Reviewerly/exHarmony huggingface dataset:
Blockchain - Future of Healthcare: The Review

If there’s one thing that has become abundantly clear over the course of the last one year, it’s that a robust, well structured healthcare system is the need of the hour. Centralized systems have one singular entity controlling the flow, management and usage of data which can give rise to misuse of sensitive medical information and delays in providing care to a patient in need. In addition to delivering services and treatments to patients, it’s also important that the security of their medical data and history is maintained strongly. A robust system that accounts for maximum patient participation is a way to ensure transparency, security and consent. Blockchain is an online ledger that is decentralized in its operation and thus, efficient at not just managing health records but also facilitating better control and access for the patients and a better ease of use to the healthcare professionals. Blockchain also has better interoperability which means data across different systems can be exchanged and accessed easily. This is a feature that facilitates faster care and service to a patient and can reduce administrative delays. Employing blockchain in a healthcare framework would help the structure become faster, more efficient, more expandable to accommodate more users and would also result in cost reduction by removing third party moderators.
Privacy-Preserving Framework for Deep Learning Cybersecurity Solutions

Experimentation with artificial intelligence (AI) and machine learning (ML) has rapidly advanced in the last decade, mainly due to the progress made in data storage and processing technologies. As this technology is implemented in everyday devices, ranging from smart appliances to personal assistants, we observe an increase in interest in more rigid to change sectors, like governmental and military. Utilizing a machine learning service is a commodity for today’s society, introducing data privacy challenges for data owners and security concerns for model developers. As the EU plans to build a network of AI-enabled Security Operations Centers across Europe and NATO believes in including artificial intelligence in its decision-making process, we need to focus intensely on the security and privacy offered by machine learning-based applications. This paper contains our summarization of the current concerns regarding privacy-preserving machine learning and an analysis of the present frameworks highlighting possible attacks and methodology. Additionally, we present our approach as a potential solution for privacy-preserving models used in cybersecurity applications. Our research shows that even though all three methods guarantee privacy at a certain level, a holistic approach proves to be more efficient.
Improving Pose Estimation Through Contextual Activity Fusion

This research presents the idea of activity fusion into existing pose estimation architectures to enhance their predictive ability. This is motivated by the rise in higher level concepts found in modern machine learning architectures, and the belief that activity context is a useful piece of information for the problem of pose estimation. To analyse this concept we take an existing deep learning architecture and augment it with an additional 1 $$\times $$ 1 convolution to fuse activity information into the model. We perform evaluation and comparison on a common pose estimation dataset, and show a performance improvement over our baseline model, especially in uncommon poses and on typically difficult joints. Additionally, we perform an ablative analysis to indicate that the performance improvement does in fact draw from the activity information.
Key Frame Extraction Algorithm of Surveillance Video Based on Quaternion Fourier Significance Detection

With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data.Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval.It can extract key frame sets that can express video content from massive videos.However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately.To this end, this paper proposes a key frame extraction algorithm of surveillance video based on quaternion Fourier saliency detection.Firstly, the algorithm used colors, and intensity features to perform quaternion Fourier transform on surveillance video sequences.Next, the phase spectrum of the quaternion Fourier transformed image was obtained, and he image visual saliency map was obtained according to the quaternion Fourier phase spectrum.Then, the image visual saliency map of two adjacent frames is used to characterize the change of target motion state.Finally, the frames that can accurately express the motion state of the target are selected as key frames.The experimental results show that the method proposed in this paper can accurately capture the changes of the local motion state of the target while maintaining the integrity of the image information.
Distributed Streaming Big Data Analytics for Internet of Things (IoT)

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.
Efficient Seamline Network Generation for Large-Scale Orthoimage Mosaicking

