Reviewerly/exHarmony huggingface dataset:
What’s up with Image &amp; Video Forensics?

This talk will be a journey through more than 20 years of research in video image forensics, which was born to respond to challenges of the digital era such as integrity and authenticity verification and source attribution. We will examine some of the characteristics that have made it possible, from the use of intrinsic features in the acquisition devices, such as lens aberration or demosaicing filters to the traces left by the postprocessing, in order to grasp how methods have evolved from making use of relatively simple statistical models to much more complex ones that rely on deep learning. In particular, we will see this evolution with **two classic problems**: 1. The identification of the device that captured a given image, to which the use of the PRNU (Photo Response Non-Uniformity) opened the door and which has currently expanded the type of fingerprints that can be extracted and used successfully 2. The detection and localization of image manipulations through the traces of double compression, which now benefits from the ability of DNNs to learn models with richer dependencies. But not only forensic techniques have evolved: first, the popularization of computational photography with lens distortion corrections, high-dynamic range algorithms, video stabilization or AI-based filters, etc.; second, the ever more prevalent use of social networks for image and video exchange often with proprietary transcoding; and third, the advent of Deep Fakes, imply that many of the techniques developed in the past must be continuously revised to respond to all these novel and demanding challenges. Last, we will briefly discuss the ethical and legal implications of image and video forensics, especially when it comes to the use of AI.
Detect and Defense Against Adversarial Examples in Deep Learning

Despite the enormous performance of Deep Neural Networks (DNNs), recent studies have shown their vulnerability to Adversarial Examples (AEs), i.e., carefully perturbed inputs designed to fool the targeted DNN. The literature is rich with many effective attacks to craft such AEs. Meanwhile, many defense strategies have been developed to mitigate this vulnerability. However, this latter showed their effectiveness against specific attacks and does not generalize well to different attacks. This talk presents a framework for defending the DNN classifier against adversarial samples. The proposed method includes a separate detector and a denoising block. The detector aims to detect AEs by characterizing them through natural scene statistics (NSS), where we demonstrate that the presence of adversarial perturbations alters these statistical features. The denoiser is based on Block Matching 3D (BM3D) filter fed by a threshold estimated by a Convolutional Neural Network (CNN) to project back the samples detected as AEs into their data manifold. We conduct a complete evaluation on three standard datasets: MNIST, CIFAR-10, and Tiny-ImageNet, compared with state-of-the-art defenses. Our experimental results have shown that the proposed detector achieves a high detection accuracy while providing a low false positive accuracy. Additionally, we outperform the state-of-the-art-defense techniques by improving the robustness of DNN’s.
Exploiting prunability for person re-identification

Recent years have witnessed a substantial increase in the deep learning (DL) architectures proposed for visual recognition tasks like person re-identification, where individuals must be recognized over multiple distributed cameras. Although these architectures have greatly improved the state-of-the-art accuracy, the computational complexity of the convolutional neural networks (CNNs) commonly used for feature extraction remains an issue, hindering their deployment on platforms with limited resources, or in applications with real-time constraints. There is an obvious advantage to accelerating and compressing DL models without significantly decreasing their accuracy. However, the source (pruning) domain differs from operational (target) domains, and the domain shift between image data captured with different non-overlapping camera viewpoints leads to lower recognition accuracy. In this paper, we investigate the prunability of these architectures under different design scenarios. This paper first revisits pruning techniques that are suitable for reducing the computational complexity of deep CNN networks applied to person re-identification. Then, these techniques are analyzed according to their pruning criteria and strategy and according to different scenarios for exploiting pruning methods to fine-tuning networks to target domains. Experimental results obtained using DL models with ResNet feature extractors, and multiple benchmarks re-identification datasets, indicate that pruning can considerably reduce network complexity while maintaining a high level of accuracy. In scenarios where pruning is performed with large pretraining or fine-tuning datasets, the number of FLOPS required by ResNet architectures is reduced by half, while maintaining a comparable rank-1 accuracy (within 1% of the original model). Pruning while training a larger CNNs can also provide a significantly better performance than fine-tuning smaller ones.
INTELLECTUAL CODE IN THE FICTION BY IVAN FRANKO: THE DIMENSION OF PROBLEMS

