Reviewerly/exHarmony huggingface dataset:
Simultaneous Calibration of Multiple Revolute Joints for Articulated Vision Systems via SE(3) Kinematic Bundle Adjustment

We propose a vision-based approach to calibrate kinematic structure of low degree-of-freedom (DoF) articulated systems. Standard hand-eye calibration yields excellent eye-to-hand relations by explicitly estimating end-effector mounted camera poses from the Perspective-n-Point (PnP) problem of a single calibration rig. However, these methods struggle when the ideal kinematic model is unknown or inaccurate, which are typical in customized serial chains such as articulated vision systems. Inspired by bundle adjustment in structure-from-motion methods, we proposed an approach, dubbed KBA, to simultaneously localize multiple joint axes at the kinematic level instead of in the 3D space. To achieve this, we design a robust multi-checkerboard detector to enlarge the kinematic coverage of end-effector samples, which are usually limited by sensor field of view. The overall optimization problem is formulated and solved using Lie group SE(3)—suitable for integration into simultaneous localization and mapping systems. In addition to simulated scenes, the experimental results with articulated monocular (AMV) and binocular vision (ABV) confirm that the proposed method not only is applicable to real serial kinematic chains but also enables challenging 3D vision tasks such as stereo reconstruction in motion.
Geometry-guided compact compression for light field image using graph convolutional networks

Light field records the information of the light in space and contributes to regenerate the content effectively, which makes immersive media more promising. In this paper, we propose a geometry-guided compact compression scheme (GCC) for light field image. We regard that the geometry of GCC includes the structure in a single sub-aperture image (SAI) and the relationship among SAIs, which can be used to fully explore the compact representation for light field image. The light field image is grouped into key SAIs and non-key SAIs. The key SAIs are obtained by down-sampling in the angular domain and arranged into the pseudo-sequence that needs to be compressed. We consider the superpixel-based segmentation algorithm to detect the contours and obtain the sketch map for the non-key SAIs. Meanwhile, the graph model is used to establish the relationships among the SAIs by the vertices and edges. On the decoder side, the light field image is reconstructed by the graph convolutional networks, and the sketch map optimizes the details of the recovered images to some extent. Experimental results show the benefit of GCC in terms of rate-distortion performances compare with several state-of-the-art methods for the real-world and synthetic light field datasets. Besides, the proposed GCC is able to generalize over datasets not seen during training.
Tailor : Datacenter Load Balancing For Accelerating Distributed Deep Learning

Accelerating Distributed Deep Learning draws increasing attention recently. Existing solutions like gradient compression, pipelining computation/communication, flow scheduling based on gradient disparities, neglect load balancing for Distributed Deep Learning (DDL) flows in DataCenter Network. In this paper, we review the PS traffic pattern in usual DDL training and propose that minimizing tail Flow Completion Time (FCT) of DDL flows is objective for DDL traffic Load Balancing. Besides, traffics of DDL differ from traditional DataCenter in temporal and spatial distribution. Current DCN Load Balancing become unadaptable to optimization requirements of DDL traffic, so we propose Tailor, a brand new load balancing scheme for DDL traffics training in DCN. Tailor is a solution without complex switch modification that sufficiently leverages the known parameter amount of DDL flows to discover congestion and reroutes by ECMP-like hash function to compensate bandwidth for slower flows. With large-scale simulation, we validate the effectiveness of Tailor. Tailor behaves well in both symmetric and asymmetric topology. It reduces time cost of DDL training by decreasing about 20-30% tail FCT than other load balancing mechanisms.
English Text Analysis System Based on Genetic Algorithm

In order to accurately extract the useful information in English, this paper studies English text analysis combined with a genetic algorithm and establishes a text analysis system. In this method, a text tendency analysis algorithm based on a genetic algorithm language model is proposed, and a Doc2vec text feature representation algorithm integrating the LDA model is designed; the parallelization technology of text analysis algorithm is studied, and the parallelization model of the algorithm by using spark big data platform is designed; the process of English text tendency analysis is studied, and a Chinese text analysis system is designed and implemented based on big data platform, including corpus intake, corpus annotation, corpus storage, model training, model verification, and other modules. In order to verify the feasibility of this subject, the accuracy of the Doc2vec text feature representation algorithm of the fused LDA model designed in the prototype system is tested. The experimental results show that the fused text representation model has high recognition degree, and the AUC value of the ROC curve reaches 0.95. At the same time, this paper tests the text analysis-related algorithms involved in the system. The test results show that the parallel algorithm can greatly improve the efficiency of the system.
Context Enhanced Traffic Segmentation: traffic jam and road surface segmentation from aerial image

