Reviewerly/exHarmony huggingface dataset:
Automatic Analysis of Amateur Film and Video Collections

Amateur film and video collections are often only sparsely documented and the contents of the material are only vaguely known. The progress in deep learning based computer vision methods over the last decade provides an opportunity to automatically generate additional descriptions that would help exploring and finding content, even if they do not meet the level of professional archive documentation. While the automatically generated metadata can facilitate interactive search and exploration, it will always need to be used with a human user in the loop, as it will not be perfect. In this paper, we describe a framework including visual content analysis tools and a web-based visualisation of the extracted metadata.
Two-sites’ spin chain as a good statistical representative of an infinite one

We apply information-theory based symbolic quantifiers, related to complexity studies, in order to investigate interacting spin chains, in which there is competition between one-body and two-body quantum interactions. Our system of reference is a well-known XY model with N-spins. We show that most of its thermal features can already be detected with N=2.
Patrol Control System Using AI

Visualization frameworks are used to monitor almost all open and private areas. The continuous operation of these components adds additional power to the visual action. In such cases, any suspicious or decisive action is detected by examining the camera's continuous video transmission through these frameworks. For the most part, the viewing action is an additional manual, that is, a standard CCTV video film is screened by security personnel in a specific control room. It is difficult for anyone to monitor CCTV continuously and diligently without rest. In this way, the excitement and adequacy of such a view depends on the capabilities of the security equipment, (i.e., the individual), and therefore represent the need for additional staff of non-stop vision. This paper explores an independent system that will not only reduce human effort but will also increase the legal expertise of the city's public spaces.
Incremental Refinement of Goal Models with Contracts

Goal models and contracts offer complementary approaches to requirement analysis. Goal modeling has been effectively used to capture designer’s intents and their hierarchical structure. Contracts emphasize modularity and formal representations of the interactions between system components. In this paper, we present CoGoMo (Contract-based Goal Modeling), a framework for systematic requirement analysis, which leverages a new formal model, termed contract-based goal tree, to represent goal models in terms of hierarchies of contracts. Based on this model, we propose algorithms that use contract operations and relations to check goal consistency and completeness, and support incremental and hierarchical refinement of goals from a library of goals. Model and algorithms are implemented in a tool which enables incremental formalization and refinement of goals from a web interface. We show the effectiveness of our approach on an illustrative example motivated by vehicle platooning.
Optimistically Compressed Hash Tables &amp; Strings in theUSSR

Modern query engines rely heavily on hash tables for query processing. Overall query performance and memory footprint is often determined by how hash tables and the tuples within them are represented. In this work, we propose three complementary techniques to improve this representation: Domain-Guided Prefix Suppression bit-packs keys and values tightly to reduce hash table record width. Optimistic Splitting decomposes values (and operations on them) into (operations on) frequently- and infrequently-accessed value slices. By removing the infrequently-accessed value slices from the hash table record, it improves cache locality. The Unique Strings Self-aligned Region (USSR) accelerates handling frequently occurring strings, which are widespread in real-world data sets, by creating an on-the-fly dictionary of the most frequent strings. This allows executing many string operations with integer logic and reduces memory pressure. We integrated these techniques into Vectorwise. On the TPC-H benchmark, our approach reduces peak memory consumption by 2-4× and improves performance by up to 1.5×. On a real-world BI workload, we measured a 2× improvement in performance and in micro-benchmarks we observed speedups of up to 25×.
Instant Messaging Application Traffic Recognition

As a basic work of network security, network traffic recognition plays an important role in network resource management and abnormal network traffic monitoring. At present, network traffic identification has become one of the hottest issues in academic research. In the past research, network traffic analysis was mainly done by Port Matching, Deep Packet Inspection. However, these methods are not perfect, and they are not suitable for today. This paper implements a traffic recognition method based on deep learning and machine learning. Besides, this paper implements unsupervised clustering of traffic. On the UNB ISCX data set, the experimental results are quite good.
Path Planning of UAV Based on Error Correction

