Reviewerly/exHarmony huggingface dataset:
Bayesian Hyper-Parameter Optimisation for Malware Detection

Malware detection is a major security concern and has been the subject of a great deal of research and development. Machine learning is a natural technology for addressing malware detection, and many researchers have investigated its use. However, the performance of machine learning algorithms often depends significantly on parametric choices, so the question arises as to what parameter choices are optimal. In this paper, we investigate how best to tune the parameters of machine learning algorithms—a process generally known as hyper-parameter optimisation—in the context of malware detection. We examine the effects of some simple (model-free) ways of parameter tuning together with a state-of-the-art Bayesian model-building approach. Our work is carried out using Ember, a major published malware benchmark dataset of Windows Portable Execution metadata samples, and a smaller dataset from kaggle.com (also comprising Windows Portable Execution metadata). We demonstrate that optimal parameter choices may differ significantly from default choices and argue that hyper-parameter optimisation should be adopted as a ‘formal outer loop’ in the research and development of malware detection systems. We also argue that doing so is essential for the development of the discipline since it facilitates a fair comparison of competing machine learning algorithms applied to the malware detection problem.
Personalizing Dialogue Agents for Russian: Retrieve and Refine

Currently, the development of automatic dialogue systems is in demand not only for traditional applications (increasing the level of automation of contact centers) but also for relatively new cases (development of virtual assistants, smart speakers, interactive robots). Adding information about characteristics of a person, which the agent must take into account when generating a response, i.e. personification of conversational agents, helps to increase user loyalty and engagement. This paper presents a study of Retrieve and Refine models for automatic generation of utterances of a personalized Russian-speaking dialogue agent. To train models in Russian, the Toloka Persona Chat Rus dataset is used. Refine models that used an adaptation of the BlenderBot model for the Russian language showed worse performance than for datasets in English. For Retrieve models, a solution based on the BERT encoder model was proposed, which made it possible to obtain the value of the metrics hits@1=0.705 for the model without a person, and hits@1=0.717 for the model with a person.
Unsupervised domain adaptation via discriminative feature learning and classifier adaptation from center-based distances

Current feature adaptation methods align the joint distributions across domains. But they may be limited because the difference between distributions cannot be completely eliminated. Existing classifier adaptation methods find the shared classifier across domains based on the original features or Manifold features. However, the shared classifier may be ineffective due to the high granularity at the category level of the features. Inspired by these, we propose the unsupervised domain adaptation via Discriminative feature learning and Classifier adaptation from Center-based Distances (DCCD). First, we define the data-centers and class-centers. Second, Discriminative feature learning from Center-based Distances (DCD) is established by using the data-centers and class-centers to align the joint distributions across domains and maximize the intra-class compactness and inter-class separability of features at the category level. Specifically, the optimization objective of DCD is constructed by minimizing the maximum mean discrepancy (MMD) between two domains and Class-center-Sample distances (CS), and maximizing the Data-center-Class-center (DC) and Class-center-Nearest-Class-center distances (CNC). Next, we propose Classifier adaptation from Center-based Distances (CCD). In detail, CCD applies Structural Risk Minimization (SRM), dynamic distribution alignment, and the constructed Laplacian Regularization to solve the shared classifier, where the constructed Laplacian Regularization extra considers CS and CNC to measure the local structure of features. Benefited from CCD, the joint distributions can be further aligned at the classifier level. Besides, integrating with the learned features from DCD, the shared classifier can be effective on the two domains. Finally, extensive experiments on four benchmark datasets show that DCCD outperforms the state-of-the-art UDA methods.
Comparative Study of Latent-Sensitive Processing of Heterogeneous Data in an Experimental Platform for 3D Video Holographic Communication

Holographic communication is a complete human-computer-machine interface that combines augmented reality and virtual reality telepresence to create real-life digital scans and realistic 3D avatars of subjects that are displayed in a mixed reality environment and used for real-time communication and interaction between remote users. The latency that exists while relaying the realistic impression of presence in a remote location to a local human user is impeding the acceptability of immersive telepresence systems. Motion sickness, caused by the misalignment between the user's motion and the visual response, is one of the main reasons for the slow adoption of these systems. In this paper we explore two use case scenarios to measure the calculated transmission time of heterogeneous data, including key points of the 3D model of the body, face or hands, so as to meet the limitations of bandwidth and latency in current and future networks. Depending on the way the information is transmitted and received, the speed of transmission and the amount of information, as well as other important factors, different modes of operation for the proposed experimental platform are considered, allowing it to be flexible by adapting to the available resources.
Online English Teaching Quality Assessment Based on K-Means and Improved SSD Algorithm

