Reviewerly/exHarmony huggingface dataset:
A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning

Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters. For complex machine learning models such as deep neural networks, it is difficult to determine their hyperparameters. In addition, existing hyperparameter optimization algorithms easily converge to a local optimal solution. This paper proposes a method for hyperparameter optimization that combines the Sparrow Search Algorithm and Particle Swarm Optimization, called the Hybrid Sparrow Search Algorithm. This method takes advantages of avoiding the local optimal solution in the Sparrow Search Algorithm and the search efficiency of Particle Swarm Optimization to achieve global optimization. Experiments verified the proposed algorithm in simple and complex networks. The results show that the Hybrid Sparrow Search Algorithm has the strong global search capability to avoid local optimal solutions and satisfactory search efficiency in both low and high-dimensional spaces. The proposed method provides a new solution for hyperparameter optimization problems in deep learning models.
Information Retrieval for Cancer Cell Detection Based on Advanced Machine Learning Techniques

In this research paper, we focus on designing and developing a fully automated gene regulation from cancerous cell heterogeneity using advanced machine learning techniques. There are several modern technologies developed to make DNA sequencing easier and cheaper. Among them, gene regulation produces the longest read sequences and the lengths of the reads are growing day by day. Machine learning technique like Support Vector Machine (SVM) is developed to align these gene sequences. Every technique faced some challenges, but facing the desired challenges introduce some new challenges on the other side. So, no one tool is perfect for every work. The SVM technique is a new aligner tool that does a tradeoff and performs better from different aspects. For the model with the best generator loss, an average maximum validation accuracy of 91.29% was achieved. The gene regulation with SVM is like a mini-map that takes a few times more memory to index the whole genome of a reference sequence. The single-cell data are the main target of SVM. It is shown that it would help the SVM and similar techniques to align better with long insertions and deletions of single-cell gene regulation. Single-cell data is run against the well-known reference sequence and a randomly generated synthetic reference.
ICT and academic transformation in universities

The Covid-19 pandemic transformed the way people interact, necessitating the global adoption of Information and Communication Technologies (ICT) as both established and emerging resources. Although the University of La Guajira had been integrating face-to-face and virtual modalities, resistance was identified among the teaching staff. This research aimed to clarify the role of teachers in the use of these technologies within teaching and learning processes, employing a quantitative, descriptive approach and a non-experimental field design. The study population consisted of 1,062 students, and the sample included 282 students. Data collection was conducted via a Likert scale questionnaire, which obtained a reliability of 0.859 through Cronbach's Alpha. The study concluded that teachers demonstrate command of technology, utilize basic support equipment, and provide procedural support through explanations, collaboration, guidance, and motivation to enhance performance in accordance with the individual needs of each student.
Policy-based usage control for a trustworthy data sharing platform in smart cities

Although data is a key part in smart cities, traditionally there has been no systematic effort to enable the sharing of data in a trustworthy manner among applications or services. In order to promote sharing of data, mechanisms need to be put into place to provide the different actors — data producers, data consumers, etc. means to control and visualize how their data or requests are being processed and used. In this paper we deal with a key issue involved in trust which is usage control, i.e., how data is used once access to it has been granted. We propose a Data Usage Control Model (DUPO) to capture the diversity of obligations and constraints that data providers impose on the use of their data. Based on the DUPO model and semantic technologies, we propose a trustworthy data sharing platform which enhances transparency and traceability of data usage in smart cities. Lastly a proof-of-concept is developed to evaluate our solution and results show that the performance of the added trust does not impact negatively on the system.
Evaluation of lossless and lossy algorithms for the compression of scientific datasets in netCDF-4 or HDF5 files

Abstract. The increasing volume of scientific datasets requires the use of compression to reduce data storage and transmission costs, especially for the oceanographic or meteorological datasets generated by Earth observation mission ground segments. These data are mostly produced in netCDF files. Indeed, the netCDF-4/HDF5 file formats are widely used throughout the global scientific community because of the useful features they offer. HDF5 in particular offers a dynamically loaded filter plugin so that users can write compression/decompression filters, for example, and process the data before reading or writing them to disk. This study evaluates lossy and lossless compression/decompression methods through netCDF-4 and HDF5 tools on analytical and real scientific floating-point datasets. We also introduce the Digit Rounding algorithm, a new relative error-bounded data reduction method inspired by the Bit Grooming algorithm. The Digit Rounding algorithm offers a high compression ratio while keeping a given number of significant digits in the dataset. It achieves a higher compression ratio than the Bit Grooming algorithm with slightly lower compression speed.
Effects of Virtual Reality-Based Exercise Imagery on Pain in Healthy Individuals