Abstract: Due to the limitation of system structure, UAV can not accurately locate itself. Once the positioning error accumulates to a certain extent, it may lead to mission failure. Therefore, the aircraft needs to go through several calibration points to correct vertical and horizontal error. In this paper, an improved ant colony optimization algorithm is used to realize path planning of the aircraft based on error correction. Firstly, mathematical model of path planning is established based on constraints of location error. Secondly, ant colony optimization is used to realize multi-objectives including maximizing success rate reaching the destination, minimizing the number of correction nodes and minimizing length of the path. To find feasible solutions for different problems, constraints are strengthened or relaxed and search strategies of the next node are set. Finally, the algorithm with adaptive parameters is verified by an example, the optimal path with 100% arrival rate, shorter length, and less correction nodes shows its effectiveness.
Concept Formation in Computational Creativity: a Comparative Study of Algorithmic Approaches

The doctoral thesis presented in this paper addresses the latest advancements in machine learning architectures for creative artifact generation through the lenses of Design, Philosophy and Cognitive Science. The research adopts a trans-disciplinary approach, looking for opportunities to decompartmentalize knowledge and formalize efficient guidelines that facilitate adoption and operation of such technologies. Its main objective is to uncover the hidden assumptions embedded in these tools and formalize a theoretical framework able to describe the different concept ontologies employed during human-machine collaboration and their inter-operability. Three studies will be conducted to validate the framework, each addressing a specific domain (music, language, visual).
An Interactive Data Assignment Algorithm for Non-Replicated DDBMSs

In this work, we propose an optimized data assignment algorithm for time dependent non-replicated distributed database systems based on a modified Dijkstra shortest path algorithm findings. Fragment assignment is determined from the access cost matrix and a threshold value obtained from the query matrix and site access record. Transmission, storage and computational overhead costs are computed and compared with existing algorithms. This model uses fragment preferred site technique to ensure nonreplication at sites and to avoid constraints violation if fragment replication occurs. The algorithm is demonstrated through detailed example and proved robust in terms of transmission cost while it lags behind in terms of storage cost.
Cloud Task Prediction Model Based on Singular Spectrum Analysis, Empirical Mode Decomposition and Temporal Convolutional Network

As we all know, the nonlinearity and non-stationarity of the cloud task series bring great challenges for accurate prediction. To solve this problem, this paper proposes a hybrid method which consists of empirical model decomposition (EMD) and time convolutional networks (TCN) to predict the numbers of the future tasks arriving the datacenters. In this approach, the singular spectrum analysis is used to eliminate the noises and extracts the trend information of the original cloud task series; the cloud task series was decomposed into several subsequences by EMD, which attenuates the interaction between subsequences in the original sequence; and then the TCN is used to predict the sub-sequences and get the prediction result of the trend of each sub-sequences. The prediction results of all the sub-sequences are reconstructed to obtain the final prediction results. Experimental results show that the proposed EMD-TCN algorithm has higher prediction accuracy.
Effectiveness of PowerPaper presentation as an innovative instructional material in teaching science 5 in the selected public schools in Taytay, Rizal

PowerPoint Presentation (PPT) is a common instructional software or program used by teachers in ICT-rich classrooms.However, many public schools in the country still lack or have insufficient access to ICT resources.Therefore, this study sought to assess the effectiveness PowerPaper Presentation (PPP).PowerPaper Presentation is a teacher-designed low-cost and low-tech innovative alternative to PowerPoint Presentation in promoting student learning of selected topic in Science 5 using a quasi-experimental method that adopted the pre-test-post-test design.Three groups of student participants from selected public elementary schools totaling 136 participated in the study composed of 45 students constituting the control group from Corazon C. Aquino Elementary School, 46 students serving as experimental group A from Bagong Pag-Asa Elementary School Annex, and 45 students serving as experimental group B from San Juan Elementary School Annex from the Division of Rizal.The intervention was like teaching a selected topic in Science 5 using PowerPaper Presentation for the experimental groups and without PowerPaper Presentation for the control group.First, a pre-test was administered to the three groups before the teaching process.Then, the same test was issued as a post-test after the teaching process.Finally, the results were statistically analyzed.Analysis of the pre-test results using one-way analysis of variance revealed no significant differences in the pre-test scores of the control group and the two experimental groups.However, as regards the post-test results, analysis of variance revealed that the post-test scores of the two experimental groups are statistically comparable.In contrast, the post-test scores of the control group are significantly lower than the experimental groups.Furthermore, the normalized learning gains of the two experimental groups, while statistically comparable, are substantially higher than the learning gains of the control group.These results imply that PowerPaper Presentation significantly improved students' learning of the selected topic in Science 5
국가수준 학업성취도 평가의 시 교육 평가틀 연구 - 2015~2019년 중학교 학업성취도 평가를 중심으로 -

