Reviewerly/exHarmony huggingface dataset:
Approximate Pattern Matching in Massive Graphs with Precision and Recall Guarantees

There are multiple situations where supporting approximation in graph pattern matching tasks is highly desirable: (i) the data acquisition process can be noisy; (ii) a user may only have an imprecise idea of the search query; and (iii) approximation can be used for high volume vertex labeling when extracting machine learning features from graph data. We present a new algorithmic pipeline for approximate matching that combines edit-distance based matching with systematic graph pruning. We formalize the problem as identifying all exact matches for up to k edit-distance subgraphs of a user-supplied template. We design a solution which exploits unique optimization opportunities within the design space, not explored previously. Our solution is (i) highly scalable, (ii) supports arbitrary patterns and edit-distance, (iii) offers 100% precision and 100% recall guarantees, and (vi) supports a set of popular data analysis scenarios. We demonstrate its advantages through an implementation that offers good strong and weak scaling on massive real-world (257 billion edges) and synthetic (1.1 trillion edges) labeled graphs, respectively, and when operating on a massive cluster (256 nodes/9,216 cores), orders of magnitude larger than previously used for similar problems. Empirical comparison with the state-of-the-art highlights the advantages of our solution when handling massive graphs and complex patterns.
Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks

Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a software agent that performs sentiment analysis and another performing stress analysis on keystroke dynamics data has been designed and implemented. The proposal consists of a set of new agents that have been integrated into a multi-agent system (MAS) for guiding users interacting in online social environments, which has agents for sentiment and stress analysis on text. We propose a combined analysis using the different agents. The MAS analyzes the states of the users when they are interacting, and warns them if the messages they write are deemed negative. In this way, we aim to prevent potential negative outcomes on social network sites (SNSs). We performed experiments in the laboratory with our private SNS Pesedia over a period of one month, so we gathered data about text messages and keystroke dynamics data, and used the datasets to train the artificial neural networks (ANNs) of the agents. A set of experiments was performed for discovering which analysis is able to detect a state of the user that propagates more in the SNS, so it may be more informative for the MAS. Our study will help develop future intelligent systems that utilize user data in online social environments for guiding or helping them in their social experience.
Gaze-based Authentication in Virtual Reality

Authentication in virtual reality (VR) is a challenging topic since the common input modalities in VR (e. g., hand-held controllers) are limited and easily observable from the perspective of a bystander. Yet, as applications in VR are increasingly allowing access to private information and commercial applications appear (e. g., virtual shopping, social media), the secure identification and verification of a person is a major concern. This challenge is aggravated, as the wearer of a head-mounted display (HMD) does not perceive the surrounding real environment through the HMD. As more and more HMDs are released to the market with built-in eye-tracking functionality, we seek to understand how we can seamlessly utilize gaze-based authentication and connected methods in VR applications.
Proof-of-Concept (PoC) of Land Mortgaging Process in Blockchain-based Land Registration System of Thailand

Land title deeds are important documents that can be used to verify ownership and transfer history of lands. In Thailand, the Department of Lands operates and facilitates the transfer of ownership and other operations related to lands. The current technology used in the operations often encounters problems with the reliability of operations and complexities that cause delays. This paper investigates a proof-of-concept (PoC) of the integration of blockchain in the land registration system to improve the reliability of storage and reduces the steps in the process. The process of loan contracts with banks is used in the PoC and an Ethereum-based private blockchain is developed and tested. Performance evaluation of our proposed system can handle at least 26 transactions per second over a range of a number of transactions of interest. The results show promising potential both in the reduction of process and the performance of blockchain for the proposed use case.
Multi-View Spectral Clustering With Incomplete Graphs

Traditional multi-view learning usually assumes each instance appears in all views.However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing.How to effectively cluster this kind of partial multi-view data has attracted much attention.In this paper, we propose an incomplete multi-view clustering method, namely Multi-view Spectral Clustering with Incomplete Graphs (MSCIG), which connects processes of spectral embedding and similarity matrix completion to achieve better clustering performance.Specifically, MSCIG recovers missing entries of each similarity matrix based on multiplications of a common representation matrix and corresponding view-specific representation matrix, and in turn learns these representation matrices based on the complete similarity matrices.Besides, MSCIG adopts the p-th root integration strategy to incorporate losses of multiple views, which characterizes the contributions of different views.Moreover, we develop an iterative algorithm with proved convergence to solve the resultant problem of MSCIG, which updates the common representation matrix, view-specific representation matrices, similarity matrices, and view weights alternatively.We conduct extensive experiments on 9 benchmark datasets to compare the proposed algorithm with existing state-of-the-art incomplete multi-view clustering methods.Experimental results validate the effectiveness of the proposed algorithm.
Content to cash: Understanding and improving crowdsourced live video broadcasting services with monetary donations

