Reviewerly/exHarmony huggingface dataset:
Reducing the Breach between Simulated and Real Data for Top View Images.

The advances in Artificial Intelligence have provided powerful tools for perception in road applications, such as Traffic Monitoring or Autonomous vehicles. However, detecting and classifying objects from top-view or aerial imagery becomes more challenging, especially in urban cities; due to the variation in size and scale of the objects; in addition to the lack of the datasets that contain information about the object classes and their 3D positions. This paper introduces a novel algorithm to refine the semantic images captured from CARLA simulator; refining the semantic images, creating a new dataset with more classes of the objects, and calculating its 3D bounding box positions. The proposed method and dataset have been validated by training a CNN - based 2D object detector, and inferring with real aerial images from VisDrone-2019, and from fixed infrastructure cameras i.e. cameras mounted on Traffic lights. The obtained results illustrates the improvements of using the proposed dataset in the object detection process, with real top-view images in complex environments.
Property Assertion Constraints for an Informed, Error-Preventing Expansion of Knowledge Graphs

The expansion of knowledge graphs (KGs) by new triples is an elementary process, which is needed for enriching and extending the represented knowledge. In order to function correctly and reliably, it is of utmost importance for many knowledge-driven applications that the expansion only adds semantically correct statements to the KG. Existing validation methods however, including description logic reasoning, SHACL and ShEx, can only detect wrong statements after they have materialized in the KG. They are of no or limited value for preventing errors when expanding KGs. To solve that problem, Property Assertion Constraints (PAC) are introduced as main contribution of this paper. For the context of a given instance and property, a PAC can identify all valid instances, which result in semantically correct property value assertions. By only offering them to users as options to choose from, the creation of semantically wrong statements is prevented and users can greatly benefit from this informed preselection. The main principle of PAC consists in the restriction of a property’s range definition by additional logic, which needs to be fulfilled additionally to the range. Similar to SHACL, PAC utilize SPARQL for defining the constraints, which can comprise almost arbitrarily complex conditions or business logic. The fundamental difference to SHACL and other integrity constraint approaches however is that PAC quasi negate the principle of formalizing constraints from constraints that detect erroneous (already materialized) facts into constraints that do the complete opposite, namely finding all semantically correct assertions (before materializing them).
Classifications as Linked Open Data. Challenges and Opportunities (Chapter 2)

Linked Data (LD) as a web-based technology enables in principle the seamless, machine-supported integration, interplay and augmentation of all kinds of knowledge, into what has been labeled a huge knowledge graph. Despite decades of web technology and, more recently, the LD approach, the task to fully exploit these new technologies in the public domain is only commencing. One specific challenge is to transfer techniques developed preweb to order our knowledge into the realm of Linked Open Data (LOD). This paper illustrates two different models in which a general analytico-synthetic classification can be published and made available as LD. In both cases, an LD solution deals with the intricacies of a pre-coordinated indexing language. The Universal Decimal Classification (UDC) approach illustrates a more complex solution driven by the practical requirements that the LD model is expected to fulfill in the bibliographic domain, and within the constraints of copyright protection. The Basic Concepts Classification (BCC) is a new classification with a novel approach to classification structure and syntax for which LD is an important vehicle for increasing the scheme’s visibility and usability. The report on these two cases illustrate some of the challenges of the representation of knowledge organization systems as LD and the possibilities that analytico-synthetic and interdisciplinary or phenomenon-based systems present for the representation of knowledge using LD.
Generic Approach of Measuring Text Semantic Similarity

Text Semantic Similarity can be viewed as one of the challenging tasks as evident from current profound interest in NLP research community that has created achievable milestones through active participation in SemEval task series of the recent decade. Amidst these developments, it was realized that exploring text to compare its semantics largely depends on valid grammatical structures of sentences and sentence formulation types. In this paper, the computation of text semantic similarity is addressed by devising a novel set of generic similarity metrics based on both, word-sense of the phrases constituting the text as well as the grammatical layout and sequencing of these word-phrases forming text with sensible meaning. We have used the combination of word-sense and grammatical similarity metrics over benchmark sentential datasets. Having obtained highest value of Pearson’s correlation coefficient (0.89) with mean human similarity scores, when compared against equivalent scores obtained through closely competent structured approach models, plagiarism-detection classification task was revisited on well-known paragraph-phrased Rewrite corpus articulated by Clough and Stevenson (2011) using our model to provide generic utility perspective to these novel devised similarity metrics. Here also, nearly competent classification model performance (with accuracy 76.8%) encouraged authors to work in directions that are more promising where the performance can be enhanced by improving upon dependency (grammatical relations) component in order to raise the count of true-positives and false-negatives.
Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018.

