Reviewerly/exHarmony huggingface dataset:
An Unsupervised Deep Learning Method for the Super-Resolution of Radar Sounder Data

Radar sounders (RSs) are widely used to image profiles (radargrams) of the subsurface of planetary bodies and the Earth. However, despite the huge scientific return from radargram analyses, their horizontal and vertical resolutions are limited by technical factors. Even if methods exist for improving the resolution, these are still limited by technical factors and introduce artifacts. This paper proposes an unsupervised deep-learning method that synthesizes accurate super-resolved radargrams overcoming these limitations. The method adopts the Cycle-Consistent Adversarial Network (CyleGAN) that learns the mapping function between the low- and high-resolution data distributions. The network is adapted to match the low- and high-resolution radargram characteristics, including the differences in dimensions and radiometric properties. The proposed method was successfully validated on airborne data at higher resolution and simulated data with lower resolution.
Area Efficient Compression for Floating-Point Feature Maps in Convolutional Neural Network Accelerators

Since Convolutional neural networks (CNNs) need massive computing resources, lots of computing architectures are proposed to improve the throughput and energy efficiency of the computing. However, those computing architectures need high data movement between the chip and off-chip memories, which causes high energy consumption on the off-chip memory; thus, the feature map (fmap) compression has been discussed for reducing the data movement. Therefore, the design of fmap compression becomes one of the main researches on CNN accelerator for energy efficiency on the off-chip memory. In this brief, we proposed floating-point (FP) fmap compression for a hardware accelerator which includes hardware design and a compression algorithm. This can apply quantization methods such as ternary neural quantization (TTQ), which only quantized weights with little or no degradation in accuracy and reduces the computation cost. In addition to the zero compression, we also compress nonzero values in the fmap based on the FP format. The compression algorithm achieves low area overhead and a similar compression ratio compared with the state-of-the-art on ILSVRC 2012 dataset.
Facilitating NeuroIS Research Using Natural Language Processing: Towards Automated Recommendations

Facing rapidly growing numbers of scientific publications, attempts to structure and interconnect scientific articles are becoming increasingly relevant. Various approaches to scientific article mapping are discussed in academic literature. However, these are mainly developed upon citation-based similarity measures. We propose an extension to these concepts of literature mapping by introducing a knowledge graph-based approach. Our contribution is twofold. We introduce a holistic concept to systematically capture literature in the field of NeuroIS research based on a relational mapping of main research concepts in the field. Additionally, we implement a content-based recommender system for literature in the NeuroIS domain that serves us as a proof-of-concept and a performance baseline for further developments. By providing a relational overview of the research domain and a dedicated recommender system, we hope to facilitate the entry to the field for new researchers and enhance further research.
Evaluation of Word Embeddings for Toxic Span Prediction

Toxic span prediction task is to predict the span of toxic content in social media posts, to identify objectionable parts of them. This task is dependent on word embeddings that represent the tokens as vectors and the model used for span prediction. This paper describes our experiment to evaluate word embeddings ranging from static and contextual to transformer-based embeddings for toxic span detection. Motivated by the top performers of SemEval 2021 Task 5, we chose to follow the sequence tagging approach for span prediction, hence, a stacked BiLSTM layer followed by CRF is used to predict the toxicity of each token. Our model with RoBERTa embeddings, fine-tuned on toxicity classification task has achieved the highest performance of 70.26 F1 score among all the word embeddings, which is on par with the top two performers of this task that are ensemble models. Our experiments compared word embeddings in terms of F1 score, training, and inference time of the model.
TauPad

Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective test data augmentation tool of point clouds to generate high-quality data to test the robustness of the target model. However, although common mutation methods can increase the amount of point clouds, the quality of the augmented data still needs to be improved based on the specify of the spatial structure of the point clouds. In this paper, we develop a point clouds augmentation tool, namely TauPad, of which the specific mutation direction is guided by adversarial attacks. Based on the point clouds pre-processing, point clouds adversarial mutation, and spatial distribution restoration, TauPad can generate augmented test data that are significantly deceptive to the target model. Preliminary experiments show that TauPad can reliably and effectively augment point clouds for testing. Its video is at https://youtu.be/Y9nDIEW13_g/ and TauPad can be used at http://1.13.193.98:2600/.
Autoexplorer: Autonomous Exploration of Unknown Environments using Fast Frontier-Region Detection and Parallel Path Planning