The article deals with the fiction by Ivan Franko, in particular, with the main issues that enable to propose the intellectual code for reading the writer’s text. The analysis of a set of Franko’s novels and stories proves that the intellectualization of the fiction by Ivan Franko is mainly provided by national and ideological, social and political, philosophical as well as literary criticism problems. The author raises the issue of the people’s self-identification, voluntary or forced assimilation, the crisis of national identity, the dialogue between intellectuals and ordinary people, unity and cohesion, which, under the condition of honest purposeful activity, can lead to the desired goals, namely, the welfare of society and consolidated nation. The philosophical problems are represented by the issues of the relativity of progress and truth, the meaning of human existence, relationship between a human person and God, another person or other self, the mystery of divine providence, the struggle between good and evil in the world. In his fiction, Ivan Franko also draws attention to the literary criticism problems, namely, to the essence and purpose of art, psychology of creation and perception of an artistic text, the plurality of interpretations along with the different levels of understanding the literary work.
Why We Do What We Do – HCI and Societal Impact

In the spirit of INTERACTs founder Brian Shackel, this conference was founded to bring together academics and industrial practitioners in the field of human computer interaction (HCI) to recognize the profound societal impact that research and knowledge on humans interacting with various forms of technology has. HCI has grown from being a small obscure discipline for computer scientists that were not that good on programming and needed to pick a field that was easy, to being one of the most important and fastest growing professions in the IT sector, through the role that user experience (UX) play. The scientific studies and published papers in HCI have grown much more than many of the other disciplines. How has HCI come to undergo such a development? I believe that there are several potential reasons for this. The transdisciplinary nature, involving true collaboration and a joint understanding between disciplines of very different natures including computer science, behavioral science and design, among other things that are fundamental to the impact. Another value is the ambitions not to bend for the complex, “wicked” problems that human computer interaction addresses but develop the methodology to fit the nature of doing research in the wild on complex problems where many of the involving factors are difficult to control is one of the reasons for the success. The values of addressing and caring about users, regardless of abilities, knowledge level, age or gender is another positive quality that has contributed to the impact. To be able to make the best out of our discipline we should more recognize and cherish the contribution that HCI can make, educate students and the society to understand the impact and make use of all the valuable methods, we need carefully to choose research problems and approaches to maximize the impact and we should to a higher extent take an active role in the contemporary development of the field, actively engage in politics and policy making on AI, digitalization and development for change and make sure that the values of our HCI field permeates all development.
MENINGKATKAN KEMAMPUAN MENGENAL HURUF MELALUI KEGIATAN JEMUR HURUF KELOMPOK B TK KARYA BANRI KABUPATEN BULUKUMBA

Problem in this research ability language, especially the ability to master letters has not yet developed compared to other capabilities. Children look difficult at times name the letter symbols. So that tesearchers provide a solution by carrying out letter drying activities in the hope that it will make it easier for children to master language, especially the ability to recognize letters. The purpose of this research is to find out how the application of letter drying activities can improve the ability to recognize letters in group B children at Karya Banri kindergarten, Bulukumba Regency.this research is a class action research with quantitative descriptive data analysis and data collection techniques through observation an documentation. The subject of this research is group B kindergaerden of Karya Banri Bulukumba regency totalling 20 children. The results of the study showed that in the first cysle as a whole only reached 62,9% which met the sufficient criteria. While the re-doing of research in the second cycle as a whole has reached 75,5% and has met the good criteria and is at the level of ability to develop according to expectations.
Adversarial Machine Learning

Machine-learning algorithms are widely used for cybersecurity applications, including spam, malware detection, biometric recognition. In these applications, the learning algorithm must face intelligent and adaptive attackers who can carefully manipulate data to purposely subvert the learning process. As machine learning algorithms have not been originally designed under such premises, they have been shown to be vulnerable towell-crafted attacks, including test-time evasion and training-time poisoning attacks (also known as adversarial examples). This talk aims to introduce the fundamentals of adversarial machine learning and some techniques to assess the vulnerability of machine-learning algorithms to adversarial attacks. We report application examples including object recognition in images, biometric identity recognition, spam, and malware detection.
The Super Neuron Model - A new generation of ANN-based Machine Learning and Applications

Operational Neural Networks (ONNs) are new generation network models targeting to address two major drawbacks of conventional Convolutional Neural Networks (CNNs): the homogenous network configuration and the “linear” neuron model that can only perform linear transformations over previous layer outputs. ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators. This is a great leap towards expanding the neuron’s learning capacity in CNNs, which thus far required the use of a single nodal operator for all synaptic connections for each neuron. This restriction has recently been lifted by introducing a superior neuron called the “generative neuron” where each nodal operator can be customized during the training in order to maximize learning. As a result, the network is able to self-organize the nodal operators of its neurons’ connections. Self-Organized ONNs (Self-ONNs) equipped with superior generative neurons can achieve diversity even with a compact configuration. We shall explore several signal processing applications of neural network models equipped with the superior neuron.
THE USE OF TAXONOMIC CLUSTERING MECHANISMS IN THE PROCESS OF PSYCHOLOGICAL DIAGNOSTICS OF SOCIAL SUCCESS OF YOUNGER SCHOOLCHILDREN

