Reviewerly/exHarmony huggingface dataset:
Supervised Learning Based Online Filters for Targets Tracking Using Radar Measurements

In the field of the radar target tracking, the state filtering plays an important role in estimating the target state. One of the widely-adopted filter is the Bayesian filter, which requires the prior information and an accurate modeling of the real tracking scene. Thus, the matching degree of the dynamic model has a key impact on the state estimation accuracy of the Bayesian filter. However, the target dynamic and radar measurement models cannot be approximated perfectly in an unknown and complicated environment, and the state estimation accuracy of the model-based filter is limited. To address the limitations of the Bayesian filter, a supervised learning based online filter for target tracking is proposed in this paper. In the proposed filter, a mapping among the radar measurements is first established in the context of the polar coordinate system. Then, based on data-driven, the state filtering is directly implemented to obtain the state estimate by using this mapping relationship. As such, the prior information is not required in the proposed filter, hence the proposed filter inherits a good estimation accuracy in unknown and complicated environments. Finally, simulation experiments clarify the effectiveness of our proposed filter via comparing with the traditional filter.
The Need for MORE: Need Systems as Non-Linear Multi-Objective Reinforcement Learning

Both biological and artificial agents need to coordinate their behavior to suit various needs at the same time. Reconciling conflicts of different needs and contradictory interests such as self-preservation and curiosity is the central difficulty arising in the design and modelling of need and value systems. Current models of multi-objective reinforcement learning do either not provide satisfactory power to describe such conflicts, or lack the power to actually resolve them. This paper aims to promote a clear understanding of these limitations, and to overcome them with a theory-driven approach rather than ad hoc solutions. The first contribution of this paper is the development of an example that demonstrates previous approaches' limitations concisely. The second contribution is a new, non-linear objective function design, MORE, that addresses these and leads to a practical algorithm. Experiments show that standard RL methods fail to grasp the nature of the problem and ad-hoc solutions struggle to describe consistent preferences. MORE consistently learns a highly satisfactory solution that balances contradictory needs based on a consistent notion of optimality.
Learning Effective Value Function Factorization via Attentional Communication

How to achieve efficient cooperation among agents in partially observed environments remains an overarching problem in multi-agent reinforcement learning (MARL). Value function factorization learning is a promising way as it can efficiently address multi-agent credit assignment problem. However, existing value function factorization methods have been focusing on learning fully decentralized value functions, which are not effective for some complex tasks. To address this limitation, we propose a framework which enhances value function factorization by allowing communication during execution. Communication introduces extra information to help agents understand the complex environment and learn sophisticated factorization. Furthermore, the proposed mechanism of communication differs from existing methods since we additionally design a descriptive key along with the message. By the descriptive key, agents can dynamically measure the importance of different messages and achieve attentional communication. We evaluate our framework on a challenging set of StarCraft II micromanagement tasks, and show that it significantly outperforms existing value function factorization methods.
"Case studies" and research design in the formation of competencies for bachelors in the field of "Biotechnology"

Abstract In modern Russian education there is a process of transition to the subject-subject interaction of a teacher and a student in the learning process. Truly innovative pedagogical technologies are initially built on a competency-based approach and are aimed in learning outcomes at the future professional activity of a graduate. The case method is one of the most significant ones for modern higher education. A consistent combination of pedagogical technology of situational analysis and research design based on the principles of contextual training of students in the direction of training "Biotechnology" at the Federal Budgetary Educational Institution of Higher Education "Altai State University" within the framework of academic disciplines allows successful development of professional and universal competencies, as well as realization of personal qualities. The result of the work of the teacher and students is an intellectual product of a biotechnological research project.
A Survey Paper on Movie Trailer Genre Detection

Like a lot of movies are being made and their trailer videos are being uploaded on the internet and there are different types of algorithms and models that are used in detecting the genre of the movie genre. Here we have tried to implement the model and compare it with other models and suggest which model is better to implement. In most of the paper, the common genre which was used to detect were drama, action, thriller, and horror. But according to IMDb, there are 28 genres of videos. So still, there is a long way to go in this field.
Exploration and Practice of China Unicom Multi-scene Capability Construction and Open Platform for Large-Scale Heterogeneous Data

