Reviewerly/exHarmony huggingface dataset:
An Efficient Systematic Approach to Find All Cyclic Quorum Sets with All-pairs Property

The use of cyclic quorum sets has proved to be an efficient method for distributed systems to solve t asks requiring massive computations. In all-pairs data interaction problem, cyclic quorum sets can be used to avoid communication completely after initial data placement. However, searching for cyclic quorum sets with all-pairs property for a given number of objects, p, in a distributed system is a daunting task that involves massive computations as it is a combinatorial problem requiring full search. No time complexity reduction method has been found thus far. In other applications of cyclic quorum sets such as cycle-based routing problem, different cyclic quorum sets provide similar fault coverage yet generate significant resource utilization difference. Inspired by the need for all cyclic quorum sets with all-pairs property, we develop a methodology to optimize the search process by avoiding the search space where no new set could be found. After studying all possible cyclic quorum sets for a given p, we develop insights into the properties of all quorum sets that helps us to reduce the number of searches significantly. A s a result, for small values of p, when doing brutal-force search, our method not only reduces the time to find the first cyclic quorum base set with all-pairs property but is also able to find a ll of them in a reasonable amount of time. Moreover, we notice that as p grows, up to several orders of magnitude of search reduction could be achieved compared to naïve search. For large values of p, with existing results of base sets, we also propose a heuristic method with constant time complexity that computes a feasible subsets-assigning solution that meets the all-pairs requirement.
Development and Application of Robot Automatic Inspection System Technology Based on Machine Learning

With the development of artificial intelligence technology, computer technology and other related technologies, there are more and more researches on mobile robots. The robot has gone through three stages: from a simple teaching and reproducing robot to a low-level intelligent robot with vision, hearing, touch and certain adaptability, and then to a high-level intelligent robot with various advanced sensors and self-adaptive and self-learning functions. Intelligent voice inspection robot can be effectively used in petrochemical, oilfield, nuclear industry and other industries, especially in areas where people are inconvenient to reach, such as pipelines, tanks and towers. The robot is not taught what actions to take, but it evaluates the current state through a value function to select actions to maximize the cumulative reward in the long-term goal. This paper focuses on the design and implementation of intelligent voice inspection robot system, hoping to play a certain reference role for the further improvement and development of intelligent voice inspection robot system in China.
Range minimum queries in minimal space

We consider the problem of computing a sequence of range minimum queries. We assume a sequence of commands that contains values and queries. Our goal is to quickly determine the minimum value that exists between the current position and a previous position i. Range minimum queries are used as a sub-routine of several algorithms, namely related to string processing. We propose a data structure that can process these command sequences. We obtain efficient results for several variations of the problem, in particular we obtain O(1) time per command for the offline version and O(α(n)) amortized time for the online version, where α(n) is the inverse Ackermann function and n the number of values in the sequence. This data structure also has very small space requirements, namely O(ℓ) where ℓ is the maximum number of active i positions. We implemented our data structure and show that it is competitive against existing alternatives. We obtain comparable processing time, in the nanosecond range, and much smaller space requirements.
Object Tracking Using Moderate Derivative Gain Kalman Filter

Object tracking in a video sequence is becoming challenging and important research area, and it becomes more complex when there are multiple objects. Many researchers have proposed several algorithms for detection and tracking in various videos including sports. In this paper, Kalman gain is calculated using moderate derivative of Grunwald–Letnikov function, and the modified Kalman filter is used to track the object in video sequence. The performance of the proposed method is analyzed by root mean square error (RMSE) metric and also compared with other methods. The results demonstrate that the proposed algorithm showing improvement by up to 12%.
Differentiation of the functions of social networks in the media environment of students (on the example of MGIMO University)

The paper represents the results of an empirical case, the purpose of which was to characterize social networks as a source of information in the media consumption of students. Based on the analysis of the results of focus group interviews with MGIMO students, as well as on the secondary analysis of quantitative data collected by the Russian Public Opinion Research Center (VCIOM), the author discovers a clear differentiation of functions between various social networks, due to their varying ability to satisfy information, communication and leisure needs of students. Qualitative differences were revealed in the practices of using social networks like Telegram, Instagram, VKontakte, TikTok and YouTube. The author concludes that sociological study of social networks is essential, taking into account the specifics of different functions in various target groups of users. Besides, the research has shown the risks of using social networks perceived by students, among which the most obvious are the spread of fakes, clip thinking, and learning disabilities.
A Spectrum Sharing System: Visualized Interference Classification and CNN Approach

