Reviewerly/exHarmony huggingface dataset:
Smoothing filter design: A general framework

Signal filtering/smoothing is a challenging problem arising in many applications ranging from image, speech, radar and biological signal processing. In this paper, we present a general framework to signal smoothing. The key idea is to use a suitable linear (time-variant or time-invariant) differential equation model in the regularization of an optimization problem. The presented approach has many applications in signal processing. Specifically, the quadratic variation (QV) regularization and smoothness priors are special cases of the proposed framework. These two methods are particularly suited for polynomial signal smoothing, as they use the derivatives of the signal in the regularization term of the optimization problem. Therefore, their performance is significantly decreased for signals that cannot be well modeled with a low order polynomial function. The approach presented in this paper was used to overcome this limitation. As proof of other applications, it was employed for designing an extension of QV regularization to remove the baseline wander in electrocardiogram (ECG), simultaneous tracking of powerline interference and baseline wander, and T-wave amplitude computation.
U-MEDCHAIN A Blockchain Based System for Medical Records Access and Permissions Management

In recent years, the interest in using wireless communication technologies and mobile devices in the healthcare environment has increased. However, despite increased attention to the security of electronic health records, patient privacy is still at risk for data breaches. Thus, it is quite a challenge to involve an access control system especially if the patient’s medical data are accessible by users who have diverse privileges in different situations. Blockchain is a new technology that can be adopted for decentralized access control management issues. In this work, blockchain based frame work Electronic Medical Records (EMR) is applied. The proposed frame work aims at providing interoperable, secure, and efficient access to EMRs by health providers, patients and third parties while maintaining the patient’s privacy. We propose a timed-based smart contracts whose design meet the demands of EMRs. These contracts are employed in the blockchain for governing the transactions, monitoring the computations performed on the EMRs through the enforcement of the acceptable usage policies and managing the use of data after transmission. This work employs the blockchain technology with a collection of encryption techniques and hash functions. Sensitive information that are placed on the blockchain are encrypted to decrease the possibility of being accessed by unauthorized entity. Advanced cryptographic techniques are also adopted by the proposed framework for providing further security. The use of proxy re-encryption technique is employed to solve the problem of transferring encrypted messages among nodes with no need to share symmetric key. This adopts the distributed ElGamal re-encryption schema with distributed blinding technique. Our proposed framework employs the hashing method SHA-256, to ensure data integrity. The proposed system employs a new incentive mechanism integrated with the Proof of Authority (PoA) consensus algorithm for crating, validation, and appending new block. Now security and access control are maintained by the adoption of advanced encryption and authentication techniques throughout the blockchain. Interoperability, auditability, and accessibility are provided by the use of comprehensive logs. Our proposal gives efficient security and accessibility for medical records in an effective manner.
Combining feature fusion link prediction with graph neural networks

At present, Graph Neural Network (GNN) methods usually follow the node centered message passing process and rely heavily on smooth node characteristics rather than graph structure. In view of this limitation, based on the heuristic method and graph attention mechanism, a feature fusion link prediction model (SAFL) combined with graph neural network is proposed. This model extracts the enclosing subgraphs around the target, combines the attention mechanism to assign neighbor weights to learn useful structural features, considers the impact of different nodes on the link, and fuses the graph neural network with the characteristics of input nodes to predict the link. The experiment on OGB dataset shows that the proposed link prediction model based on heuristic method enhances the graph structure characteristics, effectively represents the connectivity of enclosing subgraphs, and achieves better performance in link prediction.
The participatory value-sensitive design (VSD) of a mHealth app targeting citizens with dementia in a Danish municipality