Virtual reality (VR) is an advanced technology that can be used to attenuate pain. The present study aimed to investigate which method was more effective for pain management: VR combined with exercise imagery or VR distraction.Fifty-two healthy students participated in this randomized cross-over controlled trial. One VR-based task aimed to passively use the imagery of driving a car as a distraction intervention (the driving group), whereas the other VR-based task aimed to use exercise imagery (running) to actively engage the participants in movement (the running group). The mechanical pressure pain thresholds of the quadriceps and forearm and the heat pain threshold of the hand of each subject were measured before, during, and after each VR task. The differences between the values at each time point and the differences between the groups were analyzed.The pressure and heat pain thresholds were significantly greater during VR task than those before VR task in both driving and running groups. The changes in the pressure pain thresholds that occurred during VR task were significantly higher in the running group than in the driving group. The difference between groups gradually declined after VR task. Conversely, there was no significant difference in the changes in the heat pain thresholds between the groups both during VR task and after VR task.VR combined with exercise imagery has a greater effect on pressure pain thresholds, but not heat pain thresholds, than VR distraction.
Psychology, not technology, is our biggest challenge to open digital morphology data

The past two decades have seen a revolution in digital imaging techniques for capturing gross morphology, offering an unprecedented volume of data for biological research. Despite the rapid increase in scientific publications incorporating those images, the underlying datasets remain largely inaccessible. As the technical barriers to data sharing continue to fall, we face a more intimate, and perhaps more complicated, obstacle to open data – the one in our minds.
Spatio-temporal Anomaly Detection in Intelligent Transportation Systems

With the increased availability of real-time data streams in different domains comes the opportunity of using this data to provide valuable insights into the performance of the systems generating such data. In this paper, we are proposing an anomaly detection method to be applied on road traffic data in intelligent transportation systems. The proposed scheme is based on multi-channel singular spectrum analysis (MSSA), and aims to characterize the spatio-temporal properties of the transportation network. By simultaneously analyzing the spatial and temporal attributes of the network, the proposed anomaly detection scheme is able to detect contextual and collective anomalies that are otherwise undetectable using only spatial or temporal anomaly detection techniques. This is indicated through the results shown in the experiments and results section.
Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes

NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental. However, as we tackle more social scientific tasks, it is possible user attributes might be of primary importance and the document supplemental. Here, we systematically investigate the predictive power of user-level features alone versus document-level features for document-level tasks. We first show user attributes can sometimes carry more task-related information than the document itself. For example, a tweet-level stance detection model using only 13 user-level attributes (i.e. features that did not depend on the specific tweet) was able to obtain a higher F1 than the top-performing SemEval participant. We then consider multiple tasks and a wider range of user attributes, showing the performance of strong document-only models can often be improved (as in stance, sentiment, and sarcasm) with user attributes, particularly benefiting tasks with stable “trait-like” outcomes (e.g. stance) most relative to frequently changing “state-like” outcomes (e.g. sentiment). These results not only support the growing work on integrating user factors into predictive systems, but that some of our NLP tasks might be better cast primarily as user-level (or human) tasks.
Organ Donation Decentralized Application Using Blockchain Technology

The proposed system is an organ donation decentralized app using blockchain technology. It would be a web application for patients to register their information-most importantly medical ID, blood type, organ type and state. The system would work on a first-in, first-out basis unless a patient is in critical condition.
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optimization methods for solving machine learning problems. However, AdaMM is not suited for solving black-box optimization problems, where explicit gradient forms are difficult or infeasible to obtain. In this paper, we propose a zeroth-order AdaMM (ZO-AdaMM) algorithm, that generalizes AdaMM to the gradient-free regime. We show that the convergence rate of ZO-AdaMM for both convex and nonconvex optimization is roughly a factor of $O(\sqrt{d})$ worse than that of the first-order AdaMM algorithm, where $d$ is problem size. In particular, we provide a deep understanding on why Mahalanobis distance matters in convergence of ZO-AdaMM and other AdaMM-type methods. As a byproduct, our analysis makes the first step toward understanding adaptive learning rate methods for nonconvex constrained optimization.Furthermore, we demonstrate two applications, designing per-image and universal adversarial attacks from black-box neural networks, respectively. We perform extensive experiments on ImageNet and empirically show that ZO-AdaMM converges much faster to a solution of high accuracy compared with $6$ state-of-the-art ZO optimization methods.
Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies.
Blended Learning Model in the Innovative Electronic Basis of Technical Engineers Training

