Reviewerly/exHarmony huggingface dataset:
Predicting the quantum texture from transmission probabilities

Inverse formulas for the energy potential level and the effective electron mass within an unknown quantum material are obtained, solely based on the transmission probabilities of obliquely incident beams through a thin lamellar sample. The estimator is robust-to-noise and does not require phase unwrapping of the wave functions; importantly, it is very reliable for low-contrast samples and high impinging energies. Even when the proposed method fails to deliver an accurate prediction, it can serve well the purpose of disclosing the identity of the medium in the case it has been picked from a well-known list. The introduced scheme may constitute the first step toward flawless generic estimation of quantum texture if implemented multiple times for various energies or incidence angles, and the collected data are post-processed by suitable algorithmic routines.
On nonlinear expectations and Markov chains under model uncertainty

The aim of this work is to give an overview on nonlinear expectations and to relate them to other concepts that describe model uncertainty or imprecision in a probabilistic framework. We discuss imprecise versions of stochastic processes with a particular interest in imprecise Markov chains. First, we focus on basic properties and representations of nonlinear expectations with additional structural assumptions such as translation invariance or convexity. In a second step, we discuss how stochastic processes under nonlinear expectations can be constructed via primal and dual representations. We illustrate the concepts by means of imprecise Markov chains with a countable state space, and show how families of Markov chains give rise to imprecise versions of Markov chains. We discuss dual representations and differential equations related to the latter.
Spatial Pyramid Block for Oracle Bone Inscription Detection

The detection of Oracle Bone Inscription (OBI) is one of the most fundamental aspects of oracle bone morphology. However, the detection method depending on experts' experience requires longterm learning and accumulation for professional knowledge. This paper investigated the performance of the deep-learning-based object detection framework in the OBI dataset, then selected the one with the best performance as the baseline and made a series of optimization. Specifically, we first redesigned the sizes and ratios of the anchor box according to the data characteristics by using K- means clustering. Secondly, we extracted some typical noises from OBI for data augmentation. Finally, Focal Loss and Mixed-precision are used to improve the model precision and compress the memory footprint. To further improve the performance, the Spatial Pyramid Block is proposed, which can stabilize features and suppress noise interference. Experiments on our OBI benchmarks validate the superiority of the proposed method that achieves 82.1% F-measure suppressing several mainstream object detectors. Our dataset and algorithms will soon be available at http://jgw.aynu.edu.cn.
LoPAD: A Local Prediction Approach to Anomaly Detection

Dependency-based anomaly detection methods detect anomalies by looking at the deviations from the normal probabilistic dependency among variables and are able to discover more subtle and meaningful anomalies. However, with high dimensional data, they face two key challenges. One is how to find the right set of relevant variables for a given variable from the large search space to assess dependency deviation. The other is how to use the dependency to estimate the expected value of a variable accurately. In this paper, we propose the Local Prediction approach to Anomaly Detection (LoPAD) framework to deal with the two challenges simultaneously. Through introducing Markov Blanket into dependency-based anomaly detection, LoPAD decomposes the high dimensional unsupervised anomaly detection problem into local feature selection and prediction problems while achieving better performance and interpretability. The framework enables instantiations with off-the-shelf predictive models for anomaly detection. Comprehensive experiments have been done on both synthetic and real-world data. The results show that LoPAD outperforms state-of-the-art anomaly detection methods.
Limits on the Efficiency of (Ring) LWE Based Non-interactive Key Exchange

$$\mathsf {LWE}$$ based key-exchange protocols lie at the heart of post-quantum public-key cryptography. However, all existing protocols either lack the non-interactive nature of Diffie-Hellman key-exchange or polynomial $$\mathsf {LWE}$$ -modulus, resulting in unwanted efficiency overhead. We study the possibility of designing non-interactive $$\mathsf {LWE}$$ -based protocols with polynomial $$\mathsf {LWE}$$ -modulus. To this end, Overall, our results show possibilities and challenges in designing simple (ring) $$\mathsf {LWE}$$ -based non-interactive key exchange protocols.
Reading from print, laptop computer, and e-reader: Differences and similarities for college students’ learning

Digital and print readings differ in their affordances. Past research supports a print advantage, but few studies have used within-subjects approaches or compared handheld devices and multiple reading genres. We compared college students’ reading of several excerpts on paper, a laptop, and an e-reader. Students read on all platforms, completed comprehension tasks, and answered questions on their perceptions and preferences. Results indicated that students’ learning experience with print was superior, with the laptop a close second. In contrast, students’ learning from and perceptions of the e-reader were lower. There was no interaction with reading genre. This research suggests that students’ preference for print remains strong. When digital materials are used, e-readers appear to be a less viable option for academics relative to laptops.
Towards successful global software development