Classroom teaching quality is a key content to measure the teaching level, and the teaching effect can be intuitively reflected from the students’ listening state. In order to improve the teaching quality, this paper proposes an online English teaching quality evaluation model based on K-means and an improved SSD algorithm. In the SSD algorithm, the backbone network is replaced by DenseNet with a dense connection to improve detection accuracy. The network structure of quadratic regression is designed to solve the problem of unbalance between positive and negative samples in the default box of the candidate region. A feature graph scaling method is used to fuse feature graphs without introducing additional parameters. The number of default boxes and the optimal aspect ratio were obtained by k-means clustering analysis. Finally, the state of students in the teaching process is predicted through the dual-mode recognition model of facial expression and posture, and the state of students in class is judged. Experimental comparison and analysis were conducted on the public data set and a self-built classroom teaching video data set. Experiments show that compared with other comparison algorithms, the algorithm presented in this paper performs better in the index of detection accuracy.
Improving open data web API documentation through interactivity and natural language generation

Widely adoption of Information Technologies has resulted in the continuous growing of open data available on the Web. However, the lack of suitable mechanisms to understand open data sources hampers its reusability. One way to overcome this limitation is by means of Web Application Programming Interfaces (APIs) with proper documentation, nowadays being the existing very rudimentary, hard to follow, and sometimes incomplete or even inaccurate in most cases. In order to improve the documentation of Web APIs that access open data, this paper proposes a novel approach to automatically generate interactive Web API documentation, both machine and user readable. This process starts by analysing the documentation of an API to obtain important information, automatically constructing Natural Language descriptions of the main Web API concepts by applying Natural Language Processing (NLP), and specifically, language generation techniques. Then, the documentation is made interactive by making it available as a Web interface, offering easy access to open data provided by Web APIs. Therefore, the use and comprehension of the Web APIs is facilitated, thus promoting the reusability of open data. The feasibility of our approach is presented through a case study and an experiment with users, both showing the benefits of our approach.
Towards adoption of mobile data collection for effective adaptation and climate risk management in Africa

Abstract The collection and use of data on climate change and its impacts are crucial for effective climate adaptation and climate risk management. The revolution in internet access, technology and costs has led to a shift from using traditional paper‐based data collection to the use of Mobile Data Collection using Personal Digital Assistants (PDA) such as smartphones and tablets. In this paper, we report our experiences using both approaches for a household and business survey during a climate adaptation study in two Nigerian cities—Makurdi and Calabar. The focus of this paper is to evaluate and compare the effectiveness of using traditional paper‐based data collection and PDAs as data collection tools for climate change study in African societies. In Calabar, data were collected using paper questionnaires, while in Makurdi the questionnaires were developed on Open Data Kit (ODK) and administered using PDAs. Results show that data collection using PDA was faster, cheaper, more accurate and resulted in fewer omissions than paper‐based data collection. There was a time saving of four (4) minutes per questionnaire and a 24% cost saving when using PDA. PDA provides additional benefits where platforms can collect images, videos and coordinates. This significantly improved the credibility of the data collection process and provided further data that allowed for the mapping of environmental phenomena by linking survey research with geo‐referenced data in a geographic information systems platform to provide spatial representations of social and environmental system convergence. PDA offers a tool for collecting data that will make necessary socio‐environmental data available in a faster, reliable and cheaper manner; future research can build on this study by discovering other possible but less highlighted benefits of PDA. Although, with great benefits, there are lessons to be learnt and issues to consider when deploying PDA in large‐scale household surveys.
Orchestrating special education during the COVID-19 lockdown. A mapping study of the technologies and challenges

