Reviewerly/exHarmony huggingface dataset:
A Target Detection Algorithm in the Aerial Image Guided by the Density Map of the Target Center Point

Accurate and efficient UAV target detection has broad application prospects in intelligent transportation, crop detection, military security and other aspects. Aiming at the characteristics of UAV images such as large size, complex background, low resolution and uneven distribution of targets, a target detection algorithm based on density map of target center is proposed, which is mainly divided into density map generation module, region segmentation module and detection fusion module. The density map can well reflect the distribution of the pixel intensity of the whole image. According to the information provided by the density, candidate detection regions are cropped by region segmentation module. Finally, the detection results of region images and original images are fused. Experimental results on VisDrone dataset show that the proposed algorithm improves the baseline experimental results significantly and has better robustness.
FAIR AI: A Conceptual Framework for Democratisation of 21st Century AI

Popular models of AI have two significant deficiencies: 1) they are mostly manually designed using the experience of AI-experts 2) they lack human interpretability, i.e., users cannot make sense of the functionality of neural network architectures either semantically/linguistically or mathematically. This lack of interpretability is a main inhibitor of broader use of 21st century AI, e.g., Deep Neural Networks (DNN). The dependence on AI experts to create AI hinders the democratisation of AI and therefore the accessibility to AI. Addressing these deficiencies would provide answers to some of the valid questions about traceability, accountability and the ability to integrate existing knowledge (scientific or linguistically articulated human experience) into the model. This keynote abstract addresses these two significant deficiencies that inhibit the democratisation of AI by developing new methods that can automatically create interpretable neural network models without the help of AI-experts. The proposed cross-fertilised innovation will have a profound impact on the society through the increased accessibility and trustworthiness of AI beneficial to almost all areas of sciences, engineering, and humanities.
A Model for Evaluating Remaining Energy of Retired Battery Based on Improved PSO-BP Neural Network

Aiming at the insufficiency of the remaining energy detection methods for decommissioned batteries specified in the current national standards, it is proposed to use neural networks to build a model for evaluating the remaining energy of decommissioned batteries to provide a basis for the subsequent recycling and reuse of decommissioned batteries. In view of the problems of traditional BP neural network being easy to fall into local optimal solutions, it is proposed to increase the momentum term and the learning rate adjustment function at the same time; for the problems of slow convergence of the PSO algorithm, the linear decreasing weight method is used as the weight update strategy, and the learning factor is adjusted at the same time. The improved PSO algorithm is combined with the improved BP neural network to construct a PSO-BP neural network model to evaluate the residual energy of decommissioned batteries. It has been verified that the PSO-BP model has a faster convergence speed and a higher accuracy during the training process, and the accuracy rate of the residual energy evaluation result of the retired battery is 98%, which has high practical application value.
BP Neural Network for Construction of Smart Campus Security

Now, higher vocational education has accounted for half of China's higher education. Higher vocational education is facing the great challenge of large-scale and quality games. People pay more and more attention to the quality of education and become the focus of higher vocational education. In order to further improve the quality of higher vocational education, clarify the status and training objectives of Higher Vocational Education in national education, issue relevant documents to the Ministry of education, and clarify the healthy development direction of higher vocational education. From the micro perspective, this topic makes a detailed study of teachers’ teaching as the object of teaching quality evaluation and control, looks for a more reasonable way of teaching quality evaluation, and provides accurate feedback information for improving teaching quality. With the development of artificial neural network in China in this century, as a new information science learning from human brain, that is, artificial intelligence method constructed by simulating human brain nerve to transmit information, its application fields include biology, economics, sociology and so on. In view of the prominent advantages of artificial neural network in the comprehensive evaluation of nonlinear complex systems and the difficulties of higher vocational education evaluation, we choose this method to study the educational evaluation and control of higher vocational education.
Performance Analysis of Leukemic Gene Expression Profiles using Classification