Majority of the software development organizations are motivated to transform their development activities from collocated development to offshore software development outsourcing (OSDO) environment. The adoption of OSDO is complicated due to geographical distance between development teams. The requirements engineering (RE) in the context of OSDO needs special consideration as it needs more communication and collaboration. The objective of this study is to investigate the RE barriers in the domain of OSDO. This study have two main objectives (i) identification of barriers for RE process in OSDO from the existing literature and (ii) prioritizing the identified barriers by using analytical hierarchy approach (AHP). However, in this study a total of 14 barriers of RE process were identified. The investigated barriers were further categorized into three core categories i.e. coordination, project administration and human resources management.The results demonstrated that Lack of 3Cs (communication coordination and control), Strict time schedule by customer, and Incompatibility with client are the barriers having most priority in RE process in OSDO context. Moreover, we noted that the coordination is the most significant category of investigated barriers. We are confident that the findings of this study provide a model which is significant to assess and improve the RE activities in the domain of OSDO.
Expression of Fractals Through Neural Network Functions

To help understand the underlying mechanisms of neural networks (NNs), several groups have studied the number of linear regions ℓ of piecewise linear (PwL) functions, generated by deep neural networks (DNN). In particular, they showed that ℓ can grow exponentially with the number of network parameters p, a property often used to explain the advantages of deep over shallow NNs. Nonetheless, a dimension argument shows that DNNs cannot generate all PwL functions with ℓ linear regions when ℓ > p.It is thus natural to seek to characterize specific families of functions with ℓ > p linear regions that can be constructed by DNNs. Iterated Function Systems (IFS) recursively construct a sequence of PwL functions Fk with a number of linear regions which is exponential in k. We show that Fk can be generated by a NN using only O(k) parameters. IFS are used extensively to generate natural-looking landscape textures in artificial images as well as for compression of natural images. The surprisingly good performance of this compression suggests that human visual system may lock in on self-similarities. The combination of this phenomenon with the capacity of DNNs to efficiently approximate IFS may contribute to the success of DNNs, particularly striking for image processing tasks.
Abstractive Event Summarization on Twitter

This paper presents a new approach for automatically summarizing a social media event. It utilizes the BERT model as the encoder and a Transformer architecture as the decoder. The framework also includes an event topic prediction component, and the predicted event topic will help the decoder focus more on the specific aspects of the topic category when generating summary. To make the summary more succinct and coherent, the most important messages from an event cluster are selected by a message selection model and encoded by the BERT model. Our preliminary experiment shows that our approach outperforms the baseline methods.
Fast Automatic Determination of Cluster Numbers for High Dimensional Big Data

For a large volume of data, the clustering algorithm is of significant importance to categorize and analyze data. Accordingly, choosing the optimal number of clusters (K) is an essential factor, but it also is a tricky problem in big data analysis. More importantly, it is to efficiently determine the best K automatically, which is the main issue in clustering algorithms. Indeed, considering both the quality and efficiency of the clustering algorithm during defining K can be a trade-off that is our primary purpose to overcome. K-Means is still one of the popular clustering algorithms, which has a shortcoming that K needs to be pre-set. We introduce a new process with fewer K-Means running, which selects the most promising time to run the K-Means algorithm. To achieve this goal, we applied Bisecting K-Means and a different splitting measure, which all are contributed to efficiently determine the number of clusters automatically while maintaining the quality of clustering for a large set of high dimensional data. We carried out our experimental studies on different data sets and found that our procedure has the flexibility of choosing different criteria for determining the optimal K under each of them. Experiments indicate higher efficiency through decreasing of computation cost compared with the Ray&Turi method or with the use of only the K-Means algorithm.
Holistic Descriptors of Omnidirectional Color Images and Their Performance in Estimation of Position and Orientation

The use of visual sensors in robotic navigation tasks is a common approach, and numerous examples can be found in the literature.This work focuses on the problem of map building and localization using omnidirectional images as the only source of information.The main objective of this paper is to present a thorough comparison of global-appearance description techniques including the use of color information in different approaches.Some of the descriptors have been widely tested in previous works using graylevel images.In the present work we concentrate on the role and efficiency of the color information.Other descriptors are presented for the first time.To carry out this study, a database captured in different areas of an office environment is used, including two different datasets: training and test datasets.The experimental results include computational requirements in the map building and localization processes, and the accuracy in the pose estimation of the test images in a topological map, separating both position and orientation.To complete the study, the behavior of the descriptors is tested when the images present noise or occlusions, specially the effect on the color information.
Normalized Recurrent Dynamic Adaption Network: A New Framework With Dynamic Alignment for Intelligent Fault Diagnosis

