Reviewerly/exHarmony huggingface dataset:
Fighting Deepfakes Using Body Language Analysis

Recent improvements in deepfake creation made deepfake videos more realistic. Open-source software has also made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the public privacy. It is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of the countries and the world. Research into automated detection for deepfaked media is therefore essential for public safety. We propose in this work the use of upper body language analysis for deepfake detection. Specifically, a many-to-one LSTM network was designed and trained as a classification model is trained for deepfake detection. Different models trained using various hyper-parameters to build a final model with benchmark accuracy. We achieve 94.39% accuracy on a test deepfake set. The experimental results show that upper body language can effectively provide identification and deepfake detection.
Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction, for instance.Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Meanwhile, this method can also choose the optimal neural network prediction model at an adaptive learning rate so as to make the prediction and analysis result more accurate.
The Influence of Teacher Librarians' Personal Attributes and Relationship with the School Community in Developing a School Library Programme

The ability of teacher librarians worldwide to develop and maintain school library programmes has been severely constrained by a number of factors ranging from inadequate budgetary allocations, lack of support from principals and other key stakeholders such as Ministry of Education personnel. However, even with limited funding and support, some teacher librarians have used ingenious strategies to develop and maintain their library programmes. This literature review synthesizes international research obtained from peer-reviewed journals, theses and professional papers on the personal attributes that a teacher librarian should possess in order to influence the development of a school library programme and the types of relationships the teacher librarian need to have with key stakeholders to be successful. The literature examined spanned a time period mostly from 2000-2018. The literature review found that leadership, collaboration, communication and interpersonal skills were the dominant skills a teacher librarian should possess in order to develop and maintain a school library programme alongside a good relationship with the school community.
High Performance Predictive Analytics in IoT

Internet of things (IoT) is a quick-moving gathering of web associated sensors implanted in a wide-extending assortment of physical articles. While things can be any physical item (energize or lifeless) on the planet, to which you could associate or implant a sensor. Sensors can take countless potential estimations. Sensors produce gigantic measures of new, organized, unstructured, ongoing information, and structures enormous information. IoT information is exceptionally huge and confused, which can give genuine-time setting and supposition data about genuine articles or nature. Among the different challenges that the present IoT is facing, the three prime areas of concern are, need of efficient framework to receive IoT data, a need of a new scalable parallel indexing technique for efficiently storing IoT data and securing IoT generated data at all the stages i.e. from the edge devices to the cloud. A new efficient framework is introduced, which can retrieve meaningful information from these IoT devices and efficiently index it. For processing such enormous real time data generated from IoT devices, new techniques are introducing which are scalable and secure. The research proposes a general IoT network architecture. It describes the interconnectivity among the different things such as sensors, receivers and cloud. The proposed architecture efficiently receives real time data from all the sensors. The prime focus is on the elimination of the existing issues in IoT. Along with this, the provision has to make for standard future proofing against these new proposed schemes.
DOPPELGANGERS AND MATRIXES: A NEW METHOD OF COMPARATIVE ANALYSIS

The paper presents the results of the research of doppelganger topos by means of quantitative analysis, namely, by elementary approach provided by graph theory. The idea to employ the approach belongs to M. Zhuravlev. Seven canonic texts chosen by I. Golovacheva and M. Zhuravlev (E. T. A. Hoffmann’s “Die Doppeltgänger”, E. A. Poe’s “William Wilson”, Fyodor Dostoevsky’s novella “The Double”, Henry James’s “The Jolly Corner”, Joseph Conrad’s “The Secret Sharer” and Vladimir Nabokov’s Despair) are analyzed. Our point of departure was the archetypal attributes of the double highlighted in a seminal work by Otto Rank of 1914. Still, Rankean list of attributes had to be supplemented by a list of other, specifically ‘poetic’, attributes, by a set of diverse invariant motifs found in the above works of fiction, specified by I. Golovacheva and P. de Mauny. Incidence matrixes built for each of the four specific groups of attributes of the double allowed us to view the doppelganger poetics as a whole and each of the chosen seven texts in particular. The quantitative method led to several new qualitative conclusions. For example, it demonstrated that Dostoevsky’s “The Double” and Nabokov’s Despair occupy opposite places in the set of doppelganger texts: Dostoevsky’s novella and Nabokov’s short novel differ much in their comprehensiveness and typicality of doppelganger imagery. Such a result allowed Golovacheva, Zhuravlev and de Mauny to reconsider the notion of the ‘typical’ in literary theory. The incidence matrix method also revealed the similarity of “William Wilson” and Despair. The objectivist comparative analysis and the subsequent close rereading of these two fictions prove, Golovacheva argues, that Poe’s tale is one of the pre-texts of Despair, such intertextuality previously neglected in Nabokov studies.
Mobile Cloud Data Privacy using Lightweight Data Sharing System

