Reviewerly/exHarmony huggingface dataset:
Multi-objects Generation with Amortized Structural Regularization

Deep generative models (DGMs) have shown promise in image generation. However, most of the existing methods learn a model by simply optimizing a divergence between the marginal distributions of the model and the data, and often fail to capture rich structures, such as attributes of objects and their relationships, in an image. Human knowledge is a crucial element to the success of DGMs to infer these structures, especially in unsupervised learning. In this paper, we propose amortized structural regularization (ASR), which adopts posterior regularization (PR) to embed human knowledge into DGMs via a set of structural constraints. We derive a lower bound of the regularized log-likelihood in PR and adopt the amortized inference technique to jointly optimize the generative model and an auxiliary recognition model for inference efficiently. Empirical results show that ASR outperforms the DGM baselines in terms of inference performance and sample quality.
Enhancing Patch-Based Methods with Inter-Frame Connectivity for Denoising Multi-Frame Images

The 3D block matching (BM3D) method is among the state-of-art methods for denoising images corrupted with additive white Gaussian noise. With the help of a novel inter-frame connectivity strategy, we propose an extension of the BM3D method for the scenario where we have multiple images of the same scene. Our proposed extension outperforms all the existing trivial and non-trivial extensions of patch-based denoising methods for multi-frame images. We can achieve a quality difference of as high as 28% over the next best method without using any additional parameters. Our method can also be easily generalised to other similar existing patch-based methods.
A diagrammatic calculus of fermionic quantum circuits

We introduce the fermionic ZW calculus, a string-diagrammatic language forfermionic quantum computing (FQC). After defining a fermionic circuit model, wepresent the basic components of the calculus, together with theirinterpretation, and show how the main physical gates of interest in FQC can berepresented in our language. We then list our axioms, and derive someadditional equations. We prove that the axioms provide a complete equationalaxiomatisation of the monoidal category whose objects are systems of finitelymany local fermionic modes (LFMs), with maps that preserve or reverse theparity of states, and the tensor product as monoidal product. We achieve thisthrough a procedure that rewrites any diagram in a normal form. As an example,we show how the statistics of a fermionic Mach-Zehnder interferometer can becalculated in the diagrammatic language. We conclude by giving a diagrammatictreatment of the dual-rail encoding, a standard method in optical quantumcomputing used to perform universal quantum computation.
Low-Cost Eye Gesture Communication System for People with Motor Disabilities

In today's world, the use of electronic interfaces is a daily occurrence that majority of people has taken for granted. People that suffer from motor disabilities are unable to access these simple interfaces due to their inability to direct their body movement to do certain tasks such as pressing a key on the keyboard or activate a button on the device. In most cases, interaction with the digital interfaces or devices is the only means that such individuals can use to communicate with their caregivers. This has become particularly apparent for the people whose only mobility is confined to their eyes. For these individuals, the use of an Eye Gesture Communication System (EGCS) is great importance as it gives them the opportunities to convey their needs to other people through their eye movement. There are various types of EGCS available on the market. However, they are normally quite expensive. This paper discusses how a simple low-cost and yet reasonable accurate EGCS can be achieved with the use a webcam, Open CV's Haar Cascade Classifiers, and a simple algorithm using various thresholding techniques. The results show the overall accuracy of 86% in interpreting the user's needs.
Do Conversational Partners Entrain on Articulatory Precision?

The communication phenomenon known as conversational entrainment occurs when dialogue partners align or adapt their behavior to one another while conversing. Associated with rapport, trust, and communicative efficiency, entrainment appears to facilitate conversational success. In this work, we explore how conversational partners entrain or align on articulatory precision or the clarity with which speakers articulate their spoken productions. Articulatory precision also has implications for conversational success as precise articulation can enhance speech understanding and intelligibility. However, in conversational speech, speakers tend to reduce their articulatory precision, preferring low-cost, imprecise speech. Speakers may adapt their articulation and become more precise depending on feedback from their listeners. Given the potential of entrainment, we are interested in how conversational partners adapt or entrain their articulatory precision to one another. We explore this phenomenon in 57 task-based dialogues. Controlling for the influence of speaking rate, we find that speakers entrain on articulatory precision, with significant alignment on articulation of consonants. We discuss the potential applications that speaker alignment on precision might have for modeling conversation and implementing strategies for enhancing communicative success in human-human and human-computer interactions.
Jointly Learning to Detect Emotions and Predict Facebook Reactions