China Unicom has a global data aggregation and management center, which aggregates rich resource capability, computing capability, model capability, and data capability. In order to meet the demands of enterprise mixed reform and double innovation, the paper adopts mainstream big data technology routes such as Kubernetes, Docker, Hadoop to build a multi-scene capability construction and open platform for large-scale heterogeneous data. The platform encapsulates various capabilities and services, making them open to the outside world. The platform also provides a one-stop container cloud environment for application development and deployment, and supports multiple big data application scenarios. At present, the platform has been promoted and used throughout China Unicom. Besides, it introduced external partners such as Tencent and Merchants Union Consumer Finance Company Limited, involving more than ten industries such as finance, e-commerce, automobile, Internet and real estate. Practice has proved that the construction of this platform can effectively solve the problems of internal capability sharing, intensive management, intelligent scheduling, and maximum utilization, and has important reference value for the construction of the industry’s capability opening system.
Implementing micro High Performance Computing (µHPC) artifact: Affordable HPC Facilities for Academia

This Research to Practice Full Paper presents the experience of implementing micro HPC (μHPC) clusters in the education context. In developing countries, access to and experience of High Performance Computing (HPC) facilities (which are important for the economic development of those countries) has presented challenges to academic institutions due to prohibitive costs of acquisition and maintenance of HPC systems. These challenges affect the delivery of courses, which include HPC and parallel computation in their syllabi. However, due to advancements in integrated circuit technologies, credit-card-sized personal computers (PCs) have emerged with the capabilities of a fully-fledged PC. These credit-card-sized PCs offer an opportunity for system integrators to build micro HPC (μHPC) clusters which, apart from their efficiency, scalability and availability, have a number of attributes which are not shared with conventional data-center based HPC clusters These include their low power consumption, portability, diskless nodes, cost-effectiveness and excellent price to performance ratios. μHPC clusters are, therefore, ideally suited to support curricula in all educational disciplines that demand parallel computation. The purpose of this study is to investigate the implementation of μHPC to find out from students how ease of use of a μHPC system is to acquire skills and knowledge of HPC. We used focus groups and surveys to gather data in the framework of the design science research paradigm. The implementation of μHPC clusters involved a demonstration of the system in the problem context that provided valuable lessons. The results indicate that μHPC is the ease of use HPC artifact for learning about management, integration, deployment, installation, downloading, and running of parallel programs.
Comparative Analysis of the Performance of the Fasttext and Word2vec Methods on the Semantic Similarity Query of Sirah Nabawiyah Information Retrieval System: A systematic literature review

In the field of text processing or Natural Language Processing, the increasing popularity of the use of words used in the field of Natural Language Processing can motivate the performance of each of the existing word embedding models to be compared. Semantic similarities have an important role in the field of language. Keywords are the most important thing in finding information. Seeing the benefits and studying the knowledge of Sirah Nabawiyah, especially from unreliable sources, a system to retrieve information was proposed for viewing and studying Sirah Nabawiyah. Based on the explanation above, the writer conducted a Systematic Literature Review (SLR) which was related to the semantic similarity comparison of the word embedding method, namely fast text and word2vec which contributed to the information retrieval system using the knowledge discovery in database method approach to documents in the form of Sirah nabawiyah text. Analysis and comparisons of several pieces of literature have been carried out. As a result, there are several criteria to complement the advantages and disadvantages of previous research.
Immersive Visualisation In Medical Imaging

The advent of technologies emphasizing on 3-D model reconstruction, shows a tremendous potential for application in medical imaging, specifically in anomaly detection for target organs. Medical practitioners. Bearing this in mind, the objective of our research is to identify extant anomalies within the organ with an aim to reproduce approximate size and relative location of anomaly and to generate a 3-Dimensional model of a target organ with the anomaly based on scans of medical reports to visualize in an immersive environment through Augmented Reality. In this paper, we discuss the chronological order of detecting, segmenting and rendering the anomalies onto our target organ- the brain. We have employed the U-Net architecture for CNN to train on the MICCAI BraTS 2018 dataset for segmenting brain tumours, particularly high grade gliomas. Post segmentation, we discuss the pipeline of our research, with the end results being GLTF and USDZ file formats to view the 3-D model of the brain along with tumour in Augmented Reality. We conclude the paper by discussing the future scope of research on our current work.
The Development and Application of Artificial Intelligence Based on Computer Network Technology in the Background of Big Data

Artificial intelligence is an important direction for the development of computer network technology, and it is also the key to realize the intelligence of information processing. In the era of big data, the rapidly increasing amount of network information and increasingly complex network environments put forward higher requirements for the application and innovation of computer network technology. Based on the intelligent development needs of computer network technology in the era of big data, the author analyzes the basic characteristics of big data, analyzes the advantages of artificial intelligence application, and focuses on the calculation of artificial intelligence in the era of big data The application of computer network technology.
CCA-Almost-Full Anonymous Group Signature with Verifier Local Revocation in the Standard Model