Traffic jam detection and density estimation of aerial images have been widely utilized in various scenarios, such as vehicle routing and city management. Rather than directly detecting traffic jams or estimating density, traffic condition analysis based on traffic jam segmentation could yield more accurate results. Therefore, we propose a Context Enhanced Traffic Segmentation Model to simultaneously segment the traffic jam parts and road surface. However, there are two critical issues for traffic jam segmentation in aerial images: one is the scale variation problem and the other is the difficulty of accurately segmenting ambiguous traffic jam boundaries. Thus, we design a traffic estimation module to handle the scale variation problem and present a context attention module to enhance the boundary of traffic jam segmentation. Experimental results demonstrate the superiority of our proposed method.
Distance learning features in university as a vector of modern education development

We analyze the possibility of distance learning in modern conditions. The concept of distance learning, its organizational forms and means, interaction of a teacher and a student using digital technologies are given. We discuss such concepts as “independent learning activity” and “independent work”. We used theoretical methods of analysis and synthesis, as well as empirical methods, that allowed identifying the advantages and disadvantages of distance learning in higher education. The research provides examples of a conducted survey among students without single opinion on this teaching method. During its testing, revealed challenging aspects will allow considering the existing difficulties of a different nature and direct efforts to solve the identified issues in the future. Distance learning a foreign language at the Ural State University of Railway Transport is considered. The research is the base for further work on the issue in theoretical and practical terms. It helps direct the interest of both parties of interaction in education to testing various technologies in a distance-learning format with constant updates and improvements of Internet services. Distance learning brings a certain novelty to modern education and encourages each participant in the educational process to master digital competencies.
Greek Hotels’ Web Traffic: A Comparative Study Based on Search Engine Optimization Techniques and Technologies

Currently, websites rely heavily on digital marketing, notably search engine optimization (SEO), for success. In the COVID-19 era, hotels have to employ every feasible means to stay afloat despite the bleak business and travel conditions. Many of them have already invested in digital marketing, especially SEO, by applying SEO techniques to their websites to attract more visitors and bookings. This research examines hotels’ websites regarding the SEO techniques they have applied and their impact on web traffic to their websites. During a one-year observation period (February 2021–February 2022), we collected and analyzed web data from 309 top-listed Greek hotels using our own-developed software. By creating and following a specific methodology, we came to valuable conclusions. In addition, we used fuzzy cognitive mapping to develop an exploratory model. From the descriptive analysis and technical SEO perspective, we have concluded that hotels websites’ traffic and, by extension, their long-term viability are inextricably intertwined. Existing and future SEO marketers may benefit from our research’s time-accurate insights on hotel SEO tactics.
Security Operations Center Roles and Skills: A Comparison of Theory and Practice

The increasing number of interfaces between IT assets leads to a broader area prone to attacks. To address the raising security challenges, enterprises are building up Security Operations Centers (SOC) to detect and defend against cyber-attacks. In this paper, we present an empirical study based on expert interviews to investigate the differences between the roles and skills needed within a SOC in academic research and practice. Our results indicate that there is a consensus. The roles and necessary skills of the SOC analyst emerge from the investigation as a particularly critical success factor. In addition we were able to present and validate a minimum configuration for SOC roles. We also elaborate the importance of SOC interfaces into the organization to facilitate an efficient SOC operation.
Stitching participations in a touch-oriented participatory design practice

Despite the importance of Participatory Design literature concerned with democracy as a process of engaging through what arises from or in response to problems experienced in the situation, we identify the need to design alternative ontologies and relational modes, which enable us to listen to other urgencies. This paper proposes an experiential and felt shift stance by invoking touch and sentient aspects to craft new understandings of participation within a design process. We trust that proposing slowing down enables us to cultivate a distinct sensibility, hesitating to construct a common and detached world. This paper reports on episodes of an experimental prototyping practice carried out through an embroidery group. The explorations of this case lead us to nurture the relationship between mind, body, and world, introducing the idea of a touch-oriented participatory design (TOPD) when considering participation attentive to various rhythms, making democracy through storytelling and an ethos of care.
Technology Innovations for Business Growth – Impact of AI and Blockchain on Financial Services

