Reviewerly/exHarmony huggingface dataset:
On Hulls of Some Primitive BCH Codes and Self-Orthogonal Codes

Self-orthogonal codes are an important type of linear codes due to their wide applications in communication and cryptography. The Euclidean (or Hermitian) hull of a linear code is defined to be the intersection of the code and its Euclidean (or Hermitian) dual. It is clear that the hull is self-orthogonal. The main goal of this paper is to obtain self-orthogonal codes by investigating the hulls. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}_{(r,r^{m}-1,\delta,b)}$ </tex-math></inline-formula> be the primitive BCH code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{r}$ </tex-math></inline-formula> of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r^{m}-1$ </tex-math></inline-formula> with designed distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{r}$ </tex-math></inline-formula> is the finite field of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> . In this paper, we will present Euclidean (or Hermitian) self-orthogonal codes and determine their parameters by investigating the Euclidean (or Hermitian) hulls of some primitive BCH codes. Several sufficient and necessary conditions for primitive BCH codes with large Hermitian hulls are developed by presenting lower and upper bounds on their designed distances. Furthermore, some Hermitian self-orthogonal codes are proposed via the hulls of BCH codes and their parameters are also investigated. In addition, we determine the dimensions of the code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}_{(r,r^{2}-1,\delta,1)}$ </tex-math></inline-formula> and its hull in both Hermitian and Euclidean cases for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \le \delta \le r^{2}-1$ </tex-math></inline-formula> . We also present two sufficient and necessary conditions on designed distances such that the hull has the largest dimension.
Digital competency of educators in the virtual learning environment: a structural equation modeling analysis

Abstract This study integrates the educators’ digital competency (DC), as an individual characteristic construct of the task-technology fit (TTF) theory, to examine a better fit between Moodle using and teaching task, and to investigate its effect on both Moodle’s utilization and their task performance. For assessing our proposed hypotheses, an online survey was conducted with 238 teaching staff from different departments of universities in Malaysia. Using Structural Equation Modelling (SEM), our analysis revealed that all the proposed components (i.e., technology literacy, knowledge deepening, presentation skills, and professional skills) of digital competency significantly influenced the TTF. The Task-Technology Fit was also found as an influential construct, which positively and significantly affected both Moodle’s utilization and teachers’ task performance. Besides, Moodle’s utilization was confirmed to be a substantial determinant of the performance impact. In the end, this study included limitations and future directions based on how the study’s contribution can support academics and practitioners for assessing and understanding what particular components of digital competency impact TTF, which in turn may influence the system’s utilization and performance impact.
Timing covert channel analysis of the VxWorks MILS embedded hypervisor under the common criteria security certification

Virtualization technology is nowadays adopted in security-critical embedded systems to achieve higher performance and more design flexibility. However, it also comes with new security threats, where attackers leverage timing covert channels to exfiltrate sensitive information from a partition using a trojan. This paper presents a novel approach for the experimental assessment of timing covert channels in embedded hypervisors, with a case study on security assessment of a commercial hypervisor product (Wind River VxWorks MILS), in cooperation with a licensed laboratory for the Common Criteria security certification. Our experimental analysis shows that it is indeed possible to establish a timing covert channel, and that the approach is useful for system designers for assessing that their configuration is robust against this kind of information leakage.
Virtual Makerspaces: Merging AR/VR/MR to Enable Remote Collaborations in Physical Maker Activities

We present a mixed-reality system for remote collaborations, where collaborators can discuss, explore, create and learn about 3D physical objects. The system combines Hololens augmented reality, 3D Kinect cameras, PC and virtual reality interfaces, into a virtual space that hosts remote collaborators, and physical & virtual objects.
Multilingual Text &amp; Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks

The Character recognition techniques equate an illustrative identity with the image of character. Handwritten human character recognition is a machine's ability to obtain and recognize handwritten information from various sources such as papers, photos, tactile touch devices etc. Recognition of handwriting and computer characters is an evolving field of study and has broad uses in banks, offices and industries. The key objective of this research work is to develop a knowledgeable framework for “Handwritten Character Recognition (HCR) victimization Neural Network” which might effectively acknowledge selected type-format character victimization as the substitute Neural Network approach. Neural method is the best method for controlling images, thus style parts square measure less all around plot as compared to various designs. Neural computers do parallel results. Neural computers square measure run during a manner that's utterly different from traditional operation. Neural computers square measure conditioned (not programmed) in such a way, that how it's given in an explicit beginning state (data input); they either assign the information (input file or computer file) into one amongst the quantity of categories or permit the initial data to evolve to maximize an explicit fascinating property. In this research work, a purely handwritten digit recognition using machine learning model as well as character recognition matlab model is used. A translator using MATLAB to beat the barrier of various languages is designed. The projected style is also used for English, Marathi and Guajarati text to speech conversion into English language. Input is taken in English, Marathi and Gujrati text manually to the interface or image of written text or handwritten text and output can be translated in English Language by facilitating use of Optical Character Recognition (OCR) technique. The projected methodology is also used to produce help to folks that lack the ability of speech or non-native speakers. On the other hand, purely handwritten digit recognition using machine learning algorithms is used to interpret the human handwriting to the second person easily and effectively.
Decoupling Representation Learning from Reinforcement Learning

