Reviewerly/exHarmony huggingface dataset:
A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation

Video object segmentation’s primary goal is to automatically extract the principal object(s) in the foreground from the background in videos. The primary focus of the current deep learning-based models is to learn the discriminative representations in the foreground over motion and appearance in small-term temporal segments. In the video segmentation process, it is difficult to handle various challenges such as deformation, scale variation, motion blur, and occlusion. Furthermore, relocating the segmentation target in the next frame is difficult if it is lost in the current frame during the segmentation process. This work aims at solving the zero-shot video object segmentation issue in a holistic fashion. We take advantage of the inherent correlations between the video frames by incorporating a global co-attention mechanism to overcome the limitations. We propose a novel reinforcement learning framework that provides competent and fast stages for gathering scene context and global correlations. The agent concurrently calculates and adds the responses of co-attention in the joint feature space. To capture the different aspects of the common feature space, the agent can generate multiple co-attention versions. Our framework is trained using pairs (or groups) of video frames, which adds to the training content, thus increasing the learning capacity. Our approach encodes the important information during the segmentation phase by a simultaneous process of various reference frames that are subsequently utilized to predict the persistent and conspicuous objects in the foreground. The proposed method has been validated using four commonly used video entity segmentation datasets: SegTrack V2, DAVIS 2016, CdNet 2014, and the Youtube-Object dataset. On the DAVIS 2016, the results reveal that the proposed results boost the state-of-the-art techniques on the F1 Measure by 4%, SegTrack V2 by a Jaccard Index of 12.03%, and Youtube Object by a Jaccard Index of 13.11%. Meanwhile, our algorithm improves the accuracy by 8%, F1 Measure by 12.25 %, and precision by 14% on the CdNet 2014, thus ranking higher than the current state-of-the-art methods.
Extended Reality in Education and Training: Case Studies in Management Education

This paper presents and concludes the ATOMIC project, which was to create an XR-based educational environment that enables students to meet the challenges of a natural business environment such as planning and organizing, staffing and control, problem solving, critical thinking, creativity, and teamwork. Four different approaches were taken utilizing different XR technologies (projector-based AR, mobile-based AR, HMD AR, and HMD VR), and their efficacy and educational value were juxtaposed. A universal 34-question usability questionnaire was proposed that can be applied in future XR usability studies. Four versions of the application were tested among 20 students to identify the advantages and disadvantages of each approach in an educational context.
Cross-network representation learning for anchor users on multiplex heterogeneous social network

Online users are typically involved in multiple online social networks simultaneously to enjoy a variety of social network services, thus bringing about the interconnection of online social networks via bridge users called anchor users. Anchor users can be beneficial to a wide range of social network analysis applications such as cross-domain system recommendation, cross-network information diffusion, and link prediction, taking anchor user's intra-network structural information along with its cross-network structural properties into consideration. Several studies have so far tried to learn low-dimensional representations of social users by capturing their network structures inside one social network but they have not fully leveraged their intra-network structures with their cross-network structures to boost the performance of the aforementioned analysis tasks. In this paper, we present a novel deep learning model to learn Overall low-dimensional Vector Representations for Anchor Users (OVRAU), from a multiplex heterogeneous social network by investigating the intra-network as well as the cross-network structural information. Unlike previous works, our proposed model considers the multi-network scenario to encode diverse network structures of anchor users. We propose two types of embeddings to capture the different structural information of an anchor user from multiple social networks: a high-dimensional base embedding and a low-dimensional social edge embedding for each social network. In particular, we learn a function that generates social edge embeddings by sampling and aggregating structural features from an anchor user's neighborhood inside different social networks through one of three candidate aggregator functions namely mean, max-pooling and LSTM, with a self-attention mechanism. Link prediction is used as a downstream task to evaluate the effectiveness of the learned embeddings. Experiments were conducted on real-world social networks dataset, and the results demonstrate that our proposed model involving all the three variants can significantly outperform the existing network representation learning approaches when applied on the link prediction task and also achieve better performance over all compared baselines.
VideoTrain++: GAN-based adaptive framework for synthetic video traffic generation

