Reviewerly/exHarmony huggingface dataset:
Operational Resource Theory of Nonclassicality and the Mach-Zehnder Interferometer

The nonclassical properties of quantum states are of tremendous interest due to their potential applications in future technologies. It has recently been realized that the concept of a theory is a powerful approach to quantifying and understanding nonclassicality. An important goal in this endeavor is to find resource theoretic measures of nonclassicality that are operational, meaning that they also quantify the ability of quantum states to provide enhanced performance for specific tasks, such as precision sensing. Here we present such a measure: it satisfies the necessary requirements of a resource-theoretic measure, and has the closest possible relationship to precision measurement: it quantifies the quantum-enhanced precision for pure states and is a tight upper bound on this precision for mixed states. We also show that a balanced Mach-Zehnder Interferometer provides a way to experimentally extract this measure, and we examine how the measure quantifies the concept of macroscopicity.
THE TASKS OF CONTEMPORARY HIGHER EDUCATION AND PROFESSIONAL GROWTH OF STUDENTS – FUTURE SPECIALISTS

The article deals with the problem of professionalism of the activity as a qualitative characteristic of a subject of this activity, which reflects the high level of professional qualification and competence, the variety of effective professional skills and abilities, the ability to master modern algorithms and methods for solving professional tasks.These tasks have a purpose to provide the process of a professional activity with a high degree of productivity.It was determined that the most significant characteristics of professionalism is the professional self-consciousness as the integral characteristic of a person as a self-organized psychological system, which states the conformity of professional activity performed by a person according to the image of the world, which is necessary for understanding the value and the meaning of the actions constituting the content of the professional activity.
Parent’s acceptance on wearable technology for children’s safety

&lt;span&gt;This paper presents a study on parent’s acceptance on wearable technology for children’s safety. Recently, there are many applications of wearable technology used in ensuring children’s safety, such as GPS tracking devices that are seen to be related to the safety of the children. However, the Malaysian parents have not been using these technologies to protect their children due to lack of knowledge on the wearable technology, non-familiarity with the devices, privacy and also security concern of data in wearable technology. Thus, the objective of the present research is to identify the significant factors that influence parents’ acceptance on wearable technology for their children’s safety and to identify the greatest influence factors for the acceptance of these wearable technologies for children’s safety. These factors are expected to bring about usefulness, ease of use, trust, privacy risk, financial risk and intention to use these technologies. As such, quantitative survey method was employed in the present study to obtain the result for the factors acceptance. The analysis and findings were done using IBM SPSS Version 25 tools via Pearson Correlation Analysis and Multiple Linear Regressions to get the result for the acceptance and to achieve the research objectives. Based on the result, it is discovered that the factors significant to intention to use of the wearable technology are perceived usefulness, perceived ease of use and trust while privacy and financial risk are factors seen to be non-significant to intention to use the wearable technology. Lastly, trust is discovered to be the greatest factor influencing parents to accept wearable technology and intention to use the technology. However, it is recommended for further study to employ experimental method in carrying out the research in order to get the real trust feeling of intention to use the wearable technology.&lt;/span&gt;
Classifying Customer’s Written Questions by Topics Using Deep Learning(LSTM)

In response to questions or complaints posted by customers on the company"s homepage, the response time is an important measure of customer satisfaction. However, the time it takes for a customer to receive an answer includes a time for the article to be selected by the person in charge of the reply, which limits the shortening. In this study, we developed a model in which a machine, not a person, reads the article, classifies the topic, and delivers it to each person in charge of the article. The article posted on the KEPCO homepage used in this study is a short sentence consisting of an average of 49 words. Due to the scarcity of multi-frequency words, it was found that there is a limit in securing a certain level of topic modeling accuracy in unsupervised machine learning like LDA. To overcome this, we labeled topics and let the machine conduct supervised learning. Although there are limitations in improving accuracy because there are articles containing more than two topics in one article, the classification accuracy is secured up to 84% by using LSTM and Baysian Optimization. The result of this study suggests that topic classification is possible for short-term customer questions in specific fields such as the electric power industry. In addition, it is expected that a model will be developed that can provide optimal reference answers for newly received questions when the topic-labeled questions and answers are fully accumulated.
Toward cross-speaker articulatory modeling

