Reviewerly/exHarmony huggingface dataset:
Minimizing Information Leakage of Abrupt Changes in Stochastic Systems

This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need to remain private. Privacy refers to the disclosure of information of these changes through observations of the underlying Markov chain. In contrast to previous work on privacy, we study the problem for an online sequence of data. We use theoretical tools from optimal detection theory to motivate a definition of online privacy based on the average amount of information per observation of the stochastic system in consideration. Two cases are considered: the full-information case, where the eavesdropper measures all but the signals that indicate a change, and the limited-information case, where the eavesdropper only measures the state of the Markov process. For both cases, we provide ways to derive privacy upper-bounds and compute policies that attain a higher privacy level. It turns out that the problem of computing privacy-aware policies is concave, and we conclude with some examples and numerical simulations for both cases.
Where to From Here? Unraveling Wicked Problems

The evolution of ecosystem science and systems ecology as legitimate branches of science has occurred since the late 1960s. They have flourished because of their essential contributions to understanding and management of natural resources and the environment. Scientific knowledge about the structure and functioning of ecosystems, the services ecosystems provide to people, and the roles people play therein, have become commonplace. Scientists know what challenges face Earth’s environments and they know many of the solutions available to resolve them. But scientific knowledge alone is insufficient to implement change. Knowledge transfer to people who manage our lands, waters, and other natural resources is essential and they must become engaged in implementing solutions to major natural resource and environmental challenges. Adoption of new concepts and technologies is critical. Overcoming the barriers to adoption of best management practices is critically needed. Many of the barriers are created by adherence to dogmatic cultural norms and ideologies by landowners, managers, and policy makers. Behavioral, organizational, learning, and marketing professionals study behavioral change. The systems ecology paradigm must incorporate behavioral, organizational, learning, and marketing professionals as partners in implementing concepts of adoption cycles and community-based social marketing to solve wicked problems.
Segmenting Kidney on Multiple Phase CT Images using ULBNet

Segmentation of kidney on CT images is critical to computer-assisted surgical planning for kidney interventional therapy. Segmenting kidney manually is impractical in clinical, automatic segmentation is desirable. U-Net has been successful in medical image segmentation and is a promising candidate for the task. However, semantic gap still exists, especially when multiple phase images or multiple center images are involved. In this paper, we proposed an ULBNet to reduce the semantic gap and to improve segmentation performance. The proposed architecture includes new skip connections of local binary convolution (LBC). We also proposed a novel strategy of fast retraining a model for a new task without manually labelling required. We evaluated the network for kidney segmentation on multiple phase CT images. ULBNet resulted in an overall accuracy of 98.0% with comparison to Resunet 97.5%. Specifically, on the plain phase CT images, 98.1% resulted from ULBNet and 97.6% from Resunet; on the corticomedullay phase images, 97.8% from ULBNet and 97.2% from Resunet; on the nephrographic phase images, 97.6% from ULBNet and 97.4% from Resunet; on the excretory phase images, 98.1% from ULBNet and 97.4% from Resunet. The proposed network architecture performs better than Resunet on generalizing to multiple phase images.
Playfulness, Realism and Authenticity in Cultural Presence: A Case Study of Virtual Heritage Players

The intersection of virtual reality (VR) and cultural heritage, also described as Virtual Heritage (VH), is an example of a disciplinary 'cross-fertilization' between arts and technologies (Roussou, 2007: 225). Technological advances in communication systems have accelerated the advent of new aesthetic experiences. Digital technology has created a novel perceptual dimension for the viewers of VH that Erik Champion calls cultural presence (2006). The idea of immersive art goes back to the classical world, and it now reappears in the immersion strategies of today's virtual art (Grau, 2003: 25). In order to investigate the aesthetic effects of cultural presence for the player in VH, this research takes 神游敦煌 (Shenyou Dunhuang) 2018, a popular VH project from HTC Steam VR platform as a case study. The 敦煌莫高窟 (Dunhuang Mogao Grottoes) were included in the 'World Cultural Heritage' list by United Nations Educational, Scientific and Cultural Organization (UNESCO) as representative achievements of Buddhist art in ancient China. This research draws on data elicited through focus group discussion after the group's members had experienced the VH. The researcher organised a group of participants playing Shenyou Dunhuang to discuss their aesthetic experience. The discussion text was encoded via Nvivo software. The aesthetic experience of the participants is examined and discussed through the frame of three sub-concepts: playfulness, realism and authenticity. In so doing this research discusses how the ambivalence of immersion and distance in art reception influences players' cultural presence in VH.
Diversity-Based Generalization for Unsupervised Text Classification Under Domain Shift

Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance. Diversity plays the role of promoting the model to better generalize and be indiscriminate towards domain shift by forcing the model not to rely on same features for prediction. We apply this concept on the most explainable component of neural networks, the attention layer. To generate sufficient diversity, we create a multi-head attention model and infuse a diversity constraint between the attention heads such that each head will learn differently. We further expand upon our model by tri-training and designing a procedure with an additional diversity constraint between the attention heads of the tri-trained classifiers. Extensive evaluation using the standard benchmark dataset of Amazon reviews and a newly constructed dataset of Crisis events shows that our fully unsupervised method matches with the competing baselines that uses unlabeled target data. Our results demonstrate that machine learning architectures that ensure sufficient diversity can generalize better; encouraging future research to design ubiquitously usable learning models without using unlabeled target data.
A Practical Way to Improve Automatic Phonetic Segmentation Performance

Automatic phonetic segmentation is a fundamental task for many applications. Segmentation systems highly rely upon the acoustic-phonetic relationship. However, the phonemes' realization varies in continuous speech. As a consequence, segmentation systems usually suffer from such variation, which includes the intra-phone dissimilarity and the inter-phone similarity in terms of acoustic properties. In this paper, We conducted experiments following the classic GMM-HMM framework to address these issues. In the baseline setup, we found the top error comes from diphthong /oy/ and boundary of glide-to-vowel respectively, which suggested the influence of the above variation on segmentation results. Here, we present our approaches to improve automatic phonetic segmentation performance. First, we modeled the intra-phone dissimilarity using GMM with model selection at the state-level. Second, we utilized the context-dependent models to handle the inter-phone similarity due to coarticulation effect. The two approaches are coupled with the objective to improve segmentation accuracy. Experimental results demonstrated the effectiveness for the aforementioned top error. In addition, we also took the phones' duration into account for the HMM topology design. The segmentation accuracy was further improved to 91.32% within 20ms on the TIMIT corpus after combining the above refinements, which has a relative error reduction of 3.34% compared to the raw GMM-HMM segmentation in [1].
VAIM: Visual Analytics for Influence Maximization

In social networks, individuals’ decisions are strongly influenced by recommendations from their friends and acquaintances. The influence maximization (IM) problem asks to select a seed set of users that maximizes the influence spread, i.e., the expected number of users influenced through a stochastic diffusion process triggered by the seeds. In this paper, we present VAIM, a visual analytics system that supports users in analyzing the information diffusion process determined by different IM algorithms. By using VAIM one can: (i) simulate the information spread for a given seed set on a large network, (ii) analyze and compare the effectiveness of different seed sets, and (iii) modify the seed sets to improve the corresponding influence spread.
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance. The underrepresented words correspond to rare or out-of-vocabulary (OOV) words in the training data, and thereby can't be modeled reliably. We begin with graphemic lexicon which allows to drop the necessity of phonetic models in hybrid ASR. We study it under different settings and demonstrate its effectiveness in dealing with underrepresented NEs. Next, we study the impact of neural language model (LM) with letter-based features derived to handle infrequent words. After that, we attempt to enrich representations of underrepresented NEs in pretrained neural LM by borrowing the embedding representations of rich-represented words. This let us gain significant performance improvement on underrepresented NE recognition. Finally, we boost the likelihood scores of utterances containing NEs in the word lattices rescored by neural LMs and gain further performance improvement. The combination of the aforementioned approaches improves NE recognition by up to 42% relatively.
Smart Vision of School Classroom

Modern technology has become an integral part of our daily lives. Every day generates the need for new technologies so that we can coexist and integrate into contemporary life. Virtual reality (VR) is becoming imperative in some life areas, such as medicine, space, and education, for experiments and training. The start of the COVID-19 pandemic revealed the importance of using electronic devices for content delivery. In this paper, the importance of VR in the COVID-19 crises, the current situation of virtual reality, and its applications in the fields of education are discussed. Therefore, state of the art for VR is explained in this study with the new scientific contribution in this field and the new vision of VR in education. Virtual reality has advantages and disadvantages, and both current and future ones are discussed here. Based on our current data, we found that there are tremendous positive developments taking place, but at the same time, there are rules, laws, and societies with certain policies, some of which will promote the environment of virtual reality, and others will oppose it.
Critical Comparative Study of Robot Path Planning in Grid-Based Environment