In the field of intelligent fault diagnosis, distribution divergence always exists between the training and testing sets (which could be considered as a source domain with known labels and a target domain without labels), which will lead to a significant degradation in the diagnosis performance of deep network. Generally, this problem is solved by transfer learning. Specifically, adapt the marginal distribution or jointly align the marginal and conditional distributions of two domains so that the classifier trained by labeled source data merely can correctly classify target data. However, when aligning the marginal and conditional distributions simultaneously, people usually gives them the equal weight while it is not in accordance with the general situations. In this paper, we propose a new framework called normalized recurrent dynamic adaption network (NRDAN) for intelligent fault diagnosis which not only adapts the marginal and conditional distributions of two domains simultaneously but also estimates the relative importance of two distributions dynamically and quantitatively. This framework adopts long short-term memory (LSTM) as the base network combined with layer normalization (LN) and mainly consists of a feature extractor, a dynamic adaption module, and a classifier. Finally, extensive experiments including transfer tasks between not only various operating conditions but also different machines are conducted to comprehensively evaluate the proposed method.
Flexible Cloud/User-Centric Entanglement and Photon Pair Distribution With Synthesizable Optical Router

The practical roll-out of quantum communication technologies in optical networks and the adoption of novel quantum applications demand the distribution of single or entangled photons. Flexibility and dynamicity are paramount for the provision of quantum resources, in order to scale with the number of users and to meet the demand of complex network architectures. We present a quantum network architecture that features this degree of reconfigurability, without being restricted to a rigid physical-layer network based on purely passive multiplexing componentry. We leverage spectral assets of photon-pair sources, from the short-wavelength band to the L-band, and agile spatial switching at a remote optical network node, in order to realize a flexible distribution map that features different flavors, reaching from cloud-centric to user-centric quantum connectivity. Photon pair distribution is experimentally demonstrated between five users in a 17-km reach, tree-shaped optical network, with high visibility entanglement shared between three users of the network. Simultaneous distribution of photons to more than one user is enabled and the delivered photon rate can be dynamically adjusted. Penalty-free operation is confirmed for integrating a co-existing classical control channel within the quantum.
Exploring the effectiveness of e-book for students on learning material: a literature review

Abstract The process of integrating various components and activities is needed to create quality education. The effort that can be done is the innovation of teaching materials used in learning. Physics is quite difficult to master by students because it requires practicum to understand it. To make it easier for students to understand the material, it is necessary to develop an e-book containing videos, animations, and images that can support students’ understanding. Therefore, an in-depth literature review is needed to see the effectiveness of using e-book in learning.
Studying Onboarding in Distributed Software Teams

Many companies have turned towards globally distributed software development in their quest for access to more development capacity. This paper investigates how a company onboarded distributed teams in a global project, and report experience on how to study such distributed projects. Onboarding is the process of helping new team members adapt to the existing team and ways of working. The goal of the studied onboarding program was to integrate Portuguese developers into two existing Norwegian teams. Further, due to the growing trend in utilizing globally distributed projects, and the challenge of conducting studies in distributed organizations, it is crucial to find good practices for researching such projects. We collected qualitative data from interviews, observations, Slack conversations and documents, and quantitative data on Slack activity. We report experiences on different onboarding practices and techniques, and we suggest guidelines to help other researchers conduct qualitative studies in globally distributed projects.
PP-PLL: Probability Propagation for Partial Label Learning

Partial label learning (PLL) is a weakly supervised learning framework which learns from the data where each example is associated with a set of candidate labels, among which only one is correct. Most existing approaches are based on the disambiguation strategy, which either identifies the valid label iteratively or treats each candidate label equally based on the averaging strategy. In both cases, the disambiguation strategy shares a common shortcoming that the ground-truth label may be overwhelmed by the false positive candidate labels, especially when the number of candidate labels becomes large. In this paper, a probability propagation method for partial label learning (PP-PLL) is proposed. Specifically, based on the manifold assumption, a biconvex regular function is proposed to model the linear mapping relationships between input features and output true labels. In PP-PLL, the topological relations among training samples are used as additional information to strengthen the mutual exclusiveness among candidate labels, which helps to prevent the ground-truth label from being overwhelmed by a large number of candidate labels. Experimental studies on both artificial and real-world data sets demonstrate that the proposed PP-PLL method can achieve superior or comparable performance against the state-of-the-art methods.
Temporal Memory Sharing in Visual Reinforcement Learning