The recent increasing availability of comprehensive real-time MRI data of the vocal tract and concomitant progress in air-tissue boundary segmentation present novel opportunities for articulatory modeling. PCA-based articulatory models represent vocal tract configurations as weighted linear combinations of articulatory components that characterize vocal tract shaping patterns. Historically, most such models have been developed using data from a single speaker, and their direct application to data from multiple speakers may provide components that are not comparable across speakers. A technique that can address this issue is PARAFAC, which has been applied in the past to analyze data corresponding to tongue contour X-ray tracings of 10 English vowels from 5 speakers. PARAFAC introduces an additional level of weighting of the articulatory components that is constant for, and therefore characteristic of, each speaker. We revisited PARAFAC and ran a successful pilot on real-time MRI air tissue-boundaries of the entire vocal tract from 4 speakers, each uttering two repetitions of a set of 4 Shibboleth sentences, yielding 289 phones per speaker. Application on a much larger and diverse real-time MRI dataset already collected by our team will provide crucial progress toward true cross-speaker articulatory modeling. [Work supported by NIH and NSF.]
Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses.

Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely discussed or used within the NLP community. We address this gap by contrasting various hypothesis assessment techniques, especially those not commonly used in the field (such as evaluations based on Bayesian inference). Since these statistical techniques differ in the hypotheses they can support, we argue that practitioners should first decide their target hypothesis before choosing an assessment method. This is crucial because common fallacies, misconceptions, and misinterpretation surrounding hypothesis assessment methods often stem from a discrepancy between what one would like to claim versus what the method used actually assesses. Our survey reveals that these issues are omnipresent in the NLP research community. As a step forward, we provide best practices and guidelines tailored to NLP research, as well as an easy-to-use package called 'HyBayes' for Bayesian assessment of hypotheses, complementing existing tools.
Motion Reasoning for Goal-Based Imitation Learning

We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and reproduce the same goal in a completely different environment. The key challenge is that the goal of a video demonstration is often ambiguous at the level of semantic actions. The human demonstrators might unintentionally achieve certain subgoals in the demonstrations with their actions. Our main contribution is to propose a motion reasoning framework that combines task and motion planning to disambiguate the true intention of the demonstrator in the video demonstration. This allows us to robustly recognize the goals that cannot be disambiguated by previous action-based approaches. We evaluate our approach by collecting a dataset of 96 video demonstrations in a mockup kitchen environment. We show that our motion reasoning plays an important role in recognizing the actual goal of the demonstrator and improves the success rate by over 20%. We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.
Explaining the Broad-line Region through photoionisation modelling

Broad-line regions (BLR) are one of the main components that constitute the phenomenological picture of active galaxies near the vicinity of the accreting supermassive black holes. Both theoretical and observational studies have shown that the BLR is made of dense, ionized gas clumps that have a strong virialized distribution at parsec-scale distances from the nuclei. Using a theoretically motivated photoionized gas model, I constrain the ionisation parameter (U) and cloud density (n$_{\rm{H}}$) as a function of the strength of the FeII emission. Recent observations in the reverberation mapping studies have contested the standard radius-luminosity relation showing increased dispersion in the relation, in particular, after the inclusion of highly accreting quasars. I incorporate the departure coefficient that accounts for this dispersion. This departure term in terms of the dimensionless accretion rate ($\dot{\mathcal{M}}$) and Eddington ratio ($L\mathrm{_{bol}}/L\mathrm{_{Edd}}$), also includes the virial factor that accounts for the BLR geometry. I then combine the fundamental plane relation for the BLR to connect the FeII strength (R$_{\rm{FeII}}$) in terms of U and n$_{\rm{H}}$ using selected values for the shape of the broad H$β$ profile.
MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering

The primary goal of ad-hoc retrieval (document retrieval in the context of question answering) is to find relevant documents satisfied the information need posted in a natural language query. It requires a good understanding of the query and all the documents in a corpus, which is difficult because the meaning of natural language texts depends on the context, syntax,and semantics. Recently deep neural networks have been used to rank search results in response to a query. In this paper, we devise a multi-resolution neural network(MRNN) to leverage the whole hierarchy of representations for document retrieval. The proposed MRNN model is capable of deriving a representation that integrates representations of different levels of abstraction from all the layers of the learned hierarchical representation.Moreover, a duplex attention component is designed to refinethe multi-resolution representation so that an optimal contextfor matching the query and document can be determined. More specifically, the first attention mechanism determines optimal context from the learned multi-resolution representation for the query and document. The latter attention mechanism aims to fine-tune the representation so that the query and the relevant document are closer in proximity. The empirical study shows that MRNN with the duplex attention is significantly superior to existing models used for ad-hoc retrieval on benchmark datasets including SQuAD, WikiQA, QUASAR, and TrecQA.
A Novel Approach to Enhance the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