The data security problem in mobile cloud becomes more and more severe and it prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However the most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. So we propose a light weight data sharing scheme (LDSS) for mobile computing it adopts CP-ABE (Cipher text policy attribute based encryption) an access control technology using normal cloud environment but changes a structure of access control tree to make suitable for mobile cloud environment. It is important to use the resources provided by Cloud Service Provide to store and share data. Thus LDSS can effectively reduce the over head on the mobile device side when users are sharing a data in mobile cloud environment..
Development of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering

Creating a fast domain independent ontology through knowledge acquisition is a key problem to be addressed in the domain of knowledge engineering. Updating and validation is impossible without the intervention of domain experts, which is an expensive and tedious process. Thereby, an automatic system to model the ontology has become essential. This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc. The proposed model creates a complete domain independent process that helps in populating the ontology automatically by extracting the text from multiple sources by applying natural language processing and various techniques of data extraction. The ontology instances are classified based on the domain. A Jaccord Relationship extraction process and the Neural Network Approval for Automated Theory is used for retrieval of data, automated indexing, mapping and knowledge discovery and rule generation. The results and solutions show the proposed model can automatically and efficiently construct automated Ontology.
Aviation Surveillance Information Fusion Technology Based on Recurrent Neural Network

Aviation surveillance information fusion is aimed at merging the detection data from multiple sources of the same target aircraft to obtain more accurate monitoring information, including aircraft position, heading, acceleration and other information.The traditional Kalman filter-based fusion technology has shortcomings, such as poor integration in the maneuvering state, and it takes a lot of manpower and material resources to repeat the adjustment.Therefore, this paper uses the recurrent neural network to conduct the experiment of aviation surveillance information fusion.Firstly, the recurrent neural network is used to identify the maneuver state of the aircraft, and the weighted least squares method is used to predict the position of the aircraft according to the maneuvering state, so as to obtain the monitoring information of each radar at the same time.After that, the recurrent neural network model is used to fuse the monitoring information of multiple radars.The experimental results show that the maneuvering state discriminant model based on recurrent neural network can effectively identify the maneuvering state.The least square method based on maneuvering state can accurately predict the position of the aircraft.The aeronautical surveillance information fusion model based on recurrent neural network can also obtain more accurate fusion results.The whole process includes four parts: preprocessing, maneuver status discrimination, position prediction and information fusion.The total time is about 500ms.
Design and Implementation of Smart System for Interaction Between Blind, Deaf and Mute People

Difficulties faced by the deaf and dumb people and the blind people to communicate among themselves is the major motivation for the proposal of this project. The project aims at bridging this communication gap by means of an cost-effective electronic device. This is done by using a prototype hand worn glove which converts hand gestures and Braille codes into speech and text, thus enabling speech-impaired and visually impaired people to effectively communicate with everyone. MEMS sensors are used to recognize the gesture sign given by the deaf and dumb people. The gesture input is sent as an audio file which is understandable to the people. The input given by the blind people is in Braille language which is converted to text using scale-invariant feature transform (SIFT) algorithm and decoded to text and voice output through Artificial Neural Networks (ANN) by means of Back Propagation using Supervised Learning. So this ensures two way communication between blind, deaf and dumb people.
Towards Scalable and Private Industrial Blockchains

During the industry day of ICIAM 2019, we presented a novel blockchain architecture devised specifically to meet industrial standards. In this paper, we detail our proposal and discuss its advantages over existing blockchain proposals. Our proposal features a scalable and novel consensus protocol based on BFT consensus and leverages the notion of satellite chains that can privately run different consensus protocols in parallel-thereby considerably boosting the scalability premises of the system. Our solution also accounts for a “hands-off” regulator that oversees the entire network, enforces specific policies by means of smart contracts, etc. Our proposal is implemented and integrated with Hyperledger Fabric.
Detecting Deceptive Reviews Utilizing Review Group Model

