Reviewerly/exHarmony huggingface dataset:
Comparative Single-cell RNA-sequencing Cluster Analysis for Traumatic Brain Injury Marker Genes Detection

Single-cell RNA-sequencing (scRNA-seq) is a high-resolution transcriptomic approach used to discover gene expression patterns among cell types to study precise biological functions. Unsupervised machine learning (clustering) is of central importance for the analysis of scRNA- seq data. It can identify putative cell types, uncover regulatory relationships, and track cell lineages and trajectories. A key issue in clustering scRNA-seq data is determining which clus- tering method is appropriate to use, since varied methods can yield diverse results. Current approaches usually focus on a one method and manually select a seemingly meaningful result. From a biological relevance perspective, it is vital to distinguish between normal and pathogenic cell types using marker genes. We present a learning framework for comparing outcomes of multiple scRNA-seq clustering methods to determine the most optimal results. We address the challenges of model selection and validation metrics in the context of traumatic brain injury (TBI) applications. We compare clustering performance of five clustering algorithms and two dimensionality reduction techniques implemented in both Seurat and Scanpy packages.
AI-Based Secure Construction of University Information Services Platform

The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.
Tight Bounds for Hybrid Planning

Several hierarchical planning systems feature a rich level of language features making them capable of expressing real-world problems. One such feature that's used by several current planning systems is causal links, which are used to track search progress. The formalism combining Hierarchical Task Network (HTN) planning with these links known from Partial Order Causal Link (POCL) planning is often referred to as hybrid planning. In this paper we study the computational complexity of such hybrid planning problems. More specifically, we provide missing membership results to existing hardness proofs and thereby provide tight complexity bounds for all known subclasses of hierarchical planning problems. We also re-visit and correct a result from the literature for plan verification showing that it remains NP-complete even in the absence of a task hierarchy.
PetsGAN: Rethinking Priors for Single Image Generation

Single image generation (SIG), described as generating diverse samples that have the same visual content as the given natural image, is first introduced by SinGAN, which builds a pyramid of GANs to progressively learn the internal patch distribution of the single image. It shows excellent performance in a wide range of image manipulation tasks. However, SinGAN has some limitations. Firstly, due to lack of semantic information, SinGAN cannot handle the object images well as it does on the scene and texture images. Secondly, the independent progressive training scheme is time-consuming and easy to cause artifacts accumulation. To tackle these problems, in this paper, we dig into the single image generation problem and improve SinGAN by fully-utilization of internal and external priors. The main contributions of this paper include: 1) We interpret single image generation from the perspective of the general generative task, that is, to learn a diverse distribution from the Dirac distribution composed of a single image. In order to solve this non-trivial problem, we construct a regularized latent variable model to formulate SIG. To the best of our knowledge, it is the first time to give a clear formulation and optimization goal of SIG, and all the existing methods for SIG can be regarded as special cases of this model. 2) We design a novel Prior-based end-to-end training GAN (PetsGAN), which is infused with internal prior and external prior to overcome the problems of SinGAN. For one thing, we employ the pre-trained GAN model to inject external prior for image generation, which can alleviate the problem of lack of semantic information and generate natural, reasonable and diverse samples, even for the object image. For another, we fully-utilize the internal prior by a differential Patch Matching module and an effective reconstruction network to generate consistent and realistic texture. 3) We construct abundant of qualitative and quantitative experiments on three datasets. The experimental results show our method surpasses other methods on both generated image quality, diversity, and training speed. Moreover, we apply our method to other image manipulation tasks (e.g., style transfer, harmonization) and the results further prove the effectiveness and efficiency of our method.
STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly Voice Agent