The Sammen Om Demens (together for dementia), a citizen science project developing and implementing an AI-based smartphone app targeting citizens with dementia, is presented as an illustrative case of ethical, applied AI entailing interdisciplinary collaborations and inclusive and participative scientific practices engaging citizens, end users, and potential recipients of technological-digital innovation. Accordingly, the participatory Value-Sensitive Design of the smartphone app (a tracking device) is explored and explained across all of its phases (conceptual, empirical, and technical). Namely, from value construction and value elicitation to the delivery, after various iterations engaging both expert and non-expert stakeholders, of an embodied prototype built on and tailored to their values. The emphasis is on how moral dilemmas and value conflicts, often resulting from diverse people’s needs or vested interests, have been resolved in practice to deliver a unique digital artifact with moral imagination that fulfills vital ethical–social desiderata without undermining technical efficiency. The result is an AI-based tool for the management and care of dementia that can be considered more ethical and democratic, since it meaningfully reflects diverse citizens' values and expectations on the app. In the conclusion, we suggest that the co-design methodology outlined in this study is suitable to generate more explainable and trustworthy AI, and also, it helps to advance towards technical-digital innovation holding a human face.
3V3D: Three-View Contextual Cross-slice Difference Three-dimensional Medical Image Segmentation Adversarial Network

In three-dimensional (3D) medical image segmentation, it is still a great challenge to obtain the multidimensional feature information contained in voxel images using a single view for smaller segmentation targets, and the robustness of models obtained by relying solely on segmentation networks needs to be enhanced. In this article, we propose a three-view contextual cross-slice difference 3D segmentation adversarial network, in which three-view contextual cross-slice difference decoding blocks are introduced to improve the segmentation decoder’s ability to perceive edge feature information. Meanwhile, dense skip connections are used to alleviate the problem that a large amount of shallow feature information is lost in encoding and insufficient information provided by a single long skip connection during image reconstruction. The adversarial network improves the performance of the segmentation network by distinguishing true or false for each patch of the predicted image. Further, the robustness of the segmentation model is improved in the form of adversarial training. We evaluate our model on the publicly available brain tumor BraTS2019 dataset as well as the ADNI1 dataset and achieve optimal results compared to recent excellent models.
Squeezing the security budget

Organisations currently face huge economic pressures, and many of them are having to cut their budgets. But is cyber security really something you can afford to skimp on? And if savings have to be made, are there ways you can spend your security dollar more wisely?
Cross-Core and Robust Covert Channel Based on Macro-Op Fusion

Covert channels based on the CPU front-end typically utilize time or power differences caused by contention. This requires that the sender and receiver are located on the same and quiet physical core. We propose one channel exploiting the macro-op fusion in the front-end, called MFCC, which has cross-core and robust properties. Macro-op fusion is one of the optimization strategies in x86 microarchitecture, which aims to fuse two macro-ops and decode them into a single micro-op. We reverse the constraints of macro-op fusion and find that decoders stop decoding after two macro-op fusions in one cycle. Thus time differences appear in two identical loops with different virtual addresses, represent-ing one and zero, respectively. We build two types of MFCC: the sender and receiver running in the same thread but operating at different privilege levels, and the two running in two different processes. The accuracy within a single thread is almost independent of the CPU load, while the accuracy of interprocess transmission maintains more than 0.8 even when the CPU load is 100%. Finally, we propose three possible protection strategies: eliminating the macro-op fusion mechanism, adding noise to the hardware counters, and making the events delivered at a defined point in time. This paper demonstrates that even minor optimizations in the front-end can lead to covert transmission.
SIMPLE BLOCKCHAIN-BASED E-VOTE APPLICATION

This paper describes a blockchain-enabled e-voting system that uses digital wallets to represent votercredentials, with each voter receiving a single "coin" to cast their vote. The voting process includes steps for voterregistration and identification, as well as the casting of votes using encryption or hashing algorithms. The authors discussvarious methods for voter identification, including the use of private and public key pairs, ID documents, and biometricdata. They also mention the use of the SHA-256 hashing algorithm for added security. The goal of the project is to developa flexible system that can be used in any institution and that guarantees maximum security against fraud. The system isbuilt using Rust, a new experimental programming language developed by Mozilla, and makes use of "task trees" tomanage the voting process. Consideration of various methods for voter identification, including the use of private andpublic key pairs, ID documents, and biometric data, shows a commitment to providing a flexible solution that can beadapted to meet the needs of different institutions and voting scenarios. The use of the SHA-256 hashing algorithm alsoadds an extra layer of security to the system, making it more difficult for malicious actors to manipulate or tamper withthe voting process. The use of Rust and "task trees" to manage the voting process also indicates a focus on developing arobust and efficient system that can handle the complexities of large-scale voting. Rust's emphasis on safety and memorymanagement can help prevent common programming errors that can lead to security vulnerabilities, while task trees canprovide a structured approach to managing multiple concurrent voting processes. Overall, the blockchain-enabled evoting system described in this paper presents an interesting approach to addressing the challenges of secure andtransparent voting processes. However, as with any new technology, it will be important to thoroughly test and evaluatethe system to ensure its reliability and effectiveness before it can be widely adopted
Fixed-point implementations for feed-forward artificial neural networks

