Reviewerly/exHarmony huggingface dataset:
Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM

Information extraction from e-commerce platform is a challenging task.Due to significant increase in number of ecommerce marketplaces, it is difficult to gain good accuracy by using existing data mining techniques to systematically extract key information.The first step toward recognizing e-commerce entities is to design an application that detects the entities from unstructured text, known as the Named Entity Recognition (NER) application.The previous NER solutions are specific for recognizing entities such as people, locations, and organizations in raw text, but they are limited in e-commerce domain.We proposed a Bi-directional LSTM with CNN model for detecting e-commerce entities.The proposed model represents rich and complex knowledge about entities and groups of entities about products sold on the dark web.Different experiments were conducted to compare state-of-the-art baselines.Our proposed approach achieves the best performance accuracy on the Dark Web dataset and Conll-2003.Results show good accuracy of 96.20% and 92.90% for the Dark Web dataset and the Conll-2003 dataset, which show good performance compared to other cutting-edge approaches.
Extending Chatbots to Probe Users: Enhancing Complex Decision-Making Through Probing Conversations

Chatbots have become commonplace – they can provide customer support, take orders, collect feedback, and even provide (mental) health support. Despite this diversity, the opportunities of designing chatbots for more complex decision-making tasks remain largely underexplored. Bearing this in mind leads us to ask: How can chatbots be embedded into software tools used for complex decision-making and designed to scaffold and probe human cognition? The goal of our research was to explore possible uses of such "probing bots". The domain we examined was stock investment where many complex decisions need to be made. In our study, different types of investors interacted with a prototype, which we called "ProberBot", and subsequently took part in in-depth interviews. They generally found our ProberBot was effective at supporting their thinking but when this is desirable depends on the type of task and activity. We discuss these and other findings as well as design considerations for developing ProberBots for similar types of decision-making tasks.
Capturing Teens’ Voice in Designing Supportive Agents

Adolescence is a period of intense transformation that requires social interactions and emotional support. When such support is not available, technology can offer a solution. We conducted a participatory design study with the aim of producing content recommendations for a supportive conversational agent (CA) for adolescents. Twenty teens between the ages of 12 to 18 were invited to converse about issues they were experiencing and offer conversational support to each other. Analysis of participants' conversations revealed that stress was the most frequent problem participants would seek support for. Participant responses to each other's problems offered both cognitive and emotional support, including advice on changing one's behavior, seeking help from others, prioritizing one's wellbeing, and statements of unconditional emotional support, among others. The findings indicate that many of the observed conversational solutions can be programmed in a supportive CA to appeal to a large group of adolescents.
“Voice-First Interfaces in a GUI-First Design World”: Barriers and Opportunities to Supporting VUI Designers On-the-Job

Voice user interfaces (VUIs) are currently experiencing rapid growth as commercial devices like Google Home, Amazon Echo, and Apple Homepod are adopted by users. However, due to the pace of this growth, the tech industry has had to adapt quickly and vigorously to keep up with demand. Due to this, we currently have limited understanding of the environment of VUI design in industry, including the various multitude of practices and tools that are used. We also have a limited understanding of the barriers VUI designers currently still face. To address such knowledge gaps, we conducted a large-scale online survey to explore the design practices employed by VUI industry designers on-the-job, and the barriers and needs of VUI designers. We found that despite the availability of a wide range of guidelines, textbooks, tools, etc, there are significant gaps in the adoption of these tools within VUI industry design, and that designers rely on their previous experience in developing GUIs when designing VUIs. Based on our survey findings, we provide recommendations for how the HCI community may direct research efforts in developing tools to assist designers in overcoming existing barriers and build usable and adoptable VUIs.
An electro-optic reconfigurable OR to Ex-OR gate based on microring resonator loaded on Mach–Zehnder interferometric structure

In this paper, we proposed a novel electro-optic reconfigurable OR to Ex-OR gate and vice-versa. The proposed design consists of two microring resonators (MRR), employed in parallel, in a Mach–Zehnder interferometric (MZI) structure’s upper and lower arms with a tunable phase shifter attached in its lower arm, and can be controlled externally. The design will work as an OR gate when the phase of the tunable phase shifter is 0, and it will work as an Ex-OR gate when the phase is ‘π’. The micro-ring resonator employs an electro-optic modulation technique based on carrier injection, with electrical voltage sets 2 V and -1.85 V respectively for logic 1 and logic 0. We have also studied the performance of the Ex-OR gate when the phase shift deviates from the ideal required value. The extinction ratio obtained for Ex-OR operation is 17 dB and for OR operation it is 7.58 dB respectively. To demonstrate the circuit’s working operation at a data rate of 10 Gbps, @1550 nm time domain simulations were carried out with two electrical information bit streams fed into it.
A Hybrid Multimodal Data Fusion-Based Method for Identifying Gambling Websites

