Reviewerly/exHarmony huggingface dataset:
PNS1 Real-World DATA Collection in China: Challenges and Recommendations to Ensure DATA Quality

Over the past decade in China, there has been a substantial increase in real-world evidence (RWE) studies being used to support drug reimbursement and post-marketing decisions. However, due to nuances in the Chinese healthcare system, there are several challenges that must be overcome in order to ensure quality RWE generation. Possible challenges for registries, chart review studies and electronic medical records (EMR) studies in China include (1) expected loss to follow-up: often patients visit Tier 3 hospitals in the city for initial consultation, diagnosis and treatment initiation. A large proportion of patients then move to Tier 2/Tier 1 hospitals or a rural geographic location for routine visits and continued care or treatment; (2) unstructured data: often data in medical records can be unstructured and primarily contain clinician notes rather than structured medical terminology; (3) fragmented data: data within the hospital setting is often well captured however clinical aspects that take place outside of the hospital (e.g. laboratory assessments, visits to other healthcare professionals, death) will not be integrated into the patient medical records; (4) data access: in addition to ethics committee approval, Human Genetic Resources Administration of China (HGRAC) approval is needed before commencing any study sponsored by a foreign company. Contracting with study sites cannot begin until HGRAC approval is acquired and study timelines must allow for this. Hybrid design approaches (involving data collection from patient medical records combined with physician/patient reported outcomes) are needed to provide a comprehensive overview of the patient experience and mitigate risks associated with the challenges above. We provide a case example of a technology-based approach (incorporating EMR extraction and electronic PRO [ePRO]), proposed to expedite patient identification and enrolment, ensure quality data capture and allow cost-effective implementation of smaller pilot studies as well as larger retrospective studies or prospective registries.
Location Privacy Protection Algorithm Based on the Division Method of Voronoi in the Internet of Vehicles

Position fuzzy algorithm has certain security, but it doesn’t fit well with the actual situation of the Internet of vehicles in road network. So, in order to reduce the deviation from the actual situation, divide the road network into Voronoi diagram, then choose the path that satisfies k-anonymity and l-diversity, and sort the weights of each road. Then add a section of the road near the road weight of the vehicle that needs to be anonymous to the anonymous set, and combining positional blurring based on this algorithm. Not only can it meet the K-anonymity of the vehicle, but it can also guarantee K-anonymity from the space area, in addition to satisfying the l-diversity of the road network. The way effectively avoids the characteristic attacks of the road network. And the algorithm has less time complexity and relative anonymity and a higher anonymity success rate. To some extent, the disadvantages are reduced that some anonymous protection algorithms being greatly deviated from the actual situation.
Deep Generative Classifier with Short Run Inference

Author(s): Fischer, Eric | Advisor(s): Zhu, Song-Chun | Abstract: A deep generative classifier employs Short Run Markov Chain Monte Carlo inference with Langevin dynamics and backpropagation through time. In contrast to a convolutional neural network (ConvNet) with analogous architecture, the Short Run classifier approaches the same classification accuracy and (1) may synthesize data, (2) may learn unsupervised from additional unannotated data, and (3) exhibits robustness to adversarial attacks, due to the stochasticity of the Langevin equation and the top-down architecture of the generator network. The ConvNet classifier lacks the ability to perform (1) or (2) and possesses no defense against adversarial attacks, a critical concern for any deployed machine learning system. Meanwhile, the Short Run classifier demonstrates the capacity to improve in both classification accuracy and the quality of data synthesis, given additional unannotated data.
Access to Health-Related Information in a Selected Community in the Gokwe South District, Zimbabwe

The Gokwe South District is among the top districts in the Midlands Province of Zimbabwe with several health-related challenges, as indicated by the statistics from the Zimbabwe National Health Profile. The premise of this study was, therefore, to establish mechanisms that could be instituted to ensure access to health-related information for the Gokwe South community members. The study was pragmatic, through an embedded mixed-methods design. Opportunity sampling was used to select 72 community members. Expert sampling was used to select an information officer of the Gokwe Rural District Council and two members of the Zimbabwe Library Association Midlands Branch. Questionnaires were used to collect data from the community members, whereas interviews were used to generate data from the information officer, and the members of the Zimbabwe Library Association Midlands Branch. The study found that the community members faced varied challenges when accessing health-related information, which included digital illiteracy, the lack of health information centres, geographic seclusion of communities, and information format challenges. As a way forward, the study noted the need for the institution of community libraries and health information centres by the government, through the Ministry of Health and Child Welfare, in conjunction with the local council. In addition, diverse associations, such as the Zimbabwe Library Association, were recommended to play an active role in offering digital and information literacy programmes to the community members.
An Approximation Algorithm for a Semi-supervised Graph Clustering Problem