The semantics of emoji has, to date, been considered from a static perspective. We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data. We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change. In addition, we analyse select emoji in more detail, examining the effect of seasonality and world events on emoji semantics. To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.
A study of a renovated broadcast control room with a concave enclosure

Broadcast control rooms are monitoring stations which can have many operators. One such room with ellipsoidal plan shaping was recently renovated. The original room had sufficient absorptive wall treatment, such that any focusing effects were less obvious, and the occupants could perform their work in relative comfort. The renovated space included much more computer screen equipment—so much so that the original treatment was covered, and the concave surface shaping was duplicated in hard surfaces. The resulting space was distracting for the occupants, due to whispering gallery effects, and at times could become excessively loud. In this talk, we discuss our process for measuring anomalous effects in the space using room impulse response measurement techniques, as well as our computer modelling approach, which helps us identify the surfaces implicated in the measured anomalies. We will also discuss our proposed solutions, and the challenges associated with each.
Illustrative Techniques in the Primary School

The goal of this chapter is to contribute to the area of teaching about technology, especially by using illustrative aids, kits as didactic training tools in contemporary schools, and to define what is required by this field in view of the goals of the teaching process, especially of the education theory. The study summarises the issue of illustrative aids, from the view of their application to the area of general training, and formulates answers to some related practical issues of the relevant subject didactics. The chapter presents the results of past research surveys which were conducted in relation to incorporating illustrative aids - kits into education, and of the effectiveness of the implemented education in the practical reality of teaching. One subject of discussion also includes the condition/the current state of implementing education in the primary school environment. The aim of this chapter is to provide a comparative and analytical insight into the teaching of general technical subjects in primary schools in the context of technical kits, defining current trends and approaches to the implementation of educational activities. The broader intention is thus to contribute to the development of a sectoral didactics of technical subjects, targeted at lower school levels and educational practice.
Design of Booking Accounting Information System in PPTK, Cimahi Utara District

The development of technology and science is a driving factor in the development of the world economy. This study aims to design a bookkeeping accounting information system in PPTK North Cimahi District. The research method used in the form of interviews, observations and literature review. The results of the study show several display structures on the menu, several hardware and software specifications used in the accounting information system design system. Then there are several problems encountered in the designed system, The design of the information system uses the Waterfall method using the Visual Studio 2010 programming language and the database uses Microsoft Access, so that it is expected to run as needed, and the implementation and testing of this information system can be said to have been appropriate and can run well so that it can be used to facilitate data processing. According to the needs of the institution concerned.Optimal use of accounting information systems in an institution can provide benefits to the organization, namely making it easier for organizations to adapt to a constantly changing environment, better internal control systems, and improving organizational external relations.
Future of Video Forensics in IoT

Today is the hour of Internet of things (IoT), as countless machines, for instance, vehicles, smoke alarms, smart wristwatches, smart glasses, and webcams, are being related to the Internet. The number of machines that have the feature of remote access to screen and accumulate data is endlessly growing. This improvement makes, on the one hand, the human life progressively pleasant and supportive, although it moreover raises, on other hand, issues on security. Nevertheless, this progression furthermore raises troubles for the modernized operator when IoT devices are studied for criminal investigations. All the while, IoT computerized video legal devices are not reinforced by existing progressed quantifiable contraptions and methodologies, making it difficult for experts to extract data from them without the assistance of a legitimate advisor with explicit data at the present time. Without a doubt, current research asks about the composing bases on security and insurance for IoT circumstances, instead of methodologies or techniques of video forensics obtainment or examination for IoT devices. Different edges related to IoT forensics, scene examination through video recording, and variables of IoT influencing crime scene investigation are discussed in this chapter. In this chapter, we are also emphasizing on the IoT video forensic applications, limitations in 114the currently available video forensic tools, as well as the future of this research.
Visually Meaningful Image Encryption Algorithm Based on Parallel Compressive Sensing and Cellular Neural Network

