Reviewerly/exHarmony huggingface dataset:
Autosegmental Neural Nets 2.0: An Extensive Study of Training Synchronous and Asynchronous Phones and Tones for Under-Resourced Tonal Languages

Phones, the segmental units in the International Phonetic Alphabet (IPA), include isolated consonants or vowels; tones, the suprasegemental units, represent pitch and voice quality movements that may span many phones. The timings of tones and phones are loosely connected, e.g., tones may be synchronized with their associated vowels, syllable finals, or a sequence of two or three syllables depending on the language. Many past studies have investigated cross-lingual adaptation in an automatic speech recognition (ASR) tone-marked phone model, yet very few studied the interaction between cross-lingual adaptation and tone-phone synchronization. In this study, we perform an extensive study by multilingual training on four tonal languages and cross-lingual testing on the fifth, in a five-fold cross-validation framework, using four CTC-based systems that impose different degrees of synchronization between tones and phones. We discover that multilingual and cross-lingual training benefit from different training architectures. In multilingual training, when a large corpus of test-language training data is part of the training corpus, a system that requires synchronization of tones with phones produces significantly lower tone error rates than any of the systems that score tones and phones asynchronously. In cross-lingual training, however, when only limited adaptation data are available in the test language, jointly training synchronous tone-marked phones together with asynchronous phones and tones, as three separate system outputs jointly optimized using a multi-task learning framework, consistently and significantly outperforms the system that requires tone-phone synchrony.
Agile at Scale Adoption: New Perspectives from a Solely Remote Environment

Agility is a lever to achieve the entrepreneurial, innovative culture which allows companies to build close relationships with customers and quickly adapt to their environments. In this light, this study aimed to assess virtual scaled agile transformation. Qualitative research situated in a large software company based on 16 semi-structured interviews, observations and artifacts analysis was carried out to evaluate the studied agile adoption. Findings uncovered the challenges, strengths, innovative aspects, and the effect of the transformation on the firm’s effectiveness. The study concludes with a set of recommendations for other organizations. The firm successfully utilized the full potential of the online environment, which shows that online interconnectedness provides a way to advance digitalization and the development of the whole digital ecosystem. The empirical evidence from this study demonstrates that it is possible to realize a scaled agile adoption solely remotely when the global pandemic does not allow for in-person meetings.
Utilizing Hidden Observations to Enhance the Performance of the Trained Agent

The frame-skipping strategy has been widely employed in deep reinforcement learning (DRL) technology to train an agent. Specifically, this strategy repeats the action determined by the agent for a fixed number of frames. It increases computational efficiency by reducing the number of inferences by making the action decision sparse. However, previously, these consecutive changes in frames during the frame-skipping were hidden and ignored from the environment and did not affect the agent’s action decision. As a result, it can adversely affect the performance of trained agents, where the performance is more critical than computational efficiency. To alleviate these issues, we propose a new framework that utilizes these hidden frames during the frame-skipping, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$hidden$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$observation$</tex-math></inline-formula> , to enhance the performance of the trained agent. The proposed framework retrieves all hidden observations during frame skipping. It then combines batch inference and an exponentially weighted sum to calculate and merge the outputs from hidden observations. Through experiments, we validated the effectiveness of the proposed method in terms of both performance and stability with only a marginal increase in computation.
Introduction to robot operating system

The robot operating system (ROS) is currently the most widely used open-source robotics software platform. At present, the software implementation of some unmanned driving systems still relies on the ROS platform. This chapter introduces ROS, including its concepts, features, and tools; its basic programming; and an actual practice based on the Husky simulator.
Research Progress and Trend of Coflow Time-Optimal Scheduling in Data Center Network

Data-intensive applications in data center networks generate a large number of parallel data streams, where the strategy of flow scheduling and the allocation of network bandwidth have become research hotspot issues in this field. Compared with the scheduling of a single data stream, coflow that aims to improve the overall performance of parallel applications can transmit the application layer semantics to the network layer, which is conducive to scheduling decisions by taking full advantage of the application layer semantics. This paper focuses on coflow scheduling with the goal of optimizing completion time, where we review the existing scheduling frameworks and discuss the ideal characteristics in future work. The existing schemes fall into two categories of centralized scheduling and distributed scheduling. Centralized scheduling makes scheduling decisions through the global view of the central scheduler, and distributed scheduling makes scheduling decisions through the local view of adjacent switches. The existing scheduling schemes have made great progress in time optimization, while in-depth research is still needed in the future in terms of fault tolerance, scalability, and starvation avoidance.
Jurisprudential Analyses of Consent and Rape in Nigeria: A Paradigm of Chrisland’s School Incident