Video games provide a well-defined study ground for the development of behavioural agents that learn through trial-and-error interaction with their environment, or reinforcement learning (RL). They cover a diverse range of environments that are designed to be challenging for humans, all through a high-dimensional visual interface. Tangled Program Graphs (TPG) is a recently proposed genetic programming algorithm that emphasizes emergent modularity (i.e. automatic construction of multi-agent organisms) in order to build successful RL agents more efficiently than state-of-the-art solutions from other sub-fields of artificial intelligence, e.g. deep neural networks. However, TPG organisms represent a direct mapping from input to output with no mechanism to integrate past experience (previous inputs). This is a limitation in environments with partial observability. For example, TPG performed poorly in video games that explicitly require the player to predict the trajectory of a moving object. In order to make these calculations, players must identify, store, and reuse important parts of past experience. In this work, we describe an approach to supporting this type of short-term temporal memory in TPG, and show that shared memory among subsets of agents within the same organism seems particularly important. In addition, we introduce heterogeneous TPG organisms composed of agents with distinct types of representation that collaborate through shared memory. In this study, heterogeneous organisms provide a parsimonious approach to supporting agents with task-specific functionality, image processing capabilities in the case of this work. Taken together, these extensions allow TPG to discover high-scoring behaviours for the Atari game Breakout, which is an environment it failed to make significant progress on previously.
CovLets

State-of-the-art techniques for image and video classification take a bottom-up approach where local features are aggregated into a global final representation. Existing frameworks (i.e., bag of words or Fisher vectors) are specifically designed to aggregate vector-valued features such as SIFT descriptors. In this article, we propose a technique to aggregate local descriptors in the form of covariance descriptors (CovDs) into a rich descriptor, which in essence benefit from the second-order statistics along the coding pipeline. The difficulty in aggregating CovDs arises from the fact that CovDs lie on the Riemannian manifold of symmetric positive definite (SPD) matrices. Therefore, the aggregating scheme must take advantage of metrics and the geometry of the SPD manifolds. In our proposal, we make use of the Stein divergence and Nyström method to embed the SPD manifold into a Hilbert space. We compare our proposal, dubbed CovLets, against state-of-the-art methods on several image and video classification problems including facial expression recognition and action recognition.
Segmentation mask guided end-to-end person search

Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification. Besides the large intra-class variations owing to various illumination conditions, occlusions and varying poses, background clutters in the detected pedestrian bounding boxes further deteriorate the extracted features for each person, making them less discriminative. To tackle these problems, we develop a novel approach which guides the network with segmentation masks so that discriminative features can be learned invariant to the background clutters. We demonstrate that joint optimization of pedestrian detection, person re-identification and pedestrian segmentation enables to produce more discriminative features for pedestrian, and consequently leads to better person search performance. Extensive experiments on two widely used benchmark datasets prove the superiority of our approach. In particular, our proposed model achieves the state-of-the-art performance (86.3% mAP and 86.5% top-1 accuracy) on CUHK-SYSU dataset.
Lyricist Identification using Stylometric Features utilizing BanglaMusicStylo Dataset

This paper presents a profile-based approach utilizing supervised learning methods to identify the lyricist of Bangla songs written by two legendary poets & novelist Kazi Nazrul Islam and Rabindranath Tagore. The problem statement for this paper could be considered as authorship attribution using stylometric features on Bangla lyrics. We have utilized the BanglaMusicStylo dataset, which consists of 856 and 620 songs of Rabindranath Tagore and Kazi Nazrul Islam, respectively. The traditional authorship attribution works found in the literature are based on the novels written by the authors, not Bangla song lyrics. Using the Bangla song lyrics made it a challenging task, as the word choices made by the authors in songs depends on the rhythms, completeness, situation and many more. In this paper, we have tried to fusion different types of stylometric features, such as lexical, structural, stylistic etc. For experimentation, we have designed the prediction model based on supervised learning exploiting Naïve Bayes (NB), Simple Logistic Regression (SLR), Decision Tree (DT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The experimental model consists of several steps including data pre-processing, feature extraction, data processing, and classification model. After performance evaluation, we have got approximately 86.29% accuracy from SLR, which is quite satisfactory.