Reviewerly/exHarmony huggingface dataset:
Blind Image Quality Assessment for a Single Image From Text-to-Image Synthesis

A fundamental bottleneck in text-to-image synthesis is that there are rarely subjective quality evaluation metrics for a single generated image. To address this issue, this paper proposed a procedure to evaluate the single generated image, which includes a specific dataset named multiple metrics quality assessment for birds(MMQA Birds) and a learning model named blind generated image evaluator(BGIE). The motivation of our proposal is twofold. On the one hand, subjective image quality evaluation is a human perceptual task; Therefore, it tends to be a process of supervised learning. To the best of our knowledge, there are not any datasets for this study. Thus, we handle this problem via designing a specific dataset. On the other hand, we observed that the spatial content of generated image attracts more attention when humans judge its quality; According to this finding, an efficient machine-learning model that combines both pixel-level features and spatial features is proposed. Extensive experiments manifest our method can solve this problem to some extent. In the generated image dataset, BGIE surpasses the state-of-art NSS-based method by 6.3% in PLCC and SRCC. In practice, we further discuss the rationality of the MMQA Birds dataset and the application of BGIE. It proves that both in subjective and objective aspects, our method achieves convincing results.
On the Smallest Ratio Problem of Lattice Bases

Let $(\mathbfb _1, łdots, \mathbfb _n )$ be a lattice basis with Gram-Schmidt orthogonalization $(\mathbfb _1^\ast, łdots, \mathbfb _n ^\ast )$, the ratios %quantities $\|\mathbfb _1 \|/\|\mathbfb _i ^\ast \|$ for $i = 1, łdots, n$ do arise in the analysis of many lattice algorithms and are somehow related to their performances. In this paper, we study the problem of minimizing the ratio $\|\mathbfb _1 \|/\|\mathbfb _n ^\ast \|$ over all bases $(\mathbfb _1, łdots, \mathbfb _n )$ of a given n-rank lattice. We first prove that there exists a basis $(\mathbfb _1, łdots, \mathbfb _n )$ for any n-rank lattice L such that $\|\mathbfb _1\| = \min_\mathbfv \in L\backslash\\mathbf0 \ \|\mathbfv \|$, $\|\mathbfb _1 \|/\|\mathbfb _i ^\ast \| łeq i$ and $\|\mathbfb _i \|/\|\mathbfb _i ^\ast \| łeq i^1.5 $ for $1 łeq i łeq n$. This leads us to introduce a new NP-hard computational problem, namely % that is, the \em smallest ratio problem (SRP): given an n-rank lattice L, find a basis $(\mathbfb _1, łdots, \mathbfb _n )$ of L such that $\|\mathbfb _1 \|/\|\mathbfb _n ^\ast \|$ is minimal. The problem inspires a new lattice invariant μ_n (L) = \min\\|\mathbfb _1\|/\|\mathbfb _n^\ast \|: (\mathbfb _1, łdots, \mathbfb _n) \textrm is a basis of L\ $ and a new lattice constant μ_n = \max μ_n (L)$ over all n-rank lattices L: both the minimum and maximum are justified. Some properties of μ_n (L)$ and μ_n $ are investigated. We also present an exact algorithm and an approximation algorithm for SRP. This is the first sound study of SRP. Our work is a tiny step towards solving an open problem proposed by Dadush-Regev-Stephens-Davidowitz (CCC '14) for tackling the closest vector problem with preprocessing, i.e., whether there exists a basis $(\mathbfb _1, łdots, \mathbfb _n )$ for any n-rank lattice s.t. $\max_1 łe i łe j łe n \|\vecb _i ^\ast \|/\vecb _j ^\ast \| łe \textrmpoly (n)$.
Chat-Oriented Social Engineering Attack Detection Using Attention-based Bi-LSTM and CNN

As more traditional businesses, such as banking and finance, are transferred to online platforms or the cloud, the deepening of system interaction with users and the improvement of technology-based defence system make cyber attackers focus more on human beings, leading to serious financial consequences. This attack utilising social engineering often exploits human nature's weakness. Its complexity, language variability and inductivity are difficult to defend effectively. Therefore, this paper proposes a model for detecting social engineering attacks based on deep neural network by reviewing current methods for social engineering detection, in terms of phishing, deception and content-based detection, in addition to examining deep learning algorithms with excellent data performance. Through the processing and analysis of natural language in chat history, the attention-based Bi-LSTM is used to capture and mine the context semantics, and the ResNet is used to integrate user characteristics and content characteristics for classification and judgment. By describing the features of social engineering attacks and online conversations, the feasibility and effectiveness of the proposed model are demonstrated from the perspective of algorithm selection and applicability.
Quantum state preparation and its prospects in quantum machine learning