In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning. To this end, we introduce a new unsupervised learning (UL) task, called Augmented Temporal Contrast (ATC), which trains a convolutional encoder to associate pairs of observations separated by a short time difference, under image augmentations and using a contrastive loss. In online RL experiments, we show that training the encoder exclusively using ATC matches or outperforms end-to-end RL in most environments. Additionally, we benchmark several leading UL algorithms by pre-training encoders on expert demonstrations and using them, with weights frozen, in RL agents; we find that agents using ATC-trained encoders outperform all others. We also train multi-task encoders on data from multiple environments and show generalization to different downstream RL tasks. Finally, we ablate components of ATC, and introduce a new data augmentation to enable replay of (compressed) latent images from pre-trained encoders when RL requires augmentation. Our experiments span visually diverse RL benchmarks in DeepMind Control, DeepMind Lab, and Atari, and our complete code is available at \url{hidden url}.
SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.
Wind Turbine Tower Deformation Measurement Using Terrestrial Laser Scanning on a 3.4 MW Wind Turbine

Wind turbine plants have grown in size in recent years, making an efficient structural health monitoring of all of their structures ever more important. Wind turbine towers deform elastically under the loads applied to them by wind and inertial forces acting on the rotating rotor blades. In order to properly analyze these deformations, an earthbound system is desirable that can measure the tower’s movement in two directions from a large measurement working distance of over 150 m and a single location. To achieve this, a terrestrial laser scanner (TLS) in line-scanning mode with horizontal alignment was applied to measure the tower cross-section and to determine its axial (in the line-of-sight) and lateral (transverse to the line-of-sight) position with the help of a least-squares fit. As a result, the proposed measurement approach allowed for analyzing the tower’s deformation. The method was validated on a 3.4 MW wind turbine with a hub height of 128 m by comparing the measurement results to a reference video measurement, which recorded the nacelle movement from below and determined the nacelle movement with the help of point-tracking software. The measurements were compared in the time and frequency domain for different operating conditions, such as low/strong wind and start-up/braking of the turbine. There was a high correlation between the signals from the laser-based and the reference measurement in the time domain, and the same peak of the dominant tower oscillation was determined in the frequency domain. The proposed method was therefore an effective tool for the in-process structural health monitoring of tall wind turbine towers.
Privacy-preserving data sharing via probabilistic modeling

Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modelling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also including prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key data sets for research.
Domain Divergences: A Survey and Empirical Analysis

Domain divergence plays a significant role in estimating the performance of a model in new domains.While there is a significant literature on divergence measures, researchers find it hard to choose an appropriate divergence for a given NLP application.We address this shortcoming by both surveying the literature and through an empirical study.We develop a taxonomy of divergence measures consisting of three classes -Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them.Further, to understand the common use-cases of these measures, we recognise three novel applications -1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild -and use it to organise our literature.From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and Higher-order measures are more common for 2).To further help researchers choose appropriate measures to predict drop in performance -an important aspect of Decisions in the Wild, we perform correlation analysis spanning 130 domain adaptation scenarios, 3 varied NLP tasks and 12 divergence measures identified from our survey.To calculate these divergences, we consider the current contextual word representations (CWR) and contrast with the older distributed representations.We find that traditional measures over word distributions still serve as strong baselines, while higher-order measures with CWR are effective.
Controlling Dialogue Generation with Semantic Exemplars

Dialogue systems pretrained with large language models generate locally coherent responses, but lack the fine-grained control over responses necessary to achieve specific goals.A promising method for controlling generated responses is exemplar-based generation, in which models edit exemplar responses that are retrieved from training data, or hand-written to strategically address discourse-level goals, to fit new dialogue contexts.We present an Exemplar-based Dialogue GEneration model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation.We show that controlling dialogue generation based on the semantic frames of exemplars improves the coherence of generated responses, while preserving semantic meaning and conversation goals present in exemplar re-1 sponses.
Stereographic geometry of coherence and which-path information