With the development of network technology, the number of gambling websites has grown dramatically, causing a threat to social stability. There are many machine learning-based methods are proposed to identify gambling websites by analyzing the URL, the text, and the images of the websites. Nevertheless, most of the existing methods ignore one important piece of information, i.e., the text within the website images. Only the visual features of images are extracted for detection, while the semantic features of texts on the images are ignored. However, these texts have key information clearly pointing to gambling websites, which can help us identify such websites more accurately. Therefore, how to fuse image and text multimodal data is a challenge that should be met.Motivated by this, in this paper, we propose a hybrid multimodal data fusion-based method for identifying gambling websites by extracting and fusing visual and semantic features of the website screenshots. First, we fine tune the pretrained ResNet34 model to train an image classifier and to extract visual features of webpage screenshots. Second, we extract textual content from webpage screenshots through the optical character recognition (OCR) technique. We use pretrained Word2Vec word vectors as the initial embedding layer and use Bi-LSTM to train a text classifier and extract semantic features of textual content on the screenshots. Third, we use self-attention to fuse the visual and semantic features and train a multimodal classifier. The prediction results of image, text, and multimodal classifiers are fused by the late fusion method to obtain the final prediction result. To demonstrate the effectiveness of the proposed method, we conduct experiments on the webpage screenshot dataset we collected. The experimental results indicate that OCR text on the webpage screenshots has strong semantic features and the proposed hybrid multimodal data fusion based method can effectively improve the performance in identifying gambling websites, with accuracy, precision, recall, and F1-score all over 99%.
Robots Still Outnumber Humans in Web Archives, But Less Than Before

To identify robots and humans and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012 and 2019, as well as Arquivo.pt's (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the two years of IA access logs (2012 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 is greater than in IA 2019 (21% more in requests and 18% more in sessions). Robots account for 98% of requests (97% of sessions) in Arquivo.pt (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.
Efficient freeform-based pattern projection system for 3D measurements

For three-dimensional (3D) measurement of object surface and shape by pattern projection systems, we used a hybrid projection system, i.e., a combination of a projection lens and a transmissive freeform to generate an aperiodic sinusoidal fringe pattern. Such a freeform effects a light redistribution, thus leading to an effective and low-loss pattern projection, as it increases the total transmission intensity of the system and has less power dissipation than classical projection systems. In this paper, we present the conception and realization of the measurement setup of a transmissive fringe projection system. We compare the characteristics of the generated intensity distribution with the classical system based on GOBO (GOes Before Optics) projection and show measurement results of different surface shapes, recorded with the new system.
Perceptual ultra-high definition video coding based on adaptive just noticeable distortion model

Ultra-high definition (UHD) video is gradually applied into many fields. The huge amount of data in UHD video limits the realization of these applications. Perception video coding can exploit the visual redundancy, and improve the compression performance. So far, the perpetual video coding based on just noticeable distortion (JND) model cannot fully exploit the perceptual redundancy of ultra-high definition (UHD) video/image in 10-bit depth. Thus, an adaptive JND model based on perceptual noise Bayesian network (PNBN) is firstly established for UHD video/image. Then, combined perceptual noise channel encoding/decoding with human visual characteristics, hidden side information of image is dynamically estimated by PNBN. Thirdly, the adaptive JND model is considered both luminance masking effect and texture complexity, which can adaptively exploit redundancy in different texture regions of image. Finally, an effective perceptual video coding (PVC) scheme is proposed for versatile video coding. A novel distortion compensation factor is appropriately designed for rate-distortion optimization. Experimental results show that the proposed PVC scheme achieves significant bitrate reduction with better subjective video quality than the state-of-the-art PVC schemes.
Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy-Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.
An Efficient Blockchain Transaction Retrieval System

In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.
WavTrans: Synergizing Wavelet and Cross-Attention Transformer for Multi-contrast MRI Super-Resolution