We propose a fully autonomous system for mobile robot exploration in unknown environments. Our system employs a novel frontier detection algorithm based on the fast front propagation (FFP) technique and uses parallel path planning to reach the detected front regions. Given an occupancy grid map in 2D, possibly updated online, our algorithm can find all the frontier points that can allow mobile robots to visit unexplored regions to maximize the exploratory coverage. Our FFP method is six~seven times faster than the state-of-the-art wavefront frontier detection algorithm in terms of finding frontier points without compromising the detection accuracy. The speedup can be further accelerated by simplifying the map without degrading the detection accuracy. To expedite locating the optimal frontier point, We also eliminate spurious points by the obstacle filter and the novel boundary filter. In addition, we parallelize the global planning phase using the branch-and-bound A*, where the search space of each thread is confined by its best knowledge discovered during the parallel search. As a result, our parallel path-planning algorithm operating on 20 threads is about 30 times faster than the vanilla exploration system that operates on a single thread. Our method is validated through extensive experiments, including autonomous robot exploration in both synthetic and real-world scenarios. In the real-world experiment, we show that an autonomous navigation system using a human-sized mobile manipulator robot equipped with a low-end embedded processor that fully integrates our FFP and parallel path-planning algorithms.
Shoot Before You Escape: Dynamic Behavior Monitor of Bitcoin Users via Bi-Temporal Network Analytics

As the first successful decentralized cryptocurrency system, Bitcoin has gradually become a breeding ground for hiding illegal or malicious activities without a central governing authority, in recent years. It remains a challenging task to mine Bitcoin blockchain for better financial forensics and security regulation, due to the hugeness and dynamism of transaction data. In this paper, we propose BitMonitor, which enables dynamic classification of Bitcoin users in real-time. The key module of BitMonitor is the construction of Bi-Temporal transaction network. Through it, on the one hand, we can perform temporal slicing of transactions associated with wallet nodes and sequentially tag the intent of user activity at different stages. On the other hand, it enables deep backtracking and tracing of the relevant financial flows dynamically over time. We demonstrate the effectiveness of the resulting multi-order neighborhood information in a static experimental scenario. Besides, the specificity of the Bi-Temporal transaction network also allows for incremental updating of neighborhood characteristics. Finally, through a weighted voting mechanism by incorporating tags on historical slices, we evaluate the dynamic classification performance from two different aspects. Unlike the static post-mortem analysis among existing work, we are the first to conduct a dynamic behavior monitor for the purpose of identifying Bitcoin accounts as soon as possible.
Simultaneous Multiple Cameras Monitoring and Streaming Based on Auto-layout and Analyzer

This paper provides a novel implementation method to develop a simultaneous monitor and visual streaming toolkit for multiple cameras. The implementation of the research of this paper uses the four main libraries to implement: FFmpeg, OpenCV, PIL and Tkinter. This streaming toolkit can implement the visible monitoring and streaming for multiple cameras simultaneously. Moverover, this streaming toolkit can achieve the function of arranging the visual display layout windows of multiple cameras. The inside interpreter of this streaming toolkit can intelligently analyze the input text of the streaming URL string and find out the correct protocol for camera streaming. Furthermore, enough experiments have been taken to test the monitoring and streaming performance and efficiency of this toolkit. This monitoring and streaming toolkit is able to gain the multimedia equipment information of the computer and make the corresponding configuration.
Non-Cepstral Uncertainty Vector for Replay Spoofed Speech Detection

Replay spoofing attack refers to gaining unautho-rized access to Automatic Speaker Verification (ASV) system simply by replaying the pre-recorded speech. In order to make an ASV system immune towards such attacks, this paper exploits Heisenberg's uncertainty principle in signal processing frame-work for spoof speech detection (SSD). This principle is used to develop non-cepstral features, namely, u-vector, t-vector, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\omega$</tex> -vector for SSD task. On analyzing the pictorial representation of the proposed feature sets, it is concluded that t-vector is effective at detecting distortions due to replay channel and reverberation. On the other hand, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\omega$</tex> -vector is although less effective for SSD but possess an inherent ability to emphasize more on the high frequency regions than the low frequency regions, which is desirable for effective SSD. Their summation, u-vector adopts properties of both t-vector and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\omega$</tex> -vector and thus, proving it to be superior for SSD task. Another attribute of these feature representation lies in their simplicity of implementation for practical SSD deployment. Experiments performed on ASVspoof 2017 version 2.0 dataset using Gaussian Mixture Model (GMM) classifier suggest that u-vector gives an absolute reduction of 4.55% and 5.28% in EER on development and evaluation dataset, respectively, on the baseline system. Automatic Speaker Verification (ASV), Heisenberg's un-certainty principle, Time-Bandwidth Product (TBP), replay attack, u-vector.
Exploiting Named Entity Recognition for Information Extraction from Italian Procurement Documents: A Case Study

