Reviewerly/exHarmony huggingface dataset:
LOTUS-BI: A Thai-English Code-mixing Speech Corpus

Nowadays, English words mixed in Thai speech are usually found in a typical speaking style. Consequently, to increase the performance of the speech recognition system, a Thai-English code-mixing speech corpus is required. This paper describes the design and construction of LOTUS-BI corpus: a Thai-English code-mixing speech corpus aimed to be the essential speech database for training acoustic model and language model in order to obtain the better speech recognition accuracy. LOTUS-BI corpus contains 16.5 speech hours from 4 speech tasks: interview, talk, seminar, and meeting. Now, 11.5 speech hours of data from the interview, talk, and seminar acquire from the internet have been transcribed and annotated. Whereas, the rest of 5 speech hours from meeting task has been transcribing. Therefore, only 11.5 speech hours of data were analyzed in this paper. Furthermore, the pronunciation dictionary of vocabularies from LOTUS-BI corpus is created based on Thai phoneme set. The statistical analysis of LOTUS-BI corpus revealed that there are 37.96% of code-mixing utterances, including 34.23% intra-sentential and 3.73% inter-sentential utterances. The occurrence of English vocabularies is 29.04% of the total vocabularies in the corpus. Besides, nouns are found in 90% of all English vocabularies in the corpus and 10% in the other grammatical categories.
Toward unsupervised multiresident tracking in ambient assisted living: methods and performance metrics

Aging is a global challenge facing our society in the next few decades. Ambient assisted living (AAL) is a promising technology that helps people stay active, socially connected, and independent into older age. Even though ambient binary sensors, such as passive infrared (PIR) motion sensors, offer a low cost, easy to deploy, and less intrusive solution to constructing a smart environment, the limited ability of coping with multiple residents hinders the wide adoption of the AAL technology. In this work, we present three multiresident tracking algorithms, nearest neighbor with sensor graph (NN-SG), global nearest neighbor with sensor graph (GNN-SG), and multiresident tracking with sensor vectorization (sMRT), to solve the data association problem between the ambient sensor events and residents in the smart environment. We also introduce new performance metrics to evaluate the success of alternative approaches to multiresident tracking in smart homes. We evaluate all the algorithms with a recent smart home dataset recorded in real-life settings. Among the three algorithms, NN-SG and GNN-SG require sensor location and floor plan of the environment to derive the sensor graph, while sMRT does not require such information and relies solely on the unannotated sensor data. As an initiative of the unsupervised resident tracking solution, sMRT prompts additional research opportunities in multiresident tracking to improve the adoption of AAL technology in our daily life.
On the Linear Transformation in White-Box Cryptography

A linear transformation is applied to the white-box cryptographic implementation for the diffusion effect to prevent key-dependent intermediate values from being analyzed. However, it has been shown that there still exists a correlation before and after the linear transformation, and thus this is not enough to protect the key against statistical analysis. So far, the Hamming weight of rows in the invertible matrix has been considered the main cause of the key leakage from the linear transformation. In this study, we present an in-depth analysis of the distribution of intermediate values and the characteristics of block invertible binary matrices. Our mathematical analysis and experimental results show that the balanced distribution of the key-dependent intermediate value is the main cause of the key leakage.
Maximum smoothness consistent unwrapping of n-dimensional phase fields

The accurate recovery of an n-Dimensional field from observations that are wrapped into a particular interval (e.g., [−π,π]) is a problematic step in many applications, such as holography, interferometry, optical metrology, magnetic resonance imaging and analog-to-digital conversion in digital photography. While methods designed for this purpose abound (mainly in the 2-Dimensional case), most fail if the original unwrapped field contains abrupt changes that become aliased. In this paper we present Maximum Smoothness Consistent Unwrapping (MSCU), a novel and general method that overcomes said limitation. The method operates in two stages: in the first, a region without aliased changes which is as large as possible (which we call the consistent region) is found, and in the second, the unwrapped phase in the consistent region is propagated to the inconsistent areas to get the final result. MSCU has the following advantages: it is well founded theoretically, which allows the assessment of the reliability of its results; it is directly applicable to fields in any number of dimensions and when the signal is wrapped module any real number P; it has no free parameters to adjust; and it is computationally efficient and easy to implement. We present a formal derivation of the method and illustrations of its performance, both in synthetic fields –where we compare it with that of other state-of-the-art methods– and in real data (2-D data from optical speckle interferometry and 3-D data from magnetic susceptibility images obtained by magnetic resonance acquisitions). In this paper we focus on phase unwrapping applications, but the presented method may be directly applied to the case of other wrapping intervals as well, as for instance in High Dynamic Range image processing.
Review of Techniques for Automatic Text Summarization