At present, most image encryption algorithms protect the image by converting the original image into a visually meaningless noise-like image. However, the noise-like image can easily attract the attention of attackers, which increases the risk of being deciphered. Thus, a novel meaningful image encryption algorithm based on parallel compressive sensing and cellular neural network is proposed in this paper. In our scheme, the image is processed by three modules, namely, compression, encryption and hiding. Among them, the compression module is utilized to compress the plain images in parallel. After that, the encryption module converts the compressed image into a meaningless noise-like image by diffusion and scrambling. Finally, the hiding module realizes the visualization of the encrypted image by embedding the encrypted image into a carrier image, which helps reduce the interest of attacker in deciphering cipher images. Experimental results and analysis indicate that the proposed algorithm has satisfactory performance and can effectively protect image information from being leaked under the premise of reducing computational overhead.
Improved Speaker Identification System Based on MFCC and DMFCC Feature Extraction Technique

Speaker Identification (SI) pertains to the method of understanding person voice by utilizing the techniques of machine learning algorithms. Extracting the feature information from speaker utterances is an essential activity in the speaker identification process to classify the speakers accurately. In many speaker identification systems Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Mel Frequency Cepstral Coefficients (DMFCC) are used as features because of its potential to represent repetitive nature of speech signal. This work aims to consider the MFCC and DMFCC coefficient which is used to improve the recognition of speaker systems in precision. To construct the speaker identity model, the extracted MFCC and DMFCC features were fed into a Gaussian Mixture Model (GMM) and Bayesian Classifier as input. The Performance of GMM and Bayesian classifier is analyzed for different number of mixtures. The GNN Model can reach maximum accuracy of 82.7% for MFCC and 80.12% for DMFCC. On the other hand the Bayesian classifier performance is 79.43 for MFCC and 83.92% for DMFCC. To classify a speaker, the extraction of features and the classification model can be applied extensively to different types of speaker datasets.
Local Correctabilities and Dual Codes of Symmetric Reed–Muller Codes

Recently, a variant of the Reed-Muller family, called symmetric Reed-Muller codes, was proposed. In this paper, we prove that multivariable symmetric Reed-Muller codes form a class of locally-correctable codes. Then, the dual codes of symmetric Reed-Muller codes are given.
Fenomenologia, cognitivismo e teoria musicale

This paper explores the relationships between phenomenology, cognitive sciences, and music theory. West Coast school of phenomenology, by subscribing a Fregean reading of Husserl, interpreted Husserlian philosophy as strictly related to cognitive computationalism (Fodor) and Artificial Intelligence project (Minsky). In his musicological research, David Lewin was involved in this suggestion and modeled mental actions to formalize musical perceptions. The study of phenomenological intentionality and time-consciousness can be useful to improve Lewin’s model. Furthermore, the idea of embodied cognition is noteworthy in order to analyze motor intentionality in musical experiences.
The Effect of Dimension on Image Recognition using Convolutional Neural Network

We study the effect of image dimension on the accuracy and efficiency in image recognition. After preprocessing the dataset., we obtain several datasets with different image dimensions, which can be used to training the model of neural network. By comparing the predicted results and the original labels, as well as recording the accuracy, loss of each epochs, and time-consuming on each training precess, we obtain the scheme for optimal accuracy and efficiency in this task. Our results provide a new perspective on improving the image recognition.
Incorporating Human Behavior in VR Compartmental Simulation Models

A novel strand of Coronavirus has affected a large number of individuals worldwide, putting a considerable stress to national health services and causing many deaths. Many control measures have been put in place across different countries with the aim to save lives at the cost of personal freedom. Computer simulations have played a role in providing policy makers with critical information about the virus. However, despite their importance in applied epidemiology, general simulation models, are difficult to validate because of how hard it is to predict and model human behavior. To this end, we propose a different approach by developing a virtual reality (VR) multi-agent virus propagation system where a group of agents interact with the user in a university setting. We created a VR digital twin replica of a building in the University of Derby campus, to enhance the user’s immersion in our study. Our work integrates human behavior seamlessly in a simulation model and we believe that this approach is crucial to have a deeper understanding on how to control the spread of a virus such as COVID-19.
VOICE TO TEXT APP: QUALITY TEACHING AND LEARNING WITH HIGH TECH (WWW.VOPIO.TECH)

