Reviewerly/exHarmony huggingface dataset:
TapStr: A Tap and Stroke Reduced-Qwerty for Smartphones

TapStr is a single-row reduced-Qwerty that enables text entry on smartphones by performing taps and directional strokes. It is an unambiguous keyboard, thus does not rely on a statistical decoder to function. It supports the entry of uppercase and lowercase letters, numeric characters, symbols, and emojis. Its purpose is to provide an economic alternative to virtual Qwerty when saving touchscreen real-estate is more desired than the entry speed and a convenient alternative when users do not want to repeatedly switch between multiple layouts for mixed-case alphanumeric text and symbols (such as a password). In a short-term study, TapStr yielded about 11 WPM with plain phrases and 8 WPM with phrases containing uppercase letters, numbers, and symbols.
Nxt-Freedom: Considering VDC-Based Fairness in Enforcing Bandwidth Guarantees in Cloud Datacenter

In the cloud datacenter, for the multi-tenant model, network resources should be fairly allocated among VDCs (virtual datacenters). Conventionally, the allocation of cloud network resources is on a best-effort basis, so the specific information of network resource allocation is unclear. Previous research has either aimed to provide minimum bandwidth guarantee, or focused on realizing work conservation according to the VM-to-VM (virtual machine to virtual machine) flow policy or per-source policy, or both policies. However, they failed to consider allocating redundant bandwidth among VDCs in a fair way. This paper presents a bandwidth that guarantees enforcement framework NXT-Freedom, and this framework allocates the network resources on the basis of per-VDC fairness, which can achieve work conservation. In order to guarantee per-VDC fair allocation, a hierarchical max–min fairness algorithm is put forward in this paper. In order to ensure that the framework can be applied to non-congestion-free network core and achieve scalability, NXT-Freedom decouples the computation of per-VDC allocation from the execution of allocation, but it brings some CPU overheads resulting from bandwidth enforcement. We observe that there is no need to enforce the non-blocking virtual network. Leveraging this observation, we distinguish the virtual network type of VDC to eliminate part of the CPU overheads. The evaluation results of a prototype prove that NXT-Freedom can achieve the isolation of per-VDC performance, which also shows fast adaption to flow variation in cloud datacenter.
A differentially private algorithm for range queries on trajectories

We propose a novel algorithm to ensure $$\epsilon $$ -differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state of the art, our method achieves significantly lower error as it is the first data- and query-aware approach for such queries. The key challenge for answering range queries on trajectory data privately is to ensure an accurate count. Simply representing a trajectory as a set instead of sequence of points will generally lead to highly inaccurate query answers as it ignores the sequential dependency of location points in trajectories, i.e., will violate the consistency of trajectory data. Furthermore, trajectories are generally unevenly distributed across a city and adding noise uniformly will generally lead to a poor utility. To achieve differential privacy, our algorithm adaptively adds noise to the input data according to the given query set. It first privately partitions the data space into uniform regions and computes the traffic density of each region. The regions and their densities, in addition to the given query set, are then used to estimate the distribution of trajectories over the queried space, which ensures high accuracy for the given query set. We show the accuracy and efficiency of our algorithm using extensive empirical evaluations on real and synthetic data sets.
Use of keystroke dynamics and a keystroke-face fusion system in the real world

In the field of keystroke biometrics, many systems have been developed using more traditional approaches and evaluated based on their accuracies. However, with the rise in popularity of neural networks, the research in the recent years focuses mainly on deep learning approaches. We test the accuracy and maintainability of both types of classifiers and try to test a new vector distance approach using a custom distance metric to see if there could still be room for improvement in the field of more traditional systems for keystroke-based authentication. We also test a keystroke dynamics and face recognition fusion classifier to see if a multi-modal system is perhaps the best compromise in terms of maintainability and performance. We find that there is still room for research on more traditional keystroke rhythm comparison techniques and conclude that by fusing those together with a face recognition pipeline we can achieve very good results in web authentication systems.
Of Cores: A Partial-Exploration Framework for Markov Decision Processes.