Recently, it was shown that quantum entanglement is an indispensable part of the duality behavior of light. Here we report a surprisingly intimate connection between the stereographic projection and the duality-entanglement nature of a single photon. We show that the duality-entanglement relation [Optica5, 942 (2018)OPTIC82334-253610.1364/OPTICA.5.000942] naturally emerges from the stereographic projection geometry. We demonstrate that this geometry is complementarity sensitive, in the sense that it is sensitive to the particle nature, wave nature, and entanglement nature of a single photon.
Cyclic program synthesis

We describe the first approach to automatically synthesizing heap-manipulating programs with auxiliary recursive procedures. Such procedures occur routinely in data structure transformations (e.g., flattening a tree into a list) or traversals of composite structures (e.g., n-ary trees). Our approach, dubbed cyclic program synthesis, enhances deductive program synthesis with a novel application of cyclic proofs. Specifically, we observe that the machinery used to form cycles in cyclic proofs can be reused to systematically and efficiently abduce recursive auxiliary procedures.
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting

Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each others' work.Despite decades of study, computational methods for CCA have largely relied on overly-simplistic assumptions of how authors cite, which ignore several important phenomena.For instance, scholarly papers often contain rich discussions of cited work that span multiple sentences and express multiple intents concurrently.Yet, recent work in CCA is often approached as a single-sentence, single-label classification task, and thus many datasets used to develop modern computational approaches fail to capture this interesting discourse.To address this research gap, we highlight three understudied phenomena for CCA and release MULTICITE, a new dataset of 12.6K citation contexts from 1.2K computational linguistics papers that fully models these phenomena.Not only is it the largest collection of expert-annotated citation contexts to-date, MULTICITE contains multi-sentence, multi-label citation contexts annotated throughout entire full paper texts.We demonstrate how MULTICITE can enable the development of new computational methods on three important CCA tasks.We release our code and dataset at https://github.com/allenai/multicite.
SSN3D: Self-Separated Network to Align Parts for 3D Convolution in Video Person Re-Identification

Temporal appearance misalignment is a crucial problem in video person re-identification. The same part of person (e.g. head or hand) appearing on different locations in video sequence weakens its discriminative ability, especially when we apply standard temporal aggregation such as 3D convolution or LSTM. To address this issue, we propose Self-Separated network (SSN) to seek out the same parts in different images. As the name implies, SSN, if trained in an unsupervised strategy, guarantees the selected parts distinct. With a few samples of labeled parts to guide SSN training, this semi-supervised trained SSN seeks out the parts that are human-understandable within a frame and stable across a video snippet. Given the distinct and stable person parts, rather than performing aggregation on features, we then apply 3D convolution across different frames for person re-identification. This SSN + 3D pipeline, dubbed SSN3D, is proved to be efficient through extensive experiments on both synthetic and real data.
Machine Learning-Based Student Emotion Recognition for Business English Class

Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.
Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability

Unlabeled data exploitation and interpretability are usually both required in reality. They, however, are conducted independently, and very few works try to connect the two. For unlabeled data exploitation, state-of-the-art semi-supervised learning (SSL) results have been achieved via encouraging the consistency of model output on data perturbation, that is, consistency assumption. However, it remains hard for users to understand how particular decisions are made by state-of-the-art SSL models. To this end, in this paper we first disclose that the consistency assumption is closely related to causality invariance, where causality invariance lies in the main reason why the consistency assumption is valid. We then propose ECT (Explanation Consistency Training) which encourages a consistent reason of model decision under data perturbation. ECT employs model explanation as a surrogate of the causality of model output, which is able to bridge state-of-the-art interpretability to SSL models and alleviate the high complexity of causality. We realize ECT-SM for vision and ECT-ATT for NLP tasks. Experimental results on real-world data sets validate the highly competitive performance and better explanation of the proposed algorithms.
Syntax-augmented Multilingual BERT for Cross-lingual Transfer

In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning.However, due to typological differences across languages, the cross-lingual transfer is challenging.Nevertheless, language syntax, e.g., syntactic dependencies, can bridge the typological gap.Previous works have shown that pretrained multilingual encoders, such as mBERT (Devlin et al., 2019), capture language syntax, helping cross-lingual transfer.This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer.We perform rigorous experiments on four NLP tasks, including text classification, question answering, named entity recognition, and taskoriented semantic parsing.The experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks, such as PAWS-X and MLQA, by 1.4 and 1.6 points on average across all languages.In the generalized transfer setting, the performance boosted significantly, with 3.9 and 3.1 points on average in PAWS-X and MLQA.