Evaluation and diagnosis of any cancer disease is facilitated with the classification of blood cells. Once blood disorders are identified it is easy to classify diseases related to blood. Leukemia is a cancer that affects the bone marrow, which is the producer of blood. Studies reveal that Leukemia can be classified using blood smear images or by taking gene expression profiles. Classification based on gene expressions is of much interest to researchers since it provides an objective, accurate and systematic diagnosis of several cancer types. The present study is an analysis of the classification of gene expression profiles from the classic Golub et al leukemia dataset which reported how gene expression data from DNA microarrays could be used to classify new cancer cases. The present study uses the K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Logistic Regression, Random Forest and Neural Network algorithms to classify the samples into either Acute Myeloid Leukemia (AML) or Acute Lymphocytic Leukemia (ALL). Performance evaluation of the various algorithms used is given towards the end of this paper. The results reveal that Logistic Regression provides accurate results and the study reiterates the fact that Neural Networks perform efficiently when the dataset is large.
КЛЮЧОВІ КОМПЕТЕНЦІЇ ВИКЛАДАЧА-ТЬЮТОРА В УМОВАХ ЗМІШАНОГО НАВЧАННЯ

Radical transformations of the modern educational space, which arose as a result of the active introduction of innovative information technologies in the educational process, have radically changed the nature of the relationship between teacher and student. educational tools, Internet technologies have increased the importance of the distance component of blended learning. A wide range of multimedia tools (virtual classrooms and laboratories, interactive whiteboards and augmented reality tools) and their high efficiency call into question the relevance of the classic role of the teacher in the learning process.
.Compostmodernism: Textual Machinery Through Typography and Materiality in Mark Z. Danielewski’s The Familiar

This article defines “.compostmodernism” as a successor to postmodernism by explicating of Mark Z. Danielewski’s The Familiar as an example of .compostmodern textual machinery—a system of two interconnected “machines” (one abstract, the other physical) that are co-dependent and mobilized by the novel’s typography and materiality. Beginning by illustrating how The Familiar exemplifies .compostmodern textual machinery through its experimental typography which becomes the visual manifestation of literary cyber-consciousness, this article indicates how, in visually manifesting character cyber-consciousness, the novel’s typography actualizes the digitality of character interiority, ultimately drawing attention to the work’s status not only as literary artifice, but also as textual machinery. The demands of such experimental typography instantiate a physical relationship between reader and codex that emphasizes the novel’s materiality and requires the reader to engage with the text both physically and digitally. Ultimately, this digital engagement incorporates the internet not only as a crucial supplement for the reader to seek reference, translation apps, and supplementary (albeit obscure) Danielewski publications, but also as a medium for the reader to supplement the novel via social media output and online reading communities.
Infirm Fitness

Physically challenged people or the people with disabilities are those section of the society in which the society embarks no decision or gives any judgement about their physical abilities of the working body parts and the other requirements of them according to their physical fitness as well, the present generation has the awareness regarding keeping themselves fit and sportingly active but on the other hand the people who are physically challenged are unable to work on the same. The project hereby tries to implement the solution for the same by giving the various physical exercises that could be done by the physically challenges people according to their working body parts and according to their ability accordingly.
Ethical Hacking : Roles, Phases and Impact on various sectors of the Economy

This paper will discuss the topic of ethical hacking, which is also called penetration testing. It starts by briefing about the ethical hacking introduction and its key protocols. It will further discuss the varied classifications of hacking and explain the causes for the swift rise in the cyber-crimes and their impact on socio-economic growth. The advantages and limitations of ethical hacking are also listed. It will further discuss the steps involved in ethical hacking, who is allowed to conduct ethical hacking, and its importance in order to reduce the effect of these attacks, penetration tests are highly required, to consider an acceptable solution for this task. Results from the case study shows that there are negative impacts where the society suffers from cybercrimes and why the computer or networking tools are targeted for the crimes. Ethical hacking education can provide the future professionals to combat the future cyber security issues.
Lev Vekker and His Unified Theory of Mental Processes

L. M. Vekker was a leading theorist for general psychology at Leningrad State University after 1960. A student of B. G. Ananiev, he helped his teacher continue and extend the scientific principles and organizational style of I. M. Sechenov and V. M. Bekhterev, the founders of the Saint Petersburg school of psychology. Vekker’s work toward a metatheory for psychological science began with research on the sense of touch and expanded into a generalized theory of perception and mental processes; he made use of ideas from reflex theory and cybernetics and proposed a model of hierarchical levels of signal systems that managed codes. As his major works were completed in the 1980s, he fell out of favor with leaders of the Faculty of Psychology and emigrated to America; however, his ideas for a general theory of psychology lived on in the minds and the work of his Russian students.
Make It or Break It: The Impact of Information Technology Audit on Efficiency of Modern Economy