The scarcity of the low-frequency band has demanded Dynamic Spectrum Access (DSA). We implemented DSA system that detects channel usage of primary users and controls secondary users through a deep neural network in real-time. With an LTE base station that adopts Citizens Broadband Radio Service (CBRS) protocol, we implement the Navy’s active sensing radar signal through a Universal Software Radio Peripheral (USRP). We obtain visualized image dataset for CBRS system, called ViC dataset consisting of 12 classes with 500 samples per class and visualize it through spectrograms. CNN detects each class of primary-existence scenarios with an accuracy of 96.37%.
Infrared image feature analysis and application in the field of image encryption

Infrared thermal imaging technology is widely used in military and civilian surveillance fields. For high security requirements, encryption of infrared images can effectively improve their security. In this paper, the statistical characteristics of infrared images and visible images are analyzed and compared in both the space and frequency domains. The unique features of infrared images in gray histogram, bit plane distribution, and Discrete Fourier Transform (DFT) domain amplitude spectrum are pointed out. A general mathematical model for encryption of infrared images in the space and frequency domains is given. The experimental results show that the infrared image is significantly different from the visible image in terms of visual characteristics with fewer gray levels. Its main information is concentrated in the first four bit planes, and the high frequency component of the spectrum has unique features. All the above features provide a theoretical basis for infrared image encryption.
Learning-Based Near-Surface Modeling for Predictive Multirotor Landing Control

Multirotor aerial vehicles landing on moving platforms are subject to large near-surface forces which depend on the state of the multirotor and the geometry of the platform. These near-surface dynamics must be modeled accurately to minimize prediction errors in multirotor landing controllers. In this paper, we propose a novel learning-based method for modeling near-surface effects for arbitrary landing pad geometries. The model learns latent space representations of the landing pad geometries by auto-encoding robot-centered occupancy grids. Thrust and torque profiles for the multirotor under different control inputs and robot states are predicted using three neural networks that first predict single-rotor static thrusts and then correct these predictions for multi-rotor vortex interactions. The proposed method is tested using single- and multi-rotor thrust models to validate the learning approach and to ensure the thrust profiles are learnable given only on-board data. The latent space representations of the landing pads are investigated by comparing ground truth occupancy grids to reconstructions obtained from the auto-encoder sub-network. Time complexity for the method is analyzed to ensure the network can be used for real-time control applications.
Abstracting Inter-instance Relations and Inter-label Correlation Simultaneously for Sparse Multi-label

In this paper, both considering inter-instance relations and inter-label corre-lations simultaneously, a kernel Gaussian neural network sparse multi-label learning (GNN-SML) is proposed. More specifically, latent representation for sparse multi-label instance sets is constructed, both involving inter-instance and inter-label relations. The attacking problem is that instance features or label sets are too sparse to be extracted effectively hidden representation. Through both extracting inter-instance relations and inter-label correlations, it makes the learning latent representation more comprehensive, complete and accurate. At the same time, to grasp the uncertainty underlying in multi-label data, Gaussian process is introduced to denote the real underlying distribution of multi-label dataset. Additionally, this paper also incorporates self-attention mechanism to adjust its weight in the calculation of contributions of different features for the final prediction results. Finally, the effectiveness of the GNN-SML is validated on the sparse multi-label datasets.
ComParE: A User Behavior Centric Framework for Personalized Recommendations in Skill Gaming Platforms