This study analyzed the National Assessment Educational Achievement(NAEA) for middle school students that was conducted between 2015 and 2019 to discuss problems and improvement of the assessment framework of poetic education.I selected three achievement standards that are frequently presented as assessment items for poetic education. By analyzing items based on these standards, I suggest the following points. First, items are needed to identify the role and location of the speaker and deal with changes in the work according to the speaker’s positions. Second, items that gauge learners’ understanding of literary knowledge more precisely are needed. I also propose that literary knowledge needs to be described more systematically in the curriculum. Third, learners should be given the opportunity to interpret works in different ways by solving the items.Additionally, I analyzed two achievement standards that were not presented as items for poetic education and items based on them. Through this analysis, I suggest that achievement standards should be addressed more clearly and not be duplicated. Various genres of literature should be presented in a balanced way. I also argue that we need to think about whether we can evaluate the ability to create works and we should evaluate it in NAEA. Achievement standards of NAEA should be established considering the differences in achievement standards of curriculum.
Comparative analysis of scientometrics indicators of “Povolzhskaya аrkheologiya” journal

The authors discuss the results of analyzing scientometric indicators of “Povolzhskaya аrkheologiya” journal acquired to the Web of Sciences, Scopus and RSCI (Russian Science Citation Index) databases. To specify the journal position within the archeological periodicals and its possible inclusion into the Web of Science database, the study findings were compared to the analyses results for the Russian authoritative academic journals in the related domains. The rating for selected journals by Scopus and RSCI key scientometrics indicators is built. The journal position in these ratings is discussed. Within the Web of Science system, the indicator of the journal “latent citation” is obtained. The journal dynamics within the databases under examination is demonstrated. The articles published in 2015–2019 are analyzed for their territorial affiliation. Based on the analysis results, the authors determine the journal position in the national and global context and suggest that “Povolzhskaya аrkheologiya” journal has got high starting position if included into the Web of Science.
Visual Recognition of Abnormal Activities in Video Streams

Nowadays, the visual information captured by CCTV surveillance and body worn cameras is continuously increasing. Such visual information is often used for security purposes, such as the recognition of suspicious activities, including potential crime- and terrorism-related activities and violent behaviours. To this end, specific tools have been developed in order to provide law enforcement with better investigation capabilities and to improve their crime and terrorism detection and prevention strategies. This work proposes a novel framework for recognising abnormal activities where the continuous recognition of such activities in visual streams is carried out using state-of-the-art deep learning techniques. Specifically, the proposed method is based on an adaptable (near) real-time image processing strategy followed by the widely used 3D convolution architecture. The proposed framework is evaluated using the publicly available diverse dataset VIRAT for activity detection and recognition in outdoor environments. Taking into account the non-batch image processing and the advantage of 3D convolution approaches, the proposed method achieves satisfactory results on the recognition of human-centred activities and vehicle actions in (near) real time.
Artificial Cardiac Pacemaker Control design using Deep Reinforcement learning: A Continuous Control Approach

Cardiac pacemaker is a device that demands very high accuracy alongside sophisticated performance. In this work we have tried to apply the various recent and state of the art actor-critic, policy gradient, both on-policy and off-policy Algorithms for the continuous control of the artificial cardiac pacemaker. We owe this work also due to the recent development of MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> integration with the Reinforcement Learning toolbox in MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> which combines low level RL algorithm tuning down to each and every hyperparameter and the high level model based control and electrical engineering tool that is Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> .
Correlated Adversarial Joint Discrepancy Adaptation Network

Domain adaptation aims to mitigate the domain shift problem when transferring knowledge from one domain into another similar but different domain. However, most existing works rely on extracting marginal features without considering class labels. Moreover, some methods name their model as so-called unsupervised domain adaptation while tuning the parameters using the target domain label. To address these issues, we propose a novel approach called correlated adversarial joint discrepancy adaptation network (CAJNet), which minimizes the joint discrepancy of two domains and achieves competitive performance with tuning parameters using the correlated label. By training the joint features, we can align the marginal and conditional distributions between the two domains. In addition, we introduce a probability-based top-K correlated label (K-label), which is a powerful indicator of the target domain and effective metric to tune parameters to aid predictions. Extensive experiments on benchmark datasets demonstrate significant improvements in classification accuracy over the state of the art.
Placement Cell Android Application and Admin Portal