Summarization refers to the process of reducing the textual components such as words and sentences but conveying most of the information in the input text. Research in summarization is very prominent in the current scenario where the textual data available is enormous and contains valuable information. People have been interested in summarization since time immemorial. The methods adopted in the past relied on manually reading the text and based on one’s understanding of the text, manually generating the summary. In the current world, due to the explosion of data from Internet and social media, the manual process is very tedious and time-consuming. As a result, there is a great need to automate the process of summarization. In this paper, we summarize most of the researches in the field of summarization which is unique and path-breaking.
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Due to the wide applications in consumer electronics, data storage systems and communication systems, cyclic codes have been an interesting research topic in coding theory. In this paper, let p be a prime with p≥7. We determine the Hamming distances of all repeated-root cyclic codes of length 5ps over Fq, where q=pm and both s and m are positive integers. Furthermore, we find all MDS cyclic codes of length 5ps and take quantum synchronizable codes from repeated-root cyclic codes of length 5ps. By comparing the minimum distances of 5ps-length repeated-root cyclic codes to BCH codes of close lengths, we illustrated that quantum synchronizable codes constructed from repeated-root cyclic codes have in general better performance in correcting Pauli errors than those from BCH codes.
Refining Skill Classification with Interactive Machine Learning

This Innovative Practice Full Paper presents our work on using machine learning to estimate student mastery of Calculus skills based on their performance using an online Problem Solving Learning Environment. We have shown in earlier papers that we can make accurate predictions of student performance at an aggregate level using the Performance Factors Analysis (PFA) approach. We had an expert teacher label 243 Calculus problems with the skills required to solve each problem and then use PFA to predict student performance using a dataset with 1609 students. In this paper, we improved our labeling of the skills by adding new difficulty tags as well as showing how to apply interactive machine learning to help the expert teacher improve their labeling of the skills required for each problem. This can then improve the prediction accuracy for future classes doing the same problems. It can also help the expert teacher improve the definition of the skills to make them more effective features for predicting student performance. The technique we present is to use a human-in-the-loop hill climbing process where a skill is added or removed, and we test to see whether this significantly improves the predictions for that problem over all students. If so, then the instructor is asked to verify whether indeed that particular skill should be removed or added. The new skill could be the result of the expert making a mistake in labelling a problem with skills, but it could also suggest that a modification of the skill definition would yield more accurate predictions. The key idea behind this approach is to harness the strengths of both the instructor and the machine learning algorithms to improve the effectiveness of the pedagogical system.
Prediction of Good Abandonment Behavior in Mobile Search

Good abandonment behavior means that in a query, the user can obtain the required information directly through the search results without clicking any linked page or reformulating the search query, which is common in mobile search at present. In this paper, a good abandonment prediction model in mobile search was constructed from 5 groups of features: session features, query features, SERP features, mobile touch interaction features and visual attention features. The visual attention features are introduced into good abandonment prediction for the first time and proved to be able to improve the prediction accuracy.
CREPE

Software crashes are nearly impossible to avoid. The reported crashes often contain useful information assisting developers in finding the root cause of the crash. However, crash reports may carry sensitive and private information about the users and their systems, which may be used by an attacker who has compromised the crash reporting system to violate the user's privacy and security. Besides, a single bug may trigger loads of identical reports which excessively consumes system resources and overwhelms application developers.
Grammar Error Correcting by the Means of CFG Parser

In this paper, we are trying to develop an efficient adjustable model for analyzing syntactical compositions of natural language. The model of a context-free grammar was chosen for the representation of grammar rules of the English language. Based on it we build error correction grammar. The Earley parser was used with an error accumulating algorithm to bring flexibility in the model. As an outcome, an appropriate instrument for natural language parsing was created. It can be used for grammar checking and correcting. Conducted experiments proved the efficiency and correctness of proposed model.
IR-Based Obstacle Avoiding and Self-Navigating Robot

This report presents a method for autonomous navigation of a mobile robot, a concept which can be built upon with the goal of achieving long-term robotic activity without human intervention. An IR transmitter is designed consisting of a NE 555 timer (normal remote) and an IR LED, while an IR receiver is mounted to an 8051-microcontroller robot. The system was changed and improved over each iteration, to resist environmental hurdles and implement obstacle avoidance, which ultimately resulted in a navigation success rate of 93 percent over 50 trials.
Online Learning Techniques for Space Situational Awareness (Poster)