The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with “reactions” of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection. We are inspired by the idea of introducing a connection between reactions and emotions by means of First-Order Logic formulas, and we propose an end-to-end neural model that is able to jointly learn to detect emotions and predict Facebook reactions in a multi-task environment, where the logic formulas are converted into polynomial constraints. Our model is trained using a large collection of unsupervised texts together with data labeled with emotion classes and Facebook posts that include reactions. An extended experimental analysis that leverages a large collection of Facebook posts shows that the tasks of emotion classification and reaction prediction can both benefit from their interaction.
Dynamic Programming based Track-Before-Detect Methods for Airborne Radar System

Benefited from the flexibility of the aircraft platform, airborne radar shows prominent advantages such as good visibility and excellent maneuverability. In military applications, it is of great significance for airborne radar to detect and track low signal-to-noise ratio (SNR) targets, while it is intractable to traditional methods. Dynamic programming based Track-Before-Detect (DP-TBD) approach can achieve superior performance for dim targets by jointly processing multiple frames of raw data, which relies on a finite and discrete state space. For DP-TBD in ground-based radar, the state space is demarcated by the boundary of scanning area and quantized according to the criterion of discrete measurements. However, the motion of aircraft causes the ego-motion of scanning area for airborne radar system. Consequently, the demarcation and discretization strategy of ground-based radar cannot be directly applied to airborne radar because the criteria of discretization do not match each other in different frames. In this paper, an intuitive solution is presented firstly, it demarcates the state space to merely cover all scanning areas and quantizes the space by the criterion of measurements adopted in the first frame. After a thorough analysis of the previous algorithm, another method is proposed, which creates a “mapped” state space changes with scanning area to make the ground-based strategy working on airborne radar. Simulation examples demonstrate the implementation processes of the algorithms. Comparison between two algorithms and advantage in detection is also illustrated.
Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Images

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTMLong Short-Term Memory (LSTM) learning. To overcome the above challenges, 3D anisotropic hybrid network (AH-Net) transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modeling. We show the effectiveness of the 3D AH-Net on two example medical image analysisMedical image analysis applications, namely, lesion detection from a digital breast tomosynthesisDigital Breast Tomosynthesis (DBT) volume, and liver, and liver tumorLiver tumor segmentation from a computed tomography volume.
Key-Frame Extraction Based on HSV Histogram and Adaptive Clustering

Along with the fast development of digital information technology and the application of Internet, video data begins to grow explosively. Some applications with high real-time requirements, such as object detection, require strong online video storage and analysis capabilities. Key-frame extraction is an important technique in video analysis, which provides an organizational framework for dealing with video content and reduces the amount of data required in video indexing. To address the problem, this study proposes a key-frame extraction method based on HSV (hue, saturation, value) histogram and adaptive clustering. The HSV histogram is used as color features for each frame, which reduces the amount of data. Furthermore, by using the transformed one-dimensional eigenvector, the fixed number of features can be extracted for images with different sizes. Then, a cluster validation technique, the silhouette coefficient, is employed to get the appropriate number of clusters without setting any clustering parameters. Finally, several algorithms are compared in the experiments. The density peak clustering algorithm (DPCA) model is shown to be more effective than the other four models in precision and F -measure.
Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks

Weather can be elucidated as the appearance of atmosphere for a short span of time for a specific place. Weather an important factor in our day-to-day lives and also the one that controls how and what people should do. For researchers and scientists, weather has been the most demanding task. The practice of technology that has been practiced to presume the conditions of atmosphere for a given area at a specific time is weather forecasting. Rationally accurate forecasts are obtained because of modern weather forecasting methods that involve computer models, observations and information about current patterns, and trends. Due to the presence of nonlinearity in climatic data, neural networks have been the satisfactory for prediction of weather. An algorithm based on artificial neural networks is used for predicting the future weather as the artificial neural network (ANN) package supports a number of learning or training algorithms. The benefits of using artificial neural networks for predicting the weather have been presented in this paper. In this paper, weather predictions are made by building training and testing datasets with the usage of learning algorithms back propagation and resilient back propagation.
Blockchains and Distributed Databases: a Twin Study.

