Reviewerly/exHarmony huggingface dataset:
Using Social Media Background to Improve Cold-Start Recommendation Deep Models

In recommender systems, a cold-start problem occurs when there is no past interaction record associated with the user or item. Typical solutions to the cold-start problem make use of contextual information, such as user demographic attributes or product descriptions. A group of works have shown that social media background can help predicting temporal phenomenons such as product sales and stock price movements. In this work, our goal is to investigate whether social media background can be used as extra contextual information to improve recommendation models. Based on an existing deep neural network model, we proposed a method to represent temporal social media background as embeddings and fuse them as an extra component in the model. We conduct experimental evaluations on a real-world e-commerce dataset and a Twitter dataset. The results show that our method of fusing social media background with the existing model does generally improve recommendation performance. In some cases the recommendation accuracy measured by hit-rate@K doubles after fusing with social media background. Our findings can be beneficial for future recommender system designs that consider complex temporal information representing social interests.
Mobile edge AI

In this chapter, we provide a panorama view of mobile edge AI frameworks. We summarize several common frameworks of mobile edge AI, including on-device inference, computation offloading-based edge inference, data partition-based edge training, model partition-based edge training, and coded computing systems. We also include unique characteristics and a summary of existing research directions.
Behavioral model summarisation for other agents under uncertainty

Modelling other agents is one of the fundamental research challenges in multiagent decision making under uncertainty. A subject agent expects to optimise its own decisions by correctly modelling what other agents are going to act when they share a common environment and their actions have a joint impact in the transition of environmental states and their individual rewards. However, a true model of the other agent is often unknown due to limited prior knowledge, which particularly occurs to a competitive agent setting. Consequently, the subject agent needs to consider a large number of potential models (the number is infinite in theory) for the other agent, which turns to be computationally hard in its decision making process. A feasible solution is to select a subset of potential models that are representative of all the models for the other agent. In this article, we investigate such a model selection given some known behavioral stereotypes ascribed to the other agents. We propose a new measurement to diversify the selected models under the constraint of the number of models (K models). We formulate the top-K model selection as a sub-modular function optimisation problem and develop a greedy algorithm to obtain high-quality solutions. We develop the proposed model selection techniques in a well-recognised multiagent decision model, namely interactive dynamic influence diagrams, that represents individual agents’ decision making process with the consideration of other agents. By doing this, we can test the performance of the new model selection techniques over two problem domains and provide experimental results in support. Hence, our research in this article contributes to a new model selection technique for modelling other agents therefore improving decision quality of a subject agent interacting with the other agents. It also inspires a creative usage of sub-modular function optimisation in multiagent decision making.
Continuous-Variable Quantum Secret Sharing Based on Thermal Terahertz Sources in Inter-Satellite Wireless Links

We propose a continuous-variable quantum secret sharing (CVQSS) scheme based on thermal terahertz (THz) sources in inter-satellite wireless links (THz-CVQSS). In this scheme, firstly, each player locally preforms Gaussian modulation to prepare a thermal THz state, and then couples it into a circulating spatiotemporal mode using a highly asymmetric beam splitter. At the end, the dealer measures the quadrature components of the received spatiotemporal mode through performing the heterodyne detection to share secure keys with all the players of a group. This design enables that the key can be recovered only by the whole group players’ knowledge in cooperation and neither a single player nor any subset of the players in the group can recover the key correctly. We analyze both the security and the performance of THz-CVQSS in inter-satellite links. Results show that a long-distance inter-satellite THz-CVQSS scheme with multiple players is feasible. This work will provide an effective way for building an inter-satellite quantum communication network.
String diagram rewrite theory III: Confluence with and without Frobenius

Abstract In this paper, we address the problem of proving confluence for string diagram rewriting, which was previously shown to be characterised combinatorially as double-pushout rewriting with interfaces (DPOI) on (labelled) hypergraphs. For standard DPO rewriting without interfaces, confluence for terminating rewriting systems is, in general, undecidable. Nevertheless, we show here that confluence for DPOI, and hence string diagram rewriting, is decidable. We apply this result to give effective procedures for deciding local confluence of symmetric monoidal theories with and without Frobenius structure by critical pair analysis. For the latter, we introduce the new notion of path joinability for critical pairs, which enables finitely many joins of a critical pair to be lifted to an arbitrary context in spite of the strong non-local constraints placed on rewriting in a generic symmetric monoidal theory.
Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy

The rapid development of affordable and compact high-fidelity sensors (e.g., cameras and LIDAR) allows robots to construct detailed estimates of their states and environments. However, the availability of such rich sensor information introduces two challenges: (i) the lack of analytic sensing models, which makes it difficult to design controllers that are robust to sensor failures, and (ii) the computational expense of processing the high-dimensional sensor information in real time. This paper addresses these challenges using the theory of differential privacy, which allows us to (i) design controllers with bounded sensitivity to errors in state estimates, and (ii) bound the amount of state information used for control (i.e., to impose decision-making under bounded rationality). The resulting framework approximates the separation principle and allows us to derive an upper-bound on the cost incurred with a faulty state estimator in terms of three quantities: the cost incurred using a perfect state estimator, the magnitude of state estimation errors, and the level of differential privacy. We demonstrate the efficacy of our framework numerically on different robotics problems, including nonlinear system stabilization and motion planning.
Discriminative Alignment of Projected Belief Networks

The projected belief network (PBN) is a deep layered generative network with tractable likelihood function (LF) and can be used as a Bayesian classifier by training a separate model on each data class, and then classifying based on maximum likelihood (ML). Unlike other generative models with tractable LF, the PBN can share an embodiment and network parameters with a feed-forward classifier network. By training a PBN with a cost function that combines LF with classifier cross-entropy, its network weights can be ``aligned" to the decision boundaries separating the data class from other classes. This results in a Bayesian classifier that rivals state of the art discriminative classifiers. These claims are backed up by classification experiments involving spectrograms of spoken keywords and handwritten characters.
JointPose: Jointly Optimizing Evolutionary Data Augmentation and Prediction Neural Network for 3D Human Pose Estimation

3D human pose estimation plays important roles in various human-machine interactive applications, but lacking diversity in existing labeled 3D human posture dataset restricts the generalization ability of deep learning based models. Data augmentation is therefore an important method to solve this problem. However, data augmentation and pose estimation network training are usually treated as two isolated processes, limiting the performance of pose estimation network. In this paper, we developed an improved data augmentation method which jointly performs pose network estimation and data augmentation by designing a reward/penalty strategy for effective joint training, making model training and data augmentation improve each other. In particular, an improved evolutionary data augmentation method is proposed to generate the distribution of nodes in crossover and rotation angles in mutation through the process of the evolution. Extensive experiments show that our approach not only significantly improves state-of-the-art models without additional data efforts but also is extremely competitive with other advanced methods.
Perceived Vulnerability As a Determinant of Increased Risk for Cybersecurity Risk Behavior

There is interest in better understanding people's cybersecurity (CS)-related attitudes and behaviors, which are ultimately impacted by their perceived vulnerability to CS risks. There is a relationship between how risk is perceived and how someone acts, with protection motivation theory (PMT) providing a particularly salient framework for explaining this connection. Exploration of how one perceives his or her own vulnerability to CS victimization is essential to understanding this interaction, and risk mitigation of threats relies heavily on the human despite increased reliance on digital technologies such as machine learning that can be used proactively and in real time yet are still impacted by human behavior. This study sought to examine the information security attitudes and behaviors that contribute to perceived CS vulnerability. A convenience sample of 612 college students sampled from two public universities in the United States completed a brief demographic survey and the Online Security Behavior and Beliefs Questionnaire. The instrument demonstrated good internal reliability with an index of perceived vulnerability significantly and positively correlating with multiple subscales. Linear regression indicated subscales that tended to focus more on one's inner belief that he or she is capable and competent enough to understand the nature of CS risks was predictive of perceived vulnerability, potentially resulting from a social desirability response bias which yielded an overly favorable self-report. PMT suggests that knowledge is an essential factor influencing decision making and results of this study suggest that perceived vulnerability may depend upon the appraisal of experience more so than one's actual knowledge or competence.
Emerging Technologies for Quantum Computing

Despite promising proof of concept demonstrations, currently available quantum hardware suffers from fundamental scalability limitations. The field is relatively new and it is imperative to consider emerging technologies in the space and evaluate their unique tradeoff spaces to determine how to best close the gap between current devices and target applications. Here, we explore three recent developments on this front. First, we consider extensions to currently available hardware, which allow the use of higher level states, beyond the usual binary, which when used temporarily can confer circuit-level advantage. Second, we consider the use of superconducting resonant cavities to reduce hardware requirements to implement quantum error correction protocols. Finally, we consider the use of neutral atoms, which offer unique strengths and weaknesses. It is valuable to evaluate new technology early and often to determine the best path toward scalability.
A new structure of the focal element in object recognition

