Reviewerly/exHarmony huggingface dataset:
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth. The synthesized realistic depth can then be used to train task-specific networks facilitating label transfer from the synthetic domain. Unlike existing image synthesis pipelines, where geometries are mostly ignored, we treat geometries carried by the depth scans based on their own existence. We propose differential contrastive learning that explicitly enforces the underlying geometric properties to be invariant regarding the real variations been learned. The resulting depth synthesis method is task-agnostic, and we demonstrate the effectiveness of the proposed synthesis method by extensive evaluations on real-world geometric reasoning tasks. The networks trained with the depth synthesized by our method consistently achieve better performance across a wide range of tasks than state of the art, and can even surpass the networks supervised with full real-world annotations when slightly fine-tuned, showing good transferability. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
CLUZH at SIGMORPHON 2021 Shared Task on Multilingual Grapheme-to-Phoneme Conversion: Variations on a Baseline

This paper describes the submission by the team from the Department of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task 1 of the SIGMORPHON 2021 challenge in the low and medium settings.The submission is a variation of our 2020 G2P system, which serves as the baseline for this year's challenge.The system is a neural transducer that operates over explicit edit actions and is trained with imitation learning.For this challenge, we experimented with the following changes: a) emitting phoneme segments instead of single character phonemes, b) input character dropout, c) a mogrifier LSTM decoder (Melis et al., 2019), d) enriching the decoder input with the currently attended input character, e) parallel BiLSTM encoders, and f) an adaptive batch size scheduler.In the low setting, our best ensemble improved over the baseline, however, in the medium setting, the baseline was stronger on average, although for certain languages improvements could be observed.
Understanding Ethereum Transactions via Network Approach

Ethereum, which is one of the largest public blockchain-based platforms, provides an unprecedented opportunity for data mining. However, the analysis of Ethereum transaction records remains under-explored. In this chapter, we model Ethereum transaction records as a complex network and further study the problem of phishing detection and transaction tracking via node classification and link prediction, respectively, which provides a deeper understanding of Ethereum transactions from a network perspective. Specifically, we introduce time-series snapshot network (TSSN) to model Ethereum transaction records as a spatial-temporal network and present temporal biased walk (TBW) to effectively embed accounts via their transaction records, which integrates temporal and structural information of the proposed network. Furthermore, we provide a detailed and systematic analysis of various graph embedding models and compare our proposed method with these embedding technologies on realistic Ethereum transaction records. Experimental results demonstrate the superiority of our TBW in learning more informative representations, and is essential for Ethereum network analysis.
Manus manum lavat

The newly rectified CTA standard --- Common Media Client Data (CMCD) --- allows content providers to get insights into the performance of their large-scale streaming operations. Its sister standard --- Common Media Server Data (CMSD) --- is in the works and will allow servers to send hints to other servers and clients. The CMCD/CMSD combo is the long-awaited upgrade to HTTP adaptive streaming systems.
Lifted Static Analysis of Dynamic Program Families by Abstract Interpretation

Program families (software product lines) are increasingly adopted by industry for building families of related software systems. A program family offers a set of features (configured options) to control the presence and absence of software functionality. Features in program families are often assigned at compile-time, so their values can only be read at run-time. However, today many program families and application domains demand run-time adaptation, reconfiguration, and post-deployment tuning. Dynamic program families (dynamic software product lines) have emerged as an attempt to handle variability at run-time. Features in dynamic program families can be controlled by ordinary program variables, so reads and writes to them may happen at run-time.Recently, a decision tree lifted domain for analyzing traditional program families with numerical features has been proposed, in which decision nodes contain linear constraints defined over numerical features and leaf nodes contain analysis properties defined over program variables. Decision nodes partition the configuration space of possible feature values, while leaf nodes provide analysis information corresponding to each partition of the configuration space. As features are statically assigned at compile-time, decision nodes can be added, modified, and deleted only when analyzing read accesses of features. In this work, we extend the decision tree lifted domain so that it can be used to efficiently analyze dynamic program families with numerical features. Since features can now be changed at run-time, decision nodes can be modified when handling read and write accesses of feature variables. For this purpose, we define extended transfer functions for assignments and tests as well as a special widening operator to ensure termination of the lifted analysis. To illustrate the potential of this approach, we have implemented a lifted static analyzer, called DSPLNum²Analyzer, for inferring numerical invariants of dynamic program families written in C. An empirical evaluation on benchmarks from SV-COMP indicates that our tool is effective and provides a flexible way of adjusting the precision/cost ratio in static analysis of dynamic program families.
Speculative Experimentation in (Methodological) Pluriverse

