Reviewerly/exHarmony huggingface dataset:
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts

Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seed-guided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.
Minimum Entropy Principle Guided Graph Neural Networks

Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks. However, limited by the bottleneck of interpretability that deep neural networks present, existing GNNs have ignored the issue of estimating the appropriate number of dimensions for the embeddings. Hence, we propose a novel framework called Minimum Graph Entropy principle-guided Dimension Estimation, i.e. MGEDE, that learns the appropriate embedding dimensions for both node and graph representations. In terms of node-level estimation, a minimum entropy function that counts both structure and attribute entropy, appraises the appropriate number of dimensions. In terms of graph-level estimation, each graph is assigned a customized embedding dimension from a candidate set based on the number of dimensions estimated for the node-level embeddings. Comprehensive experiments with node and graph classification tasks and nine benchmark datasets verify the effectiveness and generalizability of MGEDE.
Simplifying Graph-based Collaborative Filtering for Recommendation

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets.
Design of Personalized News Recommendation System Based on an Improved User Collaborative Filtering Algorithm

To solve the problem of information overload in the field of news, this paper designs and implements a feasible news recommendation system, where the front-end web page is made by Django framework, whose performance is optimized by bootstrap and jquery, while in the back-end design, the original user similarity calculation method is improved by adding the time attenuation factor, and a news recommendation model based on user collaborative filtering (CF) algorithm is proposed. Experimental results show that the proposed algorithm achieves highest recall, accuracy, and F1 score ratio compared with other algorithms, which indicate that the proposed algorithm has better performance.
Modeling hourly solar diffuse fraction on a horizontal surface based on sky conditions clustering

The commonly used diffuse fraction models are generally expressed as a single-value function of clearness index (Kt). Whereas in fact, the same Kt occurring at different times of the day may correspond to different diffuse fraction (Kf). Using a 10-year dataset from 2005 to 2014, this study intended to model hourly solar diffuse fraction based on sky conditions. Five cities in China, including Golmud, Kashgar, Sanya, Wuhan, and Wenjiang, were selected as representatives of five solar climatic zones. The measured radiation data on a horizontal surface were used for k-means clustering of the sky conditions with two factors of Kf and Kt. The results show that the sky conditions could be divided into two groups according to time dimension: the morning (6:00–12:00) and the afternoon (13:00–18:00) segments. Subsequently, a morning-afternoon segmented Kf-Kt model for each zone was constructed based on quartic polynomial regression, with the smallest error rate at a range of 10.4%–28.5%. Comparison with the existing models further validates that the respective models for the morning and afternoon periods can assist in improving the modeling of the hourly solar diffuse fraction on a horizontal surface in variable solar climatic zones.
Synthesis of optimal covert sensor–actuator attackers for discrete-event systems

This work introduces an approach for the synthesis of optimal covert sensor–actuator attackers in the context of discrete-event systems (DES). Building upon existing works on optimal supervisor synthesis, on one hand, and the base model construction in the transformation of covert sensor–actuator attacker synthesis to supervisor synthesis, on the other hand, we show how the optimal covert sensor–actuator attacker synthesis problem could be transformed to the optimal supervisor synthesis problem, thus generalizing its logic counterpart. We consider several different types of optimization objectives, such as (1) minimization of attack energy cost, and (2) minimization of time cost to cause damage infliction; for the latter one, asynchronous event firings and concurrent event firings at the plant are both considered. We provide the necessary and sufficient conditions for the existence of an optimal attacker for both the energy minimization case and damage infliction time minimization case. Thus, this work may potentially provide a unified approach for the optimal synthesis of covert sensor–actuator attackers in different setups.
Knowledge-Augmented Methods for Natural Language Processing

Knowledge in NLP has been a rising trend especially after the advent of large-scale pre-trained models. Knowledge is critical to equip statistics-based models with common sense, logic and other external information. In this tutorial, we will introduce recent state-of-the-art works in applying knowledge in language understanding, language generation and commonsense reasoning.
Sonar glass—Artificial vision: Comprehensive design aspects of a synchronization protocol for vision based sensors

Supporting visually impaired people during their navigation is a challenging task that involves localization, tracking, navigation, obstacle avoidance, and path guidance. Many researchers have experimented with Sonar, RFID, GPS, NFC, walking sticks, waist-based devices, and even computer vision modules for blind navigation. Using five sonar sensors for obstacle detection with direction and timestamp, we developed Sonar Glass. As humans see in left, right, front, top, and bottom directions based on eye angle and the head pose, the sonar glass is designed to provide obstacle information at the same angle. The head movement of the visually impaired person activates a pair of sensors for each module. Understanding human eye movement mechanisms and developing a synchronization protocol for each pair of visual sensors on sonar glass sitting on both eyes is the main goal of this paper. The major challenge lies in understanding and simulating the human vision mechanism and to realize how the field of Artificial Intelligence can be a contributor in producing technologies for the visually impaired. We are using log-polar transform to simulate human retinal image mapping. The Scale Invariant Feature Transform (SIFT) algorithm has also been implemented. It is the first time that both human eyes can be replaced by vision sensors. After comparing the estimated obstacle information from one sensor pair with that of the other sensors, the voice track is activated. The blind person uses the nearest obstacle information to avoid the obstacle and to extract spatial information about obstacles ahead of the user and provide an early warning. Unique in its design, the sonar Glass’s synchronization protocol for a pair of related sensors provides possible object information in that direction. The executions were simulated in MATLAB and the results obtained in real time are found to be promising as the tests were carried out both indoor and outdoor. The sonar Glass design is unique of its kind and the synchronization protocol for the pair of related sensors provides the possible vision about the object information at that particular direction.
Energy-aware fully-adaptive resource provisioning in collaborative CPU-FPGA cloud environments