The development of traditional classic computers relies on the transistor structure of microchips, which develops in accordance with Moore's Law. In the future, as the distance between transistors approaches to the physical limit of manufacturing process, the development of computation capability of classical computers will encounter a bottleneck. On the other hand, with the development of machine learning, the demand for computation capability of computer is growing rapidly, and the contradiction between computation capability and demand for computers is becoming increasingly prominent. As a new computing model, quantum computing is significantly faster than classical computing for some specific problems, so, sufficient computation capability for machine learning is expected. When using quantum computing to deal with machine learning tasks, the first basic problem is how to represent the classical data effectively in the quantum system. This problem is called the state preparation problem. In this paper, the relevant researches of state preparation are reviewed, various state preparation schemes proposed at present are introduced, the processes of realizing these schemes are described, and the complexities of these schemes are summarized and analyzed. Finally, some prospects of the research work in the direction of state preparation are also presented.
A Combinatorial Interpretation for the Shor-Laflamme Weight Enumerators of CWS Codes

We show that one of the Shor-Laflamme weight enumerators of a codeword stabilized quantum code may be interpreted as the distance enumerator of an associated classical code.
Machine Learning Control for Mobile Robot by Approximation Extremals by Symbolic Regression

The control system synthesis problem is considered for mobile robot. It is necessary to find a control as function of state space vector that supplies achievement of terminal position from some compact set of initial conditions with optimal value of quality criterion. For solution of this problem symbolic regression is used. This allows to find a structure of control function mathematical expression. General properties of symbolic regression are presented. Search of mathematical expression structure for control system is performed on the space of codes, therefore here a specific forms of genetic algorithms are used. They are differed crossover and mutation operations in depend on form of mathematical expression codding. At the searching optimal mathematical expression for control system by symbolic regression methods two approaches can be applied two approaches, as in artificial neural network technology, with teacher and without one. For obtaining a training set it is necessary to solve the optimal control problem some times for set of initial conditions. It is shown, that for decreasing the search space the principle of small variations of basic solution can be used and a basic solution should be set as closed to optimal one intuitively. In this case crossover and mutation operations will be performed the same on sets of variation vectors independently on symbolic regression method. In a computational experiment the control system synthesis problem for mobile robot is considered, that must be move from an area of initial conditions to the set terminal position on the plane with phase constraints.
Phase retrieval for studying the structure of vitreous floaters simulated in a model eye

We simulate vitreous floaters in a model eye and apply a phase retrieval algorithm to recover the structure of the vitreous floaters. The algorithm requires modulating the object under study by a relatively small number of masks. The modulating masks are generated by a spatial light modulator. The retrieved phase of the vitreous floaters is compared to that measured with a surface profiler. It can be concluded that the phase of the vitreous floaters can be retrieved in presence of speckles, however, the high computational load is one of the most significant drawbacks of the algorithm. In order to apply the proposed method for real-time correction of the vitreous floaters, the speed of the algorithm must be increased and second-order tracking of the vitreous floaters must be ensured.
Squeezing and EPR correlations in divide-by-3 self-phase-locked optical parametric oscillators

We analyze the dynamics, two-mode squeezing as well as Einstein–Podolsky–Rozen (EPR) correlations of the signal–idler beams in a cavity with two resonant cascaded nonlinear crystals. The subharmonic system is trifurcated and self-phase-locked, and enables the existence of stable steady bright-beam/dark-beam solutions, leading to different properties of squeezing and EPR correlations. Compared with conventional nondegenerate optical parametric oscillators, our results can be helpful to quantum information processing as well metrology.
Toward a Quantitative Approach to the Specialness of Poetry: Taking <i>Chang Hen Ge and Chang Hen Ge Zhuan</i> as a Case Study

The differences of linguistic features between Chang Hen Ge ( Ge) and Chang Hen Ge Zhuan ( Zhuan) have rarely been mentioned in the relevant fields. Nevertheless, these differences can best highlight the specialness of poetry, for the two works were written contemporaneously by two friends on the same subject, in distinct styles. This article employs quantitative methods and indicators to provide empirical evidence for the specialness of Ge through comparisons between the two. The results show that, on the premise of expressing the same subject in different styles, Ge does have certain linguistic characteristics compared with Zhuan. Its particularity is reflected not only in fewer repeat characters and words but also in their richness, as well as in the use of more content words and fewer function words. Moreover, all of these characteristics have had a great influence on Ge’s artistic level and dissemination. Through this study, we hope that our methods provide a new perspective and shed some light on this area.
Benefits and Challenges in Information Security Certification – A Systematic Literature Review

