Reviewerly/exHarmony huggingface dataset:
Analysis of modern computer diagnostic and measurement methods in medicine

Abstract The article deals with the aspectology of determining the classification of problems in the metrological apparatus of medical support. Attention is paid to the design and synthesis of the principles of diagnostics and direct measurements in medicine. The functional and semantic component of modern computer diagnostic methods is indicated, their synergistic effect is determined. The importance of promising measurement control methods - expert systems of new generations is substantiated. The author’s consideration of the issue of solving specific problems through the methods of artificial intelligence and information technologies in mass spectroscopy is given.
A Blockchain based Framework for IoT Security

Millions of Internet of Things (IoT) devices are associated with ordinarily to trade information or data over the web. Notwithstanding, IoT protection and security are significant difficulties because of expanding the quantity of IoT gadgets that the data are gathered from IoT gadgets. Blockchain has stood out to address protection and security worry in IoT gadgets because of its trademark highlight which incorporates permanence, review capacity, and decentralization. Regular blockchain is costly in computational and has restricted versatility and devours more memory and transfer speed which causes the postponement for IoT biological systems. This paper shows two novel commitment, Lightweight adaptable blockchain and disseminated time-based agreement. The Lightweight adaptable accomplishes decentralization by shaping an overlie network where high asset gadgets together deal with the blockchain. The time-based consensus that preparing overhead and deferral. A confident approach is used by the group heads to endlessly reduce the handling overhead for confirm new squares. Blockchain is progressively being utilized to give a circulated, secure, trusted, and private structure for the energy exchange. Be that as it may, existing arrangements experience the ill effects of the absence of protection, handling and parcel overheads, and dependence on Trusted Third Parties to make sure about the exchange. To address these difficulties, this paper proposes a Secure Private Blockchain-based system as a piece of this commitment.
Effect of Point Configurations on Parameter Estimation Analysis of Circles

The Fisher Information Matrix (FIM) determinant and the precision of the circle parameters are derived for generic configurations with n data points. The conditions for maximizing the FIM determinant are examined, and analysis shows that an infinite number of point configurations exist to maximize the FIM determinant. A collinear point configuration is proposed to obtain the highest precision of the radius, as do the configurations with the maximum FIM determinant. Moreover, theoretical analysis and the Monte Carlo method are employed to reveal that the highest precision of the parameter estimation is achieved synchronously with the maximum FIM determinant. When points are limited along an arc, the distribution ratio sequence (DRS) is designed to describe point configurations. Experiments show that the DRS of the maximum FIM determinant generally outperforms other arbitrary ones when points are limited along a certain arc.
A Multitone Model-Based Seismic Data Compression

This work develops a model-based compression scheme for seismic data. First, seismic traces are modeled as multitone sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Second, a parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. Next, the residuals are compressed using entropy coding or quantization coding techniques. The corresponding compression ratios are presented. Finally, the proposed model-based compression scheme is compared with the linear predictive coding (LPC) algorithm and the distributed principal component analysis (DPCA) algorithm on a real seismic database. The performance of the proposed model based is shown to be superior to that of the LPC and DPCA.
VR System for the Restoration of Broken Cultural Artifacts on the Example of a Funerary Monument

We present a VR system that supports the restoration of broken cultural artifacts. As a case study, we demonstrate this approach for the restoration of a funerary monument. Among the challenges of this monument are a large number of 415 fragments, an unknown amount belongs to another artifact, missing pieces prevent a full reconstruction and the preserved fragments vary strongly in size. Our VR system supports the workflow of digital restoration by offering a configurable self-arranging fragment wall. It supports the user to organize all fragments in an overview representation and to identify relevant fragments quickly. For assembly, we implemented a jigsaw approach comprising two sets of manipulation techniques that allow the user to roughly align fragments first in sub-puzzles and precisely assemble them in a second step. The iterative development and assembling process was accompanied by a professional restorer. We report about the insights we gained from this process and how we optimized the VR system according to her requirements and feedback. Within 14 sessions that took 21 hours, the virtual reconstruction was finalized.
Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network