Video streaming traffic has been dominating the global network and the challenges have exacerbated with the gaining popularity of interactive videos, a.k.a.360° videos, as they require more network resources. However, effective provision of network resources for video streaming traffic is problematic due to the inability to identify video traffic flows through the network because of end-to-end encryption. Despite the promise given for network security and privacy, end-to-end encryption also provides a shield for adversaries. To this end, encrypted traffic classification and content fingerprinting with advanced Machine Learning (ML) methods have been proposed. Nevertheless, achieving high performance requires a significant amount of training data, which is a challenging task in operational networks due to the sheer volume of traffic and privacy concerns. As a solution, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesize video streaming data for two different tasks, 360°/normal video classification and video fingerprinting. The solution consists of a percentile-based data mapping mechanism to enhance the data generation process, which is further supported by novel algorithms for data pre-processing and GAN model training. Taking over 6600 actual video traces and generating over 150,000 new traces, our ML-based traffic classification results show a 5%–16% of accuracy improvement in both tasks.
Is there a value premium in cryptoasset markets?

This study examines if blockchain fundamentals determine cryptoasset prices. Previous research has shown that non-fundamental factors affect cryptoasset prices however, whether blockchain fundamentals affect cryptoasset prices, lacks empirical research. Using data for 652 cryptoassets, I specify active addresses-to-network value as a valuation metric that captures transaction benefits. I find anomalous returns that increase with active addresses-to-network value ratio, a proxy for the value anomaly. Cryptoassets with a high active addresses-to-network value ratio yield on average 3.7 percentage points higher weekly returns than cryptoassets with low active addresses-to-network value ratio, and comparable size. A four-factor model directed at capturing the value pattern in average returns performs better than the three-factor model. Importantly, my results suggest that cryptoasset prices are related to their blockchain fundamentals.
Can Elon Mask's Twitter Posts About Cryptocurrencies Influence Cryptocurrency Markets by Creating a Herding Behavior Bias?

The main purpose of this study is to examine the effects of Elon Mask's Twitter posts about cryptocurrencies on cryptocurrency markets within the scope of herding behavior bias. For this purpose, the daily price values and transaction volumes of Bitcoin and Dogecoin are analyzed by applying the EGARCH models. The results show that Elon Musk's positive Twitter posts increase dogecoin's volatility more than bitcoin in terms of price and trading volume. In addition, the effect of positive tweets has been found to increase Bitcoin and Dogecoin prices and their market transactions. According to the results, while negative tweet sharing negatively affects bitcoin returns, it manifests itself with an increase in volatility after a certain period of time. Another result is that the Dogecoin return and negative tweet interaction vary according to time intervals, but the presence of the effect on volatility cannot be determined. It is also concluded that after the negative tweet, both bitcoin and dogecoin transaction volumes increased in the first days, but their volatility was not affected. The results are important in terms of showing the effects of an influential person's social media posts on the financial markets by creating a herd behavior effect. Revealing the "influential person effect" as a behavioral finance bias is seen as the originality of the study. It is thought that the findings can be evaluated in terms of pointing out a factor that may pose a potential risk to financial stability in the global sense.
Edge-Cloud Blockchain and IoE-Enabled Quality Management Platform for Perishable Supply Chain Logistics

In perishable supply chain logistics, even a small departure from the required storage conditions at any distribution link can compromise the quality of transported products, such as food, pharmaceuticals, and other bioproducts, resulting in big losses for the businesses involved or even threats to public health. To enhance quality management (QM) and consumer confidence, an edge-cloud blockchain and Internet of Everything (IoE)-enabled QM platform is proposed to achieve low delay and rapid response for sensor data acquisition, authentication, consistency, and transparency in cold supply chain logistics. Then, we design an adaptive data smoothing and compression (ADSC) mechanism to reduce IoE data size, and analyze and store those data in the edge gateways with limited computation and storage capacity for correctly characterizing logistics operations and transactions. Moreover, to ensure the data integrity during last-mile delivery, the mobile edge gateway is adopted when the goods are temporarily off the communication range of the fixed edge gateway in the truck. Then, we propose a synchronization engine with a formal workflow applied at mobile and fixed edge gateways where data blocks are generated, validated, and synchronized with the cloud. Finally, a real-life case study on vaccine logistics is introduced to verify our proposed approach with results presented.
How do children acquire knowledge about voice assistants? A longitudinal field study on children’s knowledge about how voice assistants store and process data