We present STUDIES, a new speech corpus for developing a voice agent that can speak in a friendly manner. Humans naturally control their speech prosody to empathize with each other. By incorporating this "empathetic dialogue" behavior into a spoken dialogue system, we can develop a voice agent that can respond to a user more naturally. We designed the STUDIES corpus to include a speaker who speaks with empathy for the interlocutor's emotion explicitly. We describe our methodology to construct an empathetic dialogue speech corpus and report the analysis results of the STUDIES corpus. We conducted a text-to-speech experiment to initially investigate how we can develop more natural voice agent that can tune its speaking style corresponding to the interlocutor's emotion. The results show that the use of interlocutor's emotion label and conversational context embedding can produce speech with the same degree of naturalness as that synthesized by using the agent's emotion label. Our project page of the STUDIES corpus is http://sython.org/Corpus/STUDIES.
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields

Abstract A good understanding of geometrical concepts as well as a broad familiarity with objects lead to excellent human perception of moving objects. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer and even camouflage. How we perceive moving objects so reliably is a longstanding research question in computer vision and borrows findings from related areas such as psychology, cognitive science and physics. One approach to the problem is to teach a deep network to model all of these effects. This is in contrast with the strategy used by human vision, where cognitive processes and body design are tightly coupled and each is responsible for certain aspects of correctly identifying moving objects. Similarly, from the computer vision perspective there is evidence that classical, geometry-based techniques are better suited to the “motion-based” parts of the problem, while deep networks are more suitable for modeling appearance. In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly. We present a novel probabilistic model to estimate the camera’s rotation given the motion field. We then rectify the flow field to obtain a rotation-compensated motion field for subsequent segmentation. This strategy of first estimating camera motion, and then allowing a network to learn the remaining parts of the problem, yields improved results on the widely used DAVIS benchmark as well as the more recent motion segmentation data set MoCA (Moving Camouflaged Animals).
Entropy-Enhanced Attention Model for Explanation Recommendation

Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture the long-range dependencies between user comments. Through the sentiment analysis of user comments, we can better capture the characteristics of user interest. Information entropy can reduce the adverse impact of noise words on the construction of user interests. Information entropy is used to analyze the user information content and filter out users with low information entropy to achieve the purpose of filtering noise data. A self-attention recommendation model based on entropy regularization is proposed to analyze the emotional polarity of the data set. Specifically, to model the mixed interactions from user comments, a multi-head self-attention network is introduced. The loss function of the model is used to realize the interpretability of recommendation systems. The experiment results show that our model outperforms the baseline methods in terms of MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain) on several datasets, and it achieves good interpretability.
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs

This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification.The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes.More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence.For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance.With this structure, we achieve a 0.755 macroaverage F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in subtask B. 1
Automatic Hepatocellular Carcinoma Diagnosis using Graph Convolutional Network

Blood tests are used to screen a risk group for hepatocellular carcinoma. Various studies have utilized artificial intelligence to diagnose hepatocellular carcinoma using blood test records. However, most studies suffer from performance degradation due to insufficient data. In this paper, we propose a novel graph convolutional network-based computer-aided diagnosis model to address the data insufficiency problem. The proposed method assists training by converting data into graphs representing the relationships among the features. As a result, our diagnosis model has improved 4% accuracy compared to existing approaches with 89.3% accuracy.
Blood group detection using ML classifier

Determining blood types is very important during emergency situations before giving a blood transfusion. Currently, tests that are performed manually by technicians can lead to human error. As the tests are performed manually, if an inappropriate blood group is detected it can result in the death of an individual. Blood type determination in a short time and without human error is very essential. Many methods are employed which aims at determining the type of blood. The first method developed is determination of blood type using fiber optics. In this method, optical signals are fed into blood sample depending upon the optical variations of different blood groups the corresponding blood group is detected. But, this method fails to find the Rh (positive and negative) type of blood group. The second method employed is classification of blood type by microscopic images. Initially image processing is performed by histogram equalization, and then corresponding blood group is analyzed by Quantification Techniques. In this Technique, the major disadvantage lies in the inaccurate detection of agglutination. In order to avoid these disadvantages a new method Blood group detection using an ML classifier, Support Vector Machine (SVM) is proposed.
How the morning-afternoon cloudiness asymmetry affects the energy-maximizing azimuth direction of fixed-tilt monofacial solar panels