This article is slightly modified from the Invited Egon Guba Memorial Lecture I gave at the American Educational Research Association (AERA) annual meeting in 2021. Drs. Aaron Kuntz and Juha Suoranta served as my discussants and their individual responses and comments are linked to this article. I think of this article having many potential provocations and imagined audiences while I intentionally did not carry forward linear and logical argumentation structure. Instead, I thought about qualitative pasts, many presences of today’s qualitative inquiries and I aimed to vision potentially more diverse and inviting qualitative futures. I wrote within and through interrelated plural flows of relationality, including speculative, experimentative, and methodological flows. Furthermore, speculative experimentation inspired me as one methodological possibility as it offers opportunities for creative imagination, critical hesitation, reflective questioning, and thinking with unthinkable futures.
KaFHCa: Key-establishment via Frequency Hopping Collisions

The massive deployment of IoT devices being utilized by home automation, industrial and military scenarios demands for high security and privacy standards to be achieved through innovative solutions. This paper proposes KaFHCa, a crypto-less protocol that generates shared secret keys by combining random frequency hopping collisions and source indistinguishability independently of the radio channel status. While other solutions tie the secret bit rate generation to the current radio channel conditions, thus becoming unpractical in static environments, KaFHCa guarantees almost the same secret bit rate independently of the channel conditions. KaFHCa generates shared secrets through random collisions of the transmitter and the receiver in the radio spectrum, and leverages on the fading phenomena to achieve source indistinguishability, thus preventing unauthorized eavesdroppers from inferring the key. The proposed solution is (almost) independent of the adversary position, works under the conservative assumption of channel fading (σ=8dB), and is capable of generating a secret key of 128 bits with less than 564 transmissions.
Beyond Speculation About the Ethics of Virtual Reality: The Need for Empirical Results

OPINION article Front. Virtual Real., 12 August 2021Sec. Virtual Reality and Human Behaviour https://doi.org/10.3389/frvir.2021.687609
Reservoir Computing Leveraging the Transient Non-linear Dynamics of Spin-Torque Nano-Oscillators

Present artificial intelligence algorithms require extensive computations to emulate the behavior of large neural networks, operating current computers near their limits, which leads to high energy costs. A possible solution to this problem is the development of new computing architectures, with nanoscale hardware components that use their physical properties to emulate the behavior of neurons. In spite of multiple theoretical proposals, there have been only a limited number of experimental demonstrations of brain-inspired computing with nanoscale neurons. Here we describe such demonstrations using nanoscale spin-torque oscillators, which exhibit key features of neurons, in a reservoir computing approach. This approach offers an interesting platform to test these components, because a single component can emulate a whole neural network. Using this method, we classify sine and square waveforms perfectly and achieve spoken-digit recognition with state of the art results. We illustrate optimization of the oscillator’s operating regime with sine/square classification.
DeCanSec: A Decentralized Architecture for Secure Statistical Computations on Distributed Health Registry Data

The architectures presented in the literature, and current practices and solutions for computing statistics on data from health registries distributed across the world are manual and suffers from security and privacy problems. In this paper, we suggest a solution design with a infrastructure architecture providing improved security, automation and privacy guarantees compared to the related works. Our solution builds on top of the key research accomplishments from several areas such as distributed computing, blockchain, cryptography, and medical informatics rather than completely re-inventing the wheel from scratch for the healthcare domain. The proposed architecture is currently being prototyped in the Cancer Registry of Norway.
Research on the Application of Digital VR Technology in Snow and Ice Landscape Teaching