Cloud warehouses have been exploiting multi-tenancy in CPU-FPGA collaborative environments, so clients can share the same infrastructure, achieving scalability and maximizing resource utilization. Therefore, the distribution of tasks across CPU and FPGA must be well-balanced so performance and energy are optimized in a highly variant workload scenario. In this paper, we take a step further and, in contrast to existing approaches, exploit DVFS (Dynamic Voltage and Frequency Scaling) on the CPU, together with an intelligent CPU-FPGA resource provisioning mechanism, to further improve energy. For that, we propose EASER, an end user-transparent framework that employs multiple strategies and dynamically selects the most appropriate one to optimize resource provisioning and DVFS according to the warehouse needs, workload properties, and target architecture. Our synergistic DVFS optimization brings up to 22% additional energy gains over our dynamic provisioning alone. Compared to fixed single strategies with DVFS, EASER brings, on average, 71% of energy gains.
TransTM: A device-free method based on time-streaming multiscale transformer for human activity recognition

RFID-based human activity recognition (HAR) attracts attention due to its convenience, non-invasiveness, and privacy protection. Existing RFID-based HAR methods use modeling, CNN, or LSTM to extract features effectively. Still, they have shortcomings: 1) requiring complex hand-crafted data cleaning processes and 2) only addressing single-person activity recognition based on specific RF signals. To solve these problems, this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM. This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing. Concretely, we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes single-human activities and human-to-human interactions. Compared with existing CNN- and LSTM-based methods, the Transformer-based method has more data fitting power, generalization, and scalability. Furthermore, using RF signals, our method achieves an excellent classification effect on human behavior-based classification tasks. Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy (99.1%). The dataset we collected for detecting RFID-based indoor human activities will be published.
Inward VR: Toward a Qualitative Method for Investigating Interoceptive Awareness in VR

Immersive virtual reality (VR) technologies can produce powerful illusions of being in another place or inhabiting another body, and theories of presence and embodiment provide valuable guidance to designers of VR applications that use these illusions to "take us elsewhere." However, an increasingly common design goal for VR experiences is to develop a deeper awareness of the internal landscape of one's own body (i.e., interoceptive awareness); here, design guidelines and evaluative techniques are less clear. To address this, we present a methodology, including a reusable codebook, for adapting the five dimensions of the Multidimensional Assessment of Interoceptive Awareness (MAIA) conceptual framework to explore interoceptive awareness in VR experiences via qualitative interviews. We report results from a first exploratory study (n=21) applying this method to understand the interoceptive experiences of users in a VR environment. The environment includes a guided body scan exercise with a motion-tracked avatar visible in a virtual mirror and an interactive visualization of a biometric signal detected via a heartbeat sensor. The results provide new insights on how this example VR experience might be refined to better support interoceptive awareness and how the methodology might continue to be refined for understanding other "inward-facing" VR experiences.
Body and Time: Virtual Embodiment and its Effect on Time Perception

This article explores the effect of one's body representation on time perception. Time Perception is modulated by a variety of factors including, e.g., the current situation or activity, it can display significant disturbances caused by psychological disorders, and it is influenced by emotional and interoceptive states, i.e., "the sense of the physiological condition of the body". We investigated this relation between one's own body and the perception of time in a novel Virtual Reality (VR) experiment explicitly fostering user activity. Forty-Eight participants randomly experienced different degrees of embodiment: i) without an avatar (low), ii) with hands (medium), and iii) with a high-quality avatar (high). Participants had to repeatedly activate a virtual lamp and estimate the duration of time intervals as well as judge the passage of time. Our results show a significant effect of embodiment on time perception: time passes slower in the low embodiment condition compared to the medium and high conditions. In contrast to prior work, the study provides missing evidence that this effect is independent of the level of activity of participants: In our task, users were prompted to repeatedly perform body actions, thereby ruling-out a potential influence of the level of activity. Importantly, duration judgements in both the millisecond and minute ranges seemed unaffected by variations in embodiment. Taken together, these results lead to a better understanding of the relationship between the body and time.
Multimodal Image Fusion Framework for End-to-End Remote Sensing Image Registration