Crowdsourced live video broadcasting (livecast) services such as Twitch and Douyu have become increasingly popular in recent years. In such a service, how to allocate limited service capacities, including video transcoding and delivery capacities among numerous channels, is a critical problem. Previous studies allocate capacities based on popularity. In this paper, we analyze Douyu, a leading crowdsourced livecast website in China, with a measurement approach. We find that Douyu is deployed upon a video delivery network (VDN), and it prioritizes popular channels when allocating service capacities; we also find that viewers’ willingness to donate monetary gifts in a channel is closely related to their streaming experiences, which are decided by service capacities allocated in the channel. On the other hand, a livecast channel’s profitability is only moderately correlated to its popularity. In other words, there exists a mismatch between the popularity-based service strategies and Douyu’s business model. Motivated by our analysis, we propose that channels’ profitability as well as popularity should be considered in capacity allocating. We present proactive and reactive algorithms, which balance viewers’ satisfaction with system’s monetary profit, for allocating transcoding capacity among livecast channels. We also propose a practical strategy for VDN edge nodes to select channels to replicate, by taking channels’ popularity, profitability, and bandwidth consumptions into consideration. Experiments driven by real-world measurement data show that our proposed solutions can effectively improve the overall benefits for a crowdsourced livecast system and individual VDN edge nodes, and avoid adjusting channels’ transcoding schemes too often during livecast sessions.
An open software for bitstream-based quality prediction in adaptive video streaming

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. However, because of throughput fluctuations, video quality may be dramatically varying. Also, stalling events may occur during a streaming session, causing negative impacts on user experience. Therefore, a main challenge in HAS is how to evaluate the overall quality of a session taking into account the impacts of quality variations and stalling events. In this paper, we present an open software, called BiQPS, using a Long-Short Term Memory (LSTM) network to predict the overall quality of HAS sessions. The prediction is based on bitstream-level parameters, so it can be directly applied in practice. Through experiment results, it is found that BiQPS outperforms four existing models. Our software has been made available to the public at https://github.com/TranHuyen1191/BiQPS.
Curating the use of digital media in higher education: a case study

Digital technology constitutes a substantial presence in higher education and notions like ‘digital natives’ and ‘21st century digital skills’ dominate educational discourse. However, within the last decade, scholars have started questioning the prevailing techno-optimism and practitioners have started debating whether and how to regulate the use of digital devices. Based on a systematic qualitative analysis of 100 written evaluations and mapping sessions with five students, this study explores students’ experiences in an undergraduate course in which the teacher restricted the use of digital devices in discussion classes. We found that students expressed mostly positive attitudes towards resulting class engagement and mostly negative attitudes towards taking, sharing, and searching their notes. Finally, we discuss our findings based on the notion of media affordances.
Too Much Information: Questioning Security in a Post-Digital Society

Whilst user- and people-centered design are accepted routes for digital services, they are less commonly used in the design of technologies that control access to data and the security of information. The ubiquity of both technology and programmes such as "digital by default" as well as the weaving of digital systems into the everyday fabric of society, create an environment in which people and technology become enmeshed. Such an environment might be termed "post-digital" and its security is dependent on a people-centered approach to its design. In this paper we present a study that uses critical design techniques coupled with critical security analysis to examine how security might be approached in a post-digital context. We call for a paradigm shift towards a people-centered security practice and using a case study then make practical recommendations as to how this shift might be achieved.
A neural model for joint event detection and prediction

Event prediction aims to predict the future possible event given a sequence of previously happened events. Event prediction is important since it can benefit the government, agencies and companies for avoiding damages by taking proactive actions. A further related task is event detection, which is to classify each event to predefined types, helping users quickly find relevant information. Event prediction is related to event detection, since salient information of events is universal between the tasks. In this paper, we propose a novel neural model for joint event detection and prediction, which classifies the events to predefined types as well as predicts the next probable event by generating a sequence of words describing it. In addition, we propose a hierarchical attention mechanism to enable the model to capture important information at both word level and event level for next event prediction. Empirical experiments on a real-world dataset reveal that our joint model with hierarchical attention achieves substantial improvements on event prediction, advancing state-of-the-art models. With joint learning, our model also improves the performance on event detection.
FriSBE

