Reviewerly/exHarmony huggingface dataset:
An Initial Research Work on the Evaluation of Professor’s Digital Competencies: Metared Overview

This research work was developed withing the scope of the metared – Portugal framework which consisted in identifying and evaluating the level of proficiency of digital competencies of Higher Education professors. The study involved 701 participating professors from several Higher Education institutions and outlined five dimensions of evaluation in the scope of digital proficiency that served as the guiding parameters for the sample analysis.
Enity Relation Extraction of Industrial Robot PHM Based on BiLSTM-CRF and Multi-head Selection

This paper extracted the entity relation from unstructured text in the industrial robot prognostics and health management (PHM) data for the construction of a knowledge graph. Traditionally, there is a disadvantage of error propagation in the pipeline method. At the same time, the industrial robot PHM corpus has a relation overlap problem, which reduces the accuracy of relations extraction. To solve this problem, we proposed a joint entity relations extraction model combining BiLSTM-CRF and multi-head selection. In this model, the encoding layer is shared by entity extraction tasks and relations extraction tasks. Moreover, BiLSTM-CRF is introduced for named entity recognition tasks, and the entity recognition information is utilized by the multi-head selection structure to solve the problem of overlapping relations. The results show that the model can effectively extract the entity relations of unstructured text in the robot PHM data, and the overall F1-score for entity relations extraction reaches 87.64. It is an increase of 2.92% and 13.5%, compared with the BiLSTM-ED-CNN model and the pipeline method using the same model.
Path Planning of a Multi Dimensional Robot in Static Environment Using Meta Heuristic Techniques

This paper focuses on finding the shortest and correct path for the robots without any collision with the objects in its environment. Generally global (static) and local environments are used for path planning of mobile robots. In this work, static environment is considered and an optimized path is evaluated for efficient robot movement from the start position to the end position. Different classical and evolutionary based path search algorithms are used so as to find the optimized path, with reduced computation time and path length of the mobile robot. The results reveal that evolutionary-based algorithms provide a more effective solution to the problem under consideration.
Web Archiving as a Task of National and Local History Bibliography

The foreign experience of forming the majority of national web archives (in national libraries) and many accessible local history archives of public and university libraries has been analyzed due to the development of a model and methodology for electronic local history archives for Russian libraries. It is shown that the global activity on preserving network content is divided into national and regional segments and is national and local history in both contents. Its goals and structure fully fit into the classical forms and directions of national/local history bibliography, which turns out to be the key in all processes of selection and bibliographic processing of a new class of sources. For selection, all the criteria of connection with the country/region/territory, the place of production of the resource, its national language and content, characteristic of the national/local history bibliography; the selection itself is carried out by library specialists. The analysis has revealed the reasons why the preparation of the archived online content for use, regardless of the capabilities of automated technologies, in practice also requires the involvement of specialist in bibliography, the use of classical tools and methods of bibliographic description and indexing of content. The issue of choosing objects for archiving (sites, subdirectories, individual documents) is specially considered. The special significance, advantages and problems of treating individual documents are shown. The necessity of forming such web archiving strategies is substantiated, in which “starting points” are separate documents, and sites are evaluated and selected for productivity as their “containers”. Taking into account the global trends and the conditions of Russian libraries, a model of web archiving is proposed, the implementation of which, in the absence of a national repository and modern tools, will allow the beginning of accumulating some necessary experience and valuable network content.
Intelligent Question Answering System Based on Machine Reading Comprehension

Abstract Question answering(Q&amp;A) system is important for accelerating the landing of artificial intelligence. This paper makes an improvement on the Q&amp;A system which uses the method of retrieval-machine reading comprehension (MRC). In the retrieval phase, we use BM25 to recall some documents and split these documents into paragraphs, then we reorder the paragraphs according to the correlation with the question, so as to reduce the number of recalled paragraphs and improve the speed of MRC. In the MRC stage, we design a multi-task MRC structure, which can judge whether the paragraph contains answer and locate answer accurately. Besides, we modify the loss function to fit the sparse labels during the training. The experiments are carried out on multiple data sets to verify the effectiveness of the improved system.
Methods for decrypting the data encrypted by the latest Samsung smartphone backup programs in Windows and macOS