Seamline network generation from multiple aerial images with overlapping regions is a key issue for creating seamless and large-scale digital orthophoto maps (DOMs). In this letter, an efficient algorithm is proposed that can find adequate networks from hundreds of aerial images in several minutes. It can also be ensured that the seamline passes through weakly textured regions as much as possible. The proposed algorithm consists of two steps. First, the regions overlapped by the images are triangulated into a mesh. Second, a labeling <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> is computed that assigns an image <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{i}$ </tex-math></inline-formula> as a texture for each triangle using a Markov random field energy function. Triangulating the overlapping area of the images can greatly improve the efficiency of the seamline network generation, and by designing a smoothing term with a divergence difference between images, the seamline network passes through the weakly textured area as much as possible. In addition, we also use a hierarchical optimization strategy to improve efficiency in large-scale scenes. The proposed method can be easily combined with other data information (e.g., a digital surface model), or other auxiliary methods (e.g., building segmentation), to generate high-quality seamline networks. Experimental results on numerous challenging datasets show that the proposed algorithm can achieve high-quality seamline networks efficiently. Moreover, it outperforms several existing methods and software.
Brand Management of Archives in Ukraine: Raising the Issue

The article outlines the general state and the main tendencies of modern archival science in Ukraine. It highlights the formation of the latest trends in archivistics related to digital technologies and innovative features in the development of the information space and civil society, which led to a strong belief in the need to modernize the image of the archive as a social and communicational institution and to update the content of the profession of an archivist. A special place among these trends should be allocated to the brand management of archives which is a set of measures intended to support a system of values which, with a predetermined purpose, create a unique image of the archive and the archivist in order to sustain their favourable image among the target audience. The content of the concept of “brand management of archives” is defined and its principle foundations are explained. Attention is drawn to the types of corporate brand management. For the first time the objective characteristics of a brand are formulated, our own vision of the model for creating and developing a brand identity suitable for the archival field is proposed and its structure and the content of its components are outlined.
Hate speech content detection system on Twitter using K-nearest neighbor method

Twitter is a social media platform that many Indonesians use to express their thoughts on a variety of topics. In Indonesia, the use of social media is governed by a law known as Information and Electronic Transactions Law. However, until now, the implementation of this law has been subpar. This is because there are still violations occurring, and no legal action has been taken against these violations. Hate speech is a common violation on Twitter. The goal of this research is to create a system that can detect potential violations of content on Twitter, particularly content containing hate speech. The k-nearest neighbor (KNN) method was used in this research, along with the feature extraction method TF-IDF. The system built will detect whether the tweet you want to post violates a specific article in the Information and Electronic Transactions Law. Based on model validation, model classifier built has accuracy value is 67.86%, with K value in the KNN method is 10. Meanwhile, based on user validation, the system created has an accuracy of 77%.
Multi-Target Track Initiation in Heavy Clutter

In the heavy clutter environment, the information capacity is large, the relationships among information are complicated, and track initiation often has a high false alarm rate or missing alarm rate.Obviously, it is a difficult task to get a high-quality track initiation in the limited measurement cycles.This paper studies the multi-target track initiation in heavy clutter.At first, a relaxed logic-based clutter filter algorithm is presented.In the algorithm, the raw measurement is filtered by using the relaxed logic method.We not only design a kind of incremental and adaptive filtering gate, but also add the angle extrapolation based on polynomial extrapolation.The algorithm eliminates most of the clutter and obtains the environment with high detection rate and less clutter.Then, we propose a fuzzy sequential Hough transform-based track initiation algorithm.The algorithm establishes a new meshing rule according to system noise to balance the relationship between the grid granularity and the track initiation quality.And a flexible superposition matrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0-1 voting method in traditional Hough transform.In addition, the algorithm allows the superposition matrixes of nonadjacent cycles to be associated to overcome the shortcoming that the track can't be initiated in time when the measurements appear in an intermittent way.And a slope verification method is introduced to detect formation-intensive serial tracks.Last, the sliding window method is employed to feedback the track initiation results timely and confirm the track.Simulation results verify that the proposed algorithms can initiate the tracks accurately in heavy clutter.
Sequential Convolutional Neural Network based Face Mask Detection