Voice assistants (VAs) are available in the family context and are easy to interact with. Due to the opaqueness of these assistants, however, children in particular might be unaware of potential negative consequences (e.g., with regard to privacy). To gain a deeper understanding of children’s knowledge about VAs, of how to facilitate knowledge regarding VA technology (focusing here on parental interventions), and of how children’s knowledge and understanding affects their communicative behavior towards VAs (focusing here on telling a secret to the VA), we conducted a longitudinal field study with 16 families (N=20 children). The emphasis was on differentiating between how VAs store and process data, as both aspects are important for a sophisticated usage but vary in how they are experienced or understood. The qualitative data demonstrated that children draw observable inferences from VAs’ behavior, which explains why children know more about how data is processed than about how it is stored or accessed. Our results indicate that parental interventions positively affect children’s understanding of data processing, but that this cannot be transferred to data storage, indicating the need for self-explaining systems. Lastly, knowledge about how data is stored negatively predicts the willingness to entrust a secret to the system, which underlines the importance of a basic understanding of how technologies work, also for younger users.
Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case

Deep learning-based RF fingerprinting has recently been recognized as a potential solution tor enabling newly emerging wireless network applications, such as spectrum access policy enforcement, automated network device authentication, and unauthorized network access monitoring and control. Real, comprehensive RF datasets are now needed more than ever to enable the study, assessment, and validation of newly developed RF fingerprinting approaches. In this paper, we present nod release a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF- compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions. This dataset provides a comprehensive set of essential experimental scenarios, considering both indoor and outdoor environments and various network deployments and configurations, such as the distance between the transmitters and the receiver, the configuration of the considered LoRa modulation, the physical location of the conducted experiment, and the receiver hardware used for training and testing the neural network models.
Automatic repetition instruction generation for air traffic control training using multi-task learning with an improved copy network

To eliminate the need for human pseudo-pilots in air traffic controller training, a multi-task framework with a copy mechanism is proposed to automatically generate a repetition instruction, which can greatly reduce human and device resources. The proposed framework is implemented using a sequence-to-sequence architecture and is optimized through multi-task learning, including text instruction understanding (TIU) and repetition instruction generation (RIG). In the proposed framework, a shared encoder is designed to learn the representations from the input sequence. Two task-specific attention modules are proposed to extract task-specific features, and different decoders are applied to generate the TIU and RIG outputs. The TIU module provides both word- and sentence-level implicit semantic representations through slot filling and intent detection tasks. In the decoding procedure of the RIG task, the slot context vector is regarded as an additional input used to solve the problem of ambivalent words. Simultaneously, an improved copy network based on multi-task learning is proposed to consider the correlations between the input instruction and the output repetition in the RIG module. To avoid a loss imbalance for the TIU and RIG tasks, the gradient normalization algorithm is applied to learn the loss weights automatically by adjusting the loss gradient. Finally, the proposed framework was trained and evaluated using a real-world air traffic control corpus. The experiment results demonstrate that the proposed framework significantly outperforms other state-of-the-art methods for the RIG task, achieving a novel integrated metric of 97.19%.
Research of Text Classification Based on TF-IDF and CNN-LSTM

Abstract With the rapid development of deep learning, many deep learning models have been widely used in Natural Language Processing(NLP). The Long-Short Term memory network(LSTM) model and convolutional neural network(CNN) model can achieve high classification accuracy in text classification tasks. However, the high input dimension of text features and the need to train a large number of parameters in the deep learning model often take a lot of time. This paper uses Term Frequency-inverse Document Frequency(TF-IDF) to remove features with lower weights, extract key features in the text, extract the corresponding word vector through the Word2Vec model, and then input it into the CNN-LSTM model. We compared the model with CNN, LSTM, and LSTM-attention methods and found that the model can significantly reduce model parameters and training time in short and long text data sets. The model hardly loses accuracy in the long text, but the model will lose a certain amount of accuracy in short texts. This paper also proposes fusing original text features to make up for the accuracy loss caused by the TF-IDF feature extraction method.
LiDAR Aided Human Blockage Prediction for 6G

Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.
Phishing Detection Using URL-based XAI Techniques