In this paper we proposed a cascaded structure for extraction of the volumetric shape of the pancreas and tumor in adenocarcinoma patients. This process is a combination of an elastic atlas which is capable of fitting on 3D volumetric shape extracted from CT slices, a convolutional neural network with three forward paths to label the patches of images with coarse to fine resolutions using a multi-resolution structure, a region growing edge detection and finally a wavelet based multi-resolution rendering to extract the volumetric shape of the pancreas and tumor from planar CT slices. The atlas organs were weighted by geometrical parameters which were adjusted at global and organ levels. The classification of image patches was performed using a multiresolution convolutional neural network. The final segmentation of the pancreas and tumor was obtained by applying an edge detection method. A multiresolution wavelet is used for extraction of the volumetric shapes from segmented images. The subjects were 53–86 years of age with a mean and SD of 66 ± 10.24 years. K-fold cross validation is used for all the experiments. The results were compared with those of previously reported methods including Residual Network. The performance of the proposed algorithm was evaluated by Dice Similarity Coefficient (DSC), Jaccard index (JI), precision and recall yielded 89.67, 80.12, 91.37 and 93.63 for pancreas and 81.42, 68.66, 84.97 and 88.24 for tumor, respectively. The results of the proposed fully cascaded method overperformed all the other methods being compared.
Demystifying Compression Techniques in CNNs: CPU, GPU and FPGA cross-platform analysis

Convolutional Neural Networks (CNNs) are known for their high-performance despite its huge memory requirement and computational complexity. A wide range of compression techniques to reduce the number of parameters and hence computational and memory complexity have been exploring recently. In this paper, we analyse three widely used categories of techniques viz. quantization, pruning and tensor decomposition to make a cross-platform performance comparison on CPU, GPU and FPGA. These techniques are not mutually exclusive and hence can be combined to get better compression and a better speed-up on devices. Our focus is to highlight the contrasting impact of optimization techniques on devices and performance objectives. We observe a speed-up of 3.8 to 15.6× on CPU, 3.4 to 7.2× on GPU and 10.5 to 29.4×on FPGA across the models and compression techniques under consideration. We also achieved a compression of 93 to 97% across models with acceptable accuracy. Blended techniques have shown a better speed-up on FPGA compared to CPU and GPU as the caching effects, memory accesses and compiler optimizations slow down the inference on these general-purpose machines.
Robust Domain-Free Domain Generalization with Class-Aware Alignment

While deep neural networks demonstrate state-of-the-art performance on a variety of learning tasks, their performance relies on the assumption that train and test distributions are the same, which may not hold in real-world applications. Domain generalization addresses this issue by employing multiple source domains to build robust models that can generalize to unseen target domains subject to shifts in data distribution. In this paper, we propose DomainFree Domain Generalization (DFDG), a model-agnostic method to achieve better generalization performance on the unseen test domain without the need for source domain labels. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It aligns class relationships of samples through class-conditional soft labels, and uses saliency maps, traditionally developed for post-hoc analysis of image classification networks, to remove superficial observations from training inputs. DFDG obtains competitive performance on both time series sensor and image classification public datasets.
Psychophysiology, eye-tracking and VR: exemplary study design

In our poster we present the design of the study, which will be conducted in the Virtual Environment with the usage of psychophysiology (EDA, HR and pupil size) and eye-tracking. Although many papers implementing aforementioned methods have been carried out to date, we believe that these techniques are still not fully utilized. Therefore, the focus in our work is on the methodological steps we have taken and technical topography we created to provide proper ground for the measurement. Our aim was to increase the signal-to-noise ratio, achieve higher confounders control and obtain reliable results.
A Feminist Utopian Perspective on the Practice and Promise of Making

While makerspaces are often discussed in terms of a utopian vision of democratization and empowerment, many have shown how these narratives are problematic. There remains optimism for the future of makerspaces, but there is a gap in knowledge of how to articulate their promise and how to pursue it. We present a reflexive and critical reflection of our efforts as leaders of a university makerspace to articulate a vision, as well as our experience running a maker fashion show that aimed to address some specific critiques. We analyze interviews of participants from the fashion show using feminist utopianism as a lens to help us understand an alternate utopian narrative for making. Our contributions include insights about how a particular making context embodies feminist utopianism, insights about the applicability of feminist utopianism to makerspace research and visioning efforts, and a discussion about how our results can guide makerspace leaders and HCI researchers.
Negative Correlation Hidden Layer for the Extreme Learning Machine

