Reviewerly/exHarmony huggingface dataset:
Nonclassicality of Photon Added Bipartite Glauber Lachs State

Abstract Mixture of coherent state with thermal noise is known as the Glauber Lachs state, which is taken into consideration. Nonlocal photon addition is imposed on the two mode Glauber Lachs state by the application of bosonic creation operators. This nonlocal photon addition induces nonclassicality, revealed in the single mode and two mode squeezing properties. The ratio of the coherent noise to thermal noise is the key factor in observing the nonclassical features. The tolerance limit of the noise for the nonclassicality is discussed in single mode squeezing and two mode squeezing quadrature operators. The results show that the squeezing properties are less pronounced even for the low level of thermal noise.
An Adaptive Threshold Setting Algorithm Based on PCNN Edge Detection Model

Edge detection is an important image preprocessing technology, which plays an important role in image classification, target recognition and image understanding, but the parameter setting of various edge detection algorithms is always a difficult problem. This paper aims at the problem that the segmentation threshold in the PCNN edge detection model proposed in the previous stage needs to be manually set, utilizing the characteristics of the image edges contained in the PCNN segmentation output, through the combination of transform domain edge feature extraction and DNN prediction, realize the adaptive setting of the segmentation threshold parameters of the PCNN detection model. First, use Contourlet transform to extract sub-band feature vectors on the output of each iteration of the PCNN segmentation model, further perform median pooling processing on the extracted feature vectors, and then calculate the variance and mean value as the feature vector, and then propose an edge detection algorithm that uses DNN to realize segmentation threshold adaptive prediction. When predict the appropriate segmentation threshold, this method can get better edge detection effect of the image only after six iteration of PCNN. When the algorithm is applied to images such as Lena and BSDS500 image library, it has obtained a finer edge detection effect and has high robustness.
A Comparative Study of Different Information Entropy Index in Personalized Exercise Recommendation

It is meaningful to recommend exercises to students in an online education system with a large amount of learning resource. Many recommendation methods usually rely on strategy in the recommendation system in order to predict an exercise score. In this paper, we compare different information entropy index in the exercise recommendation. These index consider how well exercise matches the student’s knowledge ability. In order to compare different methods, we introduce an interactive platform for dynamic exercise recommendation. We conduct a set of exercise recommendation experiments, compare the effect of different index and find the optimal index in the experiment.
A Text Search Algorithm in Compressed Text

A text search algorithm within compressed text using the compression algorithm in [1] is presented. This compression algorithm compresses a pair of letters into one letter through mapping the lower bytes of their unicodes into one unicode character. It utilizes the fact that the unicodes of a given natural language alphabet have the same upper byte. The proposed text search algorithm - in this paper - searches for the compressed pattern within the compressed text using character comparisons as done by typical text search algorithm without modification. However, few additional comparisons are required to decide if a valid shift for the compressed pattern within the compressed text corresesponds to a valid shift for the pattern in the uncompressed text. The experimental study showed a slightly better text search time for the proposed algorithm than searching in uncompressed text.
KF-Based Target L&amp;T Using RSSI

The KF is the one of the most widely used approaches to solve the problem of target L&T using RSSI measurements. However, very few existing KF-based research works address the issues such as abrupt changes in the target velocity and variation in the target trajectories. The existing KF-based L&T algorithms though perform well for one type of target track; it is guaranteed to perform if the target track changes or the monitoring area is enlarged. It is very difficult to achieve high tracking accuracy with the help of traditional techniques such as trilateration alone in this context. Hence, the combination of the trilateration and KF can be a very good option to deal with the abovementioned issues. This chapter presents two range-based KF algorithms, namely, trilateration+KF and trilateration+UKF, to deal with these important issues. The chapter discusses the performance of the trilateration+KF and trilateration+UKF L&T algorithms for the changes in the target velocity trajectory, target trajectory, as well as WSN monitoring area. The proposed techniques are tested for linear as well as nonlinear target trajectories. The WSN monitoring (simulation) area is varied from 100 m × 100 m to 200 m × 200 m. To understand the effect of abrupt variations in the target velocity, we varied velocity abruptly in the range of −2 to 7 m/s at specific time instances in all the three cases. The overall target L&T performance is evaluated in terms of the localization error and RMSE. The results confirmed that the proposed target L&T algorithms are able to track the moving target with the help of WSN, irrespective of the dynamicity of the given RF environment.
An integrated framework for knowledge based obstacle information system with image processing techniques