Recommendation in outcome based platforms (eg. skill gaming, financial trading) where users get rewards based on their choices and the subsequent non-deterministic outcomes from such choices presents a unique set of challenges. Unlike other online services (e.g., e-commerce, social media), these platforms often see: (a) distinctive longitudinal behavior patterns in users’ transactions, (b) enormous content generated by the user interactions, (c) a dynamic interplay between the users’ transactional history and outcomes towards their future behavior, (d) ordinal nature of transaction elements. Motivated by these observations, we propose ComParE (Competing Parallel Networks with Expert Network), a novel personalized recommendation framework that: (i) exploits the distinct behavioral trends in the data, by training competing parallel networks as local experts, (ii) trains a global expert network to get the overall picture for the final prediction, and (iii) introduces custom loss functions to learn inherent ordering and interpretations of the classes being predicted. With the example of personalizing entry fee choices for the game of Rummy on the RummyCircle platform we show: (i) distinct and robust user Personas found based on historical entry fee selections; (ii) significant boosts in the entry fee predictions through ComParE with both neural networks and classical ML algorithms for local and global experts; (iii) substantial lifts over platform default as shown through offline analysis. ComParE significantly outperforms other baselines including well known deep learning models, as shown through offline experiments.
Deep Online Video Stabilization Using IMU Sensors

In this paper, we propose a deep learning based sensor-driven method for online video stabilization. This method utilizes the Euler angles and acceleration values estimated from the gyroscope and accelerator to assist stable video reconstruction. We introduce two simple sub-networks for trajectory optimization. The first network exploits real unstable trajectories and camera acceleration values to detect shooting scenarios. This network also generates an attention mask to adaptively choose scenario-specific features. Then the second network predicts smooth camera paths based on real unstable trajectories using long short-term memory (LSTM) under the supervision of the above mask. The output of the trajectory optimization network is filtered with a two-step modification process to guarantee smoothness. The real and smoothed camera paths are then utilized as guidance to generate stable frames in a projective manner. We also capture videos with sensor data covering seven typical shooting scenarios and design a ground truth generation method to construct pseud-labels. Moreover, the trajectory smoothing network allows the use of 3- or 10-frame buffers as future information to construct a lookahead filter. Experimental results show that our online method could outperform other state-of-the-art offline methods in several shaky video clips with fewer buffer frames for both general and low-quality videos. Furthermore, our method could effectively reduce running times without performing image content analysis, and the stabilization efficiency reaches 25 fps on 1080p videos.
Self-Supervised Source Code Annotation from Related Research Papers

Language analysis of scientific documents and analysis of source code have been done independently in the past. This work presents a network architecture and a self-supervised training approach to find alignments between published computer science research papers and their corresponding public source code by learning a representation of encodings from transformers, from which source code can be enriched with helpful information. We present our ideas, findings and plans for upcoming research.
Evaluasi kinerja staf UPT perpustakaan UNJA menggunakan analisis diskriminan

UPT Library UNJA is a Service Unit whose duties and functions are closely related to science, education, and development, especially in managing and providing various literacy, references, information, and documentation. The performance of its management staff strongly influences the management system of the UNJA Library UPT. Ideally, the performance measurement of UPT Library staff can refer to the applicable ISO standards. However, a more comprehensive performance evaluation based on the specifics and factors that affect the performance of UPT UNJA staff and the classification of staff performance evaluation is critical. UPT Library Staff Performance Evaluation based on ISO 11620: 2014 Document concludes that staff performance is good. On the other hand, the application of discriminant analysis of the performance of the Jambi University Library UPT staff analyzed 15 variables with Performance Quality as a variable consisting of two categories, namely Good and Very Good. The Stepwise Method in Discriminant Analysis reduces several independent variables so that the most significant variables in this analysis are the ability to make decisions (X6), Honesty (X8), Initiative in the line of Tasks (X9), and Skill Problem Solver (X11).
Recognition of Basic Handwritten Math Symbols Using Convolutional Neural Network with Data Augmentation

In the ever-updating digital world, automatic handwritten math symbols classification (HMC) plays many vital roles in the advancement of computer-aided systems. It is the main foundation of perfecting one of the most challenging tasks out there: recognizing handwritten mathematical formulas. As with the other similar automated handwritten characters classifications tasks, HMC also faces various difficulties while attempting to correctly classify images. As people tend to have distinct types of handwriting styles and unique ways to write symbols, a simple character may have infinite versions of itself. In our research, we focused on the classification of such images of numerous handwritten mathematical symbols. For this classification, we have developed a convolutional neural network (CNN) model and worked with three different datasets to test our model’s efficiency. We introduced different data augmentation techniques to construct various versions of the already available images. This created a virtual mimicry of people’s tendency to write the same character in many styles. Our CNN model of 11 layers (6 were convolutional layers) worked to classify 16 classes (each denoting a mathematical symbol or digit) and had an accuracy of 98.71%, 99.01%, and 99.85% respectively on three publicly available datasets. To our knowledge, our model performed better than every other research work in this field. Considering this remarkable success, we are bent on working further on this and creating a fully working app that would eventually be able to automatically classify handwritten mathematical formulas.
Non-Regular Languages

