Reviewerly/exHarmony huggingface dataset:
Light Multiscale Conventional Neural Network for MP3 Steganalysis

In this paper, we propose a light multiscale convolution neural network to detect adaptive MP3 steganography, which can be used in attacking both the MP3 steganography based on Huffman codes substitution and the method through modifying sign bit in MP3 encoding. Especially, we decrease the model size and the occupation of graphics memory based on convolution factorization. At the same time, the convolution kernels with different size are applied in one layer, which is conducive to the retaining of the detection performance. And refer to the residual structure, a shortcut connection is used in the proposed network to enhance the performance of the network. The experimental result shows the accuracy can reach more than 90% when the payload rate is high. And the model size is reduced by 70% than the previous networks.
Complex Valued Gated Auto-encoder for Video Frame Prediction.

In recent years, complex valued artificial neural networks have gained increasing interest as they allow neural networks to learn richer representations while potentially incorporating less parameters. Especially in the domain of computer graphics, many traditional operations rely heavily on computations in the complex domain, thus complex valued neural networks apply naturally. In this paper, we perform frame predictions in video sequences using a complex valued gated auto-encoder. First, our method is motivated showing how the Fourier transform can be seen as the basis for translational operations. Then, we present how a complex neural network can learn such transformations and compare its performance and parameter efficiency to a real-valued gated autoencoder. Furthermore, we show how extending both - the real and the complex valued - neural networks by using convolutional units can significantly improve prediction performance and parameter efficiency. The networks are assessed on a moving noise and a bouncing ball dataset.
Quantum metrology with Fiber Fabry-Perot cavities (Conference Presentation)

The development of laser-machined, ultralow roughness micromirrors on optical fiber tips has enabled a new type of high-finesse Fiber Fabry-Perot cavities (FFPs). These microcavities are used very successfully in an increasing number of quantum technology applications, reaching from single-photon sources with various solid-state emitters to entanglement generation in ultracold atomic ensembles. They are also attractive for general photonics applications where high cavity finesse is desired, combinin narrow resonance width and large stopband with high transmission, excellent passive stability and built-in fiber coupling. I will give a short overview of this cavity technology and describe an experiment where we are using a next-generation FFP cavity to generate long-lived spin squeezed states in an atomic clock on a chip.
Double JPEG Compression Detection Based on Markov Model

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles ($$\psi $$, $$\phi $$, and $$\theta $$). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with QF1 $$\ge $$ QF2 is a challenging task, and when the images with small size (i.e., 64 $$\times $$ 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case.
Employing multi-modal sensors for personalised smart home health monitoring.

As the prevalence of IoT sensor equipment in smart homes continues to rise, long term monitoring for personalised and more representative health tracking has become more accessible. The estimation of physiological health factors such as gait and heart rate can be captured using a range of diverse sensor equipment, while behavioural changes are now being monitored using simple binary sensors through activity classification and profiling. Combining both physiological and behavioural monitoring in fixed layout properties has already allowed us to effectively consider fall risk. However, expanding application of the system to new layouts and conditions requires consideration of differing retro fit home layouts and sensor configurations. A wider selection of sensors in varying configurations could potentially allow for the identification of other health conditions such as heart disease and stroke.
THE ORGANIZATION OF UNIVERSITY CONFERENCES. LINKING TRAINING AND RESEARCH