Due to the COVID-19 pandemic, we have encountered signs such as "No entry without face mask" in public spaces such as theatres, restaurants, shopping malls, other places. The Face mask is one among the necessary preventive measures taken against COVID-19. Many doctors and scientists have suggested the continued use of face masks even after the vaccination drive. This new change in the way of life necessitates the use of a face mask in public places. Open public spaces cannot be monitored but enclosed public spaces can be monitored. The passageway of these public spaces should monitor if an individual has put on a face mask. Without manual supervision, the CNN algorithm could potentially be used to check the face mask of the persons entering the enclosed public spaces. In this paper, a Convolutional Neural Network prototype is constructed using TensorFlow, OpenCV and, Keras to detect, if an individual has put on a face mask to protect themselves. At the entrance of these public spaces, a monitor scans the faces of the people entering and detects if they are wearing a facemask using the CNN deep learning model. We collect data of images and pre-process them to develop a CNN prototype. The above deep learning algorithm could be coordinated with the doorways of public places to ensure, no person without a face mask enters the space.
Improved RRT Autonomous Exploration Method Based on Hybrid Clustering Algorithm

In autonomous robot exploration, it is very important that the robot can autonomously plan the exploration path of the unknown environment and create a map representation of the surrounding environment. In the robot's autonomous environment traversal method, one branch is the frontier-based detection method. This method consumes more and more computing resources as the map expands and is not suitable for large-scale environment detection. Another branch is the random exploration method based on multiple rapidly-exploring randomized trees. This method uses the edge of the tree to search for the boundary, but the selection of the next exploration point is very demanding. This paper proposes an exploration point selection strategy based on the hybrid clustering method, which speeds up the RRT exploration method. Simulation experiments show that the improved RRT exploration method proposed in this paper takes less time to explore and build maps, especially in a large-scale mapping environment, which effectively improves the completeness and efficiency of mapping.
Cross-Lingual Transfer for Speech Processing Using Acoustic Language Similarity

Speech processing systems currently do not support the vast majority of languages, in part due to the lack of data in low-resource languages. Cross-lingual transfer offers a compelling way to help bridge this digital divide by incorporating high-resource data into low-resource systems. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. However, scaling up speech systems to support hundreds of low-resource languages remains unsolved. To help bridge this gap, we propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages. We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.
Preparing Universities for Living in a Cyberspace

After the sudden shift from face-to-face to face-to-display lecturing in Czech and Slovak universities as one of side effects of COVID-19, the authors completed an online survey mapping their reactions to this unexpected transformation on the peak of the second wave in May 2021. The main aim of this study was to learn to what degree they were prepared to their fully virtual environment adaptation. 172 educators participated in the study. Obtained results indicate that the universities were not properly equipped for a broad adaptation of online education. In the time of survey, not all of these problems have been solved. It may result in negative consequences in the future.
Work-In-Progress About Dynamicity as a Foundation for AMI, a Mobile Intelligent and Adaptive Learning System

If school, in its traditional form, cannot be accessible to all the children of the world, we believe that school can become accessible to them in the form of a smart learning system (smart system and smart learning), adaptative/personalized, and mobile. AMI, an intelligence-based learning system, could be a solution for children who are out of school. AMI aims to enable learner self-learning. To do this, it must be dynamic. Its dynamicity stems from a close and sustained interaction between the learner and the system, which infers its adaptability. The system is then alive and, therefore, in constant reaction to the learner’s activity. The continuous integration of new data from this learner/system interaction modifies the learner’s profile and/or the learning path in progress. Therefore, how to provide the system with dynamic and sustainable self-learning capabilities, based on the learner’s behaviors throughout his interaction with the system? More precisely, how to represent and interpret random events as messages to which the system can react to produce actions in continuous mode? This paper presents a Work-in Progress on the implementation of two of the four intelligent components of the AMI system aiming at allowing a maximum adaptability of a personalized learning offer.
Color the Word: Leveraging Web Images for Machine Translation of Untranslatable Words