We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less relevant rest of the state space. Although we identify the core using simulations and statistical techniques, it allows for rigorous error bounds in the analysis. Consequently, we obtain efficient analysis algorithms based on partial exploration for various settings, including the challenging case of strongly connected systems.
Substituting Teleportation Visualization for Collaborative Virtual Environments

Virtual Reality (VR) offers a boundless space for users to create, express, and explore in the absence of the limitation of the physical world. Teleportation is a locomotion technique in a virtual environment that overcomes our spatial constraint and a common approach for travel in VR applications. However, in a multi-user virtual environment, teleportation causes spatial discontinuity of user’s location in space. This may cause confusion and difficulty in tracking one’s collaborator who keeps disappearing and reappearing around the environment. To reduce the impact of such issue, we have identified the requirements for designing the substituted visualization (SV) and present four SVs of the collaborator during the process of teleportation, which includes hover, jump, fade, and portal.
Hybrid Deep Convolutional Neural Network with Multimodal Fusion

The Hybrid Deep Convolutional Neural Network with Multimodal Fusion (HDCNNMF) topology for the multimodal recognition of the speech, the face, the lips, and human gestures behavior is proposed. Conducted researches relate to improving the understanding of complex dynamic scenes. The basic unit of the proposed hybrid system is deep neural network topology, which combines 2D and 3D convolutional neural network (CNN) for each modality with proposed intermediate-level feature fusion subsystem. Such a feature map fusion method is based on scaling procedure with a specific combination of pooling operation with non-square kernels and allows merging different type of modalities. Also, the method for forming the audio modality feature is proposed. This method is based on eigenvectors of Mel frequency cepstral coefficients (MFCC) and Mel frequency energy coefficients (MFEC) self-similarity matrix and allows increasing informativeness of modality feature. The specific characteristics of proposed fusion operation is that the data of the same dimension without regard to the modality type are fed to the input of fusion subsystem. During the experiments, the high recognition efficiency was obtained both in cases of individual modalities and their fusion. The distinctive feature of proposed HDCNNMF topology is that the input set can be extended by new modalities types. This extension of modalities set should improve the quality of identification, segmentation or recognition in complex ambiguous visual scenes and simplify the task of affordance detection.
Retracted: Statistical approaches in literature: Comparing and clustering the alternatives of love in <i>Divan of Hafiz</i>

Hafiz is one of the finest lyric poets of Iran. He learnt a lot from scholars and writers. This research deals with the alternatives of the word ‘Love’ in Divan of Hafiz. First, these words are determined and their counts are computed. Then, we classify them into sixteen categories. Finally, the counts of the words and the categories are compared using chi-square test and cluster analysis. The clustering results showed that the word ‘Way’ and the category ‘Human’ have the most applications as the alternatives of the word ‘Love’ in Divan of Hafiz.
Long Short-Term Memory with Sequence Completion for Cross-Domain Sequential Recommendation

As the emerging topic to solve the loss of time dimension information, sequential recommender systems (SRSs) has attracted increasing attention in recent years. Although SRSs can model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time, the challenging issues of data sparsity and cold start are beyond our control. The conventional solutions based on cross-domain recommendation aims to matrix completion by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. But most existing transfer methods can’t deal with temporal information. In this paper, we propose a Long Short-Term Memory with Sequence Completion (SCLSTM) model for cross-domain sequential recommendation. We first construct the sequence and supplement it in which two methods are proposed. The first method is to use the intrinsic features of users and items and the temporal features of user behaviors to establish similarity measure for sequence completion. Another method is to improve LSTM by building the connection between the output layer and the input layer of the next time step. Then we use LSTM to complete sequential recommendation. Experimental results on two real datasets extracted from Amazon transaction data demonstrate the superiority of our proposed models against other state-of-the-art methods.
Augmented Unlocking Techniques for Smartphones Using Pre-Touch Information

Smartphones secure a significant amount of personal and private information, and are playing an increasingly important role in people’s lives. However, current techniques to manually authenticate to smartphones have failed in both not-so-surprising (shoulder surfing) and quite surprising (smudge attacks) ways. In this work, we propose a new technique called 3D Pattern. Our 3D Pattern technique takes advantage of pre-touch sensing, which could soon allow smartphones to sense a user’s finger position at some distance from the screen. We describe and implement the technique, and evaluate it in a small pilot study (n=6) by comparing it to PIN and pattern locks. Our results show that although our prototype takes longer to authenticate, it is completely immune to smudge attacks and promises to be more resistant to shoulder surfing.
Short Paper: Blockcheck the Typechain

Recent efforts have sought to design new smart contract programming languages that make writing blockchain programs safer. But programs on the blockchain are beholden only to the safety properties enforced by the blockchain itself: even the strictest language-only properties can be rendered moot on a language-oblivious blockchain due to inter-contract interactions. Consequently, while safer languages are a necessity, fully realizing their benefits necessitates a language-aware redesign of the blockchain itself.
Hyperthyroidism Progress Prediction with Enhanced LSTM