The Sensor Allocation Problem (SAP) is a stochastic control problem that can rarely be solved to optimality. Real world systems often use heuristic solutions in place of optimal solutions when optimal solutions are not computationally feasible. This work is a step toward developing an approach to solve the SAP in the Space Situational Awareness (SSA) domain using Deep Reinforcement Learning (DRL) instead of a traditional heuristic approach. DRL Agents learn and adapt from experience and thus may demonstrate favorable characteristics in dynamic environments over traditional heuristic solutions. We develop a methodology to apply DRL algorithms to problem classes for which good heuristics exist. As a proof of concept of our general approach, we apply it to an under-actuated non-linear control problem. The problem is solved with a traditional heuristic approach. We use that solution to train a DRL agent to reach a similar level of performance. Performance characteristics of each solution, as well as to that of traditional DRL training approaches, are compared. The long-term goal is to extend our approach to more complex problem classes, and ultimately to provide improved solutions to the SAP in the SSA domain.
Statistical compaction of a monitoring cloud cluster resource when processing streaming services

The scientific problem of developing a method of statistical compaction of the necessary cloud resource in the processing of streaming services in the interests of monitoring systems for various purposes is formulated and solved. The method allows you to determine the number of virtual connections for different services that can be processed in a cloud cluster with a given quality, taking into account, on the one hand, the flow of requests from sources of different services for the provision of virtual connections, and, on the other hand, the needs of these same services transfer rates, taking into Analytical relations for calculation of necessary productivity of a cloud cluster at the set value of probability of loss of a micropackage of streams of DStream are received.
Secured Framework for IoT Using Blockchain

Internet of Things (IoT) gained a great focus in recent years due to its importance in humans' everyday life. IoT applications appear in several domains for human welfare. The need for a powerful and scalable security framework is the main focus for the current research. Blockchain (BC) is a distributed write-only ledger that eliminates the need for third parity to secure and verify transactions between peers. Though BC is considered the most powerful technique for securing transactions between IoT devices, these devices, unfortunately, cannot act as peers in BC because of its limited processing and storage. In this work, Blockchain is utilized in deploying a security framework for IoT monitoring applications. The proposed framework comprises clients (who query IoT devices), device gateways, and an administrator. IoT devices access BC through gateways. These gateways are assumed to be resource-rich and can perform mining tasks. To that end, Ethereum Blockchain is utilized in addition to Ethereum Smart contracts for enforcing a set of rules defined by the system administrator.
User Needs-driven Enrichment of Ontology

This study describes the mobile-based user needs-driven knowledge management system that supports the decision making process by considering user needs and preferences. Agriculture is one of the domains, in which, users seek specific information and knowledge relevant to their needs rather than searching and accessing general information from the Web, books, magazines or other information sources. Thus, the conceptualized solution was created by applying participatory sensing, natural language processing and ontology theories and techniques in a novel way in order to satisfy the user needs. The user-centered agriculture ontology that has been developed in our previous work is extended to make an up-to-date knowledge base by capturing user needs and preferences through participatory sensing. The methods of ontology evolution from unstructured data were analyzed to build a technique to enrich the user-centered ontology. The Modified Delphi method is used for verifying the correctness and relevancy of the ontology and the application-based evaluation is applied for checking the functional correctness of the system.
Distributed Fault-tolerant Platform for Web Applications

Web applications are software applications, services or microservices that runs on a remote server. The problem of downtime for web application is important and in some cases, is critical for business. Nowadays, cluster solutions are often used to provide fault-tolerance for applications. But these solutions don’t solve the problem of downtime if all instances of application are down. This paper presents a complex approach to provide fault-tolerance for web applications even if all instances of applications in the cluster are down. The approach is based on long-polling and request queueing methods. In this work Apache Kafka and Google Protocol Buffers has been used as the core for the fault-tolerant platform.
Chaos-Based Modified Morphological Genetic Algorithm for Effort Estimation in Agile Software Development

