Reviewerly/exHarmony huggingface dataset:
Active Tangibles for Tabletop Interaction based on the Kniwwelino Prototyping Platform

To allow researchers explore the use of active tangibles in different tabletop scenarios, affordable and easy-to-use rapid prototyping tools are needed. This paper presents the design and implementation of active tangibles based on a microcontroller-based development platform called Kniwwelino. They are wirelessly connected via WiFi and can be programmed in Arduino or using a block-based visual programming language. We present the details of our implementation, propose a scenario of use, and describe our findings following the implementation of this scenario.
Parallelized Fuzzy RBF and FHM based Color Filtering for Real-Time Image Processing

Color filtering is an important application of image processing. Real-time access to color-based information that is very advantageous for in a wide range of engineering fields that utilizes applied measurement technologies, like industrial manufacturing, medical science, self-driving cars, robotics, etc. Processing color information, however, especially in large images is typically a very time-consuming procedure, but with the usage of parallel computing it can be significantly accelerated. In this paper, two fuzzy approaches are examined for color filtering and improvements are proposed in order to speed up their operation.
Content Recognition of Network Traffic Using Wavelet Transform and CNN

With the development of the Internet, the content types of the network traffic become more and more diverse, including video, news, music, image and so on. Traffic identification plays an important role in network management, security defense and performance optimization. Traditionally, the network traffic analysis focuses on the protocol identification and application classification, which has been well studied in the past two decades. However, as a large number of existing general protocols and legal applications can be abused to hide and transmit the data of different content types, illegal content may penetrate the traffic analysis system, and lead to inefficient network management and cause potential risks for internal networks. Different from the traditional work on the identification of the protocols or applications, in this paper, we propose a new method for recognizing the content types for the network traffic. The proposed method is based on two technologies including the wavelet transform and CNN. The wavelet transform is exploited to process the time-frequency signals of the observed network traffic that is further classified by the CNN. Experiment results are presented to validate the performance of the proposed scheme.
Evaluation of Tag Clusterings for User Profiling in Movie Recommendation

In the web 2.0 era, tags provide an effective mechanism to rapidly annotate and categorize items. However, tags suffer from many problems typically linked to language, like synonymy, polysemy, and ambiguity in general. To overcome this limitation, tag clustering can be used to group tags that represent similar concepts. One of the domains where tag clustering has shown to be particularly useful is the movie recommendation, where tags are used to represent users' preferences and affinities. In this context it is not yet available a golden standard that can prove the quality of a clustering technique, especially considering that the final aim is the users' satisfaction rather than an accuracy-like score. To this end, we propose an evaluation criterion for the quality of the resulting clusters based on human judgments.
A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification

Person re-identification (person re-ID) is often considered as a sub-problem of image retrieval, which aims to match pedestrians under non-overlapping cameras. In this work, we present a novel global and local network structure integrating pedestrian identities with multiple attributes to improve the performance of person re-ID. The proposed framework consists of three modules: shared one, global one and local one. The shared module based on pre-trained residual network extracts low-level and mid-level features. And the global module guided by identification loss learns high-level semantic feature representations. To achieve accurate localization of local attribute features, we propose a multi-attributes partitioning learning method and consider pedestrian attributes as supervised information of the local module. Meanwhile, we employ whole-to-part spatial transformer networks (STNs) to achieve coarse-to-fine meaningful feature locations. By applying a multi-task learning strategy, we design various objective functions including identification and multiple attributes classification losses for training our model. The experimental results on several challenging datasets show our method significantly improves person re-ID performance and surpasses most of the state-of-the-art methods. Specifically, our model achieves 87.49% of the attribute recognition accuracy on Market1501 dataset.
Mathematical Modeling and Simulation of 3-Qubits Quantum Annealing Processor

In this paper, a new model of 3-qubits quantum annealing processor is designed. The spin-chain model is used to simulate the quantum annealing processor. The Hamiltonian which describes the system is introduced and analytically solved using the unitary operator method taking into account, processor in, ground state. The generated entanglement between processor parts is quantified using, concurrence method. The effect of quantum processor parameters on the dynamics of entanglement is investigated. The results show that enough quantum correlation is generated between quantum annealing processor nodes (qubits). However, it has shown that we can control the dynamics of entanglement over the system by controlling the system parameters.
Online ranking combination