Abstract The path planning problem under two-dimensional maps is a fundamental issue in mobile robotics and other applications (unmanned vehicles, and so forth). The traditional algorithms (graph searching, artificial potential field and so forth) are used to find the shortest path that rely on grid-by-grid searching. The main disadvantage of the traditional methods is robot size is not considered and has much redundant in sharp curves which is difficult to guide a robot movement. In this paper, we compared the five baseline approaches of heuristic methods to find the optimal path which reduces the number of turns and time costs to attain a target. The experimental analysis shows that the second-order x derivative has an advantage over other baseline approaches that the search time and the number of turns are reduced.
Contribution of Temporal Sequence Activities To Predict Bug Fixing Time

The bug-fixing process challenges development teams and practitioners for best practices that may pave the way not only to efficient human resources management but also to provide information in advance on the required time to investigate and fix a bug. In this study, we proposed a temporal sequence activity model based on Hidden Markov Models to predict bug fixing time. Comprehensive evaluation results of two different scenarios based on bug reports existing in the the Bugzilla repository were provided. Our experiments demonstrate the feasibility of the proposed model in which the most accurate configuration was obtained with the 50 percent of bug records for training and test set.
Approach for Model Driven Development of Multi-agent Systems for Ambient Intelligence

Ambient Intelligence systems are smart environments, which are reactive and proactive to people making their actions safer and more efficient. In respect to the relation with users, they should be non-obtrusive, context aware, personalized, adaptive and anticipatory. Reliable way to achieve these main features is the choice of appropriate development approach that will ensure the reusability, portability, scalability and interoperability of ambient intelligence applications. The model driven development is a promising approach for development of different software applications using models at different levels of abstraction and applying model transformation to code generation. In this paper an extension to the traditional model driven development approach is suggested based on model driven architecture and compositional models as core elements for automation of expert activities, enabling the context-awareness of application. The ambient intelligence systems are represented as layer-based multi agent systems that enable the division of system elements into levels, reduce the coupling between modules and facilitates abstractions as well as the distributions of responsibilities. The suggested approach is illustrated with an example from the field of health and medicine, namely the development of Holter monitoring system using context based on O-MaSE methodology and agentTool III.
VoLTE problem location method based on big data

Abstract This paper mainly introduces the key information extracting method from User Equipment (UE) test data, the synchronous algorithm of various interfaces X Detailed Record (XDR) data, the key information filling technique of multi interfaces X Detailed Record (XDR), the analytical procedure based on big data is descript in details. An example is given to explain that more accurate reason for Voice over Long-Term Evolution (VoLTE) call not connected can be found with big data from core network, compared with UE data only. With this method VoLTE problem can be quickly solved, it is very useful for wireless operator to improve the user voice service awareness rapidly.
Toward a Binarization Framework resolving the Maghrebian Font Database challenges

Historical documents are of major importance in conserving cultural and scientific heritage. The Maghrebian font of the Arabic language had dominated in different countries. Thousands of cultural and scientific documents were written using this style. In this paper, we intend to present the built Maghrebian font database with giving the different challenges facing the development of an Optical Character Recognition system able to treat it. Also, we are going to reveal the new developed binarization framework, based on Deep Learning theory, able to get the best accuracy compared to different studied binarization approaches. The proposed framework is based on Selectional Auto-Encoders and the Energy minimization theory. The obtained binarization result on the Maghrebian font using the F -measure is 98,1%.
Survey Paper Key Generation Using Ant Colony Optimization Technique

Cryptography is the methodology in which the information is cast into a form that forestalls the abuse of it. In this advanced time of the wireless, it is imperative to guarantee the safe conveyance of the pertinent information which are otherwise exposed to disgusting goals. The procedure of transformation of plaintext to encode text is known as encryption and its converse is known as decryption. The three kinds of cryptography methods, viz., Asymmetric key, Symmetric-key, and Hash Functions. A huge segment of this contemporary method is the key; which is a specific component of extreme significance, as this is what is utilized while scrambling or descrambling the desired information. In this paper, we have compared the two techniques. Incipiently, Particle Swarm Optimization, a strategy of delivering keys to design a stream figure for text encryption. The tale methodology named Particle Swarm Optimization Key Generation Algorithm (PKGA) uses a character code table for encoding the keys. The essential ideal situation of this procedure is that it diminishes the quantity of keys to be fused and coursed. On the other hand is Ant Colony Optimization Techniques to produce keys for the encryption of the information. This entire philosophy depends on the conduct of the ants in their characteristic inquiry of food. The drawback of using Ant Colony Optimization is to downscale the number of keys to be utilized during encipherment when contrasted with different strategies of cryptography in presence. ACO would warrant the generation of such keys with utmost proficiency.
NECESSARY AND SUFFICIENT CONDITIONS FOR DOMINATION RESULTS FOR PROPER SCORING RULES