Training and Placement Cell is an important part of any educational institute, in which most of the work till now is being done manually. The training and placement officer has to inform around thousands of students about every single training and placement related activity. So, to make the placement process easy and effective for the training and placement department as well as the students, an Android application can be developed for students and a website for admin i.e., training and placement department. This website can help the placement officers to provide the details of upcoming companies and their events. And the students can register for all the eligible companies through the android application and view the event of particular company. The android application also provides facility of maintaining details of students along with the placement records of the college. And all the details of the students and the companies are stored in the firebase database. This project will be helpful in faster management of the placement related activities in the college campus.
Non-functionality analysis and verification based on Aspect-Z

Abstract The non-functional requirements of the software system usually cross the whole system, and the object-oriented software development method can lead to the entanglement and dispersion of the code. ASECT-Z proposed a formal method for non-functional requirements, it is the formal specification for the extension of Z language. The language based on set theory and mathematical logic, with strict logic, and the formal specification of reasoning. In this paper, we describe a library management system based on Aspect-Z, and then give a formal proof of the formal specification for formal verification.
How Private is Machine Learning?

A machine learning model is private if it doesn't reveal (too much) about its training data. This three-part talk examines to what extent current networks are private. Standard models are not private. We develop an attack that extracts rare training examples (for example, individual people's names, phone numbers, or addresses) out of GPT-2, a language model trained on gigabytes of text from the Internet [2]. As a result there is a clear need for training models with privacy-preserving techniques. We show that InstaHide, a recent candidate, is not private. We develop a complete break of this scheme and can again recover verbatim inputs [1]. Fortunately, there exists provably-correct "differentiallyprivate" training that guarantees no adversary could ever succeed at the above attacks. We develop techniques to that allow us to empirically evaluate the privacy offered by such schemes, and find they may be more private than can be proven formally [3].
Research on User Operation of High-density Entity Interactive Target Quantitative Simulation Interface

Abstract The paper determines the general cognitive model of people in visual information search, and summarizes the basic design criteria that conform to the user’s cognitive model. Under the premise of the consistency of user needs, we disassemble and analyse each step of the user interaction process through the tool visualization simulation model, find out the user’s inner cognitive law under the guidance of different interfaces, and discuss the interface layout the impact of the difference on the user’s search time. The study found that through the numerical comparison of the experiment combined with the simulation analysis of Cog Tool and the target interaction experiment, the intrinsic cognitive law of the user in the interaction process was extracted. Therefore, we can conclude that the design guidelines for interface layout proposed in the paper can help art designers improve the efficiency of information cognition.
Extraction of Key Concept Relevance Graphs From Fourteen Decades of Psychoanalytic Journal Publications

Automatically tracing the history of concepts and terms by text mining diachronic corpora is a new multidisciplinary area of research. An essential step in the pipeline of such research is calculating the relevance of terms, with most work relying on simple frequency measures. In this study, we extract relevance graphs of key psychoanalytic concepts from a corpus built for this task, one that represents the entire history of psychoanalysis. Different measures of relevance are examined and shown to tell a different story; when combined, however, they provide a clearer picture of a term's significance over time.
Overcoming the Knowledge Bottleneck Using Lifelong Learning by Social Agents

In this position paper we argue that the best way to overcome the notorious knowledge bottleneck in AI is using lifelong learning by social intelligent agents. Keys to this capability are deep language understanding, dialog interaction, sufficiently broad-coverage and fine-grain knowledge bases to bootstrap the learning process, and the agent’s operation within a comprehensive cognitive architecture.
A Reference Architecture for Validating Security Across Multi-Cloud Computing Systems

Correlative studies carried out by different experts have cited a number of impediments to the growth of cloud computing technology; at the top of the list is security and data privacy issues. More so, a systematic mapping study which was conducted at the start of this research, revealed that the most prevalent cloud security issues are a consequence of poor architecture. 73 percent of literature surveyed also revealed that frameworks and reference architectures are one of the most effective ways of preventing security and data privacy breaches within a cloud computing environment as these issues are addressed during the requirements phase, prior to deployment. [1], [14] This research seeks to explore a preventative approach to cloud security breaches through the use of reference architectures. Firstly, we investigate the main causes of cloud security breaches and then, we analyse existing reference architectures with the aim of designing a universal security framework usable across multi-cloud computing platforms.