As a task of high importance for recommender systems, we consider the problem of learning the convex combination of ranking algorithms by online machine learning. In the case of two base rankers, we show that the exponentially weighted combination achieves near optimal performance. However, the number of required points to be evaluated may be prohibitive with more base models in a real application. We propose a gradient based stochastic optimization algorithm that uses finite differences. Our new algorithm achieves similar empirical performance for two base rankers, while scaling well with an increased number of models. In our experiments with five real-world recommendation data sets, we show that the combination offers significant improvement over previously known stochastic optimization techniques. Our algorithm is the first effective stochastic optimization method for combining ranked recommendation lists by online machine learning.
7th international workshop on human activity sensing corpus and applications (HASCA)

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpuses and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Second Sussex-Huawei Locomotion and Transportation Recognition Competition and Open Lab Nursing Activity Recognition Challenge in special sessions.
Emerging Information Technology Issues in Higher Education

This chapter explores three emerging IT issues in higher education and how an engineering college has attempted to resolve them. The issues explored include providing or recommending personal devices to students based on their degree program for the college's bring your own device (BYOD) program, exploring how to make supplied technology accessible to students and how to ensure equitable access for all students, and exploring the idea of collaborative learning spaces and VDI environments.
A Novel Image Captioning Method Based on Generative Adversarial Networks

Although the image captioning methods based on RNN has made great progress in recent years, these are often lacking in variability and ignore some minor information. In this paper, a novel image captioning method based on Generative Adversarial Networks is proposed, which improve the naturalness and diversity of image description. In the method, matcher is added to the generator to get the feature of the image that does not appear in the standard description, then to produce descriptions conditioned on image, and discriminator to access how well a description fits the visual content. It is noteworthy that training a sequence generator is nontrivial. Experiments on MSCOCO and Flickr30k show that it performed competitively against real people in our user study and outperformed other methods on various tasks.
Use of Blockchain for Smart T-Shirt Design Ownership

This study aims to provide a reliable solution for the T-shirt designer to gain ownership of his design using the blockchain technology. As blockchain is a decentralized technology, it will provide authentication for the ownership of the artwork among various non-trusting members. In this research paper, we have explained how to resolve this issue for the designer as well as the customer. Using this method, a novel distributed application can be created.
Design, implementation and evaluation of SELEDE

About half of the deaf children using a cochlear implant show poorer language performance than their hearing peers when they enter primary school. Some studies suggest that these problems could be explained by a domain-general deficit in processing sequences of stimuli. Although these problems are widely recognized among the major causes of the spoken and written language delay in children with cochlear implants, a training specific for these learning abilities needs to be further developed. In this paper, we discuss the design, development and preliminary User Experience (UX) evaluation of a digital tool aimed at filling this gap: SELEDE (SEquence LEarning for DEaf children). We followed a co-design approach, involving all interested stakeholders (children, researchers, developers, therapists) throughout the process.
Improved Stabilization Method for Time-Delay T-S Fuzzy Systems

This paper aims to study problem of the delay-dependent stability analysis and stabilization control design for nonlinear systems with time delay in Takagi-Sugeno(T-S) fuzzy model. New membership function dependent Lyapunov-Krasovskii functional(LKF) is proposed in this paper. Wirtinger inequality is employed to estimate the integral term of derivative of the LKF, and switching rules are established to avoid the time derivative of membership functions. Then, improved stability and stabilization criteria are obtained within the form of linear matrix inequalities (LMIs). Two numerical examples are given to demonstrate the advantage and effectiveness of the proposed method in this paper.
aicracy

In this paper, we present aicracy, a critical design project that portrays a society ruled by an artificial intelligence. Five hypothetical objects from this society are presented: a bracelet that gives citizens feedback about their deeds, a patch that releases dopamine into its wearer's blood, an office chair that collapses when its user is unproductive, a shopping basket that displays different prices for different users, depending on how much they contribute to society, and a marble-based voting machine.
Another Step in the Ladder of DNS-Based Covert Channels: Hiding Ill-Disposed Information in DNSKEY RRs