We present scalable and generalized fixed-point hardware designs (source VHDL code is provided) for Artificial Neural Networks (ANNs). Three architectures are presented: multiply-and-add, multiplier-less, fully pipelined. In addition, we include two approaches for ANN binary layers: accumulation-based and fully pipelined. The fully customized hardware architectures allow for design space exploration to establish trade-offs among numerical format, processing time, resource usage, and numerical accuracy. Users can select the ANN architecture, ANN parameters (structure, weights, biases), the numerical format for both the input/output data in every layer and the network parameters (weights and biases). Results are presented in terms of resources, processing time, and numerical accuracy. The proposed architectures were implemented on modern FPGAs. These hardware designs are expected to be used as building blocks on a variety of applications such as CNNs and SNNs, as well as a platform for educational purposes.
Applying Decomposition to Research Generative Music Creation Processes

The question of description and selection of groups of generative systems through the study of methods and operations of generating musical compositions is considered. A description of known systems for generating musical compositions is proposed. An analysis of the process of creating generative musical compositions is proposed.
On the motivation of prospective students to go to university college of the Siberian State Industrial University

Among school graduates, an increased result in secondary vocational education and a decrease in interest in higher education became noticeable. This article discusses options for obtaining vocational education after graduation, puts forward hypotheses that explain the increased interest in obtaining secondary vocational education, in particular, the problem of increased fear of the unified state exam among applicants and their parents and their desire to «get around» it is studied. The article indicates the increased interest in the system of continuous education in general and in the city of Novokuznetsk in particular. To confirm the hypotheses put forward, a study was conducted in which 167 first-year students of the University College of the Siberian State Industrial University took part. The students were asked about their motives for entering the University College after the 9th grade, and about the reasons for entering the University College of the Siberian State Industrial University specifically. After analyzing the results, it was concluded that the hypotheses put forward were confirmed, and the main reason for the refusal of high school students to continue their studies at school in grades 10–11 in favor of obtaining a secondary vocational education was determined. The article notes that every year the number of those wishing to receive a secondary education at the Siberian State Industrial University will grow, therefore, the number of specialties and places for study should be expanded.
Object-Tracking Algorithm Combining Motion Direction and Time Series

Object tracking using deep learning is a crucial research direction within intelligent vision processing. One of the key challenges in object tracking is accurately predicting the object’s motion direction in consecutive frames while accounting for the reliability of the tracking results during template updates. In this work, we propose an innovative object-tracking algorithm that leverages both motion direction and time series information. We propose a loss function that guides the tracking model to learn the direction of object motion between consecutive frames, resulting in improved object localization accuracy. Furthermore, to enhance the algorithm’s ability to discriminate the reliability of tracking results and improve the quality of template updates, the proposed approach includes an attention mechanism-based tracking result reliability scoring module, which takes into account the time series of tracking results. Compressive experiment evaluation on four datasets shows our algorithm effectively improves the performances of object tracking. The ablation experiments and qualitative analysis prove the effectiveness of the proposed module and loss function.
The role of individual characteristics: How thinking style and domain expertise affect performances on visualization

Widely employed by innovative organizations, a well-designed simple data visualization has been shown to enhance user experience and aid in decision making; while a more embellished visualization may cause overload, it has the potential to create deeper processing and learning. Furthermore, individual characteristics may impact on how users seek information on these different types of visualization. This study proposes that thinking styles (analytical vs holistic) and domain expertise moderate the effects of data visualization types on decision performances in terms of decision accuracy, decision confidence, memory recall, and cognitive load. To test our hypotheses, an experimental study involving visual manipulations in the context of personal finance was conducted on two types of visualizations (simple and clutter). Results suggest that simple visualizations enhance decision accuracy and reduce cognitive load. We also find that cognitive load is further reduced when analytical thinkers are presented with simple visualizations. These findings can help designers understand how user characteristics may be considered when designing and evaluating visualizations for decision makers.
On the generation of necklaces and bracelets in R