In the Northern Hemisphere, south is the conventional azimuth direction of fixed-tilt monofacial solar panels, because this orientation may maximize the received light energy. How does the morning-afternoon cloudiness asymmetry affect the energy-maximizing azimuth direction of such solar panels? Prompted by this question, we calculated the total light energy received by a fixed-tilt monofacial solar panel in a whole year, using the celestial motion of the Sun and the direct and diffuse radiation measured hourly throughout the year in three North American (Boone County, Tennessee, Georgia) and European (Italy, Hungary, Sweden) regions. Here we show that, depending on the tilt angle and the local cloudiness conditions, the energy-maximizing ideal azimuth of a solar panel more or less turns eastward from south, if afternoons are cloudier than mornings in a yearly average. In certain cases, the turn of the ideal azimuth of such solar panels may be worth taking into consideration, even though the maximum energy gain is not larger than 5% for nearly vertical panels. Specifically, when solar panels are fixed on vertical walls or oblique roofs with non-ideal tilt, the deviation of the energy-maximizing azimuth from the south can be incorporated in the design of buildings.
Proof Theory of Skew Non-Commutative MILL

Monoidal closed categories naturally model NMILL, non-commutative multiplicative intuitionistic linear logic: the monoidal unit and tensor interpret the multiplicative verum and conjunction; the internal hom interprets linear implication. In recent years, the weaker notion of (left) skew monoidal closed category has been proposed by Ross Street, where the three structural laws of left and right unitality and associativity are not required to be invertible, they are merely natural transformations with a specific orientation. A question arises: is it possible to find a logic which is naturally modelled by skew monoidal closed categories? We answer positively by introducing a cut-free sequent calculus for a skew version of NMILL that is a presentation of the free skew monoidal closed category. We study the proof-theoretic semantics of the sequent calculus by identifying a calculus of derivations in normal form, obtained from an adaptation of Andreoli's focusing technique to the skew setting. The resulting focused sequent calculus peculiarly employs a system of tags for keeping track of new formulae appearing in the antecedent and appropriately reducing non-deterministic choices in proof search. Focusing solves the coherence problem for skew monoidal closed categories by exhibiting an effective procedure for deciding equality of maps in the free such category.
Normalization by Evaluation for the Lambek Calculus

The syntactic calculus of Lambek is a deductive system for the multiplicative fragment of intuitionistic non-commutative linear logic. As a fine-grained calculus of resources, it has many applications, mostly in formal computational investigations of natural languages. This paper introduces a calculus of beta-eta-long normal forms for derivations in the Lambek calculus with multiplicative unit and conjunction among its logical connectives. Reduction to normal form follows the normalization by evaluation (NbE) strategy: (i) evaluate a derivation in a Kripke model of Lambek calculus; (ii) extract normal forms from the obtained semantic values. The implementation of the NbE algorithm requires the presence of a strong monad in the Kripke interpretation of positive formulae, in analogy with the extension of intuitionistic propositional logic with falsity and disjunction. The NbE algorithm can also be instantiated with minor variations to calculi for related substructural logics, such as multiplicative and dual intuitionistic linear logic (MILL and DILL).
A Statistical Perspective on Advancement in Blockchain Technology

In computing, Blockchain is a decentralised, point-to-point program that provides a safe yet verifiable technique for secure distributed validation. Blockchain is a type of distributed transaction validation system. It is widely used in a variety of fields, including the finance sector, the Internet - Of - things, big data, virtualization, and edge computing, to name a few. Artificial intelligence technology, on the other hand, is having a substantial impact on the intellectual growth of a wide range of industries. Blockchain is a difficult technology that represents the important and influential vision and provide a comprehensive perspective to internet security. Blockchain is a hard technologies that represent an inventive and influential vision. When it comes to secure communication, Blockchain technology is always evolving and has the opportunity to deliver about substantial changes in how we work and live in the 21st century. Blockchain technology is continually evolving and become the next paradigms shifting technology. Our new problem is to figure out how we will keep up with the technological developments brought about by this revolutionary technology. A general overview of Blockchain technology, as well as its possibility to assist to future development, is presented in this article, which also proposes many study avenues for further investigation.
Engaging in English language Learning-Acquisition Using Edmodo and Telegram Among Open Distance Learners (ODLs)