In this work, we propose a method to predict the progress of the hyperthyroidism disease based on the sequence of the patient’s blood test data in the early stage. Long-Short-Term-Memory (LSTM) network is employed to process the sequence information in the tests. We design an adaptive loss function for the LSTM learning. We set bigger weights to the blood test data samples which are nearby the range boundaries when judging the hyperthyroidism. We have carried out a set of experiments against a real world dataset from a hospital in Shanghai, China. The experimental results show that our method outperforms the traditional LSTM significantly.
Automatic Generation Method of Basketball Continuous Pitching Action Based on Multi-Objective Machine Vision

In order to improve the effectiveness of basketball continuous pitching motion guidance, the image characteristics of basketball continuous pitching motion are analyzed with image processing method, and an automatic generation method of basketball continuous pitching motion based on multi-objective machine vision is proposed. The method comprises the following steps of: establishing a shape feature point feature matching model of a basketball continuous pitching motion fuzzy image; extracting posture key action points of the basketball continuous pitching motion fuzzy image by combining a spectral feature detection method; carrying out adaptive enhancement processing on the basketball continuous pitching motion fuzzy image by adopting a shape feature segmentation method; and obtaining sub-band pixel feature points of the basketball continuous pitching motion fuzzy image by combining a point tracking matching method and a feature extraction method. The fuzzy image of basketball continuous pitching motion is enhanced to improve the expression ability of posture information of fuzzy noise points in the image. The significant contrast enhancement processing of the fuzzy image of basketball continuous pitching motion is carried out through the point tracking recognition method, and the multi-objective machine vision features of the fuzzy image of basketball continuous pitching motion are extracted to realize automatic generation and accurate recognition of the fuzzy image of basketball continuous pitching motion. The simulation results show that the output peak signal-to-noise ratio automatically generated by using this method for basketball continuous pitching motion is higher, and the image recognition ability is better. Therefore, it is necessary to improve the performance of the system.
Viewing-Direction Dependent Appearance Manipulation Based on Light-Field Feedback

We propose a novel light-field feedback system that achieves appearance manipulation depending on the viewing-direction. Our method employs a reflection model using multiple projectors and cameras. It produces stable light-field feedback in the multiple-input and multiple-output system with decoupling using a pseudo-inverse. Through experiments, we confirmed that our method successfully enabled viewing-direction dependent appearance manipulation on the mirror reflection and the retro-reflection surface. Additionally, we verified that our method achieves robust appearance manipulation against disturbances, such as ambient light change.
Modern Codecs by Video Streaming under Use DASH Technique: An Objective Comparison Study

This paper focuses on the streaming service in Internet, in which video content is encoded using several different methods. At present, Standard H.265/HEVC is the most used method. This paper will compare it with two new encoding methods, VP9 and AV1. Two scenarios—a lossless and a lossy IP environment—were devised for the comparison. The quality of the video stream was outputted as both a MOS value and a PSNR value for both scenarios, the ultimate goal being to determine the benefits and drawbacks of using the different encoding methods for the streaming service in Internet under used the modern MPEG-DASH transport technique.
VMP360

Containing full panoramic content in a single frame and providing immersive experience for users, 360° video has attracted great attention in industry and academia. Viewport-driven tiling schemes have been introduced in 360° video processing to provide high-quality video streaming. However, treating viewport as traditional streaming screen results in frequently rebuffer or quality distortion, leading to poor Quality of Experience (QoE) of schemes. In this paper, we propose Viewpoint Movement Perception 360° Video Streaming (VMP360), an adaptive 360° video streaming system that utilizes unique factors of 360° video perception quality of users to improve the overall QoE. By studying the relative moving speed and depth difference between the viewpoint and other content, the system evaluates the perceived quality distortion based on optical flow estimation. Taking QoE into account, a novel 360° video quality evaluation metric is defined as Optical-flow-based Peak Signal-to-Noise Ratio (OPSNR). Appling OPSNR to tiling process, VMP360 proposes a versatile-size tiling scheme, and further Reinforcement Learning (RL) is used to realize the Adaptive Bit Rate (ABR) selection of tiles. VMP360 is evaluated through the client-server streaming system with two prior schemes Pano and Plato. Statistics show that the proposed scheme can improve the quality of 360° video by 10.1% while maintaining same rebuffer ratio compared with the Pano and Plato, which confirms that VMP360 can provide a promising high QoE for 360° video streaming. The code of a prototype can be found in https://github.com/buptexplorers/OFB-VR.
Efficient and robust unsupervised inverse intensity compensation for stereo image registration under radiometric changes