Blockchain has come a long way: a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. A blockchain is also a distributed system, and as such it shares some similarities with distributed database systems. Existing works that compare blockchains and distributed database systems focus mainly on high-level properties, such as security and throughput. They stop short of showing how the underlying design choices contribute to the overall differences. Our paper is to fill this important gap. In this paper, we perform a twin study of blockchains and distributed database systems as two types of transactional systems. We propose a taxonomy that helps illustrate their similarities and differences. In particular, we compare the systems along four dimensions: replication, concurrency, storage, and sharding. We discuss how the design choices have been driven by the system's goals: blockchain's goal is security, whereas the distributed database's goal is performance. We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd. We demonstrate how the different design choices in the four dimensions lead to different performance. In addition, we show that for most workloads, blockchain's performance is still lagging far behind that of a distributed database. However, the gap is not as significant as previously reported, and under high contention or constrained workloads, blockchains and databases are even comparable. Our work provides a framework for exploring the design space of hybrid database-blockchain systems.
Bounds and Estimates on the Average Edit Distance

The edit distance is a metric of dissimilarity between strings, widely applied in computational biology, speech recognition, and machine learning. Let $$e_k(n)$$ denote the average edit distance between random, independent strings of n characters from an alphabet of a given size k. An open problem is the exact value of $$\alpha _{k}(n)= e_k(n)/n$$ . While it is known that, for increasing n, $$\alpha _{k}(n)$$ approaches a limit $$\alpha _{k}$$ , the exact value of this limit is unknown, for any $$k\ge 2$$ . This paper presents an upper bound to $$\alpha _{k}$$ based on the exact computation of some $$\alpha _k(n)$$ and a lower bound to $$\alpha _{k}$$ based on combinatorial arguments on edit scripts. Statistical estimates of $$\alpha _{k}(n)$$ are also obtained, with analysis of error and of confidence intervals. The techniques are applied to several alphabet sizes k. In particular, for a binary alphabet, the rigorous bounds are $$0.1742 \le \alpha _2 \le 0.3693$$ while the obtained estimate is $$\alpha _2 \approx 0.2888$$ ; for a quaternary alphabet, $$0.3598 \le \alpha _4 \le 0.6318$$ and $$\alpha _4 \approx 0.5180$$ . These values are more accurate than those previously published.
Modern trends on quality of experience assessment and future work

Over the past 20 years, research on quality of experience (QoE) has been actively expanded even to cover aesthetic, emotional and psychological experiences. QoE has been an important research topic in determining the perceptual factors that are essential to users in keeping with the emergence of new display technologies. In this paper, we provide in-depth reviews of recent assessment studies in this field. Compared to previous reviews, our research examines the human factors observed over various recent displays and their associated assessment methods. In this study, we first provide a comprehensive QoE analysis on 2D display including image/video quality assessment (I/VQA), visual preference, and human visual system-related studies. Second, we analyze stereoscopic 3D (S3D) QoE research on the topics of I/VQA and visual discomfort from the human perception point of view on S3D display. Third, we investigate QoE in a head-mounted display-based virtual reality (VR) environment, and deal with VR sickness and 360 I/VQA with their individual approach. All of our reviews are analyzed through comparison of benchmark models. Furthermore, we layout QoE works on future display and modern deep-learning applications.
Mobile learning and accounting students’ readiness in tertiary and professional institutions in Nigeria

Mobile Learning (m-learning) a learning method facilitated by the convenience of mobile technology has a crucial role to play in the development of quality education in Nigeria which represents the fourth Sustainable Development Goal. However, Despite the advantages of mobile learning as an emerging learning method and its adoption across several disciplines, research into its adoption in accounting education and within developing contexts including Nigeria is still at infancy. This study, therefore, investigates the readiness of Accounting students in Nigeria to utilize m-learning for their accounting education. The survey research design was employed with copies of a relevant questionnaire distributed to 1,225 accounting students in three tertiary as well as three professional institutions in Nigeria, a total of 1021 of the retrieved copies were deemed suitable for data analyses. The study observes that there is a widespread usage of mobile devices and technology, especially smart devices among accounting students in both tertiary and professional institutions. It also found that the cost of internet access, instability of network as well as the level of the inexperience of students and instructors may impede the successful adoption of m-learning in Nigeria. The study recommends that m-learning adoption of m-learning in Nigerian should go in-line with the enhancement of mobile infrastructure especially network coverage and the training of students as well as instructors in the country to use mobile technology for learning. This will bring sustainable development to education in Nigeria.
Guided M-Net for High-Resolution Biomedical Image Segmentation with Weak Boundaries