Distance education differs from normal face-to-face classroom situation in terms of a learner or teacher’s physical presence. It is crucial for the distance teachers to provide optimal learning support to ODLs (open distance learners) and prepare them for educational changes. The use of Web-Mobile Platforms (WMPs); Edmodo and Telegram in English language learning-acquisition among the undergraduate ODLs enabled learners and educators to stay connected regardless of physical space and time constraint. This study aims to generate insights and sharing through online questionnaire on Edmodo and Telegram to seek and identify learners’ perceptions in terms of language learning-acquisition process in both contexts. The WMPs were used to accommodate learners’ language learning materials and online discussion forums thus functioned as the scaffolding to facilitate learners’ language learning-acquisition process. The study employs purposively sampling involving eleven undergraduate ODLs from a Malaysian university. The findings are presented in a narrative manner to illustrate the emerging themes and the pedagogical implications pertaining to how the WMPs provided learners access to English learning resources beyond the classroom learning time and opportunities to interact with their peers suggesting practicality and convenient ways of engaging in language learning-acquisition via the WMPs while complementing the existing face-to-face learning mode.
Constrained self regulating particle swarm optimization

Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results of constrained SRPSO algorithm have been compared with two other algorithms i.e. Differential evolution (DE) and PSO. DE and PSO have also been coupled with same four CHTs and evaluated on the 24 constrained benchmark problems. Statistical analysis on performance evaluation of three algorithms on the benchmark problems shows that constrained SRPSO algorithm performance is better than constrained PSO but it is found to be deficient when compared with constrained DE with 95% confidence level. Therefore, the objective of this work is to evaluate the SRPSO algorithm comprehensively for constrained optimization with different views to come up with suitability of constrained SRPSO algorithm when coupled with particular CHT for solving specific type of problems.
Enhancing the precision of a phase measurement through phase-sensitive non-Gaussianity

Encoding is an indispensable procedure in general phase measurement, and it is usually performed with a deterministic unitary transformation between the initial probe state and the final state. In order to improve the measurement precision, many studies focus on the optimization of the initial probe state. In this paper, we propose a scheme of subtracting one photon from the output state of an SU(1,1) interferometer. In addition to improving the output state, we find that this scheme can be used to optimize the encoding procedure. The performance of this type of optimization is only strong when the phase-sensitive negativity is induced in the Wigner function of the equivalent probe state. If a nearly perfect single-photon detector is used for the single-photon subtraction operation, the phase sensitivity within a narrow phase range can surpass $1/(\sqrt{\ensuremath{\nu}}N)$, where $N$ is the mean number of photons inside the SU(1,1) interferometer, and $\ensuremath{\nu}$ is the number of copies of the Gaussian probe state.
A Deep Insight into Deep Learning Architectures, Algorithms and Applications

The current scenario expresses that deep learning is the leading technology in the field of machine learning. Deep learning is a form of artificial intelligence that effectively uses neural network concepts where the computing system is essentially a multi-layered mesh architecture, which is motivated by the human brain and nervous system. The multiple hidden layers of CNN extract higher level features from large datasets and its methodology are speedily becoming a best choice for every field. Deep learning methods have improved and are highly developed in object recognition, Natural Language Processing, classification of images, medical image analysis etc. This paper provides introduction of different deep learning architectures, algorithms and the optimization methods used to improve the accuracy and performance of the deep learning model. And also, described challenges, obstacles to be faced while training a deep learning model and introduced applications of deep learning in various fields.