Reviewerly/exHarmony huggingface dataset:
Mass Personalization Strategy in Modern Education

The article analyzes mass personalization as a model for arrangement educational activities, the innovativeness of which is due to the modern technological capabilities of creating or adapting an educational product in accordance with the individual needs of the student. The author shows the dependence of the social demand for mass personalization in education via a number of mega-trends in economic and social development. The concept of mass personalization in the economic aspect is examined; the possibility of extrapolating the idea of mass personalization to the field of modern education is substantiated. The author considers the strategy of mass personalization as one of the ways to develop a more open architecture of education and increase the social effectiveness of educational practices. The first part of the article analyzes the potential of mass personalization for the development of education itself and for solving a wide range of social development problems. It is noted that the increasing variety of technological solutions allows one to design non-linear educational schemes, develop educational ecosystems, and shape the educational landscape to meet the heterogeneous social order for education. The urgency of the tasks of the development of “digital pedagogy” for the formation of motivation and subjectivity of individuals as the main condition of the effectiveness of mass personalization and for minimizing errors in determining the goals of learning is emphasized. The possibility of shifting the emphasis in the axiology of education from an instrumental interpretation of educational results to emphasizing the importance of education as a process of formation of personality in its integrity is considered. The article then moves on to examine the possible risks of implementing a mass personalization strategy. Risks of the development of educational inequality, a departure from the principles of egalitarianism in the organization of education are considered, the risks of violations of privacy and confidentiality of students associated with the use of big data are analyzed, the possibilities of manipulating the consciousness of students, managing their attention as potential threats are noted. The conclusion is made about the possible prospects for the development of mass personalization strategy.
Making transport more robust and interpretable by moving data through a small number of anchor points.

Optimal transport (OT) is a widely used technique for distribution alignment, with applications throughout the machine learning, graphics, and vision communities. Without any additional structural assumptions on transport, however, OT can be fragile to outliers or noise, especially in high dimensions. Here, we introduce Latent Optimal Transport (LOT), a new approach for OT that simultaneously learns low-dimensional structure in data while leveraging this structure to solve the alignment task. The idea behind our approach is to learn two sets of "anchors" that constrain the flow of transport between a source and target distribution. In both theoretical and empirical studies, we show that LOT regularizes the rank of transport and makes it more robust to outliers and the sampling density. We show that by allowing the source and target to have different anchors, and using LOT to align the latent spaces between anchors, the resulting transport plan has better structural interpretability and highlights connections between both the individual data points and the local geometry of the datasets.
Systems research is running out of time

Most sciences conduct experiments with a thorough understanding of the accuracy and precision of the instruments used for making measurements. Time is the most frequently used measurement in systems research, yet most of the literature does not consider the precision and accuracy of clocks. In this paper, we argue for the importance of understanding timekeeping and providing precise and accurate time for general systems research.
Unsupervised document summarization using pre-trained sentence embeddings and graph centrality

This paper describes our submission for the LongSumm task in SDP 2021.We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner.The proposed method is simple, fast, can summarize any document of any size and can satisfy any length constraints for the summaries produced.The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques.
Survey of PIR Approach and its Techniques for Preserving Privacy in IoT

Preserving privacy and security of users' information is one of the most challenges for the recent technologies like Internet of things (IoT) and its smart applications and adaptive services. Private Information Retrieval (PIR) is one of the most common and strong approach for preserving privacy in IoT applications and especially with Location Based Services. However, most of users cannot use like this technique because it requires to print a huge size of data from server provider to the cache user's device and that impossible and illogical in many of devices and cases according to the resources and overhead. This research presents new method called Fog-Pir to deal with PIR by depending on the fog computing with will solve previous issue in PIR and enhance the privacy of users. Simulation confirms the superiority of Fog-PIR to normal PIR according to performance and privacy.
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States

Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications.In this work, we incorporate explicit means into neural models to ensure topical continuity, content comprehensiveness and informativeness of automatically generated radiology reports.We propose a method to compute image representations specific to each sentential context to minimize hallucination caused by sequence-to-sequence approaches and to further eliminate redundant content by exploiting diverse sentence states.We conduct experiments to generate radiology reports from medical images of chest x-rays using MIMIC-CXR.Our model outperforms baselines by up to 18% and 29% respective in the evaluation for informativeness and content ordering respectively on objective metrics and 16% on human validations.
A Study on Privacy Preservation of Medical Certificates Using Blockchain Technology