Information security certification (ISC) gets increasingly more complex. Although certain benefits, challenges and success factors have been recognized by both scholars and practitioners in the field, little has been done to consolidate the published knowledge. This systematic literature review attempts to consolidate what is currently known on the benefits of ISC, the issues and the challenges to certification, and the success factors that organizations consider while embarking on this process. Following the guidelines of Kitchenham et al., and Kuhrmann et al., we examined 42 papers that are relevant to our area of interest. We identified 12 benefits, 15 challenges, and 8 success factors. Our most important conclusion is that the current certification process is complex and suboptimal; it is expensive and it depends on the auditor’s skills. Finally, we evaluated validity threats and derived some implications for practice and for research.
Verified Interactive Computation of Definite Integrals

Abstract Symbolic computation is involved in many areas of mathematics, as well as in analysis of physical systems in science and engineering. Computer algebra systems present an easy-to-use interface for performing these calculations, but do not provide strong guarantees of correctness. In contrast, interactive theorem proving provides much stronger guarantees of correctness, but requires more time and expertise. In this paper, we propose a general framework for combining these two methods, and demonstrate it using computation of definite integrals. It allows the user to carry out step-by-step computations in a familiar user interface, while also verifying the computation by translating it to proofs in higher-order logic. The system consists of an intermediate language for recording computations, proof automation for simplification and inequality checking, and heuristic integration methods. A prototype is implemented in Python based on HolPy, and tested on a large collection of examples at the undergraduate level.
End-to-End Deep Learning Vector Autoregressive Prognostic Models to Predict Disease Progression with Uneven Time Intervals

We propose an end-to-end deep learning method combining implicit feature extraction and an autoregressive model to predict the future course of a disease or condition. By merging the feature extraction and autoregression into one deep learning model, we can simultaneously train both models together. Our novel approach begins by fine-tuning a pretrained convolutional neural network to extract features from previously obtained images of patients. A trainable autoregression mechanism then predicts the features of the future image and a fully connected layer gives a prognosis based on the predicted features. We utilize a novel time interval scaling, allowing the model to account for uneven time intervals and allowing us to choose the final time point that we wish to predict. Experiments on the Age-Related Eye Disease Study give a testing area under the receiver operating characteristic curve, sensitivity, and specificity of 0.966 (95% CI: 0.947, 0.984), 0.878 (0.810, 0.945), and 0.930 (0.914, 0.947), respectively. This shows that the model can predict progression with good performance.
Convolutional Neural Network for Asynchronous Packet Parameter Identification in Dense Wi-Fi

Packet collisions due to overlapping Basic Service Set (BSS) interference can result in decreased network efficiency. To overcome this problem, 802.11 stations need to confirm the channel availability through the Clear Channel Assessment (CCA) mechanism. Besides energy detection, which has low sensitivity, packet detection in Wi-Fi also utilizes a more sensitive preamble correlation method to check for a valid data symbol within 4μs. Hence, a successful CCA is only possible if the received signal exceeds a certain power level or the contending station could receive the signal preamble. In this paper, we demonstrate how a deep learning-based method like Convolutional Neural Network (CNN) can be used to supplement Wi-Fi's CCA capability to increase its sensitivity without requiring the reception of the signal preamble. We also elaborate on the role of filter size and max-pooling in signal processing points of view. Our simulations show that the proposed CNN model can classify different packet formats and their modulation types, even when different symbol timing offsets occur. Based on the ns-3 simulation, we demonstrate that the proposed approach leads to better channel utilization and enhanced throughput in a multiple BSS network by prioritizing high throughput transmissions.
Ranking User-Generated Content via Multi-Relational Graph Convolution

The quality variance in user-generated content is a major bottleneck to serving communities on online platforms. Current content ranking methods primarily evaluate text and non-textual content features of each user post in isolation. In this paper, we demonstrate the utility of considering the implicit and explicit relational aspects across user content to assess their quality. First, we develop a modular platform-agnostic framework to represent the contrastive (or competing) and similarity-based relational aspects of user-generated content via independently induced content graphs. Second, we develop two complementary graph convolutional operators that enable feature contrast for competing content and feature smoothing/sharing for similar content. Depending on the edge semantics of each content graph, we embed its nodes via one of the above two mechanisms. We also show that our contrastive operator creates discriminative magnification across the embeddings of competing posts. Third, we show a surprising result-applying classical boosting techniques to combine final-layer embeddings across the content graphs significantly outperforms the typical stacking, fusion, or neighborhood embedding aggregation methods in graph convolutional architectures. We exhaustively validate our method via accepted answer prediction over fifty diverse Stack-Exchange (https://stackexchange.com/) websites with consistent relative gains of over 5% accuracy over state-of-the-art neural, multi-relational and textual baselines.
Investigation of Sign Language Motion Classification by Feature Extraction Using Keypoints Position of OpenPose

