Reviewerly/exHarmony huggingface dataset:
Multipartite entanglement to boost superadditivity of coherent information in quantum communication lines with polarization-dependent losses

Coherent information quantifies the achievable rate of the reliable quantum information transmission through a communication channel. Use of the correlated quantum states instead of the factorized ones may result in an increase in the coherent information, a phenomenon known as superadditivity. However, even for simple physical models of channels it is rather difficult to detect the superadditivity and find the advantageous multipartite states. Here we consider the case of polarization-dependent losses and propose some physically motivated multipartite entangled states which outperform all factorized states in a wide range of the channel parameters. We show that, in the asymptotic limit of infinite number of channel uses, the superadditivity phenomenon takes place whenever the channel is neither degradable nor antidegradable. Besides the superadditivity identification, we also provide a method to modify the proposed states and get a higher quantum communication rate by doubling the number of channel uses. The obtained results give a deeper understanding of coherent information in the multishot scenario and may serve as a benchmark for quantum capacity estimations and future approaches toward an optimal strategy to transfer quantum information.
Developing Prospective Teachers’ Beliefs about Digital Tools and Digital Feedback

In classrooms today, teachers are asked to support their teaching with digital tools. For this purpose, teachers require not only technological knowledge but also corresponding beliefs about the advantages of digital tools. The development of those beliefs should already be embedded in the university education of teachers. To this end, we developed a university seminar aimed at fostering prospective teachers’ confidence in the utility of digital tools, using the digital tool STACK as an example. The seminar is based on learning mathematics with the digital tool STACK, independently designing digital tasks with said tool, and finally, reflecting on a teaching experiment with school students using STACK. To make the development of prospective teachers’ beliefs visible throughout the seminar, we worked with different qualitative methods. The results of this case study show that there are four developmental phases of prospective teachers’ beliefs which include an initial situation, a purely positive phase, a disillusionment, and a phase of differentiated beliefs. It becomes apparent that it is possible to develop prospective teachers’ beliefs about digital tools in a positive way.
Structural and geochemical processes controlling the formation of Egyptian Alabaster at Wadi Mawathil area, Eastern Desert, Egypt

Abstract Wadi Mawathil area is characterized by the existence of carbonates, represented by “Egyptian Alabaster” (calcite alabaster) and “Bucchino” (recrystallized limestone). One of the most important geological heritage in Egypt is the Egyptian Alabaster quarries. The “Egyptian Alabaster” is considered the main economic resource in the study area and the recrystallized limestone occasionally can be quarried. Field study revealed that the study area is covered by Middle Eocene rocks represented by the “Sannur Formation” surrounded by quaternary sediments; it exhibits karst features. Detailed field and structure studies on the study area show the existence of NW–SE and NE–SW structural trends. The NW–SE fault trends are mainly striking from N25° to 55° W and dip toward NE, SW, and SSW. These fault intersections form channels for groundwater that dissolves limestone. Petrographically, these limestones show three microfacies types, namely foraminiferal biomicrite, sandy biosparite, and alabaster facies. Foraminiferal biomicrite and sandy biosparite microfacies reflect quite an open marine and shallow subtidal depositional environment, while the alabaster facies was precipitated from hydrothermal groundwaters within open fractures, cavities, or karst system. The X–ray fluorescence analysis of alabaster shows an increase in the content of some trace elements such as I, Cr, Th, W, Ag, U, Ni, Zn, Pb, and Ga, and they also show high concentrations of some REE such as Sc, Ce, and La. The major oxides show CaO with an average of 55.47% indicating calcite as a major mineral component in these karstified rocks. The geological, structural, and geochemical studies carried out on the rocks in the study area, indicated that the alabaster rocks were formed in two stages: the first one is the dissolving of limestone by thermal waters in the existence of heavy meteoric water during the rainy periods of the Pleistocene age. The second stage is the re-precipitation of the limestone in the form of “alabaster” and/or “recrystallized limestone” by calcium carbonate saturated meteoric water that percolated through the fractures and fissures of the Eocene limestone.
Demo Abstract: PrivacyCube: A Tangible Device for Improving Privacy Awareness in IoT

Consumers increasingly bring IoT devices into their living spaces without understanding how their data is collected, processed, and used. We present PrivacyCube, a novel tangible device designed to explore the extent to which privacy awareness in smart homes can be elevated. PrivacyCube visualises IoT devices' data consumption displaying privacy-related notices. PrivacyCube aims at assisting families to (i) understand key privacy aspects better and (ii) have conversations around data management practices of IoT devices. Thus, families can learn and make informed privacy decisions collectively.
Multi-Feature Intelligent Oral English Error Correction Based on Few-Shot Learning Technology