Abstract Group signature (GS) allows each member in a group to do signatures anonymously on behalf of the group under management of a group authority. Membership revocation has been a central issue in GS and widely studied so far. The mechanism of verifier local revocation for GS, first formalized by Boneh and Shacham, has an advantage of making the signers free from fetching the up-to-date information of the revoked users and practicality in the scenario of periodically update in the large population of group users. Most of work related to group signature with verifier-local revocation either can only achieve selfless anonymity or have inefficient constructions due to complicate primitives. Aiming to a recent chosen ciphertext attack-almost-full anonymous notion for GS, this paper presents an efficient GS with verifier local revocation in the standard model by adding a new primitive plaintext searchable encryption into Groth’s GS under Canard et al.’s framework. We prove that it has backward unlinkability to ensure that all signatures generated by the user before the revocation remain anonymous, even if it is revoked later.
Object Detection for Automatic Driving Based on Deep Learning

With the continuous developing progress of automatic driving, object detection is continuously being applied and developed. This paper presents an overview of deep learning object detection algorithm that used in the field of automatic driving. In the first part, we will introduce the research status and challenges of deep learning-based object detection in automatic driving. In the second part, we will introduce a deep learning-based object detection algorithm. Distinct algorithms will be discussed in the same structure. 1) Introduction 2) Algorithm steps 3) Advantages and Disadvantages. In the third part, we will compare different algorithms according to different principles. In the fourth part, we will introduce the practical application of deep learning-based object detection algorithm in the field of automatic driving.
Accented visualization application in interactive manuals for technical training and support

Abstract The capabilities of modern information and communication devices (tablets, glasses, helmets, etc.) that provide user interfaces with Augmented Reality (AR) are successfully used in education and maintenance support nowadays. The main benefits are concerned with the possibility to display the required information in the context of operations performed by the user. Technically it is implemented in the form of textual hints or visual 3D objects with highly realistic appearance and behavior. New features prompt new challenges: the user can be easily overloaded with lots of information, and some real objects that require user’s attention can be overlapped by virtual ones in one scene. In order to solve this problem and improve the usability and effectiveness of AR devices application in maintenance and education it is proposed to build adaptive user interfaces that consider the context and focus of the user. From information technology perspective this approach forms a promising area of application of high-potential technologies including machine learning and computer vision. In this paper there is presented an example of AR implementation as a part of an interactive manual, which provides adaptation and personalization based on the concept of accented visualization. Identification of objects and their classification is implemented using the artificial neural network.
Interlacing Data to Classify Software in Linear Regression Approach

Linear regression is widely used in prediction problems by modeling the relationship between input and output data. In this paper, we propose an approach to interlacing data to classify similar software in the linear regression approach. Multiple linear regression models are generated from the training of the interlaced feature data. By combining the multiple models, we can apply the proposed approach in classifying similar software. Experiments are performed to evaluate the proposed approach as compared to the conventional linear regression. The experimental results show that the proposed method can classify similar software more accurately compared to the conventional linear regression model. The proposed approach is expected to be an effective approach to improve the accuracy of linear regression and it is expected to be applied in various areas of prediction problems.
Practical Advantage in Microwave Quantum Illumination

Broadly speaking, in quantum illumination we can say that a proposed protocol has a “quantum advantage” if it outperforms all possible classical protocols. In the optical domain of LIDAR, this is the most useful metric as lasers can routinely produce nearly ideal classical states of light at room temperature (RT). This is not the case in the microwave domain of RADAR where the photon energy is much less than the 300K thermal energy, meaning that a real RT microwave source will always be contaminated by significant thermal noise. Thus, it is not clear if it is technologically possible to produce an ideal classical microwave signal at RT. It is therefore interesting to ask if a microwave quantum illumination protocol can have a “practical advantage” compared to the best technologically feasible RT microwave source. In this paper, we look to frame this question more precisely. As a concrete example, we present experimental results showing that, contrary to recent claims in the literature [1], an entangled microwave source amplified by a cryogenic HEMT amplifier fails to obtain any performance advantage over a simply constructed RT source and, in facts, performs significantly worse. We present a simple theory which explains the experimental results and which offers guidance on how a practical advantage might be achieved.
Demonstration of Joint Optimization between Cloud AI and On-board AI in Optical Transport Networks