Automatic translation allows people around the globe to communicate with one another. However, state-of-the art machine translation is still unable to capture fine-grained meaning. This paper introduces the idea of using Web image selections in text-to-text translation, specifically for lacunae, which are words that do not have a translation in another language. We asked human professional translators to rank Google translate translations of lacunae in German and Dutch. We then compared that ranking with a ranking based on color histograms of Web image data of the words. We found there is viable potential in the idea of using images to address lacunae in the field of machine translation. We publicly release a dataset and our code for others to explore this potential. Finally, we provide an outlook on research directions that would allow this idea to be used in practice.
Application of Artificial Intelligence in Indian Banks

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as the most significant technologies of our times and are increasingly being used across businesses and industry sectors. Agaist this backdrop, banks and financial institutions are exploring tools based on AI to transform various facets of their business. These tools are expected to bring in process automation, cost efficiencies, improved decision making, product innovations, and enhanced customer experiences. This paper discusses various banking use cases and techniques, including the AI initiatives being pursued by Indian banks. Major focus areas for application of AI/ML in banking are — financial crime and compliance management, customer insight and relationship management, credit risk, and customer service.
Research on Data Fault-Tolerance Method Based on Disk Bad Track Isolation

Disk is not only an important carrier to save data, but also a key component of storage system. It is of great significance to improve the reliability and data availability of disk. In the big data environment, the number of disks in the storage system is huge and distributed, so the maintenance for disk failure is inefficient and costly. Disk bad track is one of the most common disk faults. To solve this problem, this paper proposes a disk bad track isolation algorithm. This approach uses a data fault-tolerance mechanism to isolate physical bad tracks, effectively reducing the disk failure rate, improving service stability, and reducing system maintenance costs. The results show that the bad track isolation method can reduce the Disk IO exception by 47%, and significantly reduce the disk failure rate. It is effective in improving system stability and reducing maintenance cost.
Evoting Using Blockchain Technology

Voting is the fundamental right of each and every citizen in a democracy. Perhaps, it is the very basis of a democracy. Democracy means a government for the people, of the people, and by the people. This means many rights such as freedom of speech, right to information, to name a few. But the right that defines a democracy is voting. It means that those who are willing to form a government and serve the people stand as candidates, and the people are allowed to choose, who they wish to be served from (i.e., the government). Looking at how imperative voting is, it is also essential that all citizens are able to cast their votes during the election. Now here comes the problem, especially in countries like India. Some people are not available in the city at the time of voting, they may be traveling, etc.; for the others, the voting center might be very far away from their houses. And in India, with the vast multitude of population, a large fraction of people is not even able to receive their voter ID cards (Barański et al. in Appl Sci 10:7606, 2020 [1]). Also, some people may be handicapped and hence are not able to vote. There is a one clear solution to all this, “Evoting,” a concept that will be elaborated in this paper, stating how it is done, what technology it uses, and its advantages over the currently used voting methods.
Topic Segmentation for Interview Dialogue System

In this study, topic segmentation was performed by referring to the interview dialogue corpus. Utterance intention tags were added to the existing interview dialogue corpus, and uttered sentences were vectorized using BERT, Sentence BERT, and Distil BERT. In addition, topic classification was performed using the utterance intention tags and the features of the preceding and following uttered sentences. Consequently, the greatest accuracy was achieved when the utterance intention tag was used with DistilBERT.
Stop words detection using a long short term memory recurrent neural network

Natural language processing is a field of computer science that focuses on understanding and analyzing textual data in any given language. Analyzing textual data is very tedious and leads to erroneous results due to unnecessary and noisy data in the corpus. Stop words are considered noisy data which the English language has already predefined corpus of stop words. Stop words in other languages such as Cebuano and Filipino are not yet supported in many NLP API. In the Philippines, users use different languages to post on Facebook. In this study, a corpus of Facebook posts was utilized in automatically detecting a stop word. A neural network was created based on Bidirectional Long Short term memory (BiLSTM). Word2vec was used to provide word embedding and representation from the corpus. The experimental result shows 72% accuracy in using the model.