The research on uncertainty is a hot spot in artificial intelligence, especially in actual applications. Most research focus on measuring the uncertainty between evidence, ignoring the structure of evidence. When a sample has multiple features, the traditional structure of the focal element cannot handle and display the uncertainty between multiple features directly. Hence, we proposed a new structure of the focal element considering multiple features into one focal element and provided the corresponding basic probability assignment calculation. To apply the new structure in practices, we provided a new object recognition method combining the fractal feature and credible evidence. The new method can improve the robustness of the algorithm in a noisy environment. In the end, some experiments have illustrated the validity and correctness of the proposed method.
Public Key Encryption With Equality Test Supporting Flexible Designated Authorization in Cloud Storage

In cloud storage, public key encryption with equality test (PKEET) is suitable for testing whether two ciphertexts generated from different public keys contain the same message without decryption. Recently, (Y. J. Wang et al., 2019) have proposed a public key signcryption scheme with designated equality test (PKS-DET). In this scheme, since the public key of the tester is used in the encryption, the tester needs to use its secret key to test the ciphertexts. Without trapdoor, PKS-DET prevents the attacker from testing the user’s ciphertexts with the stolen trapdoor. Therefore, the privacy of the user can be protected. However, if the user in the PKS-DET wants to authorize the other tester to test his/her ciphertexts for some reason, the message needs to be encrypted again by the public key of the other tester. This will lead to higher computational complexity. Hence, the PKS-DET is not flexible to authorize multiple testers. In this article, we propose a construction of PKEET supporting flexible designated authorization (PKEET-FDA). The user in our PKEET-FDA can adaptively authorize multiple testers to test his/her ciphertexts, and each authorized tester must use its secret key to perform equality test for the ciphertexts. More importantly, the user does not need to encrypt the message repeatedly any more. We demonstrate the security of PKEET-FDA under two types of adversaries. Compared with the related efficient PKEET schemes, PKEET-FDA satisfies high efficiency from the point of view of the user. In terms of storage requirement, our construction is superior to the PKS-DET.
Potential of ALOS-2 PALSAR-2 StripMap data for lithofacies identification and geological lineament mapping in vegetated fold-thrust belt of Nagaland, India

• Geological mapping of vegetation-covered NFTB is done with ALOS-2 SAR data. • Second order lithological variance could be discriminated by this process. • Meso-scaled geological lineaments has been mapped and analysed. In this study statics-based textural analysis GLCM (Grey Level Co-occurrence Matrix) of high resolution L band SAR data from ALOS-2 of JAXA has been used to discriminate between lithological facies of the fold-thrust belt of Nagaland, India. Sandstones, shales, siltstones and clays have been separated in the analysis. Importantly, the lithological facies with second-order compositional variance have been distinguished from each other. The study of 27 lithological pairs indicates a Transformed Divergence index greater than 1.7 except one pair which shows an index of 1.6. The average GLCM texture values of different stratigraphic units in VV and VH polarization shows appreciable differences. Coefficient of variation of the texture samples from each descriptor show uniform distribution around their mean. The mean has thus been used as a lithological index in this study. Further, the medium scaled geological lineaments have been extracted and mapped at 1: 25,000 scale. The Compass Edge Detector on the first Principal Component of full polarized ALOS-2 scene has been used for this purpose. Fracture network, outcrop length, density, and orientation have also been analyzed. The study is successful in rock characterization within vegetation-covered landscape, using L band SAR remote sensing. It may provide an important tool in geological mapping in inaccessible terrains with limited ground data control. The fracture map and its study can provide some useful insights about reservoir characterization and reduce exploration risk in this terrain that has a proven petroleum system.
Collaborative AI in Smart Healthcare System

As technology advances, growing organizations aim to bring revolutionary changes to all aspects of industries. The healthcare industry is experiencing a twofold burden of illnesses, low benefit scope, and a deficiency of basic budgetary risk security mechanism. To maintain patient health records, many healthcare organizations still depend on local centralized database systems. The main problem is when patients’ confidential data has been stored in hospital’s database, its integrity and privacy is the main concern. In some cases, patient’s upgraded records are not stored within the database on time. Also patients are not happy to disclose their medical records for some privacy issues. Therefore, the re-diagnosis process is time-consuming and complicated which leads to high healthcare costs. To illuminate this issue, we propose a collaborative smart healthcare system with decentralized information capacity mechanism, guaranteeing the protection and interoperability of patient’s medical records by collaborative use. In the proposed system, hospitals share patient’s data in a decentralized database maintaining data privacy using differential privacy mechanism. The experimental result analysis and data accuracy using AI model show the practicality of the proposed system.
Smart Video Surveillance System For Level Crossing: A Systematic Literature Review