Biomedical image segmentation plays an important role in automatic disease diagnosis. However, some particular biomedical images have blurred object boundaries, and may contain noises due to the limited performance of imaging device. This issue will highly affects segmentation performance, and will become even severer when images have to be resized to lower resolution on a machine with limited memory. To address this, we propose a guide-based model, called G-MNet, which seeks to exploit edge information from guided map to guide the corresponding lower resolution outputs. The guided map is generated from multi-scale input to provide a better guidance. In these ways, the segmentation model will be more robust to noises and blurred object boundaries. Extensive experiments on two biomedical image datasets demonstrate the effectiveness of the proposed method.
Future Prospects of UAV Tilt Photogrammetry Technology

Abstract In recent years, China’s science and technology have developed rapidly, and the surveying and mapping industry has also made great breakthroughs. Traditional aerospace photogrammetry cannot collect more accurate information from modern surveying and mapping. In recent years, UAV tilt photogrammetry technology has been developed. UAV tilt photogrammetry technology can more realistically reflect the position and appearance of features. Traditional aerospace photogrammetry can only perform vertical data acquisition, but tilt photogrammetry can perform fast and efficient data acquisition from different angles. Not only that, tilt photogrammetry has greatly improved the image matching and measurement technology. This paper analyzes the future development trend and application fields of the development of tilt photogrammetry technology.
Going With the Flow: An Examination of Entrainment in Typically Developing Children

Purpose Conversational entrainment is the tendency for individuals to modify their behavior to more closely converge with the behavior of their communication partner and is an important aspect of successful interaction. Evidence of entrainment in adults is robust, yet research regarding its development in children is sparse. Here, we investigate the emergence of entrainment skills in typically developing children. Method Data were collected from a total of 50 typically developing children between the ages of 5 and 14 years. Children participated in a quasiconversational paradigm with a virtual interlocutor. Speech rate of the interlocutor was digitally manipulated to produce fast and slow speech rate conditions. Results Data from the fast and slow conditions were compared using linear mixed models. Results indicated that children, regardless of age, did not alter their speech to match the rate of the virtual interlocutor. Conclusions Findings suggest that entrainment in children may not be as robust as entrainment in adults and therefore not adequately captured with the current experimental paradigm. Modifications to the current paradigm will help identify a methodology sufficiently sensitive to capture the speech alignment phenomenon in children and provide much needed information regarding the typical stages of entrainment development.
FPGA-Based High-Performance Computing Platform for Cryptanalysis of AES Algorithm

Cryptanalysis is used to analyze the strength of a cryptographic algorithm. Various cryptanalytic attacks against AES algorithm target reduced-round variants. These attacks are theoretical and are generally considered infeasible due to the demand for a large number of computations. To meet this high-computational requirement, an FPGA-based High-Performance Computing (HPC) platform is presented in this paper. FPGAs are advantageous for implementing cryptanalytic attacks, as the modular arithmetic is implemented more efficiently in FPGAs as compared to GPUs. The proposed HPC platform consists of four Spartan6 FPGAs connected in a mesh topology. A brute force cryptanalytic attack on the AES algorithm with a 128-bit key is implemented on the proposed HPC platform. Four-AES key search engines are designed in each FPGA. Thus, 16-AES key search engines are instantiated in parallel to perform AES cryptanalysis using different keys in parallel. To allocate distinct the key space to the 16 AES key search engines, an efficient key generator is also proposed in this work. The proposed architecture achieves a computational complexity of 2124 for an attack against 10-rounds AES algorithm.
Crystal structure, x-ray diffraction and oblique geometry

The main mathematical difficulty in crystal geometry and X-ray diffraction stems from oblique system of coordinates as soon as we move on from the simple cubic to the body-centered cubic case with out mentioning the non-cubic systems. We show in this work that there exists a simple way for dealing with oblique geometry for all crystal systems by combining direct and reciprocal space geomet rical elements with the help of a small set of vector identities that are not usually taught in a genera l physics curriculum. This allows us to find a set of compact formulae applicable to all crystalline s ystems for directions and planes, moduli, angles and scalar products of basis vectors belonging to the direct and reciprocal lattices as well as interplanar distances and angles.