: Currently, blockchain technology has evolved into a reliable and secure platform to provide security for the data. This technology's main adoption areas are agriculture, supply chain, food sector, energy industries, Health care, Real estate, Voting system, and education sector. This paper reviewed existing related works and applications using blockchain technology. Forgery or fraud on medical documents is a significant challenge in the health care sector. Hence this paper proposed a Blockchain-based framework using Ehterum that is a public blockchain. Users required medical documents generated and issued by officials authentically in the way of a unique blockchain-based ID. Users can submit this Blockchain-based ID anywhere instead of a physical paper. Attacks or forgery on the medical document can reduce using advanced featured technology, Blockchain. Blockchain technology has the features such as immutable, distributive, secure, reliable, and transparent. Remix Etherum IDE, solidity programming, and Metamask Wallet were used to implement the proposed application. The main strength of this work is designing and deploying results and comparisons with the existing applications.
Differentially Private Query Release Through Adaptive Projection

We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy. Our algorithm makes adaptive use of a continuous relaxation of the Projection Mechanism, which answers queries on the private dataset using simple perturbation, and then attempts to find the synthetic dataset that most closely matches the noisy answers. We use a continuous relaxation of the synthetic dataset domain which makes the projection loss differentiable, and allows us to use efficient ML optimization techniques and tooling. Rather than answering all queries up front, we make judicious use of our privacy budget by iteratively and adaptively finding queries for which our (relaxed) synthetic data has high error, and then repeating the projection. We perform extensive experimental evaluations across a range of parameters and datasets, and find that our method outperforms existing algorithms in many cases, especially when the privacy budget is small or the query class is large.
Latent Representation in Human–Robot Interaction With Explicit Consideration of Periodic Dynamics

This article presents a new data-driven framework for analyzing periodic physical human–robot interaction (pHRI) in latent state space. The model representing pHRI is critical for elaborating human understanding and/or robot control during pHRI. Recent advancements in deep learning technology would allow us to train such a model on a dataset collected from the actual pHRI. Our framework is based on a variational recurrent neural network (VRNN), which can process time-series data generated by a pHRI. This study modifies VRNN to explicitly integrate the latent dynamics from robot to human and to distinguish it from a human state estimate module. Furthermore, to analyze periodic motions, such as walking, we integrate VRNN with a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons. By boosting RC into a complex domain, periodic behavior can be represented as phase rotation in the complex domain without decaying the amplitude. A rope rotation/swinging experiment was used to validate the proposed framework. The proposed framework, trained on the collected experiment dataset, achieved the latent state space in which variation in periodic motions can be distinguished. The best prediction accuracy of the human observations and robot actions was obtained in such a well-distinguished space.
Training Graph Neural Networks with 1000 Layers

The authors thank Shaojie Bai, researchers at Intel ISL, the reviewers, and area chairs for their helpful suggestions. This project is partially funded by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under Award No. OSR-CRG2019-4033.
Robust UI Automation Using Deep Learning and Optical Character Recognition (OCR)

In the complete software development life cycle (SDLC), testing and maintenance stand slowest, and the reason being entire process is manual or manually maintained automation. Over time, companies have realized that it is very costly and time consuming. The biggest reason for manual maintenance of automation is its dependency on dynamic properties of the UI elements (xpath, class, ID) which directly depend on document object model (DOM). Cloud releases are very frequent, and changes in the UI properties are expected. In this paper, we are presenting a UI technology agnostic approach, which does not depend on the DOM and UI properties of the application rather behaves like a human and visually parses the screen. We use artificial intelligence to detect the UI elements, and even, just a mock-up of a screen can be parsed this way. This also enables test-driven development (TDD) of UI, among many other use cases. We used annotated UI image data for training our deep learning model, and the method has been validated with 94% accuracy on new UI elements. This approach does not require manual maintenance of the generated automates unless there is a functional change in the application. We propose UI technology agnostic, zero touch, self-healing UI automation.
Lumping in Markov Reward Processes