The objective of this article is to explore the correlation between specific psychological profiles, which characterize the expressive and communicative abilities of primary school-aged children in narrative formats, and their social success during interactions. The clustering of schoolchildren's social success is achieved through the use of growing pyramidal networks, which structurally represent the operational environment of their activities during interactions. The input data for the clustering process consist of values obtained from psychological profiles, evaluated using appropriate scales in the testing of schoolchildren according to specific methods. The clustering procedure identifies forty psychological profiles that reflect the dynamics of their interaction in narrative formats and their corresponding levels of social success. Taxonomic clustering of the test results of junior schoolchildren reveals stable groups of interactions among them.
Survival Prediction in Patients with Nasopharyngeal Cancer Using some Machine Learning Methods

The staging system of tumor lymph node metastases currently ranks first in predicting the prognosis of nasopharyngeal cancer. However, this system alone is not sufficient for this type of prediction and patients at the same stage may show significant clinical heterogeneity and distinct oncological findings. The plasma Epstein-Barr virus (EBV) DNA titer remains the only clinically useful biomarker in patients with nasopharyngeal cancer. The factors affecting the prognosis of nasopharyngeal cancer and the total effect of these factors on the prognosis of this cancer remain an open subject for further investigation. To provide answers to related questions, this study examined prognostic factors for survival using machine-learning techniques. In this project, we have the following result: An accuracy of 97%, a precision of 100%, a recall of 90% and 95% for the F1 score, using Random Forest algorithm as well as the Voting Classifier algorithm.
Biases, Discrimination, and Fairness in Biometrics and Beyond

In the last few years, we are witnessing a growing interest in the Artificial Intelligence research community in studying bias effects when machine learning methods are applied on large amounts of data. These bias effects can stem from the data itself or from the learning process, which nowadays is clearly dominated by deep learning methods that most of the time are quite opaque. When those learning processes are related to AI applications dealing with personal information, or whose application affects people’s lives, then biases can result in unfair AI-based automated decision-making processes, very harmful in terms of undesired discrimination among population groups. This keynote will discuss the current state of the topic with special emphasis in AI applications involving face biometrics. Recent methods and approaches to reduce undesired discrimination towards fair biometrics will be also discussed.
Expert System for Diagnosing Cerebral Palsy in Children with the Case Based Reasoning Method

The rampant influence of technology certainly has positive and negative impacts on the increasingly complex development of health sciences and diseases. Technology in the field of computerization has been widely used in various lines of life. Technology is very helpful in various aspects of life, with the existence of technology humans can create various tools to facilitate human work. Cerebral Palsy is a disease or disorder that often occurs in today's development. Cerebral Palsy is not a fatal disease but a condition that affects the body, its treatment and treatment can help regulate its effects on the body. An expert system is a system that adopts human knowledge to a computer that combines basic knowledge with an inference system to replace the function of an expert in solving a problem. advances in expert systems can overcome this problem by designing a Desktop-based computer system that uses the Java programming language and MySQL database so that it can help users to diagnose disease. Various methods can be used to build expert systems, one of which is the Case Based Reasoning method. Where the process of calculating Case Based Reasoning compares new cases with old cases and divides them into four stages, namely retrieve, reuse, revise and retain, to get the results of the type of disease suffered by the patient in the form of a percentage. In this expert system, the system will ask questions like a patient is having a dialogue with an expert. Then, select yes for symptoms that are felt and no for symptoms that are not felt. The system will make a diagnosis based on the patient's symptoms. Diagnostic results include the type of Cerebral Palsy experienced and treatment options as well as the percentage of patients likely to suffer from the disease
On the direct building of 8 × 8 self-reciprocal recursive MDS Matrices effective for implementation over GF(q) using Reed-Solomon codes

MDS matrices are from the MDS codes in coding theory that are being used widely in cryptographic applications. Recursive MDS matrix is an matrix that is a power of some simple companion matrix. These matrices are so convenient for execution especially for hardware implementation using LFSRs. Therefore, these matrices have attracted the interest of many scientists. In this paper, we give a way to directly build 8 × 8 self-reciprocal recursive MDS matrices efficient for execution over the field GF(q), (q = pr, pis a prime number) using the Reed-Solomon codes. These matrices are significant in practice because they have the potential to be used in lightweight cryptographic algorithms.
Overview of Artificial Intelligence (AI) and Machine Learning (ML) in Seismology