The COVID-19 lockdown entailed changes at all levels. Particularly, closing schools and moving to online education from one day to another revealed limitations in the education system and difficulties to approach remote learning and assistance, particularly, in the case of special education needs. In order to collect how special education was orchestrated along the pandemic, what technologies were involved and the impact they had, we run a survey in which a total of 295 teachers, specialists and relatives of people with autism spectrum conditions participated. This paper contributes analyzing the orchestration of remote special education and provide applicable solutions to build a learning platform adapted to people with special needs, which covers the challenges encountered. In addition, a mapping of technologies is provided that can serve as an ICT selection criteria and benefit people on the spectrum.
NFT Marketplace Based on Ethereum Blockchain

Non-fungible tokens (NFTs) are transferrable rights to digital assets, such as images, videos, or music. The phenomenon and its markets have grown significantly since early 2021. The number of NFT marketplaces has been rising continuously since a couple of years. Majority of them employ centralized systems for storage of digital assets (files). This is contradictory to the notion of decentralized systems. In this project we propose a secure platform for trading digital assets in the form of Non-Fungible tokens. It will enable the users to create new digital assets and trade them in exchange of Ethereum-based cryptocurrency. Along with this, we aim to explore technical feasibility of a decentralized file system (using IPFS protocol) for storing the digital assets in a more secured way. In this way, we attempt to address the issue of file storage. Also, this project is an attempt to advocate the use of block chain technology.
Performance evaluation of IaaS cloud using Stochastic Neural Network

Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models.
MPF based symmetric cipher performance comparison to AES and TDES

This paper presents a performance comparison of newly created matrix power function (MPF) based symmetric cipher realized in cipher-block chaining mode (CBC) with two other standardized ciphers namely AES-128 and triple DES. The encryption rate is compared using 64 bits arithmetic operations. It is shown that the proposed cipher has a significant advantage against compared ciphers. It is achieved by consuming more memory for MPF realization. Mentioned ciphers are compared using the central processing unit (CPU) the number of clock cycles required to encrypt fixed size plaintexts. This paper also includes a comparison of a number of operations required to encrypt plaintext as well as memory requirements for each of the ciphers with pre-determined parameters. The main difference between symmetric cipher based on MPF and other standardized ciphers mentioned in the paper is the ability to work with larger matrix entries in comparison to other algorithms as well as availability to parallelize the process of encrypting a single block.
An Empirical Investigation in Analysing the Critical Factors of Artificial Intelligence in Influencing the Food Processing Industry: A Multivariate Analysis of Variance (MANOVA) Approach

In the era of digital technology, where innovation and digitization are transforming the business functions, the field of customer relationship management has witnessed sea change in the recent decade. The application of artificial intelligence in food processing has enabled enhancing the availability of food in an effective manner for all the individuals. It has been regarded that the application of labor force tends to play a crucial aspect for the overall execution of things in the different domains related to food manufacturing and processing, which are related to the enhanced involvement of individuals in the processing of food and related products, the industry could not able to meet the growing demand from the customers. So, in order to overcome these critical issues, it is noted that the application of technology such as automation and artificial intelligence is implemented for enhanced processing and enable delivering quality products to the customers at lower cost. The impact of AI in the current business world is becoming more indispensable as companies have started to unleash its potential. The role of AI in food processing is fast changing the manner in which the customer queries are addressed, enabling analysing the needs and requirements, and focus on creating improved packaging, high quality, and better shelf life. This empirical investigation is focusing on analysing the critical factors related to AI in influencing the food processing industry. The researchers intend to apply quantitative analysis using IBM SPSS package, and the results are stated in detail based on the analysis.
Marine Snow Removal

High-quality underwater images are significant for marine-exploration-related works. However, capturing high-quality images in the water medium is extremely difficult. Marine snow, consisting of various organic and inorganic materials, is one of the degradation sources. However, there are no available marine snow dataset to further research the algorithm of marine snow removal problem. This paper analyse the characteristics of marine snow from the observations of researches and real underwater images and introduces a new dataset for marine snow removal of both paired and unpaired underwater image pairs. We then treat the marine snow removal task as an image-to-image translation problem and provide benchmark experiments on our dataset for references.
The use of Fuzzy rule-based systems in the design process of the metallic products on example of microstructure evolution prediction