Scientific meetings and conferences, as well as participating in scientific networks and societies, are essential for research activity as they promote professors and researchers to share their activity, improve their professional relationships and establish a network of contacts which enhances their professional development, both at present and in the future. Furthermore, the organization of these events by universities also promotes the image and reputation of the host institution.The organization of these events can be carried out by a specialized, external company or by the universities themselves, provided they have structures that allow so. However, structures for these purposes are uncommon. In 2015, a study carried out by the Association of Congress Offices of Spanish Universities (OCUE) revealed that only two Spanish universities, of all respondents, were actively involved in the organization of conferences via an internal department. One of these universities is the Universitat Politècnica de València (UPV) which does so through its Lifelong Learning Centre (CFP).The CFP of the UPV was created in 1991 with the purpose of facilitating the organization of continuing training activities mentioned in the Third Mission. After years of experience in the field of continuing education, in 2008 it was decided that this department would offer support to conference organizers. Since then, more than 450 conferences have been organized by this department.The institutional support for the organization and management of the conferences favours the synergic actions between organizers and participants in the conference, as well as with other parties, such as those involved in the management of research, knowledge transfer, employers, or public organizations, which are related to the university. The institutional management of the conferences makes it easier to use the networks established in the university with professionals, institutions or companies and helps to establish new links among research groups.The relationship between conferences and the Third Mission within the European Space of Higher Education is extremely noticeable as a significant number of conferences incorporate training activities such as tutorials, workshops or master classes aimed at students and professionals, among others, offering a unique opportunity to receive highly specialized, forefront training in the field.In short, this work presents conferences as a meeting point between researchers, students and professionals in which research and training actions converge with institutions and enterprises.
Cybersecurity and the Internet of Things: Anticipating the Leverage of Cyber Crimes with Interconnected Devices

These days, technology is reaching a critical point in its evolution. The increase of technologies – as cloud computing, data analysis, and mobile communications – has encouraged other technologies as, for example, the Internet of Things to reach their full potential. Resulting, the Digital Business Environment has been rapidly changing, leading to an exponential growth of the number cyber crimes. The present study aims to identify the challenges the Digital Business Environment has to face and to propose viable solutions that would allow avoiding cyberattacks that result in a reinforcement of cybersecurity in the Internet of Things. So that, with the aim to deepen the impact that cybersecurity has on the Internet of Things for individuals, society, nations, and global humanity, the authors undertake a review of the literature and perform an exploratory analysis of the results on this ubiquitous topic.
Analysis of Steganography Using Patch-Based Texture Synthesis

Steganography is one of the techniques to hide secret information. Texture synthesis is a process to get large image from small piece of image without disturbing image texture. This paper focuses on patch-based texture synthesis. Patch-based texture synthesis process is a way to achieve steganography. This is performed by embedding message into image meanwhile not disturbing image properties and appearance. Message hiding is done by spreading secret information throughout the image. The distortion present in image is very fine sharp and person trying to interfere this cannot able to obtain required information. A secret message is stored in the form of patches which uses texture synthesis. At the receiver end, opposite action carried out to obtain original secret message and texture image. Using patch-based texture synthesis process, embedding capacity increased as well as image quality.
Representing Temporal Dependencies in Human Activity Recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.
Decentralized Q-Learning with Constant Aspirations in Stochastic Games

In decentralized stochastic control, coordination among control agents is typically required in order to achieve acceptable system performance. In practice, pertinent information about the system—in the form of the cost function, state transition probabilities, and past actions of other agents—is often unavailable to some or all agents, and this serves as an obstacle to finding optimal control policies. In this paper, a decentralized control problem is modelled as a stochastic game in which (i) the specific game being played is unknown, and (ii) players never observe the actions used by other agents. This information structure exacerbates the already difficult challenge of decentralized policy evaluation in stochastic games. This paper presents a two-timescale reinforcement learning algorithm for stochastic games, in which players engage in decentralized policy evaluation during the finer timescale and update their baseline policies in the coarser timescale. The algorithm presented here comes with provable convergence guarantees and uses only local information, in the form of local cost readings, the history of local actions, and the state information.
WORLD WIDE EDUCATION OF CULTURE HERITAGE USING NEW TECHNOLOGIES – "3D DIGITAL SILK ROAD" PORTAL CASE STUDY

New Information and Communication Technologies (ICT) are changing our life. Society, and especially its younger part, got used to everyday life in the digital space. People spend more and more time in this space. In many countries, the number of hours spent online exceeds eight per day. These statistics are dominated by young people who know the world using digital media. These trends must be used in many areas, including non-school and informal education of young people in the field of cultural heritage.The aim of the paper is to present an example of the use of new technologies for historical and cultural heritage education. The educational tool is an internet portal on which information on historical monuments lying on the medieval trade route called Silk Road is collected and made available to everyone free of charge. This route was the longest in history and connected the Chinese and European economy. Many cities were created, enriched and developed along it. Their remains are a worldwide cultural heritage. The portal is called 3D Digital Silk Road. It provides textual and multimedia information, using such new technologies as 3D models of museum artefacts, 3D panoramas of large architectural objects and virtual reality (VR). In addition to images, films and files for VR glasses, the portal contains reports from conferences and scientific expeditions related to the digitization of cultural heritage objects. The structure of the portal, the information available on it, and the form of it sharing are directed to increasing the interest of young people in the cultural heritage of Silk Road.The portal and its content is the result of international cooperation of computer science specialists and museologists.
Maps of Dynamics