Abstract One of the most important reasons of car accidents are collisions with vehicles that are not visible to a driver. This is why driver warning systems are developed. The main threat for a driver on the highway comes from the surrounding vehicles especially when the driver is not aware of the close presence generally known as driver’s blind spot. An area in and around the vehicle that cannot be directly observed by the driver are known as blind spots. Image processing plays a vital role in this scenario to safe guard drivers from sudden obstacles and blind spots. The pre-processed sequences of images acquired using front and rear camera of a vehicle are considered to train the case base reasoning (CBR) model, which detects the presence of dangerous objects in the blind spot area. To distinguish near and far obstacles, the same CBR model is used with a specified threshold in the vehicle blind spot area. The proposed knowledge based obstacle information system obtains promising results with standard blind spot camera which can improve safety of the driver and has the potential to be applied in vehicular applications for the detection of obstacles and blind spot area.
Differences in the types of kelingkan shawls in Malaysia

The art of kelingkan embroidery is a valuable traditional heritage for women. Kelingkan shawls are still worn in the community during important ceremonies, especially weddings. This heritage is a Malay handicraft that is pioneered by women and kelingkan is still made on a small scale in the states of Sarawak, Selangor, and Kelantan. Understanding regarding the types of kelingkan shawls is important for the community, especially those who are kelingkan shawl enthusiasts, so that this form of traditional kelingkan shawl will endure, though some changes have occurred in the creation of kelingkan due to modernization. To conclude this problem, two questions have emerged - What are the types of kelingkan shawls in Malaysia and What are the differences between traditional and modern kelingkan shawls? This study applies the descriptive qualitative approach through fieldwork such as observations, collection studies in Malaysia, specifically the Malay Peninsula in Kuala Lumpur, Selangor, Kelantan and Terengganu, as well as in Kuching, Sarawak, where interviews were held with embroiderers and library research was conducted. As a result, this study has found that traditional kelingkan shawls can be divided into two types, namely square or rectangular ones, using silver or gold kelingkan thread. Meanwhile, modern kelingkan shawls can be divided into 3 types - square, rectangle and triangle, which all still use silver and gold kelingkan thread, as well as colorful metal strips. The implications of this study is that it can provide knowledge and understanding to the community, especially those who are interested in or are studying kelingkan shawls in Malaysia. It will encourage them to know more about the changes that are happening to Malay artistic heritage and how it can be sustained in Malaysia.
Decentralized Data Fusion with Probabilistically Conservative Ellipsoidal Intersection

When performing decentralized data fusion, one of the major challenges is the well-known “track-to-track” correlation problem. One approach to handling this correlation is to use correlation-agnostic fusion techniques that generate conservative estimates of the fused probability distribution, irrespective of the amount of common information present. When working with Gaussian distributions, past approaches have only considered the covariance of the input distributions to compute the covariance of the fused distribution. In this paper, we introduce another constraint that considers the means of the input distributions. While this constraint cannot 100% guarantee the output is conservative, it can “probabilistically” - to a user-specified certainty - ensure the output is conservative. This introduction of a probabilistic bound enables fused probability distributions with less excess covariance. Specifically, we modify the ellipsoidal intersection technique to include the probabilistically conservative constraint. We present results that show using the probabilistic bound generates a fused distribution that is still conservative but with less excess covariance than Ellipsoidal Intersection without the probabilistic constraint.
Interaction with Objects and Humans Based on Visualized Flow Using a Background-Oriented Schlieren Method