Online product and store reviews play an important role in product and service recommendation for new customers.However, due to economic or fame reasons, dishonest people are employed to write fake reviews which is also called "opinion spamming" to promote or demote target products and services.Previous research has used text similarity, linguistics, rating patterns, graph relation and other behavior for spammer detection.It is difficult to find fake reviews by a glance of product reviews in time-descending order while It's more easy to identify fraudulent reviews by checking the list of reviews of reviewers.We propose sieries of novel review grouping models to identify both positive and negative deceptive reviews.The review grouping algorithm can effectively split reviews of reviewer into groups which participate in building new model of review spamming detection.Several new features which are language independent based on group model are constructed.Additionally, we explore the collusion behavior between reviewers to build group collusion model.Experiments and evaluations show that the review group method and relevant models can effectivly improve the precision of 4%-7% in deceptive reviews detection task especially those posted by professional review spammers.
Matching Between SIEM Tools and Smart DLC Systems

Nowadays, cybersecurity data management has become a challenging issue especially with the emergence of Big Data. This paper introduces the System of Systems (SoS) paradigm to design a new generation SIEM POC (Security Information Event Management Proof Of Concept) made up of an open source Big Data platform ELK and integrated with other open source security and load-balancing tools. To do this, we first focused on the Big Data and Smart Data requirements to model a data lifecycle from the literature named Smart DLC to the System of 7 Systems, So7S. Second, we used the proposed cycle as SoS tools design, implement and test the proposed SIEM POC by matching the cybersecurity tools to each system of the SoS modeled. The proposed open source SIEM is operational and meets all cybersecurity monitoring requirements with challenging results and may interest small and medium-sized companies dealing with cybersecurity issues.
Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

The aim of this paper is to introduce a new model of fuzzy time series (FTS) based on hedge algebras (HA) with three improvements compared with previous ones.Firstly, the new way of partitioning the universe of discourse is applied, secondly, the relationship groups following time points to determine relationships among fuzzy time series values are used, thirdly, weighs following time points are employed to build formula for computing forecasting values.The empirical results indicate that the proposed model outperform the existing models.
Supplemental Material for Planning When to Say: Dissociating Cue Use in Utterance Initiation Using Cross-Validation

Contrasting turn gap w precisionSome recent studies have used a measure of response precision (the absolute amount of time elapsed between turns; Corps et al., 2018Corps et al., , 2019) ) to see how well individuals coordinate in production.We calculated this for our three experiments to contrast with turn gap (offset of prior turn minus onset of current turn).While there are some numeric differences-means for precision are always larger than turn gap, and mean differences in precision are reduced for smaller values compared to larger ones because of the non-linear nature of the transformationthe same conclusions are drawn from both measures.
Research On Sentimental Techniques

The sentimental analysis may be a dominant role in opinion mining is in addition stated as sentiment analysis due to clear sort, review sites, blogs, and tweets square measure on the market in digital sort. Sentiment analysis is that the sphere of study that analyses consumer opinion, feedback, sentiment analysis, attitudes and feeling from communication. At intervals fraction second, we've got an inclination to classify the text in many manner in several seconds. It’s one all told the active analysis areas in communication method. There are a unit vary of techniques we would like to classify the opinion reviews. the most problematic within the sentiment analysis is to grasp the usage of negation and also the taxonomy of positive and negative sentiments recorded by the users in the group. the most aim of this paper boon a survey relating to the presently accessible technique, application and drawback that seem within the field of opinion mining.
Enhanced Assessment System to Improve The Efficiency of Current Generation Students

Assessment leads to learning. The present generation students properly guided in their learning. The correct assessment improves the learning of the students. In the paper we present a new assessment model “E-Brahma” to assess the performance of the students in current academic scenario and it also enhance the learning system also. In this paper we propose an assessment algorithm. The experimental results show the efficiency of the proposed system.
Implementing Complex Personal Authentication System by a Biometrics Pattern Algorithm