This note introduces a code snippet in R aiming to generate necklaces as well as bracelets. Among various uses, necklaces are useful tools to manage traces of products of random matrices. Functionality for necklaces and bracelets is provided with some examples of applications such as Lyndon words and de Bruijn sequences. The routines are collected in the Necklaces package available from the Comprehensive R Archive Network.
An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

Summary Efficient utilization of resources is a challenging task in cloud computing due to heterogeneity of resources and fluctuating resource demands of multifarious applications. This article presents an autonomic resource allocation approach for energy efficient utilization of resources while satisfying QoS needs of the end‐users. The proposed approach is based on antlion optimization and makes use of resource utilization history for autonomic allocation and efficient use of resources. It can operate in one of the three different modes namely, energy‐saving, performance, and balanced mode depending on the projected workload of an application. The performance analysis of the proposed approach is carried out in CloudSim simulator under different workload conditions.
Developing a shared SOC

A security operations centre (SOC) provides a critical security function to detect and defend from cyber security attacks. It is the last line of defence as part of an organisation's overall security control set. But not every organisation can, or should, run its own SOC.
Prediction of Photovoltaic Output Power Based on Match Degree and Entropy Weight Method

In this study, an algorithm, which had combined the match degree and entropy weighting method, was proposed to predict the efficiency of the photovoltaic output power. First, the key characteristic quantities were selected by the correlation analyses of output power history data and meteorological data of the photovoltaic power generation system. Second, according to the Euclidean distances between the predicted points and the historical data points, a method for the selections of similar sample points was proposed based on point-to-point theory. Finally, the match degrees between each characteristic quantity of the prediction points and the similar sample points were determined using the Mamdani reasoning method in fuzzy mathematics. The fitting weighting was then solved using the entropy weighting method, which was applied to fit the match degrees of the same characteristic quantities between the similar sample points. Next, the correlation coefficients were utilized as the weighting to fit the match degrees of the different characteristic quantities, resulting in a total match degree. This total match degree was then used to fit the photovoltaic output power of the similar sample points to the prediction points. We had proven that this method could have effective predictions and good adaptabilities in various weather statuses with high accuracy and real-time performance, especially, in the case of sudden weather change.
Optical-mode hyperconversion in the bad-cavity regime

The ability to convert photons between different electromagnetic modes with low intensity has the potential application in future information technologies. Here, we explore optical-mode-conversion characteristics in a bimodal cavity coupled to a pair of two-level atoms instead of a single atom using experimentally realistic parameters. In our scheme, the two cavity modes, involving a vertically ($V$-) polarized mode and a horizontally ($H$-) polarized mode, are not directly coupled to each other owing to their orthogonal polarizations and the $V$-polarized mode is coherently excited by an external weak pump laser. Via analytical calculations and numerical simulations, we find that optical hyperconversion phenomenon from the $V$-polarized mode to the $H$-polarized mode can be realized in the bad-cavity regime, which is reminiscent of optical hyperradiance introduced in the previous studies. We also provide an approximate analysis which gains deeper understanding into the fundamental reason behind this behavior. Furthermore, we identify the parameter ranges for generating optical-mode subconversion, superconversion, and hyperconversion. The obtained results will deepen the understanding of mode-conversion micromechanisms in the weak-coupling multiatom cavity architectures.
Criticità nella comunicazione interculturale tra spagnoli e italiani | Puntos críticos en la comunicación intercultural entre españoles e italianos

It is often believed that expressions, gestures, and habits are natural and common to all individuals. These elements have a deep cultural matrix based on the patterns that characterise one society but differentiate it from another. The causes that can generate misunderstandings can be traced back to linguistic factors, differences in values, non-verbal codes. In this volume, these aspects are presented in the context of intercultural communication between Italians and Spanish. The purpose of the volume is to focus on a few key points of intercultural communication and has no pretension of exhaustiveness; the interviews, although aimed at offering as diversified an overview as possible, can certainly not offer a mapping of the infinite nuances of human behaviour: communicating, as a matter of fact, is not a matter of cultures but of people, each with their own original way of re-elaborating the stimuli and conditioning that their culture offers.