In determining the idea of consent in rape situations, it’s difficult and preliminary. Consent analysis, as with any analysis, is problematic, greatly because, the categorical thoughts that would be undertaken in studying ambiguities are the source of our ambiguities. However, such an examination is prefatory because it is the only mechanism for determining whether or not sexual intercourse is a wrong from which women(girls minor) should be safeguarded and when it is not. In the rape case, it is significantly imperative to understand that the issue of consent is still obscure in its determination, especially when its comes to a minor victim, contradicting what’s obtainable in defilement. Thus, this subject shall be properly checked from the recent incident at chrisland School in Lagos State Nigeria. Having this in mind, this paper has succinctly observed what constitutes the offence of rape, The ingredients of rape, what amounts to consent in rape although the concept is being misconstrued and Perhaps is complicated to unravelling, the proofing of rape commission, penetration and submission, defilement, who an abettor is and the penalty for abetting, also, corroborating evidence and legal effect of un Corroborated evidence are elaborated, and the punishment to be levelled upon person that committed the offence of rape. Finally, conclusion is made after the examination aided by existing statutory provisions and judicial authorities which are municipally and internationally defined.
Automatic Text Summarization Using Intelligent Systems

Text summarizer is an artificial intelligence (AI) application that helps in summing up the content. We can transfer our information and this application gives us the synopsis of that information in almost many quantities of lines as we want. The item is fundamentally a content summing up utilizing deep learning ideas. The superfluous sentences will be disposed of to get the main sentences. Text summarization is the way toward making a consolidated type of text archive which keeps up with critical data and the general significance of input text. Programmed text synopsis turns into a significant method of discovering important data exactly in a large content in a brief time frame with little endeavors. There are principally two kinds of summarization methods namely, extractive and abstractive. In this paper, we mainly focus on the extractive method which removes the main sentences by computing the significance of each sentence to fabricate the outline.
Interactive Learning for Assisting Whole Slide Image Annotation

Owing to the large dimensions of the histopathology whole slide images (WSI), visually searching for clinically significant regions (patches) is a tedious task for a medical expert. Sequential analysis of several such images further increases the workload resulting in poor diagnosis. A major impediment towards automating this task using deep learning models is that it requires a huge chunk of laboriously annotated data in the form of WSI patches. Our work suggests a novel CNN-based, expert feedback-driven interactive learning technique to mitigate this issue. The proposed method seeks to acquire labels of the most informative patches in small increments with multiple feedback rounds to maximize the throughput. It requires the expert to query a patch of interest from one slide and provide feedback to a set of unlabelled patches chosen using the proposed sampling strategy from a ranked list. The experiments on a large patient cohort of colorectal cancer histological patches (100K images with nine classes of tissues) show a significant reduction ( $$\approx 95\%$$ ) in the amount of labelled data required to achieve state-of the-art results when compared to other existing interactive learning methods (35%–50%). We also demonstrate the utility of the proposed technique to assist a WSI tumor segmentation annotation task using the ICIAR breast cancer challenge dataset ( $$\approx 12.5$$ K patches per slide). The proposed technique reduces the scanning and searching area to about $$2\%$$ of the total area of WSI (by seeking labels of $$\approx 250$$ informative patches only) and achieves segmentation outputs with $$85\%$$ IOU. Thus our work helps avoid the routine procedure of exhaustive scanning and searching during annotation and diagnosis in general.
FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

In recent years, with the development of the natural language processing (NLP) technologies, security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity, achieved great progress.However, we found that the existing frameworks often ignored the complex internal structure of instructions and didn't fully consider the long-term dependencies of instructions.In this paper, we propose firmVulSeeker-a vulnerability search tool for embedded firmware images, based on BERT and Siamese network.It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus.Then, a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage.Finally, it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it's a real vulnerability manually.We evaluate the accuracy, robustness, scalability and vulnerability search capability of firmVulSeeker.Results show that it can greatly improve the accuracy of matching semantically similar functions, and can successfully find more real vulnerabilities in real-world firmware than other tools.
Hybrid Classification with Score Level Fusion for Human Activity Recognition