Search has for a long time been an important tool for users to retrieve information. Syntactic search is matching documents or objects containing specific keywords like user-history, location, preference etc. to improve the results. However, it is often possible that the query and the best answer have no term or very less number of terms in common and syntactic search can not perform properly in such cases. Semantic search, on the other hand, resolves these issues but suffers from lack of annotation, absence of WordNet in case of low resource languages. In this work, we have demonstrated an end to end procedure to improve the performance of semantic search using semi-supervised and unsupervised learning algorithms. An available Bengali repository was chosen to have seven types of semantic properties primarily to develop the system. Performance has been tested using Support Vector Machine, Naive Bayes, Decision Tree and Artificial Neural Network (ANN). Our system has achieved the efficiency to predict the correct semantics using knowledge base over the time of learning. A repository containing around a million sentences, a product of TDIL project of Govt. of India, was used to test our system at first instance. Then the testing has been done for other languages. Being a cognitive system it may be very useful for improving user satisfaction in e-Governance or m-Governance in the multilingual environment and also for other applications.
일본이 부정하는 한국의 독도교육

In the 21st century, Japan conservative politicians have provoked Korea about the sovereignty of Dokdo. Since Japan enacted 'Dokdo Day' in 2005, Dokdo has been described as Japanese territory in the textbooks of social studies at elementary and junior high schools. Therefore, Korea strongly resisted and strengthened Dokdo education. As a result, the textbooks of Korea and Japan described the contradictory contents concerning the sovereignty of Dokdo. Korea called for the withdrawal of distorted Dokdo contents from Japanese textbooks. Japan also has created a logic to deny the contents of Korean textbooks that make its own sovereignty over Dokdo damaged. The purpose of this study is to examine Korea's Dokdo education which is denied by Japan. The differences in the position of Dokdo education between the two countries are due to differences in interpretation of historical documents.
Designing good healthcare : Improving ulcer monitoring in home care

This thesis seeks to improve the ways in which healthcare professionals can monitor and treat ulcers using mobile technology. The work focuses on designing a well rounded user experience which is u ...
Point Clipping Algorithm on Employee Presence Application for Geolocation of Employee Position

In organizations, companies or agencies such as the Office of Communication and Information Technology, presence has become one of the critical things in assessing the performance of workers/employees. There are several ways to do attendance, where one of them can facilitate the present process with digital tools/machines such as smartphones that utilize the GPS feature to run APIs such as Google Geolocation on Android. Also, a system is needed that can manage the results of the present process, such as web-based applications that can also be accessed through either a computer or smartphone. Therefore, the need to build a presence application both in the presence and in management that can help agencies such as Office of Communication and Information (Diskominfo) to handle internal processes, especially in the management of its employees. In this study, the application in the construction of its application is used Point Clipping algorithm as a tool for the present process that can take advantage of the GPS feature. Based on the results of the tests conducted, the accuracy value of the incoming and returning attendance process was obtained in the location area with an average of each side of the area of approximately 11.21 meters at the entry point and 18.05 meters at the presence of going home outside the office, where the location area boundary outside the specified office which is around 15 meters and the time test is obtained the time delay value in the attendance process with an average of each side of the area of about 9.6 seconds at the incoming attendance and 8.3 seconds at the home attendance.
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse

Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fundus image processing, especially retinal vessel segmentation, has been extensively studied. Built upon the known operator theory, interpretable deep network pipelines with well-defined modules have been constructed on fundus images. In this work, we firstly train a modularized network pipeline for the task of retinal vessel segmentation on the fundus database DRIVE. The pretrained preprocessing module from the pipeline is then directly transferred onto OCT-A data for image quality enhancement without further fine-tuning. Output images show that the preprocessing net can balance the contrast, suppress noise and thereby produce vessel trees with improved connectivity in both image modalities. The visual impression is confirmed by an observer study with five OCT-A experts. Statistics of the grades by the experts indicate that the transferred module improves both the image quality and the diagnostic quality. Our work provides an example that modules within network pipelines that are built upon the known operator theory facilitate cross-modality reuse without additional training or transfer learning.
Evaluating a Player’s Network Class in a Multiplayer Game with Fuzzy Logic