Abstract The challenge on the contemporary market of consumer goods is a quick response to customer needs. It entails time restrictions, which a semi-finished products’ (including metal products) manufacturer must meet. This issue must be addressed during a design phase, which for the most of semi-finished products suppliers, takes part during a quotation preparation process. Our research is aimed at investigating possibility of application of Fuzzy Reasoning methods for shortening of a design process, being a part of this process. We present a study on application of simplified models for solving technological tasks, allowing obtaining expected properties of designed products. The core of our concept is replacing numerical models and classical metamodels with a rule-based reasoning. A quotation preparation process can be supported by solving a technological problem without numerical experiments. Our goal was to validate the thesis basing not only on the presentation of some potential solutions but also on the results of simulation studies. The problem is illustrated with an example of thermal treatment of aluminum alloys, aimed at evaluation of a summary fraction of precipitations as a function of time and technological parameters. We assumed that it is possible to use both unstructured and point numerical experiments for knowledge acquisition. Implementation of this concept required the use of hybrid knowledge acquisition methods that combine the results of point experiments with expert knowledge. A comparison of obtained results to the ones obtained with metamodels shows a similar efficiency of both approaches, while our method is less time and laborious.
An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities

In this paper, we address a new image forensics task, namely the detection of fake flood images generated by ClimateGAN architecture. We do so by proposing a hybrid deep learning architecture including both a detection and a localization branch, the latter being devoted to the identification of the image regions manipulated by ClimateGAN. Even if our goal is the detection of fake flood images, in fact, we found that adding a localization branch helps the network to focus on the most relevant image regions with significant improvements in terms of generalization capabilities and robustness against image processing operations. The good performance of the proposed architecture is validated on two datasets of pristine flood images downloaded from the internet and three datasets of fake flood images generated by ClimateGAN starting from a large set of diverse street images.
A Vision of Teaching and Learning with AI

Rapid development of AI technologies with breakthroughs in algorithmic machine learning and autonomous decision making have generated unprecedented opportunities for technological innovation. There is consensus from policy report that in the next years AI could revolutionise the way teaching and learning is designed and delivered to students leading to seamless intelligent services that are more tailored to student&#x2019;s needs and interests. Current impetus on AI-based research in education has mainly focused on a knowledge-based approach inherently prevalent in Intelligent Tutoring Systems employed in specific domains. The aim of this paper is to proliferate a compendious framework that classifies teaching practice with a spectrum of AIED applications and tools. The framework acts as a point of departure for teachers that envisage to use AI for enhancing the way learning and teaching is manifested. It may also serve as a blueprint for AIED developers to design AIED systems that focus on specific teaching and learning instances.
Robust Human Activity Recognition Using Generative Adversarial Imputation Networks

Human activity recognition (HAR) is widely used in applications ranging from activity tracking to rehabilitation of patients. HAR classifiers are typically trained with data collected from a known set of users while assuming that all the sensors needed for activity recognition are working perfectly and there are no missing samples. However, real-world usage of the HAR classifier may encounter missing data samples due to user error, device error, or battery limitations. The missing samples, in turn, lead to a significant reduction in accuracy. To address this limitation, we propose an adaptive method that either uses low-power mean imputation or generative adversarial imputation networks (GAIN) to recover the missing data samples before classifying the activities. Experiments on a public HAR dataset with 22 users show that the proposed robust HAR classifier achieves 94% classification accuracy with as much as 20% missing samples from the sensors with 390 μJ energy consumption per imputation.
Exploring Recent Trends in Computer Vision

Computer vision is Artificial Intelligence (AI) is a field of digital imagery, recovering meaningful information from videos and other visual inputs helps computers and systems - and take action or make recommendations based on that information. The main purpose of animal ecology is to observe living things in the natural world. The cost and challenge of data collection often restricts the scope of ecological research. Clinical image analysis involves the development, classification and diagnosis of a clinical picture. Computer Vision Syndrome (CVS) is a set of symptoms associated with prolonged work on a computer display. The answers to the diagnostic features can be displayed as thermo grams in leaf pictures, the answers to the diagnostic features can be displayed as thermo grams in leaf pictures, systematic and pale with the help of computer vision plenty for botanical studies the leaves are ready to make new contributions.