DASH streaming for 360-degree content is gaining more traction in research and commercial areas. The usefulness of H.265/HEVC Motion Constrained Tile Sets (MCTS) and viewport-dependent streaming is recognized. Streaming high quality tiles for the viewport and lower quality tiles in the surrounding area provide considerable bandwidth saving. However, required bit rate estimation is difficult for a changing viewport with variable tiles, prompting research in development of new ABR algorithms. In this paper, we present Full Sphere Bit rate Estimation (FriSBE), an Adaptive Bit Rate (ABR) technique for tiled omnidirectional content that reuses existing ABR algorithms developed for 2D video. We implemented FriSBE in an OMAF-based DASH player and found that it is able to adapt to both head motion and changing network conditions with minimal stalls. Our experiments showed no stall events for stable conditions with or without head motion, and on average 1s stall duration for varying network conditions when bandwidth was sufficient for at least the lowest quality video, despite a short 3 second buffer.
Embodied cognition?

The present chapter summarises three different theoretical perspectives on the fostering effect of pointing and tracing gestures on learning. Research on haptic processing, visual attention and cognitive load is thereby discussed, representing different views on embodied cognition. The derived future directions on how to proceed with research on the pointing and tracing effect include recommendations for the measurement of three distinctive and relevant aspects of the learning process with haptic methods. Measuring learning performance in a promising way, visual attention by eye-tracking, and cognitive load by current methods are outlined to integrate the previously discussed perspectives in future research as a key for further insights. The chapter closes with final conclusions and practical implications.
Outsourced privacy-aware task allocation with flexible expressions in crowdsourcing

In crowdsourcing, task allocation service helps to screen out suitable workers to realize more extensive perceptual data collection. However, due to the vulnerability of existing technologies, it is also subject to security and privacy issues. Although there exist some related works that enable to protect the privacy of both requesters and workers, they cannot well support flexible and expressive matching polices for multi-keyword in multi-worker scenarios. Since the inner-product encryption (IPE) mechanism enables to support multiple types of constraint expressions, a construction of privacy-aware task allocation system based on two forms of IPE is proposed in this paper. In such a scheme, only the crowdsourcing service provider (CSP) has the ability to perform fine-grained inner-product policy matching over the encrypted position and other attributes. Moreover, the CSP could calculate the corresponding conversion ciphertext for workers who meet the conditions, thus reducing the amount of user calculation. And meanwhile, through the security proof, we prove the indistinguishability for task content and location information, respectively. Furthermore, the implementation analysis and evaluation show that the scheme is feasible and effective.
Cryptographic Randomness Test of the Modified Hashing Function of SHA256 to Address Length Extension Attack

Length Extension attack is vulnerable to SHA256. It is a type of attack where certain types of hashes are misused as message authentication codes and allowing for inclusion of extra information. In this study, the researchers introduced an improved padding scheme and hashing process for SHA256 to address this problem. To prove that the modified hash function is cryptographically secure, statistical tests using Strict Avalanche Effect, Frequency Test (Monobit), Frequency Test within a Block, and Run Test were performed to evaluate the results of the Message Digest. Test results show that the number of ones and zeros in each element is distributed uniformly random in the final hash value. Additionally, runtime execution is much faster in generating the hash code since it has reduced the number of rounds to 32.
Optimal feasible step-size based working set selection for large scale SVMs training

Efficient training of support vector machines (SVMs) with large-scale samples is of crucial importance in the era of big data. Sequential minimal optimization (SMO) is considered as an effective solution to this challenging task, and the working set selection is one of the key steps in SMO. Various strategies have been developed and implemented for working set selection in LibSVM and Shark. In this work we point out that the algorithm used in LibSVM does not maintain the box-constraints which, nevertheless, are very important for evaluating the final gain of the selection operation. Here, we propose a new algorithm to address this challenge. The proposed algorithm maintains the box-constraints within a selection procedure using a feasible optional step-size. We systematically study and compare several related algorithms, and derive new theoretical results. Experiments on benchmark data sets show that our algorithm effectively improves the training speed without loss of accuracy.
Automated access control system using face recognition