A smartphone is a personal device, so the information is always tied to its user. Possibly, as the smartphone usage increase, more relevant data of the respective users end up. The smartphone manufacturers, who provide data backup services, prevent users from losing data in the event of physical damage, such as loss or breakage of the smartphone. The backup data store the same data as the user data in the smartphone, but the personal data related to the user are encrypted, and some data related to data restoration and information are stored in plain text format. When it is difficult to analyze the smartphone itself in a digital forensic investigation, the backup data are a useful analysis target to replace the data from the smartphone itself. In particular, smartphones made by Samsung, a leading manufacturer in the smartphone market, have been continually studied. In this paper, we analyzed the latest version of Smart Switch, a backup program provided by Samsung, in Windows and macOS environments. We analyzed the encryption method used in the latest version of Smart Switch and found that nine algorithms were used in both Windows and macOS environments. Using the analysis results, we decrypted all encrypted backup data and classified the backup data based on the encryption method. We identified the differences through comparison with previous studies. In addition, assuming that the PIN could not be obtained, we measured the time and resources required to recover the PIN. To the best of our knowledge, it is the first time Smart Switch has been analyzed in the macOS environment.
Probabilistic Collision Avoidance For Multiple Robots: A Closed Form PDF Approach

This paper proposes a novel method for reactive multiagent collision avoidance by characterizing the longitudinal and lateral intent uncertainty along a trajectory as a closed-form probability density function. Intent uncertainty is considered as the set of reachable velocities in a planning interval and distributed as a Gaussian distribution over the robot's instantaneous velocity. We utilize the Time Scaled Collision Cone(TSCC) approach, which characterizes the space of instantaneous collision avoidance velocities available to the ego-agent. We introduce intent uncertainty into the characteristic equation of the TSCC to derive the closed-form probability density function, which allows the collision avoidance problem to be rewritten as a deterministic optimization procedure. The formulation also allows the flexibility for the inclusion of confidence intervals for collision avoidance. We thus demonstrate the results and ablation studies of this derived collision avoidance formulation on various confidence intervals.
Sharpening anticipatory design senses for sustainable ‘scentory’ futures

This article investigates speculative design and the olfactory concerning potential future life forms and styles for more long term sustainable and survivable futures. Referring to Anticipatory Design, the article links anticipatory speculative inquiry literature from design, culture and posthumanism. Included is mention of cultural historical and contemporary material on the olfactory and sensory related design. The article reaches beyond current consumerist market practices is cosmetics and perfume. It poses and projects alternative relations between the environment and bio-materials, the olfactory, embodiment, the sensory, and the perceptual. These perspectives are related to leading olfactory projects and emerging trends and are embedded in a speculative olfactory project called XIANGVEI. Through a suite of speculative project artifacts, future life forms and life styles are seen having potential to contribute to wider systemic and collective systems of shaping futures knowledge by ‘scentory’ design. These life forms and styles are discussed in terms of in an eco-cultural-techno anticipatory design futures frame.
Cloud Storage for Management System

The development of education informatization has been highly valued by our government. It conforms to the development of education in the intelligent environment and is of great significance to promote the development of education informatization in our country. Based on the Internet platform and information technology and equipment, education management information system can fully reflect the development level of education informatization, and the establishment of a perfect education management information system is of great significance to promote the development of education informatization and improve the national quality. This paper studies the development process and current situation of education management information system in the process of education informatization at home and abroad, and makes a comparative analysis. Based on the relevant research of education informatization and education management information system at home and abroad, this paper analyzes the problems in the development of education management information system in China from two aspects of information system and project management, Put forward to establish a public network learning platform and exchange forum.
Dealing with the Uncertainty Between the Text Content and the Categories by a Proposed Wavelet Similarity Metric

The rise of the issues related to the uncertainty of decision-making has become a warm issue in operation research. The study is dealing with the uncertainty of text contents and text categories. The complex and ambiguous structure of texts prevents us to have clear and precise categorization. The purpose of this paper is to find the similarity distance for the different categories that the text may be related to, using hidden semantic relations. Our study proposes a new method to reveal the hypernym relations (generic terms, upper classes of the term) of the words in the text and formalize the similarity distance metrics between the given text and different categories. We proposed an original and novel measurement formula to calculate the similarity of defined categories to a specific text using discrete Wavelet transformation. Utilizing the Wavelet transformation method that has been rarely used in text analysis, the upper class “hypernym” relation and their dominance on each other is found. The strength of the results examined on the sample text (I like giraffes and I am afraid of lions. I saw them when they are standing opposite of each other near a cactus in Hoanib Desert.). In the first version that the frequencies are taken into account, sample text is categorized as “warm-blooded animals” between the 8 categories with highest similarity distance. For the normalized binary-valued version, maximum and minimum similarities are calculated in the range from 0.8639 to 0.1360, respectively.
Understanding as an uncertainty overcome