Fintech and digitization of financial services have proven disruptive to the traditional practices followed by large incumbents. The overall pace of business and the competitive ecosystem has transformed to become more inclusive, customer-centric, agile, and cost-effective. Fintech has caused the digitization of economies and the use of Artificial Intelligence and Blockchain-based technologies have aided in improving financial efficiencies. The penetration of the internet and mobile telephones have been the key enablers of this technological change. Social media is bringing in huge quantities of structured and unstructured personal data which need to be adequately analyzed for business advantage. This paper aims to study the two most popular technologies, AI and Blockchain, that have been adopted in the Financial Services sector considered to provide solutions that are secure, efficient, fast and cheap. As software products, AI and Blockchain are different with distinct applications in the Financial Services sector. The findings indicate the positive impact digitization has had on Financial Services by improving its reach, variety, overall security, convenience, transparency at much lower costs than traditional offerings made by incumbent institutions.
The Socializing Function of the University As a Condition for the Professional and Personal Formation of Graduates

The period of study of students at the university should be considered as a special one that determines the subsequent line of life, since each person becomes the subject of his life path. From the choice of an educational institution to the final result, enough time passes in order, along with the main educational function, to form the necessary professional and social skills in students through a set of organizational measures.
Multiscale Decomposition of HDR Images Using the Edge-Preserving Filters

Tone mapping is very much required to present the high dynamic range (HDR) images on conventional screens. Most popular and efficient way is to perform decomposition of the high dynamic range image into multi-layers. Large-scale intensity variations are assigned to the base layer, and small-scale intensity variations are assigned to the detail layer. Base layer is compressed retaining detail layers, and both the layers are combined; resulted image is displayed on the conventional display devices. In this process, only, contrast is reduced, whereas the details are preserved. In this paper, various base-detail layers decomposition techniques are compared using the metrics such as psnr, mPSNR, and HDRVDP3. Efficiency of edge-preserving filters to preserve the details while smoothing the contrast is analyzed.
Conversational partners’ interactions in response to co-present mobile phone usage

The goals of the two studies were to identify common communicative responses to co-present mobile phone usage and conversational partners’ reactions to that communication, and to determine whether individuals perceive their communicative responses as effective. Study 1 identified common participant responses and partner (i.e., phone user) reactions to those responses. In Study 2, researchers coded data from a new sample for these same responses and reactions and measured participants’ perceived effectiveness and likelihood to use the same responses again. The most frequent responses were demanding/requesting to stop, asking partner about their phone use, and using humor/sarcasm. Stopping, apologizing and stopping, justifying the use, and continuing to use the phone were the most common partner reactions. Directness of communicative responses was unrelated to reaction valence. Most conversational partner reactions were positive, but when negative, participants rated direct responses as significantly less effective than indirect responses.
Rewild My Heart: With Pedagogies of Love, Kindness and the Sun and Moon

Abstract The pressing problems we face from wars to pandemics erupt against the less dramatic background of the mundane continued destruction of our planet as a habitable realm. In education, technology can be environmentally destructive in a variety of ways. Educators may feel the urge to address these matters but we also know that EdTech solutionism can create new problems as fast as it fixes old ones. Something in the urgency of the fix may be the problem. It may be that we are too desperate to fill a hole in the world that we only see through a hole in ourselves. Hence, in order to truly escape our colonised and domesticated fates, we need to rewild our very thinking. We need to examine the underlying emotional tones and waves that cause us to act as we do against our own interests and that of our planet. This conundrum is explored here through the device of storytelling using a multimodal speculative auto-ethnography to push into realms beyond mere utility and out into conceptual wilds. No fixes or solutions are offered here for the planet. Lifeless celestial objects such as the sun and moon loom large by contrast. Both have walk-on parts in this tale, and watch us as we watch them, with whatever love and kindness we can.
Achieving Security in Proof-of-Proof Protocol with Non-Zero Synchronization Time

Among the most significant problems that almost any blockchain faces are the problems of increasing its throughput (i.e., the number of transactions per unit of time) and the problem of a long waiting time before block confirmation. Thus, for example, in the most common BTC blockchain, according to various estimates, throughput is from 3 to 7 tps (transactions per second), and the average block confirmation time (block is considered confirmed if it has at least 6 blocks over it) is 1 h. At the same time, it is impossible to solve these problems directly by increasing the block size or increasing block generation intensity because this leads to essentially a decrease in the security of the blockchain in the first turn against double spend and splitting attacks. Such problems lead to the inconvenience of the practical use of cryptocurrencies to pay for goods and services. Proposed a few years ago, the PoP consensus protocol potentially helps to solve the problem of increasing blockchain throughput, although it was originally intended to ensure the stability of “young” blockchains, with “small” PoW, through the use of a secure blockchain, such as BTC. A blockchain that has provable security is called the security-provided blockchain (SPB), and one that uses SPB to achieve its security is called the security-inherited blockchain. In this paper, we give explicit formulas which describe how the number of confirmation blocks in the security-inherited blockchain, which is sufficient to achieve a given security level of this blockchain to a double spend attack, depends on the parameters of both blockchains. It is essential that we use a realistic model to obtain the results, taking into account the synchronization times of both blockchains. Such a model is much closer to the real situation, but at the same time, it leads to significant analytical difficulties in obtaining results. The obtained formulas are convenient for numerical calculations, the numerous examples of which are also given in this work.
Investigation of Deep Learning architectures and features for Adversarial Machine Learning Attacks in Modulation Classifications