This paper proposes a complex personal authentication system using multimodal biometrics for advanced personal authentication in a mobile environment.For this, face recognition, fingerprint recognition and OTP type pattern algorithm were selected.Through the proposed algorithm, an improved security authentication system is realized compared with the single authentication algorithm.
Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

The Face recognition research identified with the field of automated surveillance systems in real life application has pulled in more consideration today with extensive use of vision cameras in biometrics and surveillance applications, globally. The small size images or poor quality images are generally Low Resolution images. Authenticating faces, with variation in pose, illumination, disguise and more, from such Low-resolution images, is the main purpose of Low Resolution Face Recognition (LR FR) system. We have assessed the LR FR system for various basic feature space techniques like PCA, LDA and Fisherface. Different illumination normalization techniques are applied on the cropped Yale face database prior to feature extraction and identification. In our work the low-resolution images, of size 32x32, are the down-sampled versions high-resolution facial images from cropped Yale face database. Our experiment demonstrates the encouraging performance, with recognition accuracy as 97.43%, on Low Resolution, Low quality face images.
Historical stages of development of ideas about the picture of the World

Presented is an attempt to give a historical overview of development of human ideas about the picture of the world. The basic astronomical, mathematical, natural science and philosophical knowledge that forms the foundation of the modem science of the universe, its evolution and structural elements are presented. This allows to create a specific image of the surrounding reality, and look into the future of the Universe and Person.
USING POINT COUNTERPOINT METHOD TO IMPROVE SPEAKING ABILITY OF GERMAN LANGUAGE STUDENTS AT PATTIMURA UNIVERSITY

This research is a classroom action research, which carried out to describe the development of students' speaking abilities as the effect of using the point counterpoint method. This research has been carried out in semester 5 (five) in academic year 2019/2020. The population of this study is the students of the German Language Study Program, while the selected sample is the 5th semester students in total 30 people. The procedure of this research was carried out in cycles, starting from planning, observation and reflection. Research instruments are test and non-test. The results of cycle 1 indicate that students have not yet reached a minimum score or passing grade, on average score of 58.93. The results of the second cycle test show that results increased, namely on average score of 72.5. Overall, the results of this study indicate that using a point counterpoint method can improve students' speaking abilities, especially in creative thinking and argumentation. Thus, using this method in teaching is useful to support the achievement of speaking competency at a B1-GER level.
Fish School Search

Fish School Search (FSS) is a swarm-intelligence subfamily of algorithms proposed by Bastos Filho and Lima Neto in 2008 and first published in 2009. In the FSS, the simple reactive agents are called fish, and each fish has a weight that represents the success obtained during the search. The weights' values and variations influence the individual and collective movements. The embedded mechanisms of feeding and coordinated action make the school move toward the positive gradient to gain weight (and find local and global better positions). Heavier fish have more influence in guiding the search. The idea of accumulating success along the examination indicates that a specific simple reactive agent is worth influencing others. FSS was designed for continuous optimization problems in multimodal search spaces. It has also influenced other researchers to propose variations for other issues, such as optimization in binary problems, multi-objective optimization, many-objective optimization, and multimodal optimization. In this chapter, we present a review of the advances considering FSS in the last decade, including some proposals for binary optimization, three approaches for multi- and many-objective optimizations, and two different multimodal optimization proposals. We also show two other methods for parallel processing, which aim to accelerate the processing time. We finalize the chapter giving some examples of applications of those recent approaches in real-world problems.
The CogMedia Project: Open Data and Tools for Linking Cognitive Science and Mass Media

Minds produce and consume news media. Because of this central relevance of minds, theories of mass media may be informed by the methods and measures of cognitive science. These measures could enhance understanding of the factors guiding news consumption habits. These measures could also help to explain how news themes accumulate collective attention. CogMedia is introduced here as an open resource for facilitating a connection between mind and mass communication. It contains hundreds of thousands of major headlines from large news media organizations, primarily in the United States. In addition, it has a set of open analysis tools for conducting quantitative analysis in R. This paper motivates CogMedia with background theoretical and empirical review. Theoretical discussion centers on complexity science as a framework for understanding mass-scale systems and the cognitive agents that underlie them. Following this background review and motivation, the paper introduces CogMedia, and illustrates potential quantitative analysis and data visualization using these open tools. Future empirical work is discussed, along with relevant theoretical implications.