The article discusses understanding as a human tendency to remove uncertainty through the phenomenon of «entropy». The learning process initially puts the student in a situation of constant movement from a disordered environment to an ordered one, which occurs due to the constant interruption of the student’s inner world balance. Studies of understanding through entropy allow us to consider the subject of understanding as an irreversible dynamic system, then the individuality of pedagogical influence can be explained by the degree of stability of this system. Taking into account the variety of individual differences that determine the process of understanding, the idea of building a probabilistic model of understanding is proposed.
High Power-Efficient and Performance-Density FPGA Accelerator for CNN-Based Object Detection

The Field Programmable Gate Array (FPGA) accelerator for CNN-based object detection has been attracting widespread attention in computer vision. For most existing FPGA accelerators, the inference accuracy and speed are affected negatively by the low power-efficient and performance-density. To address this problem, we propose a software and hardware co-designed FPGA accelerator for accurate and fast object detection with high power-efficient and performance-density. To develop the FPGA accelerator on CPU+FPGA heterogeneous platforms, a resource sensitive and energy aware FPGA accelerator framework is designed. In hardware, a hardware sensitive neural network quantization called Dynamic Fixed-point Data Quantization (DFDQ) is proposed to improve the power-efficient. In software, an algorithm-level convolution (CONV) optimization scheme is further proposed to improve the performance-density by paralleling block execution of CONV cores. To validate the proposed FPGA accelerator, a Zynq FPGA is used to build the acceleration platform of You Only Look Once (YOLO) network. Results demonstrate that the proposed FPGA accelerator outperforms the state-of-the-art methods in power-efficient and performance-density. Besides, the speed of object detection is increased by at most 16.5 times along with less than 1.5% accuracy degradation.
Text Analytics on Course Reviews from Coursera Platform

Ratings and reviews are always the major consideration factor by online course seekers before they join the course. However, it can be time-consuming to read all the information especially the course reviews. In this research work, our objective is to propose a text analytics pipeline that includes text cleaning, text lemmatization, sentiment analysis, text mining, and visualization that can help course seekers to gain a quick insight into the courses as well as enables them to make a quick comparison between multiple courses. The proposed text analytic pipeline was created in Python Jupyter Notebook. Three different Python-related courses were chosen for the study. The proposed text analytics pipeline solution was proved able to achieve our research objective. It can help course seekers to gain a quick insight including the positive and negative reviews into the courses as well as enables them to make a quick comparison between multiple courses. The n-gram analysis and word cloud generated were sufficient to provide an accurate and informative glance into the course. However, it fell short on sentiment analysis especially in detecting the negative reviews.
Combining Unsupervised and Text Augmented Semi-Supervised Learning For Low Resourced Autoregressive Speech Recognition

Recent advances in unsupervised representation learning have demonstrated the impact of pretraining on large amounts of read speech. We adapt these techniques for domain adaptation in low-resource—both in terms of data and compute—conversational and broadcast domains. Moving beyond CTC, we pretrain state-of-the-art Conformer models in an unsupervised manner. While the unsupervised approach outperforms traditional semi-supervised training, the techniques are complementary. Combining the techniques is a 5% absolute improvement in WER, averaged over all conditions, compared to semi-supervised training alone. Additional text data is incorporated through external language models. By using CTC-based decoding, we are better able to take advantage of the additional text data. When used as a transcription model, it allows the Conformer model to better incorporate the knowledge from the language model through semi-supervised training than shallow fusion. Final performance is an additional 2% better absolute when using CTC-based decoding for semi-supervised training compared to shallow fusion.
Attention-based DenseNet network for multi-source remote sensing classification

Remote sensing data are abundant in spatial, image information and widely applied in earth observation covering military and civil fields. As the hot spot and critical point, scene classification has attracted increasing attention. Convolutional neural networks (CNNs) behave as representative technologies in scene classification. Nevertheless, many CNNs are lack of the ability to distinguish key information from redundant information. To overcome the problem and achieve efficient feature extractions from multi-source data, we integrate DenseNet and attention mechanism into a CNN-based network. Multi-scale convolution and threedimensional convolution are applied to extract features; and then, to explore and obtain feature information in a deeper level, a Dense structure are utilized; Afterwards, the features extracted from previous procedures are weighed due to importance and merged through the convolutional attention model. The experiment validates that the presented method poses advantages of efficiently merging various feature by Dense structure and the convolutional attention model.
Fault Diagnosis Method of Road Traffic Communication Network Based on SVM