Image registration is a challenging problem for computer vision, and accurate and effective image registration is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and augmented reality. However, image registration becomes difficult due to the presence of noise and photometric changes. This paper presents a novel image registration method with unsupervised inverse intensity compensation (ICIR). This methodology uses weighted vectors to compensate for areas affected by radiometric variations. This is a 5-D vector body composed of RGB, brightness, and gradient, that is, each pixel is represented by a 5-D vector in its neighborhood. When performing image registration, the vector angle metric robust to illumination effect is used to calculate cost volumes. Then the selected cost metrics are aggregated based on RGB-Gradient tree structure. Experiments performed on stereo images of the Middlebury datasets and ours demonstrate this methodology in calculation accuracy and time all have good performance.
Digital Light Processing as One of the Promising 3D-Printing Technologies in Electromagnetics: Application on RFID

In this work, the application in RFID of a promising 3D-printing technology called Digital Light Processing (DLP) and based on the photopolymerization of liquid resins is discussed. After a brief introduction to the effectiveness of 3D-printing technology in electromagnetics, based on the research activity of the authors on this topic, the characteristics of the DLP are deeply faced. Specifically, a first characterization of the dielectric properties of a commercial resin is presented and used to simulate an antenna for UHF RFID applications with unconventional shape. This last consists in two elements interlinked one each other. The movement between them enables a tuning of the resonance frequency as well as the impedance of the antenna.
Probabilities of first-order sentences on sparse random relational structures: An application to definability on random CNF formulas

Abstract We extend the convergence law for sparse random graphs proven by Lynch to arbitrary relational languages. We consider a finite relational vocabulary $\sigma $ and a first-order theory $T$ for $\sigma $ composed of symmetry and anti-reflexivity axioms. We define a binomial random model of finite $\sigma $-structures that satisfy $T$ and show that first-order properties have well defined asymptotic probabilities when the expected number of tuples satisfying each relation in $\sigma $ is linear. It is also shown that these limit probabilities are well behaved with respect to several parameters that represent the density of tuples in each relation $R$ in the vocabulary $\sigma $. An application of these results to the problem of random Boolean satisfiability is presented. We show that in a random $k$-CNF formula on $n$ variables, where each possible clause occurs with probability $\sim c/n^{k-1}$, independently any first-order property of $k$-CNF formulas that implies unsatisfiability does almost surely not hold as $n$ tends to infinity.
S-NET: A Confusion Based Countermeasure Against Power Attacks for SBOX

Side channel attacks are recognized as one of the most powerful attacks due to their ability to extract secret key information by analyzing the unintended leakage generated during operation. This makes them highly attractive for attackers. The current countermeasures focus on either randomizing the leakage by obfuscating the power consumption of all operations or blinding the leakage by maintaining a similar power consumption for all operations. Although these techniques help hiding the power-leakage correlation, they do not remove the correlation completely. This paper proposes a new countermeasure type, referred to as confusion, that aims to break the linear correlation between the leakage model and the power consumption and hence confuses attackers. It realizes this by replacing the traditional SBOX implementation with a neural network referred to as S-NET. As a case study, the security of Advanced Encryption Standard (AES) software implementations with both conventional SBOX and S-NET are evaluated. Based on our experimental results, S-NET leaks no information and is resilient against popular attacks such as differential and correlation power analysis.
Heterogeneous Particle Swarm Optimizer and its Application in Aircraft Manufacturing Logistics

Particle swarm optimization (PSO) attracts much attention due to its ability in solving complex practical engineering problems effectively. To further improve its performance, a heterogeneous particle swarm optimizer (HPSO) is proposed in this work. Five widely used benchmark functions are selected to test its efficiency. Furthermore, five state-of-the-art improved PSO variants are selected for a comparisons purpose. The results demonstrate that HPSO is better than the other five algorithms. A logistics problem in aircraft manufacturing is then studied and solved. The results show HPSO's superiority over its tested PSO variants.
Social media as a professional development tool for academic librarians

Social media (SM) has evolved into more than just a way to stay in contact with friends and family. A plethora of information is available, from links to research reports to chats about trending topics. SM offers professionals an economical way to update skills. This chapter examines the results of a study designed to determine perceptions, which academic librarians have regarding SM as a tool for professional development. A total of 173 academic librarians participated in the study. Findings suggest that younger librarians are more likely to have online personal learning networks. Female participants were more likely to find SM compatible with their professional development needs.
Software Remodularization Using Tabu Search

One desired quality attribute of software is software modularity which is a degree to which the software is decomposed into loosely couple packages. Each software package consists of highly cohesive classes. When the software is modified due to software changes, the modularity of the software is affected. The software needs to be remodularized in order to maintain its modularity. This paper proposes an approach to remodularize the software using Tabu search for improving the modularity of the modified software. An evaluation is conducted with two examples and the results of a preliminary evaluation are reported.