We formulate the registration as a function that maps the input reference and sensed images to eight displacement parameters between prescribed matching points, as opposed to the usual techniques (feature extraction–description–matching–geometric restrictions). The projection transformation matrix (PTM) is then computed in the neural network and used to warp the sensed image, uniting all matching tasks under one framework. In this article, we offer a multimodal image fusion network with self-attention to merge the feature representation of the reference and sensed images. The integration information is then utilized to regress the prescribed points’ displacement parameters to get PTM between the reference and sensed images. Finally, PTM is supplied into the spatial transformation network (STN), which warps the sensed image to the same coordinates as the reference image, achieving end-to-end matching. In addition, a dual-supervised loss function is proposed to optimize the network from both the prescribed point displacement and the overall pixel matching perspectives. The effectiveness of our method is validated by qualitative and quantitative experimental results on multimodal remote sensing image matching tasks. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liliangzhi110/E2EIR</uri> .
Masked360: Enabling Robust 360-degree Video Streaming with Ultra Low Bandwidth Consumption

360-degree video streaming has gained tremendous growth over the past years. However, the delivery of 360-degree videos over the Internet still suffers from the scarcity of network bandwidth and adverse network conditions (e.g., packet loss, delay). In this paper, we propose a practical neural-enhanced 360-degree video streaming framework called Masked360, which can significantly reduce bandwidth consumption and achieve robustness against packet loss. In Masked360, instead of transmitting the complete video frame, the video server only transmits a masked low-resolution version of each video frame to reduce bandwidth significantly. When delivering masked video frames, the video server also sends a lightweight neural network model called MaskedEncoder to clients. Upon receiving masked frames, the client can reconstruct the original 360-degree video frames and start playback. To further improve the quality of video streaming, we also propose a set of optimization techniques, such as complexity-based patch selection, quarter masking strategy, redundant patch transmission and enhanced model training methods. In addition to bandwidth savings, Masked360 is also robust to packet loss during the transmission, because packet losses can be concealed by the reconstruction operation performed by the MaskedEncoder. Finally, we implement the whole Masked360 framework and evaluate its performance using real datasets. The experimental results show that Masked360 can achieve 4K 360-degree video streaming with bandwidth as low as 2.4 Mbps. Besides, video quality of Masked360 is also improved significantly, with an improvement of 5.24-16.61% in terms of PSNR and 4.74-16.15% in terms of SSIM compared to other baselines.
Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition

Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.
A Study of Change Blindness in Immersive Environments

Human performance is poor at detecting certain changes in a scene, a phenomenon known as change blindness. Although the exact reasons of this effect are not yet completely understood, there is a consensus that it is due to our constrained attention and memory capacity: We create our own mental, structured representation of what surrounds us, but such representation is limited and imprecise. Previous efforts investigating this effect have focused on 2D images; however, there are significant differences regarding attention and memory between 2D images and the viewing conditions of daily life. In this work, we present a systematic study of change blindness using immersive 3D environments, which offer more natural viewing conditions closer to our daily visual experience. We devise two experiments; first, we focus on analyzing how different change properties (namely type, distance, complexity, and field of view) may affect change blindness. We then further explore its relation with the capacity of our visual working memory and conduct a second experiment analyzing the influence of the number of changes. Besides gaining a deeper understanding of the change blindness effect, our results may be leveraged in several VR applications such as redirected walking, games, or even studies on saliency or attention prediction.
T-PASS: A Blockchain-based NFT Enabled Property Management and Exchange System

Everybody is witnessing a tremendous rise in the tokenization of all types of assets, including stock, funds, debt, and intellectual property, as a result of the explosive growth of decentralized finance (DeFi). Blockchain technology enables users to convert physical or digital assets into Non-Fungible Tokens (NFTs) and trade using cryptocurrencies. Blockchain technology uses a Distributed Ledger Technology (DLT) system to enable immutable, traceable, and safe trading. NFT’s use is currently restricted to fancy digital artwork, collectibles, games, etc., but its distinctive capabilities and platforms could be used for more real-world problems. This paper extends the DeFi applications with the real estate property assets as NFTs, examines the requirements of NFT-enabled property management and exchange system, and proposes a model for the same. We discuss the system design specifications and implementation approach extensively. The proposed model gives key components and direction for using NFTs in actual property management issues.
Internet threats and ways to protect against them: A brief review

Since the 21st century is the century of information technology, there are now a large number of Internet users, and the number of these users is steadily growing. Most people know the useful aspects of this network, but there are dangerous aspects of the Internet, which are reviewed in this paper. Today there are many types of threats such as phishing attacks, spam messages, malware, worms, spyware, Trojans, rooters, botnets. The main principle of protection against these threats is information and cyber literacy of using the Internet. The main purpose of this article is a summary of Internet threats based on a review of articles by previous researchers, as well as the definition of types of threats and attacks. As a result of the study, threats were filtered into two main types: threats of social engineering, where the main emphasis is on the information carrier, and technical threats, where various methods, algorithms and software implementations are used to hack directly into a computing device containing information.