Both large and medium-sized companies are concerned with maintaining good procurement relationships with their preferred suppliers and engaging previously unknown ones. To this end, potentially interesting suppliers are periodically monitored and evaluated by interacting with them via e-procurement platforms. Such a task rapidly becomes time-consuming and error-prone since buyer-side users ask documents of different types to suppliers, before manually evaluating the compliance status of every single document. To overcome this problem, we integrated Information Extraction capabilities, based on supervised Named Entity Recognition (NER), in the EPICS e-procurement platform. The solution has been evaluated both quantitatively and qualitatively on real-world procurement documents. Results show that the proposed approach is able to achieve good information extraction accuracy concerning different procurement document categories written in the Italian language.
Signal Detection of OFDM Systems Based on Convolutional Neural Network

This paper proposes a convolutional neural network structure to improve the signal detection capability of an Orthogonal Frequency Division Multiplexing (OFDM) receiver. The network structure can replace channel estimation and signal detection modules of conventional OFDM receivers, improving performance while making the network structure simpler. The network structure mainly includes: input layer, two convolution-residual units, full connection layer and classification layer. In this paper, the random data generated through the 3GPP TR38.901 channel model is used to train the network model, and the trained model is finally applied to the OFDM system, which replace the channel estimation and signal detection module in the traditional OFDM receiver. Theoretical analysis and simulation results show that the method in this paper not only reduces the number of parameters, shortens the training hours and reduces the memory occupancy, but also achieves good SER performance.
Real-time High-accuracy 3D Reconstruction Based on Texture and Geometric Features

We present a robust approach for reconstructing accurate 3D model based on image features combine with geometric features using RGB-D camera.First,the depthimagecapturedby RGB-D camera is filtered by our efficient algorithm to remove the intrinsic noise. This allows us to perform high-fidelity reconstruction. Then the point cloud is extracted texture and geometric features from color and depth image. Meanwhile, some mismatch point pairs are filtered by point color and distance statistics information. A new point cloud registration based on internal relative vector is proposed to obtain the current camera pose. In additional, a voxel count algorithm is proposed to register feature point and acquire an excellent fusion result. With the registered feature point, the 6DoF pose can be easily estimated from RGB-D image. Experimental results demonstrate that the proposed method have an excellent in both reconstruction and pose estimation.
Hand Motion Estimation using Super-Resolution of Multipoint Surface Electromyogram by Deep Learning

This paper proposes a method for hand motion estimation using super-resolution of multipoint surface electromyogram for prosthetic hands.In general, obtaining more EMGs (ElectroMyoGraphy) improves the accuracy of hand motion estimation, but it is costly and hard to use.Therefore, this method improves the accuracy of hand motion estimation by estimating a large number of EMG signals from a small number of EMG signals using super-resolution.This super-resolution is achieved by learning the relationship between few and many myoelectric signals using a deep neural network.Then, hand motions are estimated from the high-resolution signal using a deep neural network.Experiments using actual EMG signals show that the proposed method improves the accuracy of hand motion estimation.
Correlation Encoder-Decoder Model for Text Generation

Text generation is crucial for many applications in natural language processing. With the prevalence of deep learning, the encoder-decoder architecture is dominantly adopted for this task. Accurately encoding the source information is of key importance to text generation, because the target text can be generated only when accurate and complete source information is captured by the encoder and fed into the decoder. However, most existing approaches fail to effectively encode and learn the entire source information, as some features are easy to be missed along with the encoding procedures of the encoder. Similar problems also confuse the implementation of the decoder. How to reduce the problem of information loss in the encoder-decoder model is critical for text generation. To address this issue, we propose a novel correlation encoder-decoder model, which optimizes both the encoder and the decoder to reduce the problem of information loss by enforcing them to minimize the differences between hierarchical layers by maximizing the mutual information. Experimental results on two benchmark datasets demonstrate that the proposed model substantially outperforms the existing state-of-the-art methods. Our source code is publicly available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Infrared Object Detection Algorithm Based on Spatial Feature Enhancement