Traditional teaching and learning, or public talks, are case examples in which a fraction of materials presented can easily be marred with ambiguity, misunderstanding or total lack of understanding. These could be caused by diverse reasons or as a result of different scenarios. Such as, English language being a secondary language of the professor or student. Other possible factors are language barriers or moments of distraction or too poor/advanced vocabulary. On the other hand, and naturally so, many students usually find it difficult to take notes during classes or meetings while trying to simultaneously absorb the information being presented to them. In this project, we explore avenues to solve the aforementioned problems and present technological resource (Vopio) cum parameters to boost understanding of materials taught as well as improving the quality of teaching and learning. Vopio is an interactive voice to text application that provides clickable keywords that are hyperlinked to online dictionaries. The functionalities of Vopio include software versions that include machine learning features that enhance Personal Dictionary. This feature facilitates in-class learning and teaching even for users with hearing needs. The captured texts in any session can be saved in many formats such as word, or pdf. The app can be synchronized with PowerPoint, zoom and other presentation platforms. The app is compatible with modern web browsers such as Firefox, Chrome, and Safari, as well as compliance with WCAG standards for digital accessibility (Web Content Accessibility Guidelines 2.2). It is fun to use and non-distracting. From feedback received from pilot testers, students left their classroom more resourceful, fulfilled and with comprehensive complementary materials for future referencing/consultations.
Large-Scale Video Analytics Through Object-Level Consolidation

As the number of installed cameras grows, so do the compute resources required to process and analyze all the images captured by these cameras. Video analytics enables new use cases, such as smart cities or autonomous driving. At the same time, it urges service providers to install additional compute resources to cope with the demand while the strict latency requirements push compute towards the end of the network, forming a geographically distributed and heterogeneous set of compute locations, shared and resource-constrained. Such landscape (shared and distributed locations) forces us to design new techniques that can optimize and distribute work among all available locations and, ideally, make compute requirements grow sublinearly with respect to the number of cameras installed. In this paper, we present FoMO (Focus on Moving Objects). This method effectively optimizes multi-camera deployments by preprocessing images for scenes, filtering the empty regions out, and composing regions of interest from multiple cameras into a single image that serves as input for a pre-trained object detection model. Results show that overall system performance can be increased by 8x while accuracy improves 40% as a by-product of the methodology, all using an off-the-shelf pre-trained model with no additional training or fine-tuning.
Study on Deduplication on Distributed Cloud Environment

Deduplication methods were designed to destroy copy information which bring about capacity of single duplicates of information as it were. Information Deduplication diminishes the circle space needed to store the back-ups in the extra room, tracks and kill the second duplicate of information inside the capacity unit. It permits as it were one case information event to be put away initially and afterward following occasions will be given reference pointer to the first information put away. In a Big information stockpiling climate, immense measure of information should be secure. For this legitimate administration, work, misrepresentation identification, investigation of information protection is an significant theme to be thought of. This paper inspects and assesses the common deduplication procedures and which are introduced in plain structure. In this review, it was seen that the secrecy and security of information has been undermined at numerous levels in common strategies for deduplication. Albeit much exploration is being done in different zones of distributed computing still work relating to this point is inadequate. To get rid of duplicate data which results in storage of single copies of data, data deduplication techniques were used. Data deduplication helps in decreasing storage capacity requirements and eliminates extra copies of same data inside storage unit. Proper management, work, fraud detection, analysis of data privacy are the topics to be considered in a big data storage environment, since, large amount of data needs to be secure. At many levels in general techniques for deduplication it is observed that safety of data and confidentiality has been compromised. Even though more research is being carried out in different areas of cloud computing still work related to this topic is little.
Electric Power-grid Friendly Characteristic Data Center Energy Consumption Optimization Method

Abstract With the acceleration of the digitization process, the load of the data center on the power grid continues to increase. During the operation of the data center, the load demand on the power grid is relatively large, and the load demand on the power grid fluctuates greatly. In the Power industry, the average PUE(power usage effectiveness) of data center is above 2, which is extremely unfriendly to the power grid. This paper proposes and summarizes the optimization parameters at various levels, the energy efficiency of software and hardware, and provides a method for optimization through global variables for high-efficiency and energyconsuming data centers, which can minimize cooling energy consumption and IT energy consumption. To build a electric power grid-friendly characteristic data center with high efficiency and low energy consumption.