So far, on the premise of using a monocular optical camera, sign language motion classification has been performed using a wristband and color gloves with different dyeing on each finger. In this method, the movement of sign language is detected by extracting the color region using color gloves. However, this method has problems such as the burden on the signer of wearing color gloves and the change in color extraction accuracy due to changes in ambient light, resulting in difficulty in ensuring stable classification accuracy. Therefore, we used OpenPose, which can detect the movements of both hands without the need for colored gloves, to classify sign language movements. Feature element extraction was performed using the keypoint position obtained from OpenPose. Then, we proposed three methods as feature element for classifying each motion and compared their classification accuracy. In method 1, feature element is obtained directly from the keypoint positions of the neck, shoulder, elbow, and wrist. Method 2 is a scheme of obtaining from the relative distance from the target keypoint position around the neck. In method 3, the feature element was 30 elements, which is the sum of the 24 elements obtained in method 1 and the 6 elements obtained in method 2. In the classification experiment, cross-validation was performed using the feature quantity obtained from the sign language motion videos of five people, and the accuracy of each method was investigated. In method 1, B (68.05%), A (62.56%), C (62.19%), D (61.49%), E (56.75%), average 62.21%, in order from the signer with the highest average classification accuracy. In method 2, B (75.31%), A (75.09%), D (73.28%), E (69.97%), C (69.81%), average 72.69%. Method 3 gave B (70.72%), A (69.65%), C (66.13%), D (64.27%), E (62.72%), and an average of 66.30% classification accuracy.
Comparative analysis of frameworks used in automated testing on example of TestNG and WebdriverIO

The article presents a comparative analysis of frameworks supporting the development of automated tests for defined test scenarios. The comparative study concerned the TestNG and WebDriverIO frameworks. The overview of the tool has been analyzed both in terms of the test development process as well as the speed and efficiency of their execution. The website github.com was used for the purposes of the work. This application was used to run test scripts written in both frameworks. The results were compared by five defined criteria: the time of running the test files in different browser operation modes, the average value of memory and CPU usage during the test execution. The summary includes the evaluation of the compared frameworks.
Future Threats to Connected and Automated Vehicles

Automated Vehicles rely on intelligent systems to enable safe and efficient transportation. Thanks to robust perception and reliable communication, automated vehicles will reshape transportation services. However, the security of automated vehicles has to be guaranteed at the component level. In this chapter, we provide an overview of two threats to connected and automated vehicles (CAV), namely adversarial AI in perception system, and impact of Quantum Computer on CAV security.
Classification of masked image data

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation—called a masked form—can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.
A qualitative analysis of users’ attitudes towards the Italian contact tracing app

This research investigates Italians’ attitudes towards the contact tracing app Immuni, promoted by the Italian government to track the spread of Covid-19 and prevent possible future outbreaks. Through a variety of qualitative methods (virtual ethnography, interviews with users and secondary users), the study explores what has motivated Italians to download (or fail to download) Immuni. The study documents how multiple factors – in particular, the perceived benefits, barriers and efficacy of the app – have shaped attitudes towards the app and the willingness to adopt it. The usability of the app is another important factor. To conclude, some practical and theoretical implications of the study are drawn in order to contribute to the design of more effective apps and communication campaigns.
User Individual Characteristics and Perceived Usability in Immersive HMD VR: A Mixed Method Explorative Study

The association between personality and individual tendency for adopting and optimally interfacing with new technologies has often been proposed in the literature. However, only few published studies report experimental data. This study aims to provide evidence on the association between several individual variables and usability experience in modern immersive visual technologies. Our results are inconclusive regarding a relationship between participants’ personality and experienced sense of presence with perceived usability. The severity of the simulator sickness symptoms during the virtual experience is showed to be negatively associated with reported system usability. However, due to the small sample size and the number of variables examined, our results have only an explorative value. Qualitative analyses show that, despite positive attitude towards the use of VR for training, the ergonomic limitations of the VR headset and the suboptimal realism of the simulated scene were reported by the participants.