Abstract The development of road traffic communication technology makes communication means more diverse. With the increase of the complexity of urban road traffic communication network, the fault diagnosis of road traffic communication network has been paid more and more attention. The alarm data in the communication network is the manifestation of network failure. Network operation and maintenance personnel can understand the current network operation status according to the alarm information displayed in the system, and then analyze, locate and handle the fault. Therefore, the identification of alarm information is very important for the timely treatment of network failure. In this paper, based on the existing communication network theory and network equipment alarm information, this paper proposes a method using SVM, which can effectively solve the fault identification of network communication input anomaly.
Learning to Synthesize and Remove Rain Unsupervisedly

Most existing single image deraining networks are trained in a supervised way, which relies on paired images including one clean image and one rain image. In most cases, the rain images are synthesized from the clean ones manually to obtain sufficient paired images. However, not only huge time costs but expert knowledge are needed to ensure the synthesized images are realistic enough. In addition, the superior performance of these deraining networks trained on manually synthesized rain images is hard to be maintained when testing on real rain images. To address these issues, we propose a scene adaptive asymmetric CycleGAN (SAA-CycleGAN) which transfers clean images to their rainy counterparts automatically so that adequate realistic rain images can be obtained for training deraining networks in a supervised way. Moreover, SAA-CycleGAN can both remove rain from rainy images and synthesize rain on clean images benefiting from the cycle consistency strategy. Since the information is not symmetric during the rain synthesis process and the deraining process, the generators are designed with different architecture accordingly for these two processes. Comprehensive experiments show that the SAA-CycleGAN is able to synthesize more lifelike rain images and achieve similar deraining performance compared with the state-of-the-art deraining methods.
Dependency to Semantics: Structure Transformation and Syntax-Guided Attention for Neural Semantic Parsing

It has long been known that the syntactic structure and the semantic representation of a sentence are closely associated [, ]. However, it is still a hard problem to exploit the syntactic-semantic correspondence in end-to-end neural semantic parsing, mainly due to the partial consistency between their structures. In this paper, we propose a neural dependency to semantics transformation model – Dep2Sem, which can effectively learn the structure correspondence between dependency trees and formal meaning representations. Based on Dep2Sem, a dependency-informed attention mechanism is proposed to exploit syntactic structure for neural semantic parsing. Experiments on Geo, Jobs, and Atis benchmarks show that our approach can significantly enhance the performance of neural semantic parsers.
Survey of Cervical Cancer Prediction Using Machine Learning

Cervical malignancy is perhaps the deadliest disease perceived and is likewise a key research territory in image processing. It is a threatening illness that is created in the cells of the cervix or the neck of the uterus. The fundamental issue with this malignancy is that it cannot be recognized easily, as it does not show any side effects until the last stages. To identify malignant growth in each stage, RGB2LAB shading transformation, entropy estimation, and classification are to be performed. In this paper, the Gaussian mixture model (GMM) is used in the sample image to process information in its pixels. Neural networks (NN) are a directed learning strategy which examines information and perceives designs utilized for factual characterization and relapse examination. In a probabilistic neural network (PNN), the activities are composed into a multilayered feed forward system. A four-layer neural system of the sort can outline input example to any number of groupings. Coming to the realization, there has therefore been progress in the dissemination of the screening test equipment for the timely identification of the precancerous cervical cells. The proposed calculation removes highlights from the pictures extra precisely.
Residual Knowledge Retention For Edge Devices

This paper proposes an approach for continual learning, Knowledge Retention (KR), that learns new information without accessing data from previous tasks. A KR unit is based on the embedding layer that identifies the important kernel in the convolution layer, which preserves key parameters and allows the weights to be reused across tasks. To construct higher-order generalization, we design a Residual Knowledge Retention (RKR) architecture that facilitates the network to stack deeper layers. Additionally, we rethink the benefits of different residual blocks respectively after employing depthwise convolutions. A surprising observation is that the basic block taking advantage of depthwise convolutions achieves higher representational power and builds a more lightweight model than the bottleneck block counterpart. On the Alternating CIFAR10/100 benchmark, we empirically show that the KR unit can be integrated into diverse networks and effectively prevents catastrophic forgetting. Finally, we demonstrate that RKR significantly outperforms the existing state-of-the-art continual learning methods with at least 6 times lower model complexity in two different scenarios for continual learning, which supports that the proposed approach is more competitive for resource-limited edge devices.