The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics.
APLIKASI KAMUS IRREGULAR VERBS BERBASIS ANDROID

Irregular is verbs on English do not add with ed or d but change agree with rule. To listen andcomprehend only memorize from verb base make past tense and past participle. Because this resultmany people so think difficult in learning English specially of irregular verbs. Aim of this research is to make application dictionary irregular verbs base android to make support in listen English specially irregular verbs. Scheme of system use Unified Modelling Language, java programming and sqlite database. This research produce a application dictionary irregular verbs to use device mobile base android
EMPOWERING MOBILE ROBOTICS UNDERGRADUATE COURSES BY USING JUPYTER NOTEBOOKS

Mobile robotics has surged in popularity with the emergence of applications for commercial and industrial use such as autonomous cars, drones, or warehouse robots. Accordingly, new practitioners versed both in the theoretical and the practical aspects of the field are on high demand. Supporting material is key for the training of such stakeholders, especially in a multidisciplinary field such as robotics, which includes numerous and heterogeneous concepts from mathematics, statistics, physics, etc. In this regard, these complex concepts are better understood when presented in the context in which they are applied, where the environment, goals, and actions can be clearly visualized. This fact calls for modern and quality material using methodologies and tools applied to real use cases, seamlessly introducing theoretical concepts and their implementation and moving away from the traditional theory/exercise pedagogical approach. This paper presents a collection of educational Jupyter Notebooks for use in undergraduate robotics courses, which have been built from the ground up to meet these issues. First, they make use of the Python programming language, praised both for its ease of use and the breadth of its library support. It has gained particular relevancy in Computer and Data Science applications, which can be of use in a field increasingly reliant on machine learning approaches as mobile robotics. Second, they are implemented using the Jupyter Notebook technology [1], widely resorted to in well-known online learning platforms such as Coursera [2] or Udacity [3]. Jupyter Notebooks permit us to combine at the same place theoretical explanations through text, images, mathematical equations, videos and/or links to additional resources, as well as executable code, this way producing comprehensive and contextualized material that incorporates the interactivity, dynamic visualizations and possibilities of an application. The notebooks provide a broad view of the robotics field, with a particular emphasis on mobile robotics, as they cover aspects like: probability bases, robot motion, sensing, localization, mapping, and motion planning. The developed notebooks are meant to be an engaging tool for mobile robotics lecturers, students and practitioners seeking to enhance their knowledge basis. Currently they are being used in undergraduate courses at the University of Málaga (Spain) with promising results. The student version (without solutions) of the presented notebooks is publicly available at https://github.com/jotaraul/jupyter-notebooks-for-robotics-courses, while the complete version can be requested individually by any interested lecturer. This learning tool welcome any contribution from the mobile robotics community. References:[1] Kluyver, Thomas, Benjamin Ragan-Kelley, Fernando Pérez, Brian E. Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley et al. "Jupyter Notebooks-a publishing format for reproducible computational workflows." In ELPUB, pp. 87-90. 2016.[2] Coursera webpage: https://www.coursera.org/[3] Udacity webpage: https://www.udacity.com/
OPTIMALISASI PENERAPAN OTOMASI SLIMS (SENAYAN LIBRARY MANAGEMENT SYSTEM) PADA RUANG BACA PENDIDIKAN ADMINISTRASI PERKANTORAN FKIP UNS