In a multiplayer game environment, smoothness of a game depends on factors such as game’s netcode, player’s hardware, network connection and server’s response time. Players with bad network conditions and spiking network synchronization is always a problem for multiplayer games for both PCs and gaming devices based interactive sessions. Previous implementations to prevent such occasions involve disconnection based on their connection statistics. However usually used schema involves constant boundary values and mostly biased for disconnection decision. Disconnecting players with medium to worse network status is typically not an optimal practice as rendering the game unplayable for the person as if we think about player’s hardly earned money. Today, fast CPU and graphics processors offer rendering of game and receiving the game state with high frame rates. Hence, quality of the game depends on network connection of participants. Games prefer measuring latency between the server and the player by sending simple ping packets similar to well-known Unix tool. An administrator evaluates this value whether the player should be disconnected or not. In this paper, we discuss evaluating the player’s complex network statistics and automatically deciding for absence of the player with pattern recognition techniques.
A Structure-based Attack on the Linearized Braid Group-based Diffie-Hellman Conjugacy Problem in Combination with an Attack using Polynomial Interpolation and the Chinese Remainder Theorem

This doctoral thesis is dedicated to improve a linear algebra attack on the so-called braid group-based Diffie-Hellman conjugacy problem (BDHCP). The general procedure of the attack is to transform a BDHCP to the problem of solving several simultaneous matrix equations. A first improvement is achieved by reducing the solution space of the matrix equations to matrices that have a specific structure, which we call here the left braid structure. Using the left braid structure the number of matrix equations to be solved reduces to one. Based on the left braid structure we are further able to formulate a structure-based attack on the BDHCP. That is to transform the matrix equation to a system of linear equations and exploiting the structure of the corresponding extended coefficient matrix, which is induced by the left braid structure of the solution space. The structure-based attack then has an empirically high probability to solve the BDHCP with significantly less arithmetic operations than the original attack. A third improvement of the original linear algebra attack is to use an algorithm that combines Gaussian elimination with integer polynomial interpolation and the Chinese remainder theorem (CRT), instead of fast matrix multiplication as suggested by others. The major idea here is to distribute the task of solving a system of linear equations over a giant finite field to several much smaller finite fields. Based on our empirically measured bounds for the degree of the polynomials to be interpolated and the bit size of the coefficients and integers to be recovered via the CRT, we conclude an improvement of the run time complexity of the original algorithm by a factor of n^8 bit operations in the best case, and still n^6 in the worst case.
Deep Spatio-Temporal Modeling for Object-Level Gaze-Based Relevance Assessment

The current work investigates the problem of objectlevel relevance assessment prediction, taking into account the user's captured gaze signal (behaviour) and following the Deep Learning (DL) paradigm. Human gaze, as a sub-conscious response, is influenced from several factors related to the human mental activity. Several studies have so far proposed methodologies based on the use of gaze statistical modeling and naive classifiers for assessing images or image patches as relevant or not to the user's interests. Nevertheless, the outstanding majority of literature approaches only relied so far on the use of handcrafted features and relative simple classification schemes. On the contrary, the current work focuses on the use of DL schemes that will enable the modeling of complex patterns in the captured gaze signal and the subsequent derivation of corresponding discriminant features. Novel contributions of this study include: a) the introduction of a large-scale annotated gaze dataset, suitable for training DL models, b) a novel method for gaze modeling, capable of handling gaze sensor errors, and c) a DL based method, able to capture gaze patterns for assessing image objects as relevant or non-relevant, with respect to the user's preferences. Extensive experiments demonstrate the efficiency of the proposed method, taking also into consideration key factors related to the human gaze behaviour.
Robust Image Watermarking Method using Wavelet Transform

In this paper a robust watermarking method operating in the wavelet domain for grayscale digital images is developed.The method first computes the differences between the watermark and the HH1 sub-band of the cover image values and then embed these differences in one of the frequency sub-bands.The results show that embedding the watermark in the LH1 sub-band gave the best results.The results were evaluated using the RMSE and the PSNR of both the original and the watermarked image.Although the watermark was recovered perfectly in the ideal case, the addition of Gaussian noise, or compression of the image using JPEG with quality less than 100 destroys the embedded watermark.Different experiments were carried out to test the performance of the proposed method and good results were obtained.