The article proposes a solution to the problem of information and communication technologies applying in the training system of engineers.The methodological basis for the use of information and communication technologies is blended learning.Effective blended learning realization is caused by different factors which are identified by the authors on the university, teacher and student levels.The article proposes four functional models of blended learning: a supporting model, a replacement model, a mobile model, and a model of an electronic linguistic center.Results of the pedagogical experiment conducted in Peter the Great St. Petersburg Polytechnic University proved our hypothesis that supporting model of blended learning is the most appropriate and effective for training technical English and students' professional competence development.The main differences in the design of the blended learning model from traditional learning can be considered the choice of a blended learning model relevant to the goals, objectives and learning conditions, information and communication technologies as learning tools used in this model, as well as the distribution of educational material, types of cognitive activities and forms of work between full-time and remote component.Highlighted regulations can be considered the key to the creation or modification of any course on the blended learning model basis in the training system of engineers.
Medical image segmentation: hard and soft computing approaches

Segmentation divides an image into discrete provinces containing pieces of pixels with analogous attributes. To be expressive and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Different soft computing and hard computing methods are used for medical image segmentation for efficient accuracy. These are computing methods where hard computing is the conventional methodology, which relies on the principles of accuracy, certainty, and inflexibility. Conversely, soft computing is a modern approach premised on the idea of the approximation, uncertainty, and flexibility. Accurate segmentation is very necessary for medical images for better treatment planning. This article provides an efficient analysis of medical images using hard and soft computing. Further, it will explain data used, results obtained, and observation of the current literatures available for medical image segmentation.
Knowledge-Graph Augmented Word Representations for Named Entity Recognition

By modeling the context information, ELMo and BERT have successfully improved the state-of-the-art of word representation, and demonstrated their effectiveness on the Named Entity Recognition task. In this paper, in addition to such context modeling, we propose to encode the prior knowledge of entities from an external knowledge base into the representation, and introduce a Knowledge-Graph Augmented Word Representation or KAWR for named entity recognition. Basically, KAWR provides a kind of knowledge-aware representation for words by 1) encoding entity information from a pre-trained KG embedding model with a new recurrent unit (GERU), and 2) strengthening context modeling from knowledge wise by providing a relation attention scheme based on the entity relations defined in KG. We demonstrate that KAWR, as an augmented version of the existing linguistic word representations, promotes F1 scores on 5 datasets in various domains by +0.46∼+2.07. Better generalization is also observed for KAWR on new entities that cannot be found in the training sets.
Neural Watermarking Method Including an Attack Simulator against Rotation and Compression Attacks

We have developed a digital watermarking method that use neural networks to learn embedding and extraction processes that are robust against rotation and JPEG compression. The proposed neural networks consist of a stego-image generator, a watermark extractor, a stego-image discriminator, and an attack simulator. The attack simulator consists of a rotation layer and an additive noise layer, which simulate the rotation attack and the JPEG compression attack, respectively. The stego-image generator can learn embedding that is robust against these attacks, and also, the watermark extractor can extract watermarks without rotation synchronization. The quality of the stego-images can be improved by using the stego-image discriminator, which is a type of adversarial network. We evaluated the robustness of the watermarks and image quality and found that, using the proposed method, high-quality stego-images could be generated and the neural networks could be trained to embed and extract watermarks that are robust against rotation and JPEG compression attacks. We also showed that the robustness and image quality can be adjusted by changing the noise strength in the noise layer.
Who Did They Respond to? Conversation Structure Modeling Using Masked Hierarchical Transformer

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. Previous work usually took a pair of utterances to decide whether one utterance is the parent of the other. We believe the entire ancestral history is a very important information source to make accurate prediction. Therefore, we design a novel masking mechanism to guide the ancestor flow, and leverage the transformer model to aggregate all ancestors to predict parent utterances. Our experiments are performed on the Reddit dataset (Zhang, Culbertson, and Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In addition, we also report experiments on a new larger corpus from the Reddit platform and release this dataset. We show that the proposed model, that takes into account the ancestral history of the conversation, significantly outperforms several strong baselines including the BERT model on all datasets.
An authentication protocol based on chaos and zero knowledge proof

Abstract Port Knocking is a method for authenticating clients through a closed stance firewall, and authorising their requested actions, enabling severs to offer services to authenticated clients, without opening ports on the firewall. Advances in port knocking have resulted in an increase in complexity in design, preventing port knocking solutions from realising their potential. This paper proposes a novel port knocking solution, named Crucible, which is a secure method of authentication, with high usability and features of stealth, allowing servers and services to remain hidden and protected. Crucible is a stateless solution, only requiring the client memorise a command, the server’s IP and a chosen password. The solution is forwarded as a method for protecting servers against attacks ranging from port scans, to zero-day exploitation. To act as a random oracle for both client and server, cryptographic hashes were generated through chaotic systems.
Learning in situ: a randomized experiment in video streaming

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a simple scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.
A New Approach to Detection of Changes in Multidimensional Patterns

Abstract Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.