Reviewerly/exHarmony huggingface dataset:
Sensor Fusion and Device Collaboration based Smart Plug Hub Architecture for Precise Identification of ADL in Real-Time

In this paper, we propose a Smart Plug Hub Architecture for real-time measurement of Activities of Daily Living (ADL), a medical index indicating daily living performance. Currently, ADL is evaluated through patient interviews, behavioral videos, or behavioral tracking through wearable devices. However, the above methods have problems such as low accuracy and privacy violation. SPH overcomes the above problems through sensor fusion and device collaboration. SPH predicts the situation by detecting environmental changes according to human behavior through various environmental sensors. In order to analyze continuous environmental data effectively, a technique of analyzing data divided by each part is used by receiving a segmentation triggering a signal that allows data to be analyzed by region from surrounding IoT devices. By visualizing the collected data, SPH was able to predict in real-time specific human behavior in various environments.
Design of Robust Complex-Valued Feed-Forward Neural Networks

This paper addresses the problem of designing robust complex-valued neural networks in order to reduce their sensitivity to adversarial perturbations. The robustness is guaranteed by imposing a bound on the Lipschitz constant of the network. We present a new architecture (RCFF-Net), for which we derive tight Lipschitz constant bounds. A constrained learning strategy is then developed to train the proposed structure, while controlling its global Lipschitz constant. The proposed approach is evaluated in an audio signal denoising task. The achieved results demonstrate the effectiveness of the aforementioned design method.
Optimization of information service technologies in the Central Scientific Agricultural Library

The article considers changes in the information service technologies conditioned by digitalization in the Central Scientific Agricultural Library (CSAL), which has been creating information resources of its own generation since 1992. 24 document and lexicographic databases (DB) find their application for the information servicing. The main information product is the polythematic database “AGROS”, which provides comprehensive information about current receipts to the library collections. BD of own generation, posted on the website of CSAL, mainly in free access, have created conditions for the full-fledged remote user service. The library has converted almost all traditional services into the remote ones, developed convenient and simple services for its users to work independently with digital resources, which ensures their demand by specialists. The author shows positive dynamics of requests to the website of CSAL. Digital technologies have made it possible to perfect services; optimization of the Service Department, its merging with the Storage Department, transformation of service areas have increased the efficiency of work. As an information center, CSAL creates industry-wide linguistic tools, synopsizes and analytical reviews, makes them ready for the press.The library determines as perspective such directions of servicing its users as the formation and development of related data, which allow creating qualitatively new analytical information, provision of access to integrated information through a single window, organization of the single industry knowledge space, origination and provision of open data. For this purpose, industry- wide linguistic tools for indexing and searching for information will be developed, databases of own generation will be created and constantly updated; monitoring of the Internet resources and creating navigators and manuals on the problems of the agro-industrial complex will be carried out.
A Methodology for Large-Scale, Disambiguated and Unbiased Lexical Knowledge Acquisition Based on Multilingual Word Alignment

In order to be concretely effective, many NLP applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded lexical information. However, state-of-the-art knowledge models rarely adopt such a comprehensive and cross-lingual approach to semantics. In this paper, we propose a novel automatable methodology for knowledge modeling based on a multilingual word alignment mechanism that enhances the encoding of unbiased and naturally disambiguated lexical knowledge. Results from a simple implementation of the proposal show relevant outcomes that are not found in other resources.
Contrasting Quadratic Assignments for Set-Based Representation Learning

The standard approach to contrastive learning is to maximize the agreement between different views of the data. The views are ordered in pairs, such that they are either positive, encoding different views of the same object, or negative, corresponding to views of different objects. The supervisory signal comes from maximizing the total similarity over positive pairs, while the negative pairs are needed to avoid collapse. In this work, we note that the approach of considering individual pairs cannot account for both intra-set and inter-set similarities when the sets are formed from the views of the data. It thus limits the information content of the supervisory signal available to train representations. We propose to go beyond contrasting individual pairs of objects by focusing on contrasting objects as sets. For this, we use combinatorial quadratic assignment theory designed to evaluate set and graph similarities and derive set-contrastive objective as a regularizer for contrastive learning methods. We conduct experiments and demonstrate that our method improves learned representations for the tasks of metric learning and self-supervised classification.
Retraining a BERT Model for Transfer Learning in Requirements Engineering: A Preliminary Study