Focusing on the problems of CenterNet in infrared images, such as feature loss and insufficient information utilization, an improved algorithm based on spatial feature enhancement is proposed. Firstly, a frequency-space enhancement module is used to enhance the details of the target region. Secondly, a module that can count global information is introduced into the backbone network to model the feature graph globally. Finally, in the case of no increase in computation and complexity, the residual mechanism is adopted to redesign the overall structure of the algorithm, which strengthens the feature interaction simply and efficiently. Experimental tests are carried out on the self-established infrared object detection dataset G-TIR and public infrared object detection dataset FLIR. The proposed algorithm improves the accuracy of the baseline by 8.4% and 15.3% respectively, and is better than many mainstream object detection algorithms in recent years. Meanwhile, the detection speed reaches 72 FPS, which balances the detection accuracy and speed well, then meet the real-time detection requirements.
Study of Diverse Machine Translation Systems for Punjabi Language

Machine Translation (MT) is an automated way of transforming one language text into another language text without human efforts. MT research has been conducted for over more than 65 years, but it is still considered a tough task. MT is used as communication technology for text conversion of different scripts. A highly automated MT system is extremely difficult to construct. There are several approaches to MT systems, including rule-based, corpus-based, and hybrid-based approaches. Each approach has its advantages and limitations. In this paper, a study of diverse MT systems for Punjabi language is conducted. The researchers have used different techniques to develop an MT system that contains Punjabi as the source or target language. To evaluate the MT system, both manual and automated techniques were used. This paper also discusses the accuracy of different MT systems.
Using Archivematica and Omeka S for Long-Term Preservation and Access of Digitized Archive Materials

This study proposes a workflow using Archivematica, an application for long-term preservation of digital archives, and Omeka S for system accesses, for the long-term preservation of digitized archival materials and their access based on the Open Archival Information System (OAIS) Reference Model. The workflow requires three steps. First, metadata for ensuring authenticity is analyzed based on the OAIS information model. Second, information packages for preservation and accessing are generated using Archivematica. Third, an original tool is developed for metadata interoperability between Archivematica and Omeka S. Consequently, a consistent preservation and access workflow for digitized materials is successfully established.
PEDAGOGICAL CONDITIONS FOR FORMING THE EXPERIENCE OF CREATIVE ACTIVITY OF STUDENTS IN THE EDUCATIONAL PROCESS OF THE DISCIPLINARY LEVEL

The purpose of the article is to specify the pedagogical conditions for the formation of educational creative activity of students. Creative activity is considered to be that independent activity of students, which is aimed at acquiring new knowledge, regardless of whether its final product has social value and novelty or not. Increasing the share of objective creativity today is the task of the university. The quality and intensity of training activities are factors that significantly determine the effectiveness of training, and thus the quality of the results of training specialists. The solution of this problem is impossible without increasing the role of educational and creative activity of students, strengthening the responsibility of teachers for the formation of skills and abilities of independent creative activity of students. The organization of educational activities carries a pronounced function of management. At the same time, there is a need for a clear formulation of the educational and cognitive task, which makes it possible for the teacher to foresee the nature of the student’s activity and manage this process.
A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text

Software Requirement Specification (SRS) describes a software system to be developed that captures the functional, non-functional, and technical aspects of the stakeholder’s requirements. Retrieval and extraction of software information from SRS are essential to the development of software product line (SPL). Albeit Natural Language Processing (NLP) techniques, such as information retrieval and standard machine learning, have been advocated in the recent past as a semi-automatic means of optimising requirements specifications, they have not been widely embraced. The complexity in the organization’s information makes requirement analysis intricately a challenging task. The interdependence of subsystems and within an organisation drives this complexity. A plain multi-class classification framework may not address this issue. Hence, this paper propounds an automated non-exclusive approach for classification of functional requirements from SRS, using a deep learning framework. Specifically, Word2Vec and FastText word embeddings are utilised for document representation for training a convolutional neural network (CNN). The study was carried out by the compilation of manually categorised relevant enterprise data (AUTomotive Open System ARchitecture (AUTOSAR)), which were also employed for model training. Over a convolutional neural network, the impact of data trained with Word2Vec and FastText word embeddings from SRS documentation were compared to pre-trained word embeddings models, available online.
One-Time Programs from Commodity Hardware

One-time programs, originally formulated by Goldwasser et al. [26], are a powerful cryptographic primitive with compelling applications. Known solutions for one-time programs, however, require specialized secure hardware that is not widely available (or, alternatively, access to blockchains and very strong cryptographic tools). In this work we investigate the possibility of realizing one-time programs from a recent and now more commonly available hardware functionality: the counter lockbox. A counter lockbox is a stateful functionality that protects an encryption key under a user-specified password, and enforces a limited number of incorrect guesses. Counter lockboxes have become widely available in consumer devices and cloud platforms. We show that counter lockboxes can be used to realize one-time programs for general functionalities. We develop a number of techniques to reduce the number of counter lockboxes required for our constructions, that may be of independent interest.