Undoubtedly one of the most destructive natural catastrophes is an earthquake. Around the world, they frequently result in significant losses in terms of people, structures, economies, and societies. It is still impossible to accurately anticipate the location and timing of catastrophic occurrences, despite the fact that analytical and measuring techniques have advanced steadily over the previous few decades. The chapter provides an overview of applications of machine learning (ML) and Artificial Intelligence (AI) in seismology so that above mentioned problems in this field can be resolved. The ML Model helps to identify unseen signals and patterns to extract features that might improve our physical understanding of earthquakes. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving some problems in geosciences will be highlighted. ML algorithms in seismology address various problems like earthquake detection, phase picking, EEW, ground-motion prediction, seismic tomography, and earthquake geodesy. AI-based algorithms are used for earthquake analysis and prediction, deciphering complex stress development patterns, and developing fully automatic seismic event detection using SVM. ML techniques, such as ANN and GA, estimate earthquake source parameters, while classification algorithms like Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to study to identify shallow focus (depth < 70 km) tsunami genic earthquakes at a regional distance. Also, a lot of ML and AI models can be used in seismology to resolve various key problems of seismology from detection to prediction tasks.
SHAPING SMART HOME PRODUCT SERVICE SYSTEM (SH-PSS) REFLECTION CRITERIA CARDS FOR ‘TILES IOT INVENTOR TOOLKIT’

The internet of things (IoT) is growing rapidly as a technological tool and solution to various problems. Developing Internet of Things (IoT) technology also generates new opportunities for smart home products, services and systems (SH-PSS). Previous research has shown how challenging it is for designers to generate ideas for such smart product-service systems. Tiles IoT Inventor is a card-based toolkit that helps identify and learn the consumer's problems and explore the design brief's technical capabilities while ideating SH-PSS. We do this through the IoT Ideation toolkit comprising many domain-specific cards. The toolkit, consisting of a deck of cards such as scenario, persona, missions, things, human actions, sensors, services, feedback and reflection criteria, was created and assessed by user experience designers, product designers, and human-computer interaction (HCI) students in user workshops. We have identified five attractive characteristics of SH-PSS which influence the technology adoption. In this study, we provide these attractive characteristics as SH-PSS Reflection Criteria Cards for Tiles IoT Inventor Toolkit. Research questions are formulated to address the idea generation ability enhancement, creativity enhancement and technology adoption discontinuity identification of SH-PSS Reflection Criteria Cards for Tiles IoT Inventor Toolkit. We have formulated a hypothesis to answer these research questions and test their experiments. These hypotheses should be tested in the experiment with the control and experiment groups. This study will be a significant contribution to design education. It is expected that design practitioners and researchers will get help in identifying consumer problems and exploring the technical capabilities of SH-PSS.
COMBATTING LONELINESS BY INTRODUCING A START-UP PROJECT FOR FIRST-YEAR STUDENTS

The first year of a student’s higher education significantly affects their performance and probability of completion. Tinto found that the first few weeks are critical for the students regarding motivation, connecting socially and connecting academically. Most of the students who decide to quit or drop out do so during the first weeks of their education. Thus, it is imperative to integrate students into their new academic environment as effectively as possible, meaning that universities must be aware of and utilize this window of opportunity to give the students a meaningful start. Every four years a survey is conducted to map how the students in Norway feel (SHoT). In recent years there has been an increase in reported loneliness amongst the students. In SHoT 2022 36% reported that they either miss someone to be with, feel left outside or feel isolated often or very often. This is an increase from 2018 where 30% of the students reported this. Another striking result from this research is that 34% report that they only have a few friends and 7% report that they do not have any friends. At the University of Agder the civil engineering first-year students, of both bachelor and master, were assigned a start-up project in their first week. This start-up project was designed by the some of the employees at the civil engineering department. The students were divided into groups of 4-5 students. The purpose of the start-up project was to design a small student cottage were functionality, sustainability and design comprised the main assessment criteria. They also had to build a model in a scale of 1:20. The students had three days to complete the project and presented their final products in front of the rest of their class, and a jury consisting of employees at the civil engineering department. After having the presentation of the final projects, a survey was distributed to the students. 88% rated the project as 4 (to a large degree) and 5 (to a very large degree) when it came to academic collaboration. Furthermore, when asked to which extent the project prompted a social arena and contributed to gaining new friends, 90% and 79% respectively, replied either 4 or 5. The results of this study can provide other educational institutions with insights on how to meet the first-year students, and how to provide a social arena. By having a start-up project which is both a theoretical and physical task, the students find other students to befriend and work with.