This paper provides an overview of the adoption and expansion of the Information Technology (IT) following the revolution in the early 1990s. The importance of IT in the financial development has been touched upon drawing inferences from various studies conducted in this field. It has become an indispensible part of the business operation in the modern world. For sustainable development of the IT facilities, IT Audit is inevitable. However, there are certain intricacies to be considered while framing an audit in general, and IT in audit in particular. We mention some of the research suggesting the possible problems and corresponding overcoming of these issues. Therefore, the imperative of further research in this area to come up with ideas about better implementation and designing of audits to overcome the loopholes is tangibly present.
АЛГОРИТМ ВИКОРИСТАННЯ КРИТЕРІЮ МАКНАМАРА ДЛЯ СТАТИСТИЧНОЇ ОБРОБКИ ПЕДАГОГІЧНИХ ВИМІРЮВАНЬ

The article describes the peculiarities of using nonparametric methods of mathematical statistics, provides an algorithm for using the McNamara criterion to test the reliability of statistical hypotheses as assumptions about the results of pedagogical research, states the conditions of application of the criterion, and gives an example of mathematical processing of pedagogical measurements.
SiloSolver: Developing an Algorithm for Automatic Aggregation and Testing of Isolated Customer Segments in Facebook Ads Campaigns

Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that (a) retrieves performance metrics for different customer segments across all campaigns, (b) aggregates the values for each customer segment in mean values, and (c) runs a statistical comparison (Student&#x2019;s t-test) on the performance differences between the segments. The algorithm is evaluated using a real Facebook Ads dataset from an e-commerce company consisting of hundreds of campaigns from over five years. Using SiloSolver, advertisers using Facebook Ads are better able to understand their market segments across multiple seemingly disparate campaigns.
Testing on Anti-jamming Performance of Tracking Loop for Frequency Hopping System

Frequency Hopping (FH) system is widely researched by its strong anti-jamming performance. Focusing on the common jamming of tracking loop for FH system, the typical jamming signal features are analyzed, which including full-band jamming, partial-band jamming and tracking jamming signals. The anti-jamming performance of the synchronous tracking loop on FH system is tested, which focused on the modulation of QPSK. Simulation testing results shows that FH system has strong anti-full-band jamming and anti-partial-band jamming performance, the system has weak anti-single-tone-tracking jamming and anti-double-tone-tracking jamming performance, and the anti-tracking jamming performance will be weaker with the DTR increasing of the jamming signal.
Aspect-Level Sentiment Classification of Chinese Patient Comments Based on Pre-trained Sentiment Embedding

With the development of information technology, online health care service platforms have collected a large amount of patient comment information. Through fine-grained sentiment classification of this information, we can provide references for patients to seek medical treatment and help doctors understand their work. Therefore, we proposed a model that integrated pre-trained emotional information and semantic information at the word and character level for aspect-level sentiment classification. Specifically, we employed adversarial learning for training sentiment word embeddings and the two-layer bidirectional long short-term memory network to extract the sentiment embedding of the entire sentence in a specific aspect. We also combined the pre-trained sentiment feature vector with the structured semantic information by linear weighting and the multi-head self-attention mechanism, enabling the model to pay more attention to the information most relevant to a given aspect category. We performed experiments on the Chinese patient comments data set constructed by our research team and the proposed model outperformed the state-of-the-art methods, which proved the effectiveness of the proposed model for aspect-level sentiment classification of Chinese patient comments.
An Object-Based Splicing Forgery Detection Framework with Noise Features

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Decentralization of Finance and Global Businesses