Air flow is a ubiquitous phenomenon that can provide important insights to extend our perceptions and intuitive interactions with our surroundings. This study aimed to explore interaction methods based on flow visualization using background-oriented schlieren (BOS) in three case studies. Case 1 involved visualization of the airflow around humans or objects, which demonstrated that visualized flow provides meaningful information about the human or object under investigation. Case 2 involved the testing of a prototype sensor, where visualized flow was used to sense fine airflow. Stabilization of the flow was required for operation of the sensor. Case 3 involved the testing of a prototype system to investigate the use of flow as an input interface for playing a video game. The system did not operate as expected, but the design of the flow can be improved. Overall, interaction using flow visualization allows for the perception of air flow in a broad sense, and presents new opportunities in the field of human computer interaction.
Enhance Conversation-Based Tutoring System with Blended Human Tutor

Conversation-based learning technology is playing important role in adaptive instructional systems (AIS). As a part of the adaptivity of an instructional system it would be ideal to incorporate a human tutor to deal with conversation that is beyond the capability of a chat-bot or virtual tutor. Moreover, it is possible to answer many research questions if experiments are performed with a blended human tutor. In this research we have implemented a prototype that blends a human tutor with a virtual tutor in a typical conversation-based tutoring system (i.e., AutoTutor). We performed R&D with server-based and serverless implementations. Additionally, we have implemented audio-visual blending through WebRTC so that the conversation between students and teachers can take place through spoken language with video. We found that the serverless chat blending with AutoTutor is fast, easy to implement, and reliable. We made the so-called serverless implementation possible by using some very powerful features of a learning record store (LRS).
Design and Application of Tag Identification APP for Warehouse Sorting System

In the development process of automated warehouse, the tags in the sorting system are miscellaneous and disorderly, resulting in the labor and time consumption of sorting operations. Therefore, this paper designs a label recognition APP for warehouse sorting system. Based on Zxing barcode recognition technology and ultra-lightweight OCR technology, this paper fully taps the ability of the latest text detection algorithm DB and the classical text recognition algorithm CRNN, and designs an Android APP that can identify various labels. Finally, select a 13. 56 MHz high-frequency RFID tag, and read the information by using the NFC technology on Android phones. With the help of the automatic identification and extraction technology and the frequency identification reading and writing technology, the label identification of the automatic sorting system is completed.
How to Revise Beliefs from Conditionals: A New Proposal

Author(s): Hartmann, Stephan; Hahn, Ulrike | Abstract: A large body of work has demonstrated the utility of the Bayesian framework for capturing inference in both specialist and everyday contexts. However, the central tool of the framework, conditionalization via Bayes’ rule, does not apply directly to a common type of learning: the acquisition of conditional information. How should an agent change her beliefs on learning that “If A, then C”? This issue, which is central to both reasoning and argumentation, has recently prompted considerable research interest. In this paper, we critique a prominent proposal and provide a new, alternative, answer.
A Feature-Based Monocular Visual Odometry Solution

This paper presents a solution to the problem of estimating a camera's ego motion using its image data, namely, visual odometry. The implementation considered both synthetic and real-world scenarios. Analysis and performance comparisons of the algorithm against a defined benchmark are offered, identifying that the algorithm is resilient to gaussian image noise at SNR36dB, yet prone to scale ambiguity and gross errors in sparsely featured environments.
Investigation the City Environment with the Help of Typographic Communication Design Perspectives