We propose training-testing-decoupled (TTD) mode between cloud artificial intelligence (AI) and on-board AI to speed up the AI application. The experimental results show that TTD mode could reduce 12.5% of the time cost.
Toward Optimal Learning Rate Schedule in Scene Classification Network

Stochastic gradient descent (SGD) and adaptive methods, including ADAM, RMSProp, AdaDelta, and AdaGrad, are two dominant optimization algorithms for training convolution neural network (CNN) in scene classification tasks. Recent work reveals that these adaptive methods lead to degraded performance in image classification tasks compared with SGD. In this letter, a learning rate schedule named switching from constant to step decay (SCTSD) is proposed to further improve the classification accuracy of SGD. SCTSD begins with a constant learning rate and switches to step decay learning rates when appropriate. Theoretical evidence is provided on the superiority of SCTSD compared with other manually tuned schedules. Comparison experiments have been conducted among adaptive methods, SCTSD, and other manual schedules with three CNN architectures. The experiment results on various scene classification data sets show that SCTSD has the highest accuracy on the test set and it is state of the art. In the end, some suggestions on hyperparameters selection of SCTSD are given for scene classification.
Food Safety Traceability Technology Based on Block Chain

With the emergence and widespread application of intelligent technologies such as the Internet of Things and big data, many researchers have also begun to combine emerging technologies with food traceability systems. These studies have made innovations in data query and reading platforms or related encryption technologies. However, in traditional food traceability systems, data management information is still scattered, difficult to share, and difficult to trace. The purpose of this article is to study the food safety traceability technology based on blockchain technology. Based on the theoretical basis of blockchain technology, this paper proposes a traceability scheme formed by the combination of reverse search and recursive algorithm. Finally, this paper designs and implements a food safety traceability system. The experimental results show that the scheme proposed in this paper is more efficient than traditional traceability models. And by testing the performance of the system, the test results show that the system can meet the demand. In this paper, through testing the traceability rate of different schemes, the traceability rate of the scheme proposed in this paper is stable at about 1000 ms.
Trenchcoat: Human-Computable Hashing Algorithms for Password Generation

The average user has between 90–130 online accounts [17], and around $$3\times 10^{11}$$ passwords are in use this year [10]. Most people are terrible at remembering "random" passwords, so they reuse or create similar passwords using a combination of predictable words, numbers, and symbols [16]. Previous password-generation or management protocols have imposed so large a cognitive load that users have abandoned them in favor of insecure yet simpler methods (e.g., writing them down or reusing minor variants). We describe a range of candidate human-computable "hash" functions suitable for use as password generators - as long as the human (with minimal education assumptions) keeps a single, easily-memorizable 'master' secret - and rate them by various metrics, including effective security. These functions hash master-secrets with user accounts to produce sub-secrets that can be used as passwords; $$F_R($$ s $$, w) \longrightarrow y$$ , which takes a website w and produces a password y, parameterized by the master secret s, which may or may not be a string. We exploit the unique configuration R of each user's associative and implicit memory (detailed in Sect. 2) to ensure that sources of randomness unique to each user are present in each F. An adversary cannot compute or verify $$F_R$$ efficiently since R is unique to each individual; in that sense, our hash function is similar to a physically unclonable function [37]. For the algorithms we propose, the user need only complete primitive operations such as addition, spatial navigation or searching. Critically, most of our methods are also accessible to neurodiverse, or cognitively or physically differently-abled persons. Given the nature of these functions, it is not possible to directly use traditional cryptographic methods for analysis; so, we use an array of approaches, mainly related to entropy, to illustrate and analyze the same. We draw on cognitive, neuroscientific, and cryptographic research to use these functions as improved password management and creation systems, and present results from a survey (n = 134 individuals, with each candidate performing 2 schemes) investigating real-world usage of these methods and how people currently come up with their passwords. We also survey 400 websites to collate current password advice.
Edge Data Security for RFID-based Devices

Radio-frequency identification (RFID) has become a preferred technology for monitoring in industrial internet of things (IIoT) applications like supply chain, medical industry, vehicle tracking and warehouse monitoring where information is required continually. Typical security threats seen in these applications are denial of service (DOS) attack, transmission attack etc. We propose a novel edge data security schema based on spike modulation along with backscatter communication technique to modulate both sensor and identification (ID) information. It is observed that this data encoding schema works well even in a multi-tag single-reader environment. Further, it uses lower power and offers a low-cost solution for Industrial IoT applications.