Decentralization of finance is usually regarded as a revolution in just the field of finance. However, thanks to chain structures (e.g. blockchain) ranging from supply chain management to enterprise resource management systems, decentralization gradually reforms all aspects of global business simultaneously. Cryptocurrency markets and decentralization of network structures may be considered as two independent chaotic processes by an uninformed observer; however, almost every step of this transformation process can be associated with a certain innovative motivation which was designed for a specific purpose.In this chapter, the implications of financial decentralization movements have been discussed with the assumption that the cryptocurrency market movements and relevant infrastructure improvements may be regarded as the end result of a chain of events which may be influenced by a number of decision-makers. The system components affecting the adoption process have been divided into four categories. Three potential scenarios have been presented for each category with an emphasis on their potential effects on the global business environment. Using intuitive logic, a pathway has been speculated for the adoption process for each component of decentralized finance. Topics discussed in this context should be regarded as pure speculation and should not be perceived as investment advices in any shape or form.
Intelligent Support System for People with Visual Impairments

This paper aims to present a prototype of augmented reality glasses that work with specialized software that will help people with visual problems more easily cope with the obstacles of their environment. The software that is developed meets the hardware and software requirements of the glasses. Simplified user interface further facilitates its use by people, regardless of the degree and severity of their visual problems.
The Generalization Ability of the Tire Model Based on Bayesian Regularized Artificial Neural Network

This paper aims to develop a practical tire model that keeps the balance between generalization ability and accuracy for Formula Student application. Up to now, the advantages of Artificial Neural Networks for tire modelling have been investigated by some studies, which are briefly introduced in this paper. However, tire models based on Artificial Neural Networks were likely to over-fit the given data, or were sensitive to the noise. And far too title attention has been paid for the generalization ability, which is essential for a tire model. In this paper, a Bayesian regularization method based on the Bayes’ theorem is proposed to solve the major problems described above by improving the generalization ability. And a large number of measured data were used for testing the trained models with different configurations. The results show that the tire models based on the Bayesian regularized artificial neural networks can achieve better generalization ability, and are practical for racing applications such as Formula Student.
Gradient Compression via Count-Sketch for Analog Federated Learning

Federated learning (FL) is an actively studied training protocol for distributed artificial intelligence (AI). One of the challenges for the implementation is a communication bottleneck in the uplink communication from devices to FL server. To address the issue, many researches have been studied on the improvement of communication efficiency. In particular, analog transmission for the wireless implementation provides a new opportunity allowing whole bandwidth to be fully reused at each device. However, it is still necessary to compress the parameters to the allocated communication bandwidth despite the communsication efficiency in analog FL. In this paper, we introduce the count-sketch (CS) algorithm as a compression scheme in analog FL to overcome the limited channel resources. We develop a more communication-efficient FL system by applying CS algorithm to the wireless implementation of FL. Numerical experiments show that the proposed scheme outperforms other bench mark schemes, CA-DSGD and state-of-the-art digital schemes. Furthermore, we have observed that the proposed scheme is considerably robust against transmission power and channel resources.
Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector

The mission of this chapter is to formalize multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. The objective is to collect all Pareto optimal policies in order to adapt them for use in a learner's situation. However, it takes huge learning costs in previous methods, so this chapter proposes the novel model-based MORL method by reward occurrence probability (ROP) with unknown weights. There are three main features. First one is that average reward of a policy is defined by inner product of the ROP vector and a weight vector. Second feature is that it learns the ROP vector in each policy instead of Q-values. Third feature is that Pareto optimal deterministic policies directly form the vertices of a convex hull in the ROP vector space. Therefore, Pareto optimal policies are calculated independently with weights and just one time by Quickhull algorithm. This chapter reports the authors' current work under the stochastic learning environment with up to 12 states, three actions, and three and four reward rules.
Using Causal Discovery to Design Agent-Based Models

Designing agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone.
ACM SIGMETRICS 2021 Student Research Competition

Every year, the Association for Computation Machinery (ACM) spearheads a series of Student Research Competitions (SRCs) at ACM-sponsored or co-sponsored conferences. These SRCs, which are sponsored by Microsoft Research, provide undergraduate and graduate students an opportunity to present their original research to a panel of judges. Participating students present posters and oral presentations of their research for the opportunity to win cash prizes. In each category (graduate and undergraduate), the 1st, 2nd, and 3rd place winners receive prizes of $500, $300, and $200, respectively. Additionally, first place undergraduate and graduate (Masters or PhD program) student winners from the SRCs held during the year are invited to present their work at the SRC Grand Finals, where a different panel of judges evaluates these winners against each other. To date, Microsoft Research has generously provided $120,000 per competition year since 2003.