The computer-aided language teaching system is maturing thanks to the advancement of few-shot learning technologies. In order to support teachers and increase students’ learning efficiency, more computer-aided language teaching systems are being used in teaching and examinations. This study focuses on a multifeature fusion-based evaluation method for oral English learning, completely evaluating specific grammar, and assisting oral learners in improving their oral pronunciation skills. This study proposes an improved method based on HMM a posteriori probability scoring, in which the only standard reference model is no longer used as the basis for scoring and error determination, and instead, the average level of standard pronunciation in the entire corpus is introduced as another judgment basis, based on a preliminary study of speech recognition technology, scoring methods, and relevant theoretical knowledge of information feedback. This strategy can reduce the score limitation caused by standard pronunciation personal differences, lower the system’s misjudgment rate in detecting pronunciation errors, and improve the usefulness of error correction information. An expert opinion database has been created based on the most prevalent forms of spoken pronunciation problems, which can successfully assist learners improve their spoken English level by combining the database’s corrected information. Finally, this study proposes an artificial scoring system for spoken English that performs activities such as identification, scoring, error judgment, and correction opinion feedback, among others. Finally, it has been demonstrated through trials and tests that adding the average pronunciation level to the system improves the system’s scoring performance and has a certain effect on increasing users’ oral pronunciation level.
A Performance Evaluation of Modern Virtualization Techniques

Nowadays, due to the extremely fast advance of the requirements, features, and expectations from the users, and as a result, the incredible, but the somewhere understandable effort of the big players in this market to surpass the competition through a continuous reinvention and attempt to meet market expectations, we are witnessing a continuous, even exponential evolution of technology. Virtualization is a complex and vast technology, one of its most key factors to consider is the resources. Because there is a clear interdependence between the volume of resources and costs, it is easy to deduce that a large number of resources involves higher costs. Modern technologies, like containerization, are coming to resolve this bottleneck, by providing more optimized and more transferable ecosystems. And a short comparison can be seen in this research paper, between virtualization and containerization.
Facilitating Decentralized and Opportunistic Learning in Pervasive Computing

We explore algorithms and develop tools to support the collaboration between devices to build personalized deep learning models for pervasive computing applications. In our work, we first investigate whether individual devices belonging to different users can learn robust models that are personalized to their user&#x2019;s own experiences while preserving the privacy of the data that resides on the devices. We term this approach Opportunistic Collaborative Learning (OppCL), and we build a resilient learning algorithm that ameliorates the negative effects when coping with unpredictable mobility patterns. Moreover, we extend OppCL from networks of homogeneous devices to heterogeneous ones by employing lossy compression techniques for deep learning models. Furthermore, we develop a programming environment for developers to test decentralized learning algorithms in scale.
Digitalization of the Education System

The article prepared for publication contains material on digitalization that is an integral part of the life of modern society, providing acceleration of economic processes and saving time and resources in almost all sectors of the economy, including education as the fundamental basis of human development. The purpose of the study is to provide in-depth justification of the prospects of digital technologies in the educational process for its qualitative improvement and subsequent growth of labor productivity and strengthening of national economic potential. Its achievement is focused on revealing the place and role of digitalization for education and substantiating the needs of relevant institutions in it with an emphasis on the pandemic period. The solution of the formulated tasks is carried out by using monographic and dialectical methods that help to systematize and improve the accumulated knowledge about education and the scientific and technical achievements used in it on the basis of the identified advantages and interpretation of their semantic content. These advantages are driving factors in the development of educational processes. The material presented in the article is useful for managers, specialists involved in the development of educational processes, as well as teachers of pedagogical directions.
A light field three-dimensional ranging system based on regularization

A high-precision light field three-dimensional ranging system based on regularization is proposed. This method transforms the sub-aperture image resolution problem into a global optimization problem. The objective function is constructed to capture the relationships between the light field views. The complementary information in the light field views is then utilized to improve the spatial resolution of the light field. In particular, regularizations of fuzzy matrix-based and optical flow-field-based use of information from multiple viewpoints can effectively enhance the light field structure of the system. The experimental results show that the proposed method can perform scene distance measurement accuracy and has better depth estimation. The proposed method effectively solves image depth distortion in traditional light field imaging. It eliminates the influence of low resolution of sub-aperture images on depth estimation to the maximum extent.
Accelerated Sign Hunter: A Sign-based Black-box Attack via Branch-Prune Strategy and Stabilized Hierarchical Search