Extreme Learning Machine (ELM) algorithms have achieved unprecedented performance in supervised machine learning tasks. However, the preconfiguration of the nodes in the hidden layer in ELM models through randomness does not always lead to a suitable transformation of the original features. Consequently, the performance of these models relies on broad exploration of these feature mappings, generally using a large number of nodes in the hidden layer. In this paper, a novel ELM architecture is presented, called Negative Correlation Hidden Layer ELM (NCHL-ELM), based on the Negative Correlation Learning (NCL) framework. This model incorporates a parameter into each node in the original ELM hidden layer, and these parameters are optimized by reducing the error in the training set and promoting the diversity among them in order to improve the generalization results. Mathematically, the ELM minimization problem is perturbed by a penalty term, which represents a measure of diversity among the parameters. A variety of regression and classification benchmark datasets have been selected in order to compare NCHL-ELM with other state-of-the-art ELM models. Statistical tests indicate the superiority of our method in both regression and classification problems. • A novel Extreme Learning Machine (ELM) architecture is proposed and benchmarked. • Negative Correlation Learning concepts are applied in this ELM model. • Diversity among the nodes in the hidden layer is explicitly promoted.
Negotiating Intersectional Non-Normative Queer Identities in India

Academic work dealing with queerness in HCI is predominantly based in the Global North and has often dealt with one identity dimension at a time. This work-in-progress study attempts to complicate the notion of queerness in HCI by highlighting how in the multi-religious, multi-ethnic, and multi-cultural context of India, LGBTQ+ movements and spaces are deeply fractured on the basis of various identity intersections. We interview 18 LGBTQ+ activists, lawyers, and allied activists in the Delhi, India to understand the issues faced by queer Indians from minority groups and their use of social media and discuss how they negotiate their non-normative identities to create safe spaces, gain access to resources, and engage in care work. The argument that we are bringing into HCI scholarship through this paper is geared toward a future endeavor for designing safe space for marginalized groups in the global south keeping in mind negotiations of power, legitimacy, and resources.
Understanding the Design Space of Embodied Passwords based on Muscle Memory

Passwords have become a ubiquitous part of our everyday lives, needed for every web-service and system. However, it is challenging to create safe and diverse alphanumeric passwords, and to recall them, imposing a cognitive burden on the user. Through consecutive experiments, we explored the movement space, affordances and interaction, and memorability of a tangible, handheld, embodied password. In this context, we found that: (1) a movement space of 200 mm × 200 mm is preferred; (2) each context has a perceived level of safety, which—together with the affordances and link to familiarity—influences how the password is performed. Furthermore, the artefact’s dimensions should be balanced within the design itself, with the user, and the context, but there is a trade-off between the perceived safety and ergonomics; and (3) the designed embodied passwords can be recalled for at least a week, with participants creating unique passwords which were reproduced consistently.
Zero-Correlation Linear Cryptanalysis with Equal Treatment for Plaintexts and Tweakeys