In recent years, advanced deep learning language models such as BERT, ELMO, ULMFiT and GPT have demonstrated strong performance on many general natural language processing (NLP) tasks. BERT, in particular, has also achieved promising results on some domain-specific tasks, including the requirements classification task. However, in spite of its great potential, BERT under-performs on domain specific tasks. In this paper, we present BERT4RE, a BERT-based model retrained on requirements texts, aiming to support a wide range of requirements engineering (RE) tasks, including classifying requirements, detecting language issues, identifying key domain concepts, and establishing requirements traceability links. We demonstrate the transferability of BERT4RE, by fine-tuning it for the task of identifying key domain concepts. Our preliminary study shows that BERT4RE achieved better results than the BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> model on the demonstrated RE task.
The place of digital competency in the structure of professional competency of a foreign language teacher

The purpose of the study is to substantiate the need to include the digital competency of a foreign language teacher in the structure of his professional competency along with communicative, philological, methodological, psychological-pedagogical and personal competences. The article focuses on the importance of the formation of digital competency in the framework of professional training and retraining of foreign language teachers in connection with the ongoing process of digitalization of the education system in general and language education in particular. The scientific novelty lies in the fact that the author has determined and substantiated the place of digital competency in the structure of the professional competency of a foreign language teacher, based on a systematic analysis of publications of domestic and foreign researchers in the field of professional teacher training. As a result, the content and structure of the digital competency of a foreign language teacher are clarified and the need for its consolidation as a component of professional competency is substantiated.
A Techno-Economic Study of Spectrum Sharing with Blockchain and Smart Contracts

The wireless crunch resulted in excess demand for the use of spectrum, and spectrum sharing is increasingly being proposed as a solution. To date, little research has considered how block-chain technologies can enable greater spectrum sharing. To address this gap, we develop a stylized model to show how blockchains can be leveraged to facilitate the exchange of access rights on a well-known band. To demonstrate proof of concept, we analyze available system design options, implement a small-scale test scenario, estimate the implementation and usage costs, and demonstrate how these technologies impact spectrum sharing prospects. Our exercise shows that blockchains can alleviate some of the perceived obstacles to greater sharing of spectrum.
Identification of Bird’s Nest Hazard Level of Transmission Line Based on Improved Yolov5 and Location Constraints

Bird’s nest is a common defect in transmission line, which seriously affects the safe and stable operation of the line. This paper presents a method of bird’s nest hazard level identification based on improved yolov5 and location constraints, which solves the problem of bird’s nest multiple identification and hazard level classification. We integrate GhostModule and ECA to design a lightweight attention mechanism convolution module (LAMCM). The original yolov5 is improved by using LAMCM and adding a prediction head, which improves the detection ability of small targets and alleviates the negative impact of scale violence. We only identify the bird’s nest on the panorama of UAV patrol, and classify the hazard level of the bird’s nest according to the location constraints of the bird’s nest and insulator. Experiments on coco dataset and self built transmission line dataset (TL) show that our algorithm is superior to other commonly used algorithms. In particular, the recall rate of bird’s nest hazard level identification has increased significantly. Compared with the original yolov5, the recall rate of the three levels of bird’s nest improved by our proposed improved yolov5 is more than 3%.
Ensuring students’ information security in the process of using the project method in foreign language teaching at the university

In contemporary conditions of globalization, there is a general informatization in all spheres of society. Informatization of the learning process has become the dominant direction in the modernization of the national system of higher education. The introduction of new information and communication technologies in the educational process leads to the emergence of completely new types of information threats and risks, ignoring which leads to destabilization of the learning process and students’ mental and physical health disorders. In this regard, ensuring information security is one of the main conditions for an effective educational process. Ensuring information security is based on the identification of real and potential threats in the information space. The classification of the main types of information threats is given. The identified threats, in conjunction with the provisions of the regulatory documents of the Russian Federation, provide for the development of a teacher’s competence in the field of information security. The content aspects of competence are revealed, which reflect the features of information threats and risks that are relevant at each stage of teaching. Based on the content aspects of competence, recommendations are formulated for teachers and students on ensuring information security as part of the process of foreign language teaching.
Individual Source Camera Identification with Convolutional Neural Networks