Current multi-contrast MRI super-resolution (SR) methods often harness convolutional neural networks (CNNs) for feature extraction and fusion. However, existing models have some shortcomings that prohibit them from producing more satisfactory results. First, during the feature extraction, some high-frequency details in the images are lost, resulting in blurring boundaries in the reconstructed images, which may impede the following diagnosis and treatment. Second, the perceptual field of the convolution kernel is limited, making the networks difficult to capture long-range/non-local features. Third, most of these models are solely driven by training data, neglecting prior knowledge about the correlations among different contrasts, which, once well leveraged, will effectively enhance the performance with limited training data. In this paper, we propose a novel model to synergize wavelet transforms with a new cross-attention transformer to comprehensively tackle these challenges; we call it WavTrans. Specifically, we harness one-level wavelet transformation to obtain the detail and approximation coefficients in the reference contrast MR images (Ref). While the approximation coefficients are applied to compress the low-frequency global information, the detail coefficients are utilized to represent the high-frequency local structure and texture information. Then, we propose a new residual cross-attention swin transformer to extract and fuse extracted features to establish long-distance dependencies between features and maximize the restoration of high-frequency information in Tar. In addition, a multi-residual fusion module is designed to fuse the high-frequency information in the upsampled Tar and the original Ref to ensure the restoration of detailed information. Extensive experiments demonstrate that WavTrans outperforms the SOTA methods by a considerable margin with upsampling factors of 2-fold and 4-fold. Code will be available at https://github.com/XAIMI-Lab/WavTrans .
Multiuser Adversarial Attack on Deep Learning for OFDM Detection

Adversarial attack has been widely used to degrade the performance of deep learning (DL), especially in the field of communications. In this letter, we evaluate different white-box and black-box adversarial attack algorithms for a DL-based multiuser orthogonal frequency division multiplexing (OFDM) detector subject to multiuser adversarial attack. The bit error rates under different adversarial attacks are compared. The results show that, the perturbation efficiency of adversarial attack is higher than conventional multiuser interference. Virtual adversarial methods (VAM) and zeroth-order-optimization (ZOO) attacks perform the best among white-box and black-box methods, respectively. They are also effective when the attack changes the starting time. Additionally, adding the number of attackers is found useful to improve the VAM attack but not for ZOO. This letter shows that adversarial attack is powerful to generate adversarial against multiuser OFDM communications.
Emotion Analysis Using Multilayered Networks for Graphical Representation of Tweets

Anticipating audience reaction towards a certain piece of text is integral to several facets of society ranging from politics, research, and commercial industries. Sentiment analysis (SA) is a useful natural language processing (NLP) technique that utilizes both lexical/statistical and deep learning methods to determine whether different sized texts exhibit a positive, negative, or neutral emotion. However, there is currently a lack of tools that can be used to analyze groups of independent texts and extract the primary emotion from the whole set. Therefore, the current paper proposes a novel algorithm referred to as the Multi-Layered Tweet Analyzer (MLTA) that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. Graph structures are capable of capturing meaningful relationships in complex ecosystems compared to other representation methods. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.
Performance Comparison of Open Speech-To-Text Engines using Sentence Transformer Similarity Check with the Korean Language by Foreigners

This paper contains the performance comparison of four Speech-to-Text (STT) engines which are Google STT, Naver Clova CSR, IBM Watson, and Microsoft Azure STT when transcribing foreigners speaking the Korean Language. The respondents are recording themselves speaking a predetermined sentence to be compiled together and then feeding it into the STT engine one by one to generate the transcribed text. The performance is evaluated using the Sentence Transformer Python framework that checks the similarity percentage between the original sentence to each of the transcribed texts and then finds the average result. The engine’s performance is categorized into four different categories which are sentence, nationality, age, and gender. The performance comparison results can be used to help determine the optimal STT engine for the Korean Language Spoken by Foreigner to develop STT-based or AI-based applications.
DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. The experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.
Certificateless Public Key Authenticated Searchable Encryption With Enhanced Security Model in IIoT Applications

In Industrial Internet of Things, powerful cloud servers are needed to process substantial sensitive industrial data, which should be encrypted before being uploaded to the cloud. To retrieve encrypted data, certificateless public key authenticated encryption with keyword search (CLPAEKS) is proposed. It has three advantages: 1) no certificate management problem; 2) no key escrow problem; and 3) inside keyword guessing attack resistance. The basic security requirements of current CLPAEKS schemes are ciphertext indistinguishability (CI security) and trapdoor indistinguishability (TI security) in the single-challenge setting. However, these security requirements are incomplete, since some necessary real-world scenarios are not considered: 1) the multichallenge setting to capture scenarios where multiple keywords may be embedded in one file or search query and 2) the fully chosen keyword attack, which allows the adversary to obtain ciphertext or trapdoor of any keyword. In this article, we formalize two enhanced security models for the CLPAEKS system to both capture the multichallenge setting and resist against fully chosen keyword attacks. Then, we provide a cryptanalysis on some of previous CLPAEKS schemes to better understand the necessity of these security models. Furthermore, we propose the first CLPAEKS scheme provably secure in our enhanced security models. Compared with previous CLPAEKS schemes, our scheme provides stronger security guarantee for the privacy of both ciphertext keyword and target keyword. We also evaluate the performance of our CLPAEKS scheme and optimize the efficiency of our scheme by performing most of the computation offline. As a result, the online efficiency of our CLPAEKS scheme is comparable to other schemes optimized using the same techniques.