Covert channel communications are of vital importance for the ill-motivated purposes of cyber-crooks. Through these channels, they are capable of communicating in a stealthy way, unnoticed by the defenders and bypassing the security mechanisms of protected networks. The covert channels facilitate the hidden distribution of data to internal agents. For instance, a stealthy covert channel could be beneficial for the purposes of a botmaster that desires to send commands to their bot army, or for exfiltrating corporate and sensitive private data from an internal network of an organization. During the evolution of Internet, a plethora of network protocols has been exploited as covert channel. DNS protocol however has a prominent position in this exploitation race, as it is one of the few protocols that is rarely restricted by security policies or filtered by firewalls, and thus fulfills perfectly a covert channel’s requirements. Therefore, there are more than a few cases where the DNS protocol and infrastructure are exploited in well-known security incidents. In this context, the work at hand puts forward by investigating the feasibility of exploiting the DNS Security Extensions (DNSSEC) as a covert channel. We demonstrate that is beneficial and quite straightforward to embed the arbitrary data of an aggressor’s choice within the DNSKEY resource record, which normally provides the public key of a DNSSEC-enabled domain zone. Since DNSKEY contains the public key encoded in base64 format, it can be easily exploited for the dissemination of an encrypted or stego message, or even for the distribution of a malware’s binary encoded in base64 string. To this end, we implement a proof of concept based on two prominent nameserver software, namely BIND and NDS, and we publish in the DNS hierarchy custom data of our choice concealed as the public key of the DNS zone under our jurisdiction in order to demonstrate the effectiveness of the proposed covert channel.
Eliciting Scientific Delusions as a Control Education Methodology with Examples on the System Identification

The paper proposes an approach to activate students’ graduate experience and motivate their cognition abilities in the corresponding field of science in order of preparing to independent research activity. Such activation and motivation are proposed to be implemented via special assignments on analysis of “real”, what means actually published, scientific results in order to find delusions, errors, etc. therein. The pre-sent paper demonstrates ways and methods, both simulation and analytical, to be applied to analyze delusions concerned with various attempts of generalization of conventional techniques and notions relating to such a branch of the control theory as the system identification. The paper presents methods and formulates corresponding assignments to reveal the delusions concerned with information theoretic approaches: application of the mutual (Shannon) information and attempts of generalization of the notion of entropy.
Nearest-neighbor-intersection algorithm for identifying strong predictors using high-dimensional data

Increasingly large amounts of data are being collected, but data quantity may fail to translate to predictability, when all data are affected by the same broad trends. That makes it important to identify the most important predictors for relevant outcomes. In this paper we assume that a large set of dependent attributes is available as well as a large set of independent ones, and that the data quantity is used for evaluating the predictability of the former based on the latter. This problem can be identified with the task of correlation analysis, which has been available for a long time. However, traditional correlation analysis techniques are not suited to the high-dimensional problems. We propose a nearest-neighbor-based approach for examining the strength of relationship between input and output. We demonstrate the effectiveness and efficiency of this approach for time series data that are cut in half so as to produce the two sets of related attributes.
Tabu Cryptanalysis of VMPC Stream Cipher

Abstract In the era of global informatization, transmitting and storing information in digital form it is very important to ensure an adequate level of security of ciphers used. Cryptanalysis deals with studying the level of security, thus exposing the weakness of theoretical and implemented cryptographic solutions. In this paper cryptanalysis of stream cipher VMPC using Tabu Search is shown. From estimates made on a full version of VMPC cipher we concluded that about 2 157 possibilities needs to be checked in order to find the proper one, which would be the best attack known so far.
The value-semantic paradigm of the library science’ knowledge: choice of evolutionary suitable foundations

The value-semantic paradigm of the library science’ knowledge undergoes constant changes understandable only in the context of its evolution. It is due to a number of factors, namely: transformation of the scientific knowledge system as a whole; changes of this cognitive construction levels; necessity to solve practical tasks connected with libraries’ adaptation to constant changes in the society; intellectual upgrading of the industrial sphere, etc. The article objective is to prove that value-semantic structure of the modern library science’ knowledge must be based on principles confirming their evolutionary fitness in the context of modern historical reality. The authors conclude that changing social reality, followed by the library one, makes the professional community to form a new system of methodological approaches and notions, and the task of the society spiritual-moral evolution predetermines creating an adequate disciplinary matrix.
Deep Fully Convolutional Networks for Mitosis Detection