In this paper we consider the issue of digital camera identification which matches the area of digital forensics. This problem is well-known in the literature and many algorithms based on camera’s fingerprint have been proposed. However, one may find that there is a little number of methods providing a fast and accurate digital camera identification. This problem is especially observed in terms of today’s digital cameras, producing images of big sizes. In this paper we discuss several existing approaches based on convolutional neural networks (CNN). We try to find out whether it is possible to speed up the process of learning the networks by the images. One of the findings include replacing the ReLU with SELU activation function. We experimentally show that using SELU speeds up significantly the process of learning. We also compare the identification accuracy of all considered methods. The experiments are held on extensive image dataset, consisting of many images coming from modern cameras.
Fine-Grained Visual Entailment

Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. Because we lack fine-grained labels to train our method, we propose a novel multi-instance learning approach which learns a fine-grained labeling using only sample-level supervision. We also impose novel semantic structural constraints which ensure that fine-grained predictions are internally semantically consistent. We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18% accuracy at this challenging task while significantly outperforming several strong baselines. Finally, we present extensive qualitative results illustrating our method’s predictions and the visual evidence our method relied on. Our code and annotated dataset can be found here: https://github.com/SkrighYZ/FGVE .
Learning Local Implicit Fourier Representation for Image Warping

Image warping aims to reshape images defined on rectangular grids into arbitrary shapes. Recently, implicit neural functions have shown remarkable performances in representing images in a continuous manner. However, a standalone multi-layer perceptron suffers from learning high-frequency Fourier coefficients. In this paper, we propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes. Local textures estimated from a deep super-resolution (SR) backbone are multiplied by locally-varying Jacobian matrices of a coordinate transformation to predict Fourier responses of a warped image. Our LTEW-based neural function outperforms existing warping methods for asymmetric-scale SR and homography transform. Furthermore, our algorithm well generalizes arbitrary coordinate transformations, such as homography transform with a large magnification factor and equirectangular projection (ERP) perspective transform, which are not provided in training. Our source code is available at https://github.com/jaewon-lee-b/ltew .
Data Stream Unsupervised Partitioning Method

Data stream partitioning has attracted more and more attention in processing large-scale data. The use of parametric methods to perform this task requires for each application an empirical tuning of the different parameters. In practice, this step is difficult to perform and often does not lead to an optimized partitioning result. To avoid this difficulty and provide an objective and optimized partitioning, we propose in this paper an unsupervised and non-parametric algorithm of data stream which employs the Optimized Fuzzy C-Means algorithm. The partitioning is first performed on a series of data chunks and the final partition is obtained by a fusion process of the intermediate classes formed before. The performance of the proposed algorithm is evaluated and compared with a recent state-of-the-art algorithm on hyperspectral image databases.
Extracting a Definition and Taxonomy for Audio Augmented Reality (AAR) Using Grounded Theory

The concept of Augmented Reality (AR) has evolved over the years since its inception in 1957. However, as of today, AR is mostly realized using a visual medium. Almost all applications of AR rely on the user’s ability to see. A few recent works have attempted to explore a new modality for AR–e.g., audio. However, the concept of using audio to augment reality has been considered debatable and there is no specific definition of the concept. To better understand this new concept, we launched a study using Grounded Theory to develop a definition and taxonomy for the concept of Audio Augmented Reality (AAR). This paper shares the preliminary results based on the activities conducted thus far, and hopes to generate discussion on using audio to augment reality within the Human Factors community.
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks

With the increasing abundance of pretrained models in recent years, the problem of selecting the best pretrained checkpoint for a particular downstream classification task has been gaining increased attention. Although several methods have recently been proposed to tackle the selection problem (e.g. LEEP, H-score), these methods resort to applying heuristics that are not well motivated by learning theory. In this paper we present PACTran, a theoretically grounded family of metrics for pretrained model selection and transferability measurement. We first show how to derive PACTran metrics from the optimal PAC-Bayesian bound under the transfer learning setting. We then empirically evaluate three metric instantiations of PACTran on a number of vision tasks (VTAB) as well as a language-and-vision (OKVQA) task. An analysis of the results shows PACTran is a more consistent and effective transferability measure compared to existing selection methods.
A Deep Moving-Camera Background Model