The signboard can be said to be the face of the store. Well-designed typefaces, symbolic images, and unique colours help express the business's individuality and make viewers deeply impressed. A unique sign is a good sign. Signboards that are beautifully formative expressions with enough margins make viewers feel visually enjoyable. It is also an important medium that allows designers to help the business parties and shops enhance their brandings and increase their business credibility. From the customer's perspective, a beautiful sign is a good sign. No matter how fantastic a signboard is, it cannot impress people if it does not fit their surroundings. A sign that looks good in harmony with the characteristics of the area where the business is located, and its shape and colour is a good sign. This study investigated the application of communication design theories in signboard design among Asian cities includes but not limited to Hong Kong, Seoul, Tokyo.
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes

We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounded memory states ( $$\mathbf {BMS}$$ ) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states. We give the complete map of all pairwise relations for an established collection of criteria of successful learning. Most prominently, we show that non-U-shapedness is not restrictive, while conservativeness and (strong) monotonicity are. Some results carry over from iterative learning by a general lemma showing that, for a wealth of restrictions (the semantic restrictions), iterative and bounded memory states learning are equivalent. We also give an example of a non-semantic restriction (strongly non-U-shapedness) where the two settings differ.
Differentiable Rendering-based Pose-Conditioned Human Image Generation

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.
Sample-free white-box out-of-distribution detection for deep learning

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in- distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.
DESIGNING APPLICATIONS FOR CARRYING OUT VOCATIONAL GUIDANCE WORK. SUBSITE DESIGN PROBLEMS

The article investigated the current problem of promoting the image of the university. One aspect of this may be the creation of subsites to the main site of the university. It will become the newest tool, a communication platform for entrants. A modern website is a tool, a communication technology platform, a new space for interaction between teachers and students. It's not just about reputation and public relations. This is a new practice of society in the age of informatization. The key role in the competitive confrontation of consumer needs in educational services, as well as for the worldwide recognition of universities, is assigned to its corporate website. In order for the site to take a worthy place in the rankings, it is necessary to take action to improve the internal factors of the site. Therefore, the development of both the university website and its subsites should be approached responsibly. It is necessary to pay attention at development to stylistic registration of pages (it should be executed in the same style, as the main site). Nowadays, it is also necessary to adhere to cross-browser and take into account search engine optimization. This is a set of measures that would raise the position of the site in the results of search engines for certain user queries in order to promote the site. It is also necessary to pay attention to the development of the navigation system of the subsite. An interesting solution that will distinguish this subsite from others was the use of geographic information systems to create a navigation map. The final stage of resource development should be its testing. In this way it is possible to create an effective website of the university, which will guarantee the transparency of educational and scientific activities of the university, directly and indirectly affecting its competitiveness and demand in the higher education market by the target audience, primarily entrants.
Efficient Industrial Control Systems risk assessment using the attack path to the critical device

In cyberattacks against Industrial Control Systems (ICS), one of the final goals of attackers is to impair ICS operations. Recent ICS tend to be connected with other networks and are often implemented with common technologies in order to increase production efficiency and reduce costs. This leads to the creation of new attack surfaces that increase the cyber risks of these systems. Although preventing system intrusion without fault could be difficult, if attacks can be prevented at an early stage before they reach the physical world, damage can be minimized. Thus, appropriate cybersecurity risk assessment methods for ICS are necessary. ICS risk assessment methods proposed in previous research require more time to administer, and they heavily depend on the analysts’ particular expertise and skill to efficiently examine attack scenarios and attack paths. In this research, we propose an efficient quantitative risk assessment method to evaluate the risk of possible attacks on critical ICS devices. This method uses packet capture data in the ICS network to automatically enumerate possible attack paths and calculates the risk level of each attack path. It then comprehensively evaluates the risk level of an entire ICS by utilizing multiple metrics such as the severity of vulnerabilities that exist in specific devices or communication methods, security measures that have been taken, and the features of the attack paths to the critical devices.
Impact of Different Belief Facets on Agents’ Decision—A Refined Cognitive Architecture to Model the Interaction Between Organisations’ Institutional Characteristics and Agents’ Behaviour

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent’s actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents’ behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.