Having learned a lot about regular languages, different ways of describing them, and a little about their applications, you will have rightly come to understand that they are important. As you’ve seen, the union of two regular languages is a regular language, and the same goes for intersection, complement, concatenation, and iteration. So, given just a few simple regular languages—for example, \(\{a\}\) and \(\{b\}\)—you can build a large number of new regular languages.
A Robust and Learning Approach for Multi-Phase Aerial Search with UAVs

Unmanned aerial vehicles (UAVs) have been attracting more and more attention in the research and industry field. Aerial search is a common mission and is intrinsically fit for UAVs, e.g. disaster rescue, remote sensing and environmental monitoring. With the improvement of UAV hardware and software, UAVs tend to achieve better autonomy and accomplish more complex tasks. However, current UAV aerial search is usually hardcoded, which limits their adaptability, autonomy and robustness in realistic scenarios. In this paper, we propose to address this problem by leveraging reinforcement learning (RL) and a recent control architecture, behavior trees (BTs). We develop robust and adaptive UAV systems that can automatically conduct multi-phase complex aerial search, including search, communication and refueling. Experimental results in a 3D robot simulator verify the effectiveness and robustness of the proposed approach, which achieves better performance than the baseline.
Initiatives Related to Industry-Academia Joint Research Projects for the Purpose of Data Science Education

In this paper, we introduce the management method of the joint industry-university research project with private companies and local governments, which we have been working on for the purpose of “practical education with emphasis on contact with the society.” First, we describe the characteristics of collaborative research projects in higher education institutions in terms of “project members,” “problem setting,” “knowledge and skills,” and “handling of data and publication of results.” Next, we describe the management method of the collaborative research project that we have been working on. Specifically, it consists of three phases: problem extraction, data analysis, and verification and operation. Finally, we introduce a case study of a joint research project with a private company. Moreover, we summarize some insights into data science education gained through the operation of the project.
FRuDA: Framework for Distributed Adversarial Domain Adaptation

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is a lack of research on how uDA algorithms, particularly those based on adversarial learning, can work in distributed settings. In real-world applications, target domains are often distributed across thousands of devices, and existing adversarial uDA algorithms – which are centralized in nature – cannot be applied in these settings. To solve this important problem, we introduce FRuDA: an end-to-end framework for distributed adversarial uDA. Through a careful analysis of the uDA literature, we identify the design goals for a distributed uDA system and propose two novel algorithms to increase adaptation accuracy and training efficiency of adversarial uDA in distributed settings. Our evaluation of FRuDA with five image and speech datasets show that it can boost target domain accuracy by up to 50% and improve the training efficiency of adversarial uDA by at least <inline-formula><tex-math notation="LaTeX">$11\times$</tex-math></inline-formula> .
A Novel Modified Harmonic Mean Combined with Cohesion Score for Multi-document Summarization

The abundance of textual information that is generated on a daily basis on the web, social media, and other repositories makes it critical and difficult to extract important information from a large corpus. Automatic Text Summarization (ATS) works well in this direction, which can review many documents and pull out the relevant information from them. But the computational bottlenecks associated with ATS need to be removed by finding efficient workarounds. Although existing research works have focused on this direction for further improvements, there are still many limitations and challenges which need to be addressed. The current work proposes a semantic-based word similarity combined with sentence similarity to summarize a corpus of text documents. Finally, a relative entropy-based technique using KL-divergence is proposed, which arranges the sentences in the final summary as per their importance. Experimental results on DUC datasets are promising and show the potential of the proposed technique compared to the other state-of-the-art approaches.
TEP2MP: A text-emotion prediction model oriented to multi-participant text-conversation scenario with hybrid attention enhancement

With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an affective space mapping is constructed, which represents the original conversation-text as an n-dimensional affective vector so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.