The Internet has been growing exponentially and expanding facilities, such as payments and online purchases. Likewise, the number of criminals using electronic devices to commit theft or hijacking of information has increased. Many scams still require interaction with the victim, who in many cases is persuaded to access a malicious link sent by email, which is classified as phishing. This technique is one of the biggest threats for users and one of the most efficient for criminals. Several studies show different sorts of protection using Artificial Intelligence, which despite being very efficient, do not describe the reasons for categorizing them or using a URL as phishing. This paper focuses on detecting phishing using explainable techniques, i.e., Local Interpretable Model-Agnostic Explanations and Explainable Boosting Machine, to lighten up new advances and future works in the area.
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks

Abstract Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modelling wind at a high resolution brings forth considerable challenges given its turbulent and highly nonlinear dynamics. In developing countries, where wind farms over a large domain are currently under construction or consideration, this is even more challenging given the necessity of modelling wind over space as well. In this work, we propose a machine learning approach to model the nonlinear hourly wind dynamics in Saudi Arabia with a domain-specific choice of knots to reduce spatial dimensionality. Our results show that for locations highlighted as wind abundant by a previous work, our approach results in an 11% improvement in the 2-h-ahead forecasted power against operational standards in the wind energy sector, yielding a saving of nearly one million US dollars over a year under current market prices in Saudi Arabia.
Common Misunderstandings of Deterrence Theory in Information Systems Research and Future Research Directions

In the 1980s, information systems (IS) borrowed deterrence theory (DT) from the field of criminology to explain information security behaviors (or intention). Today, DT is among the most commonly used theories in IS security research. Our review of IS research applying DT highlights that many fundamental assumptions of DT are unrecognized and therefore unexamined. This may have resulted in misunderstandings and conceptual confusions regarding some of the basic concepts of DT. For example, some IS studies confuse general deterrence with specific deterrence or do not recognize the difference between the two. Moreover, these fundamental assumptions, when directly examined, may provide important information about the applicability of DT in certain IS security contexts. This research commentary aims to identify and discuss some of the fundamental assumptions of DT and their implications for IS research. By examining these assumptions, IS researchers can study the previously unexplored aspects of DT in different IS contexts. Further, by recognizing these assumptions, IS scholars can revise them and build new variants of DT to better account for specific characteristics of IS behaviors and contexts.
A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability

In many domains that employ machine learning models, both high performing and interpretable models are needed.A typical machine learning task is text classification, where models are hardly interpretable.Topic models, used as topic embeddings, carry the potential to better understand the decisions made by text classification algorithms.With this goal in mind, we propose two new fuzzy topic models; FLSA-W and FLSA-V.Both models are derived from the topic model Fuzzy Latent Semantic Analysis (FLSA).After training each model ten times, we use the mean coherence score to compare the different models with the benchmark models Latent Dirichlet Allocation (LDA) and FLSA.Our proposed models generally lead to higher coherence scores and lower standard deviations than the benchmark models.These proposed models are specifically useful as topic embeddings in text classification, since the coherence scores do not drop for a high number of topics, as opposed to the decay that occurs with LDA and FLSA.
Efficient Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles Using Deep Learning

Unmanned Aerial Vehicles (UAV) have gained major attention in recent years due to its numerous benefits. The navigation of UAVs is heavily reliant on sensors such as the Global Positioning System (GPS). However, sensor attacks that specifically target the GPS are a major concern. Equipment such as the software-defined radio can be used to launch GPS spoofing attacks. Existing techniques used to monitor the GPS and channel characteristics as a way to detect these attacks are insufficient due to the clear knowledge of the structure of GPS and channel characteristics which gives adversaries a blueprint to launch stealthy attacks. In this work, we developed various deep learning models that rely on UAV flight logs and telemetry data to detect GPS spoofing attacks in real time. We generate UAV data for different UAV models and use the data to train the models to detect the GPS spoofing attacks through classification. We developed two types of detectors: a Long Short-Term Memory (LSTM) binary classifier and an LSTM autoencoder-based one-class classifier (OCC). For each type, we compared two variations namely a UAV-specific detector that is customized for different UAV types and a UAV-generalized detector that can work with any model of UAV. We test and compare the efficiency of detection models in simulation. Hardware validation experiments were also performed using Intel® NCS2. For the binary classifier, we get a detection accuracy of 97.79% for the UAV-generalized detector and up to 99.56% for the UAV-model-specific detector. While for the OCC, we get 94.98% for the UAV-generalized detector and up to 99.24% for the UAV-model-specific detector.