Image recognition plays a vital role in the medical image analysis field, which depends on different medical image analysis algorithms with input data, features, parameters, and type of learning. Three crucial morphological features on Hematoxylin and Eosin 1991 (H&E) related to the classifying the diseases for breast cancer, mitosis count, tubule formation and nuclear pleomorphic. Mitosis counts plays an essential role and an important diagnostic factor for breast cancer grading. Mitosis detection is still a challenging problem because the cells are part of the cell cycle to generate a new nuclear and with different stages of mitosis. However, we implemented a residual learning algorithm for optimization and easiest training; our model is ResNet18 pre-trained to classify with localized based on the Tensorflow framework (TF-DFCNN). Moreover, it is used for avoiding the degradation problem consisted of the normalization function, data augmentation and sampling method to get high accuracy detection. Our deep fully convolutional network (DFCNN) consists of two-stage, where the first stage is used for classification of MITOS-ATYPIA 2014 dataset, which achieves 85% accuracy. In the second stage, we add a new layer to detect the localization depends on Weakly-Supervised Object Localization Concept via a class activation map (CAM) technique for identifying discriminative regions to retrain our CNN model without fully connected layer by combining the framework with localized layer lead the model to be more complex and precise about 93% accuracy.
Agile Teaching Practices

In this paper, we aimed to analyze the application and contribution of the use of Test-Driven Development (TDD) and Behavior-Oriented Development (BDD) in Software Engineering teaching. As empirical research, we presented an experiment conducted in the Software Engineering Laboratory (LES) course of a Private University (Tiradentes University) in the Bachelor of Computer Science and Information Systems courses. This experiment demonstrated the fundamentals in verifying the learning difficulties of students. Collected data were subjected not only to quantitative analysis but also to appropriate statistical analysis. The results showed a reduction in student absences, higher student satisfaction rate and higher grades in the courses. Furthermore, our approach allowed students to deliver a product in a short period, representing a possibility of adoption of BDD due to their successful learning experience.
Pandas DataFrames for a FAST binned analysis at CMS

Binned data frames are a generalisation of multi-dimensional histograms, represented in a tabular format with one category per row containing the labels, bin contents, uncertainties and so on. Pandas is an industry-standard tool, which provides a data frame implementation complete with routines for data frame manipultion, persistency, visualisation, and easy access to “big data” scientific libraries and machine learning tools. FAST (the Faster Analysis Software Taskforce) has developed a generic approach for typical binned HEP analyses, driving the summary of ROOT Trees to multiple binned DataFrames with a yaml-based analysis description. Using Continuous Integration to run subsets of the analysis, we can monitor and test changes to the analysis itself, and deploy documentation automatically. This report describes this approach using examples from a public CMS tutorial and details the benefit over traditional methods.
The Hilbert Schmidt inner product, quantum illumination, and beyond

Quantum Illumination (QI)<sup>1</sup> is a proposed remote targeting protocol using entangled states in which the experimenter sends a signal towards an expected target in a noisy environment that has a probability of reflecting off the surface or, in the case of no surface, being lost. Upon return, the noisy signal is jointly measured with the idler, which has been held in local memory, to determine if the surface has been detected. The idler effectively increases the brightness of the noisy signal to help distinguish it from the surrounding noise. Even though the returned mixed state is in the un-entangled regime, it has been shown that QI outperforms a conventional protocol that only sends separable states as the signal. We analyzed QI as a quantum channel discrimination protocol and circumvented computational issues that rely on diagonalization of the quantum states by using the normalized Hilbert-Schmidt (NH-S) inner product as a measure of state distinguishability.<sup>2</sup> Because the NH-S inner product is simple to compute, for a choice of entangled pure state and bipartition, we were able to rank the performance of QI entirely in terms of the dimension of the composite Hilbert space and the purity of the idler subsystem. We also showed that the greatest advantage gained by quantum illumination over conventional illumination occurs when one uses a Bell state, and for a fixed dimension d, the optimal performance of QI is achieved when the purity of the idler subsystem is minimal. In this talk, we review the results of,<sup>2</sup> and present our progress on extending this analysis to a broader class of quantum information protocols beyond QI.
The Challenges of Data Visualization for Precision Medicine

Precision medicine is driving medicine towards a new era where technology and large amounts of data come together to play an essential role in treatment. Data needed to empower and inform decision-makers can be overwhelming to interpret and poses unique challenges related to the visualization of data generated by machine learning and deep learning algorithms. Therefore, the present study aims to provide an in-depth understanding of the challenges, current trends, and opportunities concerning data visualization for precision medicine.
COMPUTER MODELING OF FOLK PATTERNS AS A TOOL FOR STUDYING THE THEME "MODELING" AND SPIRITUAL EDUCATION OF STUDENTS

The article shows that the modeling of folk patterns allows to study and experience in practice the basic provisions of the theory of modeling. The presentation is conducted in line with a holistic approach to the study of informatics at school and spiritual education.