Security to every firm or campus is an essential one nowadays, as the crime rates in recent days are increasing day by day. As the world is transforming into an advanced technological trend, automated systems are implemented in many sectors. Here in this sector, it will be much more effective. Thus many automated access control systems are being implemented such as biometric methods and fingerprint-based access systems. But these existing systems undergo many complications that lead to time consumption in long. Thus face recognition based access systems are introduced which provides us the better solution. There are many face recognition access systems with different algorithms. This paper deals with an effective and modern algorithm called the FaceNet algorithm. The access system with this algorithm uses Face encoding to detect the face and eye which works effectively on light/illumination changes. Later on the facial feature extraction is done by using the Histogram Object Gradient (HOG). Compare face function, which contains Support Vector Machine classifier to classify the face encoding and produces the output. RFID sensor and IR sensor is implemented to make the system function effectively. Also in addition a webpage is created to provide the access authority to the management of the campus or firm where they can also handle the access system manually if needed.
Reconciling noninterference and gradual typing

One of the standard correctness criteria for gradual typing is the dynamic gradual guarantee, which ensures that loosening type annotations in a program does not affect its behavior in arbitrary ways. Though natural, prior work has pointed out that the guarantee does not hold of any gradual type system for information-flow control. Toro et al.'s GSLRef language, for example, had to abandon it to validate noninterference.
Tabletop teleporter

Communication with remote persons over a video link is common today, e.g. to connect with family members abroad, particularly during the COVID-19 pandemic. However, social activities such as board games are rarely shared in this way, as common video chat software does not support this scenario well. However, interactive tabletops provide inherent support for natural tangible interaction with items on the tabletop surface.
Research on Movie Rating Prediction Algorithms

In the era of data explosion of movie and video websites, “informahon overload” and “information labyrinth” have brought serious troubles, but movie recommender systems can efficiently solve such problems. In order to solve these problems, we propose the RF that uses the users' activity and rating to select suitable experimental data and proved this method can efficiently reduce RMSE and MAE of various recommendation algorithms. On this basis, we propose the MCBF-SVD, which is a movie rating prediction algorithm based on explicit data and implicit data from movie datasets to predict the future ratings of movies from users with a certain degree of activity. Firstly, the MCBF-SVD uses weighting factors to discuss the impact of movie categories on predicting future rating behavior of users, and also improves the filtering method based on movie categories. Finally, the MCBF is combined with SVD algorithm which has good performance in CF. Compared with several existing algorithms, our MCBF-SVD greatly enhanced the accuracy of rating prediction, and improved the scalability and efficiency of personalized recommender systems.
Web OAuth-based SSO Systems Security

In the present digital world, users have to access multiple applications for carrying out their day-to-day business activities. As the amount of apps increase, the number of credentials (e.g. username/password) for each user increases and thereby the possibility of losing or forgetting them also increases. Single Sign-On (SSO) can be used to solve many problems related to web user authentication. OAuth-based SSO Systems are widely deployed by big tech companies such as Facebook and Google. In this paper we provide an in-depth review analysis of OAuth-based SSO systems security issues. In fact, previous efforts have been aimed either at finding errors in specific implementations, or at finding security problems within the specification itself. The main paper contribution is twofold: (1) describe in detail the OAuth 2.0 authorization flows and summarize the differences between the flows of each scenario that affect the security of the OAuth 2.0 protocol, and (2) examine the security problems related to the OAuth 2.0 specification and its implementation in the Web environment.
Kernel extreme learning machines (KELM): a new approach for modeling monthly evaporation (EP) from dams reservoirs

In this study, four kernels extreme learning machines (KELM): radial basis function (RBELM), polynomial (POELM), wavelet (WKELM) and linear (LNELM) extreme learning machines were compared for modelling monthly pan evaporation from Algerian dams reservoirs, according to three scenarios. In the first scenario, the model were developed using splitting ratio of 70/30%, for training and validation subset, respectively, and the POELM1 achieves better performances. For the second scenario, the best models were trained using validation dataset and tested with the training dataset. Results showed that, RBELM1 would appear to yield the most accurate results, across all four dam’s reservoirs, with R2 between 0.852 and 0.949, and NSE between 0.846 and 0.946, respectively. For the third scenario, when the models were developed using pooled data and validated at each station separately, the R2 and NSE values ranged from 0.815 to 0.937 and from 0.809 to 0.928, respectively. Generally speaking the results obtained were very encouraging. Our findings show that KELM are good and more consistent models, and can predict evaporation across large climatic zones. The findings suggest that the proposed KELM is useful to help establish more robust tools and further improve available machines learning approaches.