The original zero-correlation linear attack on a tweakable block cipher \(E_{K, T}\) (\(E_{K, T}\) is an ordinary block cipher when \(|T| = 0\)) with key K and tweak T exploits linear approximations \(\langle \alpha , x \rangle \oplus \langle \beta , E_{K,T}(x) \rangle \) with correlation zero for any fixed K and T, where the correlation is computed over all possible plaintexts x. Obviously, the plaintexts, keys, and tweaks are not treated equally. In this work, we regard the tweakable block cipher as a vectorial Boolean function \(F: \mathbb {F}_2^{ n+m+l } \rightarrow \mathbb {F}_2^{n}\) mapping \((x, K, T) \in \mathbb {F}_2^{ n+m+l }\) to \(E_{K,T}(x) \in \mathbb F_2^n\), and try to find zero-correlation linear approximations of F of the form $$ \langle \alpha , x \rangle \oplus \langle \gamma , K \rangle \oplus \langle \lambda , T \rangle \oplus \langle \beta , F(K, T, x) \rangle , $$where the correlation is computed over all possible (x, K, T)'s. Standard tools based on SAT and SMT can be employed to search for this type of zero-correlation linear approximations under a unified framework of which Ankele et al.'s work on zero-correlation analysis at ToSC 2019 by taking tweaks into account can be seen as a special case with linear tweak schedules and \(\gamma = 0\). Due to the links between zero-correlation linear approximations and integral distinguishers, we can convert the new type of zero-correlation linear distinguishers into related-tweakey integral distinguishers. We apply our method to TWINE, LBlock, and SKINNY with both linear and nonlinear tweakey schedules. As a result, we obtain the longest distinguishers for TWINE and longer zero-correlation linear distinguishers for LBlock and SKINNY when considering key/tweak schedule. The correctness of our method is verified by recovering the results of Ankele et al. and experiments on a toy cipher.
Attention-based Selection Strategy for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) task aims to recognize the object position by using only image-level labels. Some previous techniques remove the most discriminative parts for all input images or random images to capture the entire object location. However, these methods can not perform the correct operation on different images such as hiding the data or feature maps that should not be hidden. In this case, both classification and localization accuracy will be affected. Meanwhile, just erasing the most important regions tends to make the model learn the less discriminative parts from outside of the objects. To address these limitations, we propose an Attention-based Selection Strategy (ASS) method to choose images that do need to be erased. Moreover, we use different threshold self-attention maps to reduce the impact of unhelpful information in one of the branches of our selection strategy. Based on our experiments, the proposed method is simple but effective to improve the performance of WSOL. In particular, ASS achieves new state-of-the-art accuracy on CUB-200-2011 dataset and works very well on ILSVRC 2016 dataset.
Practical Changes in Reference Services: A Case Study

Between 2018 and 2019, the librarians at the Strauss Health Sciences Library improved the efficiency and reach of their reference service by implementing four small-scale changes. These changes included revising the method of collecting statistics, creating FAQs, utilizing an appointment scheduler, and launching proactive chat. This case study will provide the background and research to support these changes, details on how the changes were implemented using Springshare tools, as well as the results and implications. Finally, the librarians will share their lessons learned along with recommendations for institutions interested in adopting similar changes.
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems

Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users' trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.
Satellite images encryption Review

Rapid expansion of digital communication and multimedia application has led to the wide usage of digital image processing in every aspect of life of mankind. These include the use of remote sensing, medical field, meteorology, communications, as well as intelligent robots, just to name a few. This has brought to the issue of security particularly within the domains of storage, communication, as well as transmission of digital data including audio, video and also image. One way to assure image data security is through encryption. The application of encryption can be seen in numerous domains including medical science, military, as well as geographic satellite images. Confidentiality, authenticity, security, privacy and also integrity of image data have become a vital issue for communication and storage of images, especially when dealing with the internet, a channel that is insecure. Accordingly, there have been countless technologies of satellite images encryption being introduced in the recent years. Relevantly, this paper will discuss the notion of cryptography and images encryption. Then, different techniques of satellite images encryption will be discussed and the works associated with each technique will be reviewed. Through the scrutiny of the behavior of a number of satellite images encryption algorithms, this paper will assist in the construction of future satellite images encryption techniques.
A computational process-tracing method for measuring people’s planning strategies and how they change over time

One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms are studied is planning. To enable researchers to uncover how people learn how to plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a new computational method for measuring the nature and development of a person's planning strategies from the resulting process-tracing data. Our method allows researchers to reveal experience-driven changes in people's choice of individual planning operations, planning strategies, strategy types, and the relative contributions of different decision systems. We validate our method on simulated and empirical data. On simulated data, its inferences about the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects the plasticity-enhancing effect of feedback and the effect of the structure of the environment on people's planning strategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and to elucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, our methods can also be used to measure individual differences in cognitive plasticity and examine how different types (pedagogical) interventions affect the acquisition of cognitive skills.