We propose the Accelerated Sign Hunter (ASH), a sign-based black-box attack under l∞ constraint. The proposed method searches an approximate gradient sign of loss w.r.t. the input image with few queries to the target model and crafts the adversarial example by updating the input image in this direction. It applies a Branch-Prune Strategy that infers the unknown sign bits according to the checked ones to avoid unnecessary queries. It also adopts a Stabilized Hierarchical Search to achieve better performance within a limited query budget. We provide a theoretical proof showing that the Accelerated Sign Hunter halves the queries without dropping the attack success rate (SR) compared with the state-of-the-art sign-based black-box attack. Extensive experiments also demonstrate the superiority of our ASH method over other black-box attacks. In particular on Inception-v3 for ImageNet, our method achieves the SR of 0.989 with an average queries of 338.56, which is 1/4 fewer than that of the state-of-the-art sign-based attack to achieve the same SR. Moreover, our ASH method is out-of-the-box since there are no hyperparameters that need to be tuned.
UpHaaR: Blockchain-based charity donation scheme to handle financial irregularities

Non-profit organization raise charity donation through different sources, and raised funds often require a clean traceability. Recently, fake charity fundraisers schemes have raised a social concern, and thus a trusted and auditable approach is necessary to manage charity organizations reputations. Blockchain-based charity (BBC) schemes are proposed with decentralization that ensure trust, transparency, and chronology among donators and beneficiaries. However, the schemes suffer from the high-end complexity of certificate generation. Motivated from this, in this paper, we propose a BBC scheme, UpHaaR, where donators, defined as charity persons (CP), and beneficiaries, defined as charity beneficiaries (CB), register in blockchain, managed for diverse fundraisers by charity organizations (CO). The scheme mitigates fake fundraisers, as the charity scheme data and fund allocations to CP are managed as ledger entries in the blockchain. To maintain the security and privacy of massive CP and CB users, the scheme proposes partial key generations through certificate-less cryptography (CLC), which mitigates expensive digital signing and eliminates certificate generation through key distribution centers. For signature aspects, the scheme proposes a lightweight certificate-less signcryption scheme (CSS) that eliminates collusion against insider attacks and reduces computational costs. The scheme is evaluated for security analysis using automated validation of internet security protocols and applications (AVISPA) tool, and informal attack analysis, and cost evaluations. The proposed results indicate the scheme's viability against similar conventional approaches.
Automated Attendance System Using Face Recognition

Abstract: In the recent time automated face recognition has become a trend and has been developed very much , this is mainly due to two reasons; first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance face recognition is used to identify the person present in the class and mark his attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using machine learning techniques such as CNN algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person.. The faculty has access to add the student details such as name, USN, phone number, email-id. Then the image is captured through a high definition camera during the class hours. When the lecturing is going on faces of students are detected, segmented and stored for verification with database using the Convolutional Neural Networks (CNN) algorithm of machine learning technique
VAC-Net: Visual Attention Consistency Network for Person Re-identification

Person re-identification (ReID) is a crucial aspect of recognising pedestrians across multiple surveillance cameras. Even though significant progress has been made in recent years, the viewpoint change and scale variations still affect model performance. In this paper, we observe that it is beneficial for the model to handle the above issues when boost the consistent feature extraction capability among different transforms (e.g., flipping and scaling) of the same image. To this end, we propose a visual attention consistency network (VAC-Net). Specifically, we propose Embedding Spatial Consistency (ESC) architecture with flipping, scaling and original forms of the same image as inputs to learn a consistent embedding space. Furthermore, we design an Input-Wise visual attention consistent loss (IW-loss) so that the class activation maps(CAMs) from the three transforms are aligned with each other to enforce their advanced semantic information remains consistent. Finally, we propose a Layer-Wise visual attention consistent loss (LW-loss) to further enforce the semantic information among different stages to be consistent with the CAMs within each branch. These two losses can effectively improve the model to address the viewpoint and scale variations. Experiments on the challenging Market-1501, DukeMTMC-reID, and MSMT17 datasets demonstrate the effectiveness of the proposed VAC-Net.
“We dream of climbing the ladder; to get there, we have to do our job better”: Designing for Teacher Aspirations in rural Côte d’Ivoire