Abstract With the continuous reform and development of modern education system, it puts more emphasis on learning theoretical knowledge through practical perception to improve students’ understanding and digestion of knowledge. With the rapid development of digital VR technology, students can visit and study the real scene with the assistance of the corresponding equipment. This paper mainly discusses the application of digital VR technology in snow and ice landscape teaching to better understand the technical characteristics of digital VR technology, which can apply it in the course teaching and further improve the teaching quality of snow and ice landscape.
SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning

Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.
Autoweighted Multiview Feature Selection With Graph Optimization

In this article, we focus on the unsupervised multiview feature selection, which tries to handle high-dimensional data in the field of multiview learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their predefined Laplacian graphs are sensitive to the noises in the original data space and fail to obtain the optimal neighbor assignment. To address the above problems, we propose a novel unsupervised multiview feature selection model based on graph learning, and the contributions are three-fold: 1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Therefore, the proposed model can reveal the data relationship from the feature subset; 2) a reasonable rank constraint is added to optimize the similarity matrix to obtain more accurate information; and 3) an autoweighted framework is presented to assign view weights adaptively, and an effective alternative iterative algorithm is proposed to optimize the problem. Experiments on various datasets demonstrate the superiority of the proposed method compared to the state-of-the-art methods.
Fs-Net: Filter Selection Network For Hyperspectral Reconstruction

optimizing spectral filters for hyperspectral reconstruction has received increasing attentions recently. However, current filter selection methods suffer from extremely high computational complexity due to exhaustive optimization. In this paper, in order to reduce the computational complexity, we propose a novel Filter Selection Network (FS-Net) to select filters and learn the reconstruction network simultaneously. Specifically, we propose an end-to-end method to embed filter selection in FS-Net by setting spectral response functions as the input layer. Furthermore, we propose a non-negative Ll sparse regularization (NN-LI) to select optical filters automatically by sparsifying the input layer. Besides, we develop a two-stage training strategy for adjusting the number of selected filters. Experiments on public datasets show that our proposed method can considerably improve the reconstruction quality.
XRF and 3D Modelling on a Composite Etruscan Helmet

The presentation of X-ray fluorescence data (XRF) assays is commonly restricted to tables or graphical representations. While the latter may sometimes be in a 3D format, they have yet to incorporate the actual objects they are from. The presentation of multiple XRF assays on a 3D model allows for more accessible presentation of data, particularly for composite objects, and aids in their interpretation. We present a method to display and interpolate assay data on 3D models using the PyVista Python package. This creates a texture of the object that displays the relative differences in elemental composition. A crested helmet from Tomb 1036 from the Casale del Fosso necropolis, Veii, Italy, is used to exemplify this method. The results of the analysis are presented and show variation in composition across the helmet, which also corresponds with macroscopic and decorrelation stretching analyses.
Low Computation and High Efficiency Sobel Edge Detector for Robot Vision

Most information in an image is contained in the edge. Sobel edge detection algorithm is a classic method to realize the edge detection of image in the applications of robot vision, including motion detection and object tracking. This paper proposes an area-efficient and energy-efficient Sobel edge detector design utilizing bit-width pruning, shift-add operation and bit-width tuning techniques, to reduce required area and computation significantly with negligible edge information loss, for mobile robot vision applications. As a result, the proposed Sobel edge detector can achieve lower hardware overhead and higher energy efficiency. FPGA implementation shows that the proposed Sobel detector design achieves 33% lower power consumption while maintaining a good detection performance in terms of CP (over 99%, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{co}$</tex> ) and achieving a better processing performance in terms of frame rate (an increasing by 39.5%), as compared to the conventional Sobel detector design, which is suitable for resource-limited and energy-constrained mobile robots in edge IoT applications.
Activating model of professional training of persons with disabilities as competitive specialists in higher education establishment