This research aimed to find out: (1) the optimization of SLiMS automation system in Reading Room of Office Administration Education Program Study of FKIP UNS (Teacher Training and Education Faculty of Sebelas Maret University), (2) the constraints encountered in applying automation system in Reading Room of Office Administration Education Program Study of FKIP UNS; and solution to the application of automation system in Reading Room of Office Administration Education Program Study of FKIP UNS in order to be more maximal. This study was a descriptive qualitative research. The sampling techniques employed were purposive and snow ball sampling ones. Data sources employed were informant, place, and event, as well as archive and document. Techniques of collecting data used were interview, observation, and documentation. Data validation was carried out using source and method triangulations. Technique of analyzing data used was an interactive model of analysis. The result of research showed that the application of SLiMS (Senayan Library Management System) automation in Reading Room of Office Administration Education Program Study of Teacher Training and Education Faculty of Surakarta Sebelas Maret University included: circulation service, retrieval system (OPAC), member administration, and literature processing. The constraints encountered in SLiMS application are administrator’s poor competency in using SLiMS automation system leading to less maximal system utilization, and inadequate facilities including the availability of one computer unit only, the limited number of reading table available, and the inadequate space condition. Availability of library materials is still lacking in terms of both quantity and content. The attempts taken to deal with such constraints were: to hold training activity for SLiMS automation system conducted by administrators of reading room, to repair and to provide facilities in reading room by proposing it to the central office through study program, for example, increasing the number of air conditioners (AC). In addition, he also asked for donations from alumnus to increase the collection of library materials.
Adaptive Multi-agent-based Alert System for Diseases’ Detection

Monitoring the epidemiological situation of public health remains a challenge, given the exponential growth of health data. Indeed, these latter come from various sources such as, health professionals, biologists, experts in health organizations, etc. In this context, reconciling the search for the greatest amount of appropriate signals and the way of treating them, is the ultimate challenge. Developing alert systems in the health field is yet a necessity, but what counts, even more, is to propose a powerful and effective solution. To that purpose, we propose a multi-agent-based system that alerts the users when bizarre situations are detected. At the heart of the system, an adaptive agent is proposed which uses the neural network deep learning algorithm. To validate our proposal, we tested both its effectiveness and its efficiency and compared the obtained results against the Q-learning algorithm. Obtained results have proved the adequacy of the chosen algorithm especially in the context of health data.
High-Capacity Reversible Data Hiding in Encrypted Images Based on MSB Prediction

Reversible data hiding in encrypted images is an effective technology to embed additional data in the encrypted domain without accessing the content of the original image. During the decoding process, the secret message can be extracted accurately and the original image can be reconstructed perfectly. In this paper, an efficient reversible data hiding scheme for encrypted images based on MSB (most significant bit) prediction is proposed, which has high embedding capacity. Combining with the characteristic of prediction technology, the prediction error is first identified and its location is stored in the location map. The stream cipher is then used to encrypt the original image. According to the location map, the data-hider substitutes up to three MSBs of embeddable pixels in encrypted image with the secret message. At the receiving end, the secret message can be extracted without error, and the original image can be perfectly reconstructed by utilizing MSB prediction technology. Experimental results show that the scheme can achieve higher embedding capacity than most related methods.
Image Matching Method Based on Laplacian Feature Constrained Coupling Variance Measure

Abstract In order to overcome the current image matching algorithms, which mainly use the distance information between pixels to achieve feature matching, ignoring the variance information between images, resulting in more false matching in the matching results, this paper designs an image matching method based on Laplacian feature constrained coupling variance measure. Firstly, Harris operator is introduced to extract image features roughly. On the basis of rough extraction, Laplacian feature of pixels is used to optimize the extracted image features to obtain more accurate image features. Then, the gradient feature of the image is used to calculate the direction information of the image. Based on the gradient feature, the neighborhood of the feature points is established, and the Haar wavelet value in the neighborhood is obtained to obtain the feature vector. Finally, the regional variance model is used to measure the variance information of the image, and it is introduced into the process of image feature matching. The variance information is added on the basis of Euclidean distance measurement of feature points to achieve image feature matching more accurately. RANSAC method is used to purify the results of feature matching, eliminate mismatching and complete image matching. The experimental results show that compared with the existing matching algorithms, the proposed algorithm has better matching performance and higher accuracy, which accuracy maintained above 90%.