Reviewerly/exHarmony huggingface dataset:
Attack Intention Estimation Based on Syntax Analysis and Dynamic Analysis for SQL Injection

SQL injections lead to serious damage such as leakage of personal information and are prevalent online. Web Application Firewalls (WAFs) are used to detect SQL injections, but its detection is not perfect. Therefore, to validate detected attacks, a more detailed manual analysis is required. Furthermore, due to confidentiality and availability, it is often impossible to obtain information on monitored web servers. In this paper, we propose a method to identify the intention of SQL injections in order to shorten the analysis time in the situation where limited information is available. The proposed method analyzes only a partial SQL query in an HTTP request and estimates its intention by syntax analysis and emulating. Evaluation results revealed that the proposed method was able to correctly identify the intention with an accuracy of 83.1% and 71.9% for an artificial dataset and a real-world dataset respectively.
Fuzzy Active Contour Model Using Fractional-Order Diffusion Based Edge Indicator and Fuzzy Local Fitted Image

Local region-based active contour models (ACMs) can effectively segment images corrupted by intensity inhomogeneity, however, they always converge to local minimum and are sensitive to the initial position of contour. In this paper, a novel fuzzy ACM is proposed to tackle these problems. In order to deal with intensity inhomogeneity, the fuzzy local fitted image is first defined and utilized for constructing a local-region based fuzzy energy term, which is minimized in a variational level set framework to accurately segment inhomogeneous images. Second, the fractional-order diffusion based edge indicator is used to scale the local fuzzy energy term to reduce the effect of intensity inhomogeneity. Third, the fuzzy signed pressure force (FSPF) function defined by local image information is used for constructing the weighted area term to further improve the accuracy of the developed model. Finally, the global FSPF is formulated and used as an adaptive force, which can drive the level set function (LSF) to adaptively move up or down according to image intensity information. Therefore, the initial contour can be initialized as a constant function, which eliminates the problem caused by contour initialization. Moreover, the global FSPF makes the proposed model not easy to fall into local minimum. The results of experiments on synthetic and real images validate the accuracy of the proposed model for inhomogeneous image segmentation.
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach.

This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks. We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used. Our proposed algorithms compare positively/competitively against the state-of-the-art LCD (Learn Chain graphs via Decomposition) algorithm, depending on the algorithm that is used for Markov blanket discovery. Our proposed algorithms make a broad range of inference/learning problems computationally tractable and more reliable because they exploit locality.
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics

We argue that proven exponential upper bounds on runtimes, an established area in classic algorithms, are interesting also in evolutionary computation and we prove several such results. We show that any of the algorithms randomized local search, Metropolis algorithm, simulated annealing, and $$(1+1)$$ evolutionary algorithm can optimize any pseudo-Boolean weakly monotonic function under a large set of noise assumptions in a runtime that is at most exponential in the problem dimension n. This drastically extends a previous such result, limited to the $$(1+1)$$ EA, the LeadingOnes function, and one-bit or bit-wise prior noise with noise probability at most 1/2, and at the same time simplifies its proof. With the same general argument, among others, we also derive a sub-exponential upper bound for the runtime of the $$(1,\lambda )$$ evolutionary algorithm on the OneMax problem when the offspring population size $$\lambda $$ is logarithmic, but below the efficiency threshold.
On the Relationship between User Churn and Software Issues

The satisfaction of users is only part of the success of a software product, since a strong competition can easily detract users from a software product/service. User churn is the jargon used to denote when a user changes from a product/service to the one offered by the competition. In this study, we empirically investigate the relationship between the issues that are present in a software product and user churn. For this purpose, we investigate a new dataset provided by the alternativeto.net platform. Alternativeto.net has a unique feature that allows users to recommend alternatives for a specific software product, which signals the intention to switch from one software product to another. Through our empirical study, we observe that (i) the intention to change software is tightly associated to the issues that are present in these software; (ii) we can predict the rate of potential churn using machine learning models; (iii) the longer the issue takes to be fixed, the higher the chances of user churn; and (iv) issues within more general software modules are more likely to be associated with user churn. Our study can provide more insights on the prioritization of issues that need to be fixed to proactively minimize the chances of user churn.
A Prototype Proposal for AI based Smart Integrated Platform for Doctors and Patients

