showing that BERT with contrastive pre-training relies more on rationales for prediction. Our code and data are publicly available at: https://github.com/udel-biotm-lab/BERT-CLRE.
Classic Hebbian learning endows feed-forward networks with sufficient adaptability in challenging reinforcement learning tasks

A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. Proc 33rd Conf Neural Inf Process Systems (NeurIPS 2020). 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvaged in artificial feed-forward networks by optimizing coefficients of classic Hebbian rules to dynamically control the networks' weights instead of optimizing the weights directly. Although such models fail to capture many important neurophysiological details, allying the fields of neuroscience and artificial intelligence in this way bears many fruits for both fields, especially when computational models engage with topics with a rich history in neuroscience such as Hebbian plasticity.
Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline

Extensive, but remote oil and gas fields of the United States, Canada, and Russia require the construction and operation of extremely long pipelines. Global warming and local heating effects lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this causes changes in the spatial positions and forms of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the remoted pipelines in order to be able to perform remedial measures in time. They are therefore seeking methods for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting uncorrelated time series; generating synthetic features; robust scaling temperature series, tuning the additive regression model to forecast soil temperatures.
INTEGRATION AND USE OF CONTEMPORARY TECHNOLOGIES IN STEM EDUCATION

Based on the historical development of industry and society, as well as education, we can conclude that in the future we will see continuous progress and development. Accordingly, education should follow, because only in this way will people be able to live a quality life in the future. Given all the progress so far, the question arises what awaits us in the coming years or decades. This article focuses on the question of whether the society is ready for the changes that await, and whether contemporary devices (3D printers, laser cutting machines, etc.) and processing procedures are already included in education, especially in the field of STEM. In order to explore these issues, a short survey was carried out on a sample of 77 STEM teachers in Slovenia. Results show that Slovenian schools are not yet well enough prepared for contemporary technologies, and that these are not included in the lessons for various reasons. Therefore, a strategy (at different levels) should be developed and introduce certain changes, so that education can keep up with the pace of technological progress. Keywords: contemporary technologies, STEM education, technological development, changes in education.
Distributional Results for Model-Based Intrinsic Dimension Estimators

Modern datasets are characterized by a large number of features that may conceal complex dependency structures. To deal with this type of data, dimensionality reduction techniques are essential. Numerous dimensionality reduction methods rely on the concept of intrinsic dimension, a measure of the complexity of the dataset. In this article, we first review the TWO-NN model, a likelihood-based intrinsic dimension estimator recently introduced in the literature. The TWO-NN estimator is based on the statistical properties of the ratio of the distances between a point and its first two nearest neighbors, assuming that the points are a realization from an homogeneous Poisson point process. We extend the TWO-NN theoretical framework by providing novel distributional results of consecutive and generic ratios of distances. These distributional results are then employed to derive intrinsic dimension estimators, called Cride and Gride. These novel estimators are more robust to noisy measurements than the TWO-NN and allow the study of the evolution of the intrinsic dimension as a function of the scale used to analyze the dataset. We discuss the properties of the different estimators with the help of simulation scenarios.
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation

Kalman Filter requires the true parameters of the model and solves optimal state estimation recursively. Expectation Maximization (EM) algorithm is applicable for estimating the parameters of the model that are not available before Kalman filtering, which is EM-KF algorithm. To improve the preciseness of EM-KF algorithm, the author presents a state estimation method by combining the Long-Short Term Memory network (LSTM), Transformer and EM-KF algorithm in the framework of Encoder-Decoder in Sequence to Sequence (seq2seq). Simulation on a linear mobile robot model demonstrates that the new method is more accurate. Source code of this paper is available at https://github.com/zshicode/Deep-Learning-Based-State-Estimation.
Using Chaotic Stream Cipher to Enhance Data Hiding in Digital Images

The growing potential of modern communications needs the use of secure means to protect information from unauthorized access and use during transmission. In general, encryption a message using cryptography techniques and then hidden a message with a steganography methods provides an additional layer of protection. Furthermore, using these combination reduces the chance of finding the hidden message. This paper proposed a system which combines schemes of cryptography with steganography for hiding secret messages and to add more complexity for steganography. The proposed system secret message encoded with chaotic stream cipher and afterwards the encoded data is hidden behind an RGB or Gray cover image by modifying the kth least significant bits (k-LSB) of cover image pixels. The resultant stego-image less distorters. After which can be used by the recipient to extract that bit-plane of the image. In fact, the schemes of encryption/decryption and embedding/ extracting in the proposed system depends upon two shred secret keys between the sender and the receiver. An experiment shows that using an unauthorized secret keys between the sender and the receiver have totally different messages from the original ones which improve the confidentiality of the images.
Study of the Dependence of Solar Radiation Regarding Design Variables in Photovoltaic Solar Installations with Optimal Dual-Axis Tracking

Solar tracking is an efficient strategy to increase the radiative capture of photovoltaic collectors. Within the multiple efforts made in recent decades to improve the production of these facilities, various works have studied solutions to optimize the number of rotation axes (single or dual rotation axes), the degree of collector coverage, the distances between trackers, the geometric arrangement of trackers or the minimization of shading between collectors. However, although in this type of installation it is common to find collectors with geometric shapes other than rectangles, no studies on the influence of the shape of the collectors on the radiative incidence are found in the literature. In this connection, the present work systematically addresses the study of incident solar radiation in photovoltaic installations with dual-axis trackers with collectors of different geometric shapes. By means of the exhaustive study, the conclusion is drawn that, for dual-axis photovoltaic installations with an optimal tracking strategy, the main variables that influence the annual radiative incidence are the spacing between collectors, the coverage ratio (GCR), and the collector surface, while the type of arrangement of collectors and the shape of these do not show predictive values.