Abstract Scoring rules measure the deviation between a forecast, which assigns degrees of confidence to various events, and reality. Strictly proper scoring rules have the property that for any forecast, the mathematical expectation of the score of a forecast p by the lights of p is strictly better than the mathematical expectation of any other forecast q by the lights of p . Forecasts need not satisfy the axioms of the probability calculus, but Predd et al. [9] have shown that given a finite sample space and any strictly proper additive and continuous scoring rule, the score for any forecast that does not satisfy the axioms of probability is strictly dominated by the score for some probabilistically consistent forecast. Recently, this result has been extended to non-additive continuous scoring rules. In this paper, a condition weaker than continuity is given that suffices for the result, and the condition is proved to be optimal.
Cleaning System Based on Autonomous Patrol of UAV and Intelligent Detection of Foreign Matters

In order to remove the foreign matters on the highvoltage transmission lines and high-voltage power towers, we designed a foreign matter cleaning system based on the UAV patrolling on a fixed path, aiming to solve the problem of foreign matters cleaning on the high-voltage wire by using artificial intelligence. It can improve the speed and efficiency of wire inspection and foreign matters removal, avoid accidents effectively, and also reduce the worker's intensity of work to ensure the operation safety.
Performance Analysis of Segmentor Adversarial Network (SegAN) on Bio-Medical Images for Image Segmentation

Cancer is termed as one of the deadliest disease, and it is becoming a major health problem in the world. This deadly disease can be cured, if it is found at earlier stages. Medical imaging plays an important role in finding this type of diseases and helps in treatment planning. Automated lesion/tumor segmentation is an important and challenging clinical diagnostic task because of tumor’s different shape, volume, contrast, and locations. Since deep learning is promising in many applications, thus motivating us to apply in this important task. In this paper, we propose to do automated tumor segmentation with two challenge dataset named as BraTs 2017 brain tumor and ISIC 2018 skin lesion by using Segmentor Adversarial Network (SegAN), inspired from classical GAN.
Adaptive two-way sweeping method to 3D kidney reconstruction

This article presents a novel method of automatic kidney contour detection in computed tomography angiography images. This technique allows to read as input the entire set of CTA images. It allows to read an entire set of kidney CTA images as input and then automatically generates binary images of the detected kidney outlines for each scan separately. Its additional feature is a real-time 3D kidney model reconstruction. The main idea is based on an innovative two-way scanning technique. To adapt an algorithm, a CT is analyzed on the basis of a previous slice. The final kidney contour recognition uses the following digital image processing techniques: mathematical morphology, region growth, colorization. to assess the quality of our technique, we consulted the results with a pathology department. The F1-score of the researched method is 88 % compared to human specialist’s verification. We also conducted a comparative study of computation time, system reliability, and recognition accuracy using three recent alternative methods. In comparison to machine learning algorithms, the presented method is very precise thanks to the application of the adaptive sweeping technique. This solution can be successfully applied in CTA image analyzing, visualization, and neoplastic changes detection. computer-aided medical diagnostic is currently one of the greatest challenges for biomedical engineers. The technique can find a real-life application in medical centers and medical-pathology departments.
Study on the Headgear and Seat of the Thangka Image based on the Improved YOLOv4 Algorithm

Aiming at the problems of complex background and low accuracy in Thangka image detection tasks using deep learning algorithms, an improved object detection algorithm based on YOLOv4 is proposed. Considering that the Mish function used by YOLOv4 is static, which performs the same operation on all input samples, hence it is not enough to deal with complex scenarios. We use the DynamicReLU function to encode the global up and down into a hyperfunction, and dynamically adjust the piecewise linear activation function instead of the Mish function accordingly, which solves the defect that the activation function cannot be dynamically adjusted, and realizes the detection of the headgear and seat of the Thangka image. The experimental results in Thangka image detection show that the evaluation protocol mAP of the algorithm reaches 37.9%, which is an increase of 3.8% compared to the YOLOv4 algorithm. The algorithm improves detection accuracy without increasing the depth and width of the network.