The identification of human activities from a video sequence or still image remains a challenging task due to issues such as backdrop clutter, fractional occlusion, and transformation in size, point of view, appearance, and lighting. Various appliances, as well as video surveillance systems, human computer interfaces, and robots used to characterize human behavior, require a variety of activity classification systems. The paper proposes a new scheme for recognizing human activities in four stages. In the early stages of pre-processing, video to frame conversion and improved adaptive histogram equalization (AHE) are performed. Furthermore, water shed segmentation is performed and, from the segmented images, the improved local texton XOR pattern (LTXOR), motion boundary scale-invariant feature transform (MOBSIFT) and bag of visual words (BoVW) based features are extracted. In order to detect human activity, bidirectional gated recurrent unit (BI - GRU) and bidirectional long short-term memory (BI - LSTM) hybrid classifiers are used. Additionally, score level fusion is performed, where the combined decisions of BI-GRU and BI-LSTM classifiers are further fused and respective accuracy levels are determined. By using this score level fusion technique, we are likely to get more accurate results resulting from the analysis. Overall, the effectiveness of the deployed approach is regarded in the light of various metrics.
Perception of Undergraduate Dental Students about Use of Smartphones in Dentistry: An Efficient Teaching-Learning Tool During Covid – 19 Pandemic

Health care has transformed intensely with the Digital Era. Smartphone is one of the digital tools which change the traditional way of teaching-learning nowadays. There is the widespread use of Smartphones as a part of the ‘Learning Management System’ in dental healthcare. Amid the COVID-19 pandemic, regular classroom and clinical educational activities for dental students were almost disturbed globally. ‘WHO Guidelines’, appeal to everyone to stop the spread of pandemic by maintaining social distancing. As per the Government and University guidelines, colleges must continue education through an online platform so that students should not be deprived of education. As this current situation is unpredictable, Smartphones are routinely used by many students to continue digital education. Therefore the perception of students on the usage of Smartphones as an easily available efficient teaching-learning tool in dentistry is the need of the hour. So, the aim of the study was to assess the perception of undergraduate dental students about the use of Smartphones in dentistry as an efficient teaching-learning tool during COVID – 19 pandemic. A cross-sectional study was carried out at VSPM Dental College and Research Centre, Nagpur. Pre-validated questionnaires were provided to the dental undergraduate students containing 14 closed-ended questions and one open-ended question among approximately 500 students through Google Form. 99% of students used smartphones for e-learning. Smartphones are convenient and easily accessible devices during this pandemic situation to continue teaching and learning. Smartphones in dentistry are really an efficient digital teaching-learning tool during COVID – 19 pandemic situations.
Bitcoin Choices

Global, scalable, and secure – Bitcoin Choices is a tamperproof voting software, leveraging the Algorand blockchain to specifically offer governance as a service to the Bitcoin network. Part I provides an overview of the Bitcoin Network and the fundamental limitations causing the Bitcoin governance problem. Part II offers analysis of the Algorand blockchain as a scaling solution for Bitcoin voting. Part III discusses decentralized governance, Choice Coin and the scalable solution to the Bitcoin governance problem.
Educational Technologies as Pedagogical Tools

One of the recent developments across education priorities in South Africa is the provision of Information and Communication Technologies (ICTs) to schools to achieve the objectives of the country’s National Development Plan (NDP) agenda 2030. With the current focus on adoption of technology for teaching and learning, it is imperative to fully understand the perceptions and attitudes of teachers towards the use of ICT in their practice. Thus, this study investigated the teachers’ perceptions and attitudes on the use of ICTs for teaching and learning. The findings are based on data collected through semi-structured interviews, journal reflections, and lesson observations. The results reveal that most teachers have positive perceptions towards the use of ICT for teaching and learning. It also indicated that the attitudes of teachers are varied based on the level of ICT training and support that they receive. The results of this research might be foremost to provide clear insights that could be contributing to the effective adoption of technology for teaching and learning.
Closing the NLP Gap: Documentary Linguistics and NLP Need a Shared Software Infrastructure

For decades, researchers in natural language processing and computational linguistics have been developing models and algorithms that aim to serve the needs of language documentation projects.However, these models have seen little use in language documentation despite their great potential for making documentary linguistic artefacts better and easier to produce.In this work, we argue that a major reason for this NLP gap is the lack of a strong foundation of application software which can on the one hand serve the complex needs of language documentation and on the other hand provide effortless integration with NLP models.We further present and describe a work-in-progress system we have developed to serve this need, Glam.
2d Object Detection for Autonomous Vehicles Using Transferlearning