Artificial intelligence (AI), and specifically machine and deep learning, are emerging as essential enabling techniques for the design of future generations of wireless networks for the Internet of Things (IoT). With the exponential increase in IoT devices in recent years, AI methods have become even more beneficial in network management including for energy efficiency, spectrum utilisation and user admission control. However, the rise of AI applications, has also caused a rise in cyber-attacks, where attackers can exploit network vulnerabilities through its use. In this research, we develop a deep learning approach for Automatic Modulation Classification (AMC) with three different feature combinations, using batch normalisation and optimised with the focal loss. Our results have implications about the performance of the AMC against adversarial examples. Firstly, we find that using batch normalisation enhances the classifier’s performance against adversarial examples. Secondly, by generating white-box fast gradient sign method attacks, we show that using the phase as an input feature improves the performance of the AMC against adversarial examples.
Multimodal Video Summarization based on Fuzzy Similarity Features

The continuously growing number of user-generated videos has increased the need for efficient browsing through content collections and repositories, which in turn requires descriptive, yet compact representations. To this goal, a popular approach is to create a visual summary, which is by far more expressive compared to other approaches, e.g., textual descriptions. In this work, we present a video summarization approach that is based on the extraction and fusion of audio and visual features, in order to produce dynamic video summaries, i.e., comprising of the most important video segments of the original video, while preserving their temporal order. Based on the extracted features, each segment is classified as “interesting,” or “uninteresting,” thus included in the final summary, or not. The novelty of our approach is that prior to classification, the fused features are fuzzified, thus becoming more intuitive and robust to uncertainty. We evaluate our approach using a large dataset of user-generated videos and demonstrate that fuzzy features are able to boost classification performance, providing for more concrete video summaries.
Multi-object Visual Tracking for Indoor Images of Retail Consumers

Visual tracking of multiple objects is a challenging task that involves several steps: target detection, data association and re-identification, each with its own set of challenges. Trending deep learning methods usually combine some of these steps and report impressive results in tracking benchmarks while running at practical speeds. In this work, we develop an online, realtime multi-object tracking approach which relies on a multitask learning framework to output candidate targets and their corresponding discriminative image representations. The proposed model is also able to perform accurate retail consumer tracking without further tuning its design philosophy. Our key contribution lies at the motion forecasting part wherein standard Kalman Filters are replaced with a LSTM network which exploits long-term dependencies to essentially model target tracking. Numerical experiments of our method on the widely used MOT16 benchmark demonstrate its effectiveness. Additionally, quantitative results of in-house retail image data confirm the method’s ability for practical multi-consumer tracking.
Joint Power and Contrast Shrinking in RGB Images with Exponential Smoothing

In this study, an exponentially weighted moving average (EWMA) filter is employed to transform the intensity values of RGB colour images. The processing is applied at the YC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> colour system and targets only the component Y. The rationale behind the selection of temporal filtering with exponential weighting is to generate images with lower power simply and efficiently. This filter gives lower importance to the previous intensity of each pixel, while higher weight is given to the input, which here captures the overall brightness of the image (not an individual brightness level per pixel). Compared to the Concurrent Brightness & Contrast Scaling (CBCS) linear mapping algorithm, the exponential filter can produce lower power images, forcing higher histogram shrinkage but also high perceptual quality. The structural similarity index (SSIM) and power decay rate metrics are evaluated to verify their performance and empirically identify the optimal values for the involved parameters.
Attribute-based Gesture Recognition: Generalization to Unseen Classes

In this paper, we present a hand pose based gesture representation that can effectively classify both static and dynamic gestures. In contrast to resource and data intensive deep learning models, we postulate that hand pose alone can be used to extract compact yet discriminative features that are suitable for most applications that require real-time gesture recognition with minimal computational overhead. Building on the robustness of modern hand pose estimation frameworks and the expressive power of neural networks, we extract a fine-grained gesture description by decomposing gestures in a set of defined attributes. Our approach is highly capable of generalizing to unseen data and unseen classes, as shown by our experiments in the context of the BonsAPPs Challenge use-case.