Explicit conditions on the transition probabilities for lumping in a discrete-time Markov chain (DTMC) are well known and were given by Kemeny and Snell in 1960. They distinguish between “strong” lumpability for which the process is lumpable for any initial probability distribution on the states and “weak” lumpability for which the process is lumpable only for some initial probability distributions. This chapter obtains conditions for lumping in a continuous-time Markov reward process. It introduces the notion of “proportional dynamics” and gives necessary and sufficient conditions for it to hold. The chapter shows that proportional dynamics for a given measure is sufficient for weak lumpability for the same measure, it also implies unlumpability. It discusses the measures, such as the transient probabilities, the distribution of accumulated reward, the expected accumulated reward, and the instantaneous reward.
Extreme Points and Factorizability for New Classes of Unital Quantum Channels

We introduce and study two new classes of unital quantum channels. The first class describes a 2-parameter family of channels given by completely positive (CP) maps $M_3({\bf C}) \mapsto M_3({\bf C})$ which are both unital and trace-preserving. Almost every member of this family is factorizable and extreme in the set of CP maps which are both unital and trace-preserving, but is not extreme in either the set of unital CP maps or the set of trace-preserving CP maps. We also study a large class of maps which generalize the Werner-Holevo channel for $d = 3$ in the sense that they are defined in terms of partial isometries of rank $d-1$. Moreover, we extend this to maps whose Kraus operators have the form $t |e_j \rangle \langle e_j | \oplus V $ with $V \in M_{d-1} ({\bf C}) $ unitary and $t \in (-1,1)$. We show that almost every map in this class is extreme in both the set of unital CP maps and the set of trace-preserving CP maps. We analyze in detail a particularly interesting subclass which is extreme unless $t = -1/(d-1)$. For $d = 3$, this includes a pair of channels which have a dual factorization in the sense that they can be obtained by taking the partial trace over different subspaces after using the same unitary conjugation in $M_3({\bf C}) \otimes M_3({\bf C})$.
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting

We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001]. Histogram binning has displayed strong practical performance, but theoretical guarantees have only been shown for sample split versions that avoid 'double dipping' the data. We demonstrate that the statistical cost of sample splitting is practically significant on a credit default dataset. We then prove calibration guarantees for the original method that double dips the data, using a certain Markov property of order statistics. Based on our results, we make practical recommendations for choosing the number of bins in histogram binning. In our illustrative simulations, we propose a new tool for assessing calibration -- validity plots -- which provide more information than an ECE estimate. Code for this work will be made publicly available at this https URL.
Development and Evaluation of Usability Heuristics for Voice User Interfaces

In recent years, voice user interfaces have evolved substantially, enabling seamless and efficient human–machine interaction through spoken language. In spite of the increasing research, there is an absence of explicit evaluation methods for voice user interfaces (VUIs) for enabling their improvement. Presently, the evaluation criteria are primarily based on subjective metrics such as user reviews, ratings, or likelihood of future use. While these metrics have utility, they are often subjective to the users and offer hurdles to research such as cost of recruitment, time, and resources. Other alternatives are performance based such as response time and error rates, offering value to developers, but little insight into the design of these systems. There is a need for a usability-based evaluation method for VUI, analogous to the heuristic evaluation metrics proposed by Nielsen and Molich for screen-based interfaces. To address the same, our study presents a set of heuristics for usability evaluation of VUIs. Initially, existing literature in the domain of VUI was analysed to identify prevalent themes of usability issues. These themes were then categorised to define 11 usability heuristics. The set of heuristics will enable designers to rapidly evaluate and investigate areas of improvement in VUIs. A between-subjects study with 12 HCI professionals, involving usability evaluation of a VUI application, was conducted to test this hypothesis. The study reveals a statistically significant increase in the number and diversity of usability issues identified using the presented heuristics.
Seam Carving Detection and Localization Using Two-Stage Deep Neural Networks

Seam carving is a method to resize an image in a content aware fashion. However, this method can also be used to carve out objects from images. In this paper, we propose a two-step method to detect and localize seam carved images. First, we build a detector to detect small patches in an image that has been seam carved. Next, we compute a heatmap on an image based on the patch detector's output. Using these heatmaps, we build another detector to detect if a whole image is seam carved or not. Our experimental results show that our approach is effective in detecting and localizing seam carved images.
Local Differential Privacy for Bayesian Optimization

Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. Specifically, the rewards from each user are further corrupted to protect privacy and the learner only has access to the corrupted rewards to minimize the regret. We first derive the regret lower bounds for any LDP mechanism and any learning algorithm. Then, we present three almost optimal algorithms based on the GP-UCB framework and Laplace DP mechanism. In this process, we also propose a new Bayesian optimization (BO) method (called MoMA-GP-UCB) based on median-of-means techniques and kernel approximations, which complements previous BO algorithms under heavy-tailed payoffs with reduced complexity. Further, empirical comparisons of different algorithms on both synthetic and real-world datasets highlight the superior performance of MoMA-GP-UCB in both private and non-private scenarios.
An Analysis of Students’ Reading Interest during Learning From Home amidst the Covid-19 Pandemic

ABSTRACTThis study aimed to analyze students’ reading interest during the learning-from-home policy in the Covid-19 pandemic time, and it was conducted at the English Department of the University of Kutai Kartanegara in Tenggarong. This descriptive study employed a qualitative approach with a survey design that involved 79 students from the second, fourth, and sixth semesters as participants. The data were obtained through a questionnaire and interview, and they were then analyzed descriptively. Results of this study showed that during the learning-from-home policy students liked to read anything but lecture materials. They argued that reading was tedious and difficult. They preferred reading fiction to reading non-fiction and preferred reading online to reading offline. They read 15-30 minutes a day and only read 1-3 books within five months of learning from home. Their reading materials came from online and offline sources, from websites, and print books by using smartphones. The type of material that was mostly read was novels especially romantic ones. This study suggests that teachers should present lecture materials in such a way that students would be interested in reading them for certain reasons academically or personally.ABSTRAKPenelitian ini bertujuan untuk menganalisis minat baca siswa selama belajar dari rumah pada waktu pandemi Covid-19, dan dilakukan di Jurusan Bahasa Inggris Universitas Kutai Kartanegara di Tenggarong. Penelitian ini menggunakan pendekatan kualitatif dengan menggunakan studi deskriptif jenis survei yang melibatkan 79 mahasiswa semester dua, empat, dan enam sebagai partisipan. Data diperoleh melalui angket dan wawancara, kemudian dianalisis secara deskriptif dan disajikan dalam bentuk tabulasi frekuensi dan persentase, serta dalam bentuk naratif. Hasil penelitian menunjukkan bahwa siswa suka membaca apa saja kecuali materi perkuliahan selama belajar dari rumah. Mereka berpendapat bahwa membaca itu membosankan dan sulit. Mereka lebih suka membaca fiksi daripada membaca non-fiksi dan lebih suka membaca online daripada membaca offline. Mereka membaca 15-30 menit sehari dan hanya membaca 1-3 buku dalam waktu lima bulan belajar dari rumah. Sumber bacaan mereka berasal dari online dan offline, dari Website dan buku cetak dengan menggunakan smartphone. Jenis materi yang paling banyak dibaca adalah novel terutama novel romance. Studi ini menunjukkan bahwa terdapat berbagai alasan mengapa fenomena tersebut terjadi.
Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment

One of the most important challenges in the use of the Message Queue Telemetry Transport (MQTT) protocol in the Internet of Thing (IoT) environment is data confidentiality and user authentication. Moreover, it is easy to manipulate the default security provided by MQTT during user authentication through username and password, and it does not provide any type of encryption of the data to ensure confidentiality or integrity. In this paper, we propose a key exchange algorithm to provide a secret key that can be used for data encryption using permuted congruential generator PCG-Rand as well as to ensure data integrity using Spongent lightweight hash functions.
De Re Updates

In this paper, we propose a lightweight yet powerful dynamic epistemic logic that captures not only the distinction between de dicto and de re knowledge but also the distinction between de dicto and de re updates.The logic is based on the dynamified version of an epistemic language extended with the assignment operator borrowed from dynamic logic, following the work of Wang and Seligman [35].We obtain complete axiomatizations for the counterparts of public announcement logic and event-model-based DEL based on new reduction axioms taking care of the interactions between dynamics and assignments.