The number of autonomous vehicles on our roads have been steadily increasing every year. Once created to solve problems of traffic object detection, simplification as well as fuzzification in a real-world traffic milieu, an automated multi- class detection and classification algorithm has been proposed to solve the aforementioned problem in India. In a country such as India, wherein a multitude of objects can be found on the road it is critical to come up with a reliable object detection system which can classify and detect the plethora of objects available in order to help the learning model make an informed decision. This paper presents a detailed study using the YOLOv5 algorithm for detecting indigenous objects native to Indian roads which include bikes, potholes and autos. The process is defined in four stages: dataset generation, data pre-processing and augmentation, training and implementation of the model and their inference. Images have been collected from a variety of sources in the public domain covering a varied amount of lighting scenarios and vehicle models. The YOLOv5 model has been trained for 250 epochs, achieved a precision of 90.9%, recall of 82.8%, and a mAP score of 0.847 for all classes.
Lapa Card

The world is becoming a place where everything is connected and where everyone expects to use their daily objects to interact with the digital ecosystem in the most safe and seamless manner. This article proposes a smart BLE/NFC membership card system – the Lapa Card –, which can be associated with any company, club or loyalty program. It enables users to physically check-in and safely process payments by acting as a physical authenticator that combines with other authentication factors (e.g., biometric) to seamlessly validate transactions with the highest safety standards. Also, it connects with a network of Gateways to support a directional marketing platform that automates promotional events by proximity. The developed system is able to safely process transactions with the highest safety and minimum friction, and is also able to perform directional marketing in the physical world with good responsiveness while protecting the users’ privacy.
Construction of Fuzzy Classifiers by a Brain Storm Optimization Algorithm

The fuzzy classifier has such important advantages as an intuitive operation logic and high interpretability of the fuzzy rules base. The development of a fuzzy classifier includes three consecutive building steps: generating a fuzzy rules base, feature selection, and optimizing the parameters of membership functions. To create a rule base, clustering methods are most often used. Wrapper methods are used for feature selection, and parameter optimization is performed either by traditional optimization methods or by metaheuristic methods. In this paper, we use a metaheuristic called Brain Storm Optimization to construct a fuzzy classifier. The use of algorithms based on this metaheuristic made it possible to obtain comparable accuracy values comparable to counterparts such as D-MOFARC and FARC-HD, with a much smaller number of rules and features.
Resource Allocation Using Phase Change Hyper Switching Algorithm in the Cloud Environment

Cloud computing is one of the emerging technology; it provides various services like Software as a Service, Platform as a Service, and Infrastructure as a Service on demand. It reduces the cost of traditional computing by renting the resources instead of buying them for a huge cost. The usage of cloud resources is increasing day by day. Due to the heavy workload, all users cannot get uninterrupted service at some time. And the response time of some users also gets increased. Resource allocation is one of the primary issues of a cloud environment, one of the challenging problems is improving scheduling performance and reducing waiting time performance. In this research work, a new approach is proposed to distribute the heavy workload to overcome this problem. In addition to that, the resource allocation of dynamic user requests is also taken into study. The Max-Min scheduling algorithm is modified concerning Dynamic Phase Change Memory (DPCM), and the Hyper switching algorithm is implemented to speed up the resource allocation process. The proposed DPCM algorithm provides efficient performance, improves scheduling results, and minimizes the waiting time. In the experimentation analysis, it is observed that this proposed approach optimizes the response time and waiting time of the dynamic user requests and distributes the workloads effectively, compared with other existing approaches. So the performance of the resource allocation process is improved, which enhances the efficiency of the cloud system.
An Adaptive Pixel Value Ordering Based Reversible Data Hiding Scheme for Images

Pixel value ordering (PVO) is a prominent method for reversible data hiding, which has gained increasing research interest in recent years due to its effective performance producing high-fidelity marked images. PVO-based methods performance lies on block dimensions and thresholds for classification. However, most methods determine proper parameters by using exhaustive searches in restricted search spaces. In this paper, we present a novel Pixel Value Ordering technique based on Genetic Algorithms (PVO-GA) to address these issues. PVO-GA uses flexible sized blocks to take advantage of different block features, to better exploit image redundancy. A genetic algorithm is introduced to optimize the embedding process since the solutions space is greatly expanded. Results showed that PVO-GA achieves satisfactory levels in terms of the introduced distortion while significantly reduces the computational cost needed to find the optimal set of insertion parameters when compared to previous methods.