As governments in developing countries race to solve the global learning crisis, a key focus is on novel teaching approaches as taught in pedagogical programs. To scale, these pedagogical programs rely on government teacher training infrastructure. However, these programs face challenges in rural parts of Africa where there is a lack of advisor support, teachers are isolated and technology infrastructure is still emerging. Conversational agents have addressed some of these challenges by scaling expert knowledge and providing personalized interactions, but it is unclear how this work can translate to rural African contexts. To explore the use of such technology in this design space, we conducted two related studies. The first was a qualitative study with 20 teachers and ministry officials in rural Côte d’Ivoire to understand opportunities and challenges in technology use for these stakeholders. Second, we shared a conversational agent probe over WhatsApp to 38 teachers for 14-weeks to better understand what we learned in the survey and to uncover realistic use cases from these stakeholders. Our findings were examined through a theoretical lens of aspirations to discover sustainable design directions for conversational agents to support teachers in low infrastructure settings.
Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

Abstract Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decision problem that the machine learning methods were applied to. This is problematic because even domain experts cannot give complete and fully accurate descriptions of the decisions they face. To address this problem, we develop strategy discovery methods that are robust to potential inaccuracies in the description of the scenarios in which people will use the discovered decision strategies. The basic idea is to derive the strategy that will perform best in expectation across all possible real-world problems that could have given rise to the likely erroneous description that a domain expert provided. To achieve this, our method uses a probabilistic model of how the description of a decision problem might be corrupted by biases in human judgment and memory. Our method uses this model to perform Bayesian inference on which real-world scenarios might have given rise to the provided descriptions. We applied our Bayesian approach to robust strategy discovery in two domains: planning and risky choice. In both applications, we find that our approach is more robust to errors in the description of the decision problem and that teaching the strategies it discovers significantly improves human decision-making in scenarios where approaches ignoring the risk that the description might be incorrect are ineffective or even harmful. The methods developed in this article are an important step towards leveraging machine learning to improve human decision-making in the real world because they tackle the problem that the real world is fundamentally uncertain.
Improving Image Captioning via Enhancing Dual-Side Context Awareness

Recent work on visual question answering demonstrate that grid features can work as well as region feature on vision language tasks. In the meantime, transformer-based model and its variants have shown remarkable performance on image captioning. However, the object-contextual information missing caused by the single granularity nature of grid feature on the encoder side, as well as the future contextual information missing due to the left2right decoding paradigm of transformer decoder, remains unexplored. In this work, we tackle these two problems by enhancing contextual information at dual-side:(i) at encoder side, we propose Context-Aware Self-Attention module, in which the key/value is expanded with adjacent rectangle region where each region contains two or more aggregated grid features; this enables grid feature with varying granularity, storing adequate contextual information for object with different scale. (ii) at decoder side, we incorporate a dual-way decoding strategy, in which left2right and right2left decoding are conducted simultaneously and interactively. It utilizes both past and future contextual information when generates current word. Combining these two modules with a vanilla transformer, our Context-Aware Transformer(CATNet) achieves a new state-of-the-art on MSCOCO benchmark.
Cross-Modal Retrieval between Event-Dense Text and Image

This paper presents a novel approach to the problem of event-dense text and image cross-modal retrieval where the text contains the descriptions of numerous events. It is known that modality alignment is crucial for retrieval performance. However, due to the lack of event sequence information in the image, it is challenging to perform the fine-grain alignment of the event-dense text with the image. Our proposed approach incorporates the event-oriented features to enhance the cross-modal alignment, and applies the event-dense text-image retrieval to the food domain for empirical validation. Specifically, we capture the significance of each event by Transformer, and combine it with the identified key event elements, to enhance the discriminative ability of the learned text embedding that summarizes all the events. Next, we produce the image embedding by combining the event tag jointly shared by the text and image with the visual embedding of the event-related image regions, which describes the eventual consequence of all the events and facilitates the event-based cross-modal alignment. Finally, we integrate text embedding and image embedding with the loss optimization empowered with the event tag by iteratively regulating the joint embedding learning for cross-modal retrieval. Extensive experiments demonstrate that our proposed event-oriented modality alignment approach significantly outperforms the state-of-the-art approach with a 23.3% improvement on top-1 Recall for image-to-recipe retrieval on Recipe1M 10k test set.