With rise in the number of connected devices and availability of the internet even in the remote corners of the world, the health sector is the one that can leverage the benefits of this development and help to provide better services to society. A complete system that can collect, store, and analyze the data of a patient and serve as a huge advantage for the industry as most of the platforms available are not a complete system but rather an individual application. Applications that can help easily connect patients to various hospitals. In the case of healthcare devices like a thermometer, weighing scale, ECG, Heart rate monitor, etc when connected through sensors form the IoT. The data collected is encrypted for cyber security and sent to the server to be stored on a decentralized platform, like Blockchain. Furthermore, Machine Learning models can be used by the doctors to detect diseases before it becomes more problematic for the patient. All this can not only be integrated with an existing IT structure of a hospital but also can be set up anywhere such as medical camps in rural areas. In this paper, the proposal of the model described above is discussed.
Visual Perception Enhancement for HEVC Compressed Video Using a Generative Adversarial Network

The emergence of generative adversarial network (GAN) promotes the great progress of deep learning generation model. In this paper, generative adversarial network is used to remove the visual artifact of compressed video, and a visual perception enhancement algorithm for HEVC compressed video is proposed. Specifically, after HEVC compression, the reconstructed image is output by GAN generator. The output image can effectively guide the discriminator of GAN to approximate the mapping between the encoded frame and the original frame. The adversarial loss of the generator to keep learning this mapping, which not only improves the visual perception quality of compressed video, but also removes the artifact. Experimental results demonstrate the superiority of our GAN network over other methods in terms of both Perceptual Index and visual quality.
Research on data mining equipment for teaching English writing based on application

“College English teaching guide” puts in forth unique challenges and needs for College teaching skills of English. It is pressing to cultivate innovative talents with quality writing in English. Teaching in English, as a subject to check the mastery of students’ English knowledge. The successful instructional project of English writing is an assurance and support smooth growth of English writing. It can make education get double the efforts, and allow the students development.’ writing ability y, but in fact, the current situation of structural design of writing English in college is not optimistic. With the rise of “Web in addition to”, varying backgrounds have experienced emotional changes. “Web in addition to training” has become a pattern of advancement, which has brought new chances and difficulties for the educating and learning of College English composition. This research first describes the research status of data mining and College English writing at places of abroad and input the future the content of research and methods of research. Taking college English writing teaching as the research object, the association rules algorithm in data mining is applied to analyze the correlation factors of students’ writing performance and provide decision-making suggestions for teachers’ teaching.
Optical color image encryption based on a voice key under the framework of speckle-illuminated Fourier ptychography

In this paper, an optical color image encryption scheme based on a voice key under the framework of speckle-illuminated Fourier ptychography is proposed. The human voice serves as the secret key directly, and the random phase masks generated from the human voice using the chaotic map and secure hash algorithm are just used as interim variables. With the help of voice-based random phase masks located at different diffraction distances, the primary color image can be encrypted into a series of noise-like intensity cipher images under different speckle patterns’ illumination. During the decryption process, the primary color image can be recovered from the noise-like intensity cipher images with a Fourier ptychographic recovery algorithm, voice-based random phase masks, and their conjugate. Since the voice key is strongly linked with the user, it does not need need to be transmitted from the sender to the authorized receiver via the networks. The security of the proposed encryption scheme will be enhanced greatly. Additionally, the speckle patterns for illumination and diffraction distances can also provide additional security to the proposed encryption scheme. Simulation results and analysis show that the proposed encryption scheme has high security and can resist some common attacks.
Towards a unified proof framework for automated fixpoint reasoning using matching logic

Automation of fixpoint reasoning has been extensively studied for various mathematical structures, logical formalisms, and computational domains, resulting in specialized fixpoint provers for heaps, for streams, for term algebras, for temporal properties, for program correctness, and for many other formal systems and inductive and coinductive properties. However, in spite of great theoretical and practical interest, there is no unified framework for automated fixpoint reasoning. Although several attempts have been made, there is no evidence that such a unified framework is possible, or practical. In this paper, we propose a candidate based on matching logic, a formalism recently shown to theoretically unify the above mentioned formal systems. Unfortunately, the (Knaster-Tarski) proof rule of matching logic, which enables inductive reasoning, is not syntax-driven. Worse, it can be applied at any step during a proof, making automation seem hopeless. Inspired by recent advances in automation of inductive proofs in separation logic, we propose an alternative proof system for matching logic, which is amenable for automation. We then discuss our implementation of it, which although not superior to specialized state-of-the-art automated provers for specific domains, we believe brings some evidence and hope that a unified framework for automated reasoning is not out of reach.
On Leveraging Pretrained GANs for Generation with Limited Data

Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so generated are not contained in the training dataset, suggesting potential for augmenting training sets with GAN-generated data. While this scenario is of particular relevance when there are limited data available, there is still the issue of training the GAN itself based on that limited data. To facilitate this, we leverage existing GAN models pretrained on large-scale datasets (like ImageNet) to introduce additional knowledge (which may not exist within the limited data), following the concept of transfer learning. Demonstrated by natural-image generation, we reveal that low-level filters (those close to observations) of both the generator and discriminator of pretrained GANs can be transferred to facilitate generation in a perceptually-distinct target domain with limited training data. To further adapt the transferred filters to the target domain, we propose adaptive filter modulation (AdaFM). An extensive set of experiments is presented to demonstrate the effectiveness of the proposed techniques on generation with limited data.
Meet Your Personal Cobot, But Don’t Touch It Just Yet

This paper reports on a research project aimed at introducing a collaborative industrial robot into a makerspace (a public machine shop equipped with digital manufacturing technologies). Using an ethnographic approach, we observed how collaborations between researchers and non-experts are facilitated by makerspaces, how robot safety is being construed and negotiated by the actors involved in the project; and how knowledge about collaborative robot safety and applications is produced in a context previously unforeseen by the creators of the technology. The proposed analysis suggests that the sociotechnical configuration of the studied project resembles that of a trading zone, in which various types of knowledge and expertise are exchanged between the researchers from the interdisciplinary project team and makerspace members. As we shall argue, the trading zone model can be useful in the analysis and organization of participatory HRI research.
CO-Optimal Transport

Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation relies on the existence of a cost function between the samples of the two distributions, which makes it impractical for comparing data distributions supported on different topological spaces. To circumvent this limitation, we propose a novel OT problem, named COOT for CO-Optimal Transport, that aims to simultaneously optimize two transport maps between both samples and features. This is different from other approaches that either discard the individual features by focussing on pairwise distances (e.g. Gromov-Wasserstein) or need to model explicitly the relations between the features. COOT leads to interpretable correspondences between both samples and feature representations and holds metric properties. We provide a thorough theoretical analysis of our framework and establish rich connections with the Gromov-Wasserstein distance. We demonstrate its versatility with two machine learning applications in heterogeneous domain adaptation and co-clustering/data summarization, where COOT leads to performance improvements over the competing state-of-the-art methods.
TMT: A Transformer-Based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-Aware Dialog

Audio Visual Scene-aware Dialog (AVSD) is a task to generate responses when discussing about a given video. The previous state-of-the-art model shows superior performance for this task using Transformer-based architecture. However, there remain some limitations in learning better representation of modalities. Inspired by Neural Machine Translation (NMT), we propose the Transformer-based Modal Translator (TMT) to learn the representations of the source modal sequence by translating the source modal sequence to the related target modal sequence in a supervised manner. Based on Multimodal Transformer Networks (MTN), we apply TMT to video and dialog, proposing MTN-TMT for the video-grounded dialog system. On the AVSD track of the Dialog System Technology Challenge 7, MTN-TMT outperforms the MTN and other submission models in both Video and Text task and Text Only task. Compared with MTN, MTN-TMT improves all metrics, especially, achieving relative improvement up to 14.1% on CIDEr. Index Terms: multimodal learning, audio-visual scene-aware dialog, neural machine translation, multi-task learning
Semantic and syntactic anchor object information interact to make visual search in immersive scenes efficient