A level crossing is an intersection where a railway line crosses a road or path. The government has made regulation that set level crossings throughout Indonesia, Provisions on traffic and road transport in Law No. 22 of 2009. However, data from PT Kereta Api Indonesia (KAI) shows that during 2020 there was 198 accidents and around 5000 trespasses occurred at level crossings. The surveillance system currently used in level crossings is still conventional, which only relies on supervisory officers and some level crossings monitored by CCTV. Research in the field of video surveillance systems with a computer vision approach has been widely carried out in the past decade. Most researches on video surveillance systems try to replace human functions or traditional systems with video processing algorithms. This paper will review the literature on the application of artificial intelligence for the surveillance system level crossings published from 2015 to 2021. The literature review process consists of analyzing the document to determine the scope of the investigation using intelligent video surveillance systems, frameworks, methods and datasets to detect level crossing violators. In the conclusion of this paper, it will be explained about the challenges and opportunities of research on the object detected violations using intelligent video surveillance system in the future.
Extending information poverty theory to better understand the digital access and inequalities among young people who are not in education, employment or training in South Africa

Purpose The objective of this study is to present evidence regarding how young people, who are not in education, employment or training (NEET) in South Africa, lack literacy skills and access to enabling resources to actively search and navigate information services systems that are primarily web-based. Information Poverty Theory is adopted to better understand the technological and social strata challenges experienced by young NEET people. Design/methodology/approach The study used semi-structured interviews for collecting data over two months in 2018, employing snowball sampling with 24 key participants, representing a diversity of educational backgrounds and previous experience of economic participation. Findings The findings of this study suggest that most interviewed young people, who are NEET, lack advanced information literacy and digital skills to access information services. The results also indicate that access to information services that are primed for online information is a challenge for most of these NEET young people residing in underserviced communities. The high cost of an Internet connection means that the Internet is out of reach for most low-income households. In communities that are underserviced with no adequate information and communication technology (ICT) infrastructure, people residing in such areas are subjected to living in circumstances where there is poverty and thus a lack of access to online information. Research limitations/implications This paper reports on data collected in 2018 using intense interviews, while acknowledging limitations in terms of the sample size. Hence, it is not fully representative of the whole population of young people, who are NEET, residing in the Gauteng Province of South Africa. Practical implications The findings illustrate the need for further collaboration among relevant stakeholders to strengthen existing programmes and for stronger partnerships. The arguments presented herein enhance knowledge and understanding concerning the digital literacy skills divide that exists among young people who are NEET. It includes a discussion to contribute to policy development. Originality/value This study focuses on challenges young people who are NEET experience when looking for work and developmental opportunities. This qualitative study adopts Information Poverty Theory and uses prior studies to link the undertaken survey and research. It is expected that this study may serve as a pilot for future studies and may also contribute to the ongoing discussions around the use of ICTs on their use and access, especially the effect on young people when searching for information related to jobs and other developmental opportunities using online services.
The Effects of Digital Virtual Reality Game-Based Language Learning on English Language Learners’ Development of Vocabulary and Cultural Knowledge and Affective Attitudes

The current study investigated the effects of using a 3D virtual reality (VR) game on university EFL students’ development of vocabulary and cultural knowledge. In addition, it explored how this new approach affected the students’ affective attitudes such as interest, confidence, and learning motivation. Furthermore, it examined the students’ perception and attitude toward VR game-based language learning. Over one semester, an experimental group of 25 students participated in the VR game-based language learning, while a control group of 24 students followed the regular curriculum of the university EFL course. To collect data, 1) pre- and post-tests for both vocabulary and cultural knowledge, 2) a pre- and post-questionnaire for affective attitudes, 3) an online survey for students’ perceptions and attitudes, and 4) a qualitative interview were administered. The results revealed that the VR game-based language learning group outperformed the traditional learning group in both vocabulary and cultural knowledge gains. In addition, VR game instruction was effective in increasing students’ interest, confidence, and learning motivation. Furthermore, the students reported overall positive perceptions and attitudes toward this new kind of instruction. This study suggested that VR game-based language learning is a new, effective way to promote students’ vocabulary and cultural knowledge.