In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the success has been far more modest due to algorithmic and scalability challenges that arise due to the camera motion. Thus, existing MCBMs are limited in their scope and their supported camera-motion types. These hurdles also impeded the employment, in this unsupervised task, of end-to-end solutions based on deep learning (DL). Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion. Unfortunately, the former creates several problems, including poor scalability, while the latter prevents the recognition and leveraging of cases where the camera revisits previously-seen parts of the scene. This paper proposes a new method, called DeepMCBM, that eliminates all the aforementioned issues and achieves state-of-the-art results. Concretely, first we identify the difficulties associated with joint alignment of video frames in general and in a DL setting in particular. Next, we propose a new strategy for joint alignment that lets us use a spatial transformer net with neither a regularization nor any form of specialized (and non-differentiable) initialization. Coupled with an autoencoder conditioned on unwarped robust central moments (obtained from the joint alignment), this yields an end-to-end regularization-free MCBM that supports a broad range of camera motions and scales gracefully. We demonstrate DeepMCBM’s utility on a variety of videos, including ones beyond the scope of other methods. Our code is available at https://github.com/BGU-CS-VIL/DeepMCBM .
Advanced E-Voting System using Blockchain

Voting is a crucial part of the process of selection of a government. Multiple layers of encryption and authentication must secure this voting process (more like a financial transaction). During achieving this security transaction, it may fail to uphold its atomic nature. As a result of this incomplete transaction, the voting credit may be disowned, and the vote may not to be cast. Even though the voter can revert credits through manual verification (like in online money transfer), the operation’s scale makes it an unrealistic task to perform. Applying a smart contract in the block chain ledger can be executed when the transaction has been completed and the vote has been cast. Moreover, the voting credit won’t be withdrawn unless the vote for the respective id has been performed. Hence, the process prevents a vote from being stuck in between transactions owing to which voting becomes more secure, and further, it preserves the atomic nature of the transaction.
Incorporating Multilingual Knowledge Distillation into Machine Translation Evaluation

Multilingual knowledge distillation is proposed for multilingual sentence embedding alignment. In this paper, it is found out that multilingual knowledge distillation could implicitly achieve cross-lingual word embedding alignment, which is critically important for reference-free machine translation evaluation (where source texts are directly compared with system translations). Then with the framework of BERTScore, we propose a metric BERTScore-MKD for reference-free machine translation evaluation. From the experimental results on the into-English language pairs of WMT17-19, the reference-free metric BERTScore-MKD is very competitive (not only best mean scores, but also better than BLEU on WMT17-18) when the current state-of-the-art (SOTA) metrics that we know are chosen for comparison. Moreover, the results on WMT19 demonstrate that BERTScore-MKD is also suitable for reference-based machine translation evaluation (where reference texts are used to be compared with system translations).
A new artificial bee colony algorithm based on modified search strategy

Artificial bee colony (ABC) is an efficient global optimisation algorithm. It has attracted the attention of many researchers because of its simple concept and strong exploration. However, it exhibits weak exploitation capability. To improve this case, a novel ABC with modified search strategy (namely MSABC) is proposed in this work. In MSABC, some modified elite solutions are preserved and used to guide the search. In addition, MSABC uses the modified elite solutions to generate offspring to replace the probability selection in the onlooker bee phase. To evaluate the capability of MSABC, 22 classical problems are tested. Results demonstrate MSABC achieves superior performance than five other ABC variants.
Research hotspots and development trends of STEM education at home and abroad -- based on the analysis of citespace visualization

STEM education is regarded as a strategic choice for implementing the new educational reform in the 21st century. To sort out the evolution path of STEM education research at home and abroad in the past 14 years and reveal the research hotspots and trends of STEM education. In this paper, CiteSpace literature visualization analysis software is used to perform knowledge mapping visualization analysis on literature data with the theme of “STEM education” in the CNKI Chinese database and WOS core database during 2008-2021. The results show that STEM education is widely used in science education, curriculum design and information technology education at home and abroad. Domestic STEM education research focuses on K-12 education, which cultivates students&apos; core literacy and innovation ability. Foreign STEM education research focuses on student development, student academic achievement and gender research in STEM education in higher education. This situation reveals the interdisciplinary, trans-regional and pluralistic nature of STEM education at home and abroad, presenting a trend of continuous integration and remodelling between STEM education and various disciplines.