One of the most critical and important aspects of any software development project is the estimation of cost and effort, as the success or failure of the entire project is largely dependent on the accuracy of these estimations. For any software development project, several methods such as waterfall, prototyping etc. exist, but the agile methods have prevailed in terms of its efficiency and implementation in solving the problems related to the projects thus substituting the traditional methodologies. Agile methods have become much popular recently because of its ability to adopt to the changing dynamics (requirements) of software projects. This dynamic nature makes the task of estimation even more challenging than the traditional methodologies present. Thus, it becomes convenient to accurately estimate the effort and cost while adopting the agile methods, for which various techniques have already been proposed such as analogy, dis-aggregation, expert opinion etc, but none among the same have a proper mathematical model. This work has presented a novice method from the domain of evolutionary algorithms. The work is based on mathematical morphology (MM) consisting of a hybrid-artificial neuron (Dilation-Erosion perceptron (DEP)) extended from the concept of complete lattice theory (CLT). Authors have presented a chaotically modified genetic algorithm (CMGA) to build the DEP-CMGA model for solving the software development effort estimation (SDEE) problem. Calibration of the proposed model was done using data collected from 21 software projects based on agile software development (ASD). Four different statistics were used for determining the precision of the model and the results were compared with the one's obtained using the best available model in literature.
"More than just a Picture" -- The Importance of Context in Search User Interfaces for Three-Dimensional Content

While searching for two-dimensional content has become common practice in our work and leisure activities, search and retrieval of three-dimensional assets has thus far been underrepresented in research and practice. Therefore, it is unclear how to exactly support complex searches for these types of content. To contribute to fill this gap, this paper focuses on the role of context in Search User Interfaces (SUIs) for 3D objects and shows how it is envisioned by digital humanists in an exploratory user study. An evaluation of interactive mockups of a prospective search engine, contextualized by 2D, enhanced 2D and 3D maps, shows the scholars' preference for three-dimensional map views. A further qualitative analysis also suggests the need for more hybrid ways to visualize spatial context of 3D objects. This includes object properties, object relationships and spatial cues. Our findings contribute to a better understanding of SUI support for accessing the growing number of reality-based 3D models and virtual reconstructions.
Reducing Traffic Congestion by LSTM-LOF Framework

Traffic congestion is a phenomenon with which most drivers are familiar. It negatively affects drivers, city's residents and also the economic efficiency. Road pavement condition is one of the main causes of traffic congestion. Warning drivers of road pavement conditions is an effective and highly recommended solution to prevent a traffic jam before it actually occurs. Through this paper, we present a new strategy to reduce traffic congestion. We propose an unsupervised anomaly detection framework that predicts road obstacles. We suggest a well-motivated combination between the Long Short Term Memory (LSTM) and Local outlier value factor (LOF) techniques for predicting obstacles in time series. This combination can potentially offer very promising results in terms of prediction accuracy. We illustrate significant performance gains achieved by our technique with respect to the conventional methods.
Location, Context and Device aware Framework (LCDF)

The objective is to propose an unified framework to manage and optimize the data being generated by the mobile devices we regularly use, specially the smart cell phones, tablet pc, wearable devices after studying the current practiced frameworks. This study proposes a top-level framework for data consuming and sharing between mobile devices as well as the data collection and data management methods. This study has addressed some issues with the current data management frameworks and proposed the way how those issues can be avoided in future.
Multi-Task and Multi-Level Detection Neural Network Based Real-Time 3D Pose Estimation

3D pose estimation is a core step for human-computer interaction and human action recognition. However, time-sensitive applications like virtual reality also need this task to achieve real-time speed. This paper proposes a multitask and multi-level neural network architecture with a highspeed friendly 3D human pose representation. Based on this, we build a real-time multi-person 3D pose estimation system with a single RGB image as input. The network estimates 3D poses from the input image directly by the multi-task design and keeps both accuracy and speed by the multi-level detection design. By evaluation, we show our system achieves the 21 fps on RTX 2080 with only 33 mm accuracy lose compared with related works. We also provide network visualization to prove our network work as we design. This work shows the possibility for a single RGB image based 3D pose estimation system to achieve real-time speed, which is a basement for building a low-cost 3D motion capture system.
Structure and Velocity Estimation of a Moving Object via Synthetic Persistence by a Network of Stationary Cameras

Data-based, exponentially converging observers are developed for a network of stationary cooperative cameras estimating the Euclidean distance to features on a moving object (and hence, the objects' accurately scaled structure), without requiring the typical positive depth constraint and only requiring the object to remain in one camera's field-of-view. The developed observers demonstrate that a synthetic persistent view of the object relative to each camera is sufficient to maintain distance estimates. A Lyapunov-based stability analysis demonstrates that the developed geometric approach enables the developed distance observers for each camera to exponentially converge using the synthetic image of the object features generated by neighboring cameras.