Visual search in naturalistic scenes is highly efficient. One crucial reason for this is attentional guidance by our knowledge of the regularities that govern those scenes—their “scene grammar”. Our study investigated the hierarchical organization of this scene grammar, focusing on “anchor objects”, which we hypothesize are essential building blocks of environmental scenes that predict the locations of other objects (e.g., the sink predicting the soap on top, the shower predicting the shampoo inside). In a virtual reality eye tracking study, we had 24 participants search for targets in navigable, immersive scene environments that were manipulated with respect to the presence of anchor objects as well as their syntactic composition. We found that concealing the semantic identity of anchors (by replacing them with grey cuboids of matching dimensions) slowed the process of locating targets in syntactically consistent scenes (i.e., objects placed in expected locations). This was apparent in the time to the first target fixation, the number of fixations, and scanpath length of search trials. Furthermore, our motion tracking data shows that subjects exhibited more extended movement in consistent scenes without intact anchors, suggesting a greater need for costly body movements in the absence of anchor context. In scenes with inconsistent syntax (i.e., objects’ arrangement violates expectations), where search was overall much slower, we found the opposite effect with respect to manipulating anchors: Replacing anchors with cuboids speeded search and decreased movement, indicating that anchors lost their guiding function and became useless clutter in such inconsistent scenes. This shows how semantic and syntactic anchor object information are vital components of a scene’s grammar which interact to efficiently guide both eye and body movements when we search for objects, bringing us a step closer to uncovering the hierarchical nature of scene priors and its role in efficient real-world search.
Comparative study of groundwater level forecasts using hybrid neural network models

Groundwater is the world's central supply of fresh water. Water supply policies, particularly in dry seasons, thus need to be based on accurate modelling of groundwater level (GWL) fluctuations. In the work reported in this paper, a hybrid wavelet-transform-based extreme learning machine (ELM) model was investigated for predicting GWL. Two other popular models – a wavelet-transform based artificial neural network and a wavelet-transform-based adaptive neuro-fuzzy interference system – were used to evaluate the model. GWL data and mean temperatures of observation wells in an Iranian watershed between 1981 and 2017 were used in the study. The performance of the models was assessed be evaluating their root mean square error, correlation coefficient and mean absolute error. The wavelet-transform-based ELM model outperformed the other two models with a correlation coefficient of 0.983 during a 1 month period. The model was also superior to the others in terms of training and testing speeds.
Human Motion Detection based on Low Resolution Infrared Array Sensor

With the aggravating trend of aging of population, the population aged over 60 years of age is growing faster than any other age groups. Under these circumstances, the number of elderly people living alone is increasing. Therefore, there is increasing expectation for elderly monitoring services which can detect their general activities or emergency situations such as having a fall. Keeping the daily fundamental activities of the elderly is also important to prevent future accidents. In this paper, from the perspective of general versatility and privacy, an activity recognition method using a low-resolution infrared array sensor is proposed. The system can overcome the limitation of exist methods such as an invasion of privacy and have a wider application. A long short-term memory classifier is applied to this system in order to improve the accuracy. Experiments show that the system successfully achieves the aim of higher-precision human motion detection.
Optical-topological concepts in isomorphisms projecting bi-Ronchi masks to obtain 3D profiles from objects in 2D images

We introduce a technique for obtaining three-dimensional (3D) profiles of objects captured in two-dimensional (2D) flat images (FI). This technique performs a numerical approximation of the object’s topography from their image gray tones, analyzing topological concepts such as algebraic bases construction, metric functions in terms of such bases, as well as modeling and development of an isomorphism to project masks (fringe patterns) in the FI, allowing us to use the optimal 3D profilometry techniques. Among these techniques, phase shifting (four steps) is applied in the pattern shifts of the projected masks, but with a 2D shift from left to right and from top to bottom, simultaneously. Moreover, the fringe patterns in the masks are binary and with superposed periods in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>x</mml:mi> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>y</mml:mi> </mml:math> (bi-Ronchi). We show the results of the construction of the masks, as well as their projection into the FIs. We also show the 3D profilometry of the objects after the projection and phase-shifting application and a simple segmentation to observe a particular object. Subsequently, we perform the analysis of a single Mg crystal’s micrography.
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation

We introduce Exemplar VAEs, a family of generative models that bridge the gap between parametric and non-parametric, exemplar based generative models. Exemplar VAE is a variant of VAE with a non-parametric prior in the latent space based on a Parzen window estimator. To sample from it, one first draws a random exemplar from a training set, then stochastically transforms that exemplar into a latent code and a new observation. We propose retrieval augmented training (RAT) as a way to speed up Exemplar VAE training by using approximate nearest neighbor search in the latent space to define a lower bound on log marginal likelihood. To enhance generalization, model parameters are learned using exemplar leave-one-out and subsampling. Experiments demonstrate the effectiveness of Exemplar VAEs on density estimation and representation learning. Importantly, generative data augmentation using Exemplar VAEs on permutation invariant MNIST and Fashion MNIST reduces classification error from 1.17% to 0.69% and from 8.56% to 8.16%.