The purpose of the study is Assess the effectiveness of an activating model of vocational training at a university for persons with disabilities as competitive specialists. The developed model was implemented in a formative experiment that lasted from 2014 to 2018 – from the 1st to the 4th year of higher education programs in different training areas. The experimental group consisted of 18 inclusive student groups, where 416 students graduated in 2018, 71 were graduate students with disabilities. The proof of the effectiveness of the developed model is the fact that the percentage distribution of students with disabilities, who made up the experimental and control groups, according to the levels of their academic, practical, and personal readiness, significantly differed from each other by the end of the experiment. The variety of teaching methods, the alternation of traditional, standard methods with methods of activating cognitive activity, even without the use of special technical means, determine the success of memorizing educational and scientific information by persons with disabilities.
Byzantine-robust federated learning via credibility assessment on non-IID data

Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility assessment is designed to detect Byzantine attacks by combing adaptive anomaly detection model and data verification. Specially, an adaptive mechanism is incorporated into the anomaly detection model for the training and prediction of the model. Simultaneously, a unified update algorithm is given to guarantee that the global model has a consistent direction. On non-iid data, our experiments demonstrate that the BRCA is more robust to Byzantine attacks compared with conventional methods.
Linear optical deterministic and reconfigurable SWAP gate

We propose a deterministic SWAP gate for dual-rail-encoded single photon qubits. The gate is constructed from waveguide crossings, Mach–Zehnder interferometers and phase shifters which provide the gate reconfigurability. Through manipulating the phase of the phase shifters, we can apply either the SWAP or identity gates. As an essential element of nearest neighbor qubit networks, the SWAP gate has a simple structure and presents minimal overhead. Providing an efficient solution to the geometric restrictions of on-chip dual-rail qubit circuits, our design may be utilized in applying large scale arbitrary unitary transformations.
Community Enhanced Course Concept Recommendation in MOOCs with Multiple Entities

Massive Open Online Courses (MOOCs) are becoming a new way of education in the world, providing people with a large number of open courses to facilitate their access to knowledge. However, there are many courses and different course focuses influence people’s choice, which reduces the enthusiasm of online learning. To fill this gap, course concept recommendation is researched to help people better access the courses they are interested in. In this paper, we propose community enhanced course concept recommendation with multiple entities (CERec-ME). A series of heterogeneous graphs of multiple entities (e.g., users, videos, teachers, courses, concepts) in MOOCs are constructed based on meta-paths that are defined as the relationships among entities. Under different meta-paths, a set of feature representations for users and concepts are generated respectively by using graph convolutional networks (GCN). In order to extract more reasonable node representations from neighborhood nodes in a GCN, a high-level semantic of graph is explored by defining community structure and measuring the similarities of nodes in the community. The CERec-ME is an end-to-end framework. The parameters in CERec-ME are optimized through a novel loss function which is able to consider information about the community structure along with information about the node neighborhoods. Experiments are conducted on two public real-world datasets XuetangX and MOOCCube, showing that CERec-ME is more effective for knowledge concept recommendation compared with state-of-the-art baseline methods.
AST-path Based Compare-Aggregate Network for Code Clone Detection

Code clone detection remains one of the main challenges in maintaining software projects. Recently, state-of-the-art researches have shown that neural models based on abstract syntax trees (ASTs) can better represent code fragment. However, existing tree-based models are prone to gradient vanishing problems due to the large size of ASTs. In this paper, we represent a code fragment as the set of compositional paths in its abstract syntax tree (AST) and use this code representation to train a classifier to detect clone pairs. Unlike the siamese based model that obtains the embeddings of code fragments separately and then computes the similarity in vector space, our compare-aggregate based network takes two code fragments as a whole to obtain the vectors for classification. To validate our model's ability to detect code clones, we evaluated it on the publicly available dataset BigCloneBench, and the experimental results show our model outperforms the state-of-the-art model ASTNN.