Clustering problems form an important section of data analysis. In machine learning clustering problems are usually classified as unsupervised learning. Semi-supervised clustering problems are also considered. In these problems relatively few objects are labeled (i.e., are assigned to clusters), whereas a large number of objects are unlabeled.We consider the most visual formalization of a version of semi-supervised clustering. In this problem one has to partition a given set of n objects into k clusters (\(k < n\)). A collection of k pairwise disjoint nonempty subsets of objects is fixed. No two objects from different subsets of this collection may belong to the same cluster and all objects from any subset must belong to the same cluster. Similarity of objects is determined by an undirected graph. Vertices of this graph are in one-to-one correspondence with objects, and edges connect similar objects. One has to partition the vertices of the graph into pairwise disjoint groups (clusters) minimizing the number of edges between clusters and the number of missing edges inside clusters.The problem is NP-hard for any fixed \(k \ge 2\). For \(k = 2\) we present a polynomial time approximation algorithm and prove a performance guarantee of this algorithm.
A Resource Allocation Mechanism for Edge-Blockchain

Blockchain technology develops rapidly, which has a broad application prospect in solving security problem of the Internet of things (IoT). However, IoT devices with limited computing resources cannot afford high computing cost of blockchain mining. To solve this problem, edge computing is introduced to improve resource allocation efficiency in trusted IoT network based on blockchain. Therefore, IoT devices can choose to offload tasks to edge servers or local collaborative mining devices. Moreover, to maximize system revenue, a two-stage auction strategy is put forward to reasonably allocate computing resources in blockchain network. In the first stage of auction strategy, computing tasks can be offloaded to edge server, and the second stage realizes resource allocation of collaborative mining devices to utilize idle computing resource adequately. Simulation results show that the proposed strategy performs better than linear search algorithm in terms of effectiveness and rationality.
New seismological insights from the analyses of historical and recent earthquakes at Ischia Island (Southern Italy)

&amp;lt;p&amp;gt;We report here a first comparative analysis between recent and historical earthquakes, occurred in the island of Ischia (Southern Italy), which produced heavy damages and thousands of fatalities. The island of Ischia is located in the Gulf of Naples, and represents a peculiar case of resurgent caldera in which volcano-tectonic earthquakes, with low magnitude, have generated large damages and catastrophic effects, as is the case for the 4 March 1881 (I&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt;8-9 MCS) and the 28 July 1883 (I&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt;10-11 MCS) events. Both the earthquakes struck the northern area of the island, similarly to the recent 21 August 2017 earthquake. The results allowed us to assess the location, as well as the possible dimension and the related maximum magnitude of the seismogenic structure, located in the northern sector of the island, and responsible of damaging earthquakes. Our results also provide an additional framework to interpret mechanisms leading to earthquakes associated with dynamics of calderas.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Improving named entity recognition through the identification of entity roles

The complexity of natural language documents that are used in companies and organizations is a challenge for information extraction tasks. Among them, the Named Entity Recognition task (the identification of proper names of people, organizations or locations) is an essential one. However, not all named entities that appear in these types of documents are relevant or have the same purpose and meaning. This purpose and meaning is defined by the role they play in the document and, retrieving all possible named entities is not useful in certain scenarios. Therefore, in this thesis we propose a hierarchical classification of named entities according to their role with the necessary models for their identification. At the same time, the thesis proposes a method for the identification of named entities based on their role using the previously mentioned hierarchy. Both contributions have been instantiated in a real use case in the legal domain using leaked mails in a journalism investigation. Finally, this thesis presents a software library that implements the contributions of the thesis together with other tasks of the information extraction field.
Quantum Secret Sharing: Experimental Demonstration of 11‐Dimensional 10‐Party Quantum Secret Sharing (Laser Photonics Rev. 14(9)/2020)

Quantum secret sharing allows the dissemination of private information amongst many participants with unconditional security. In article number 2000012, Jonathan Pinnell and co-workers devise and experimentally realize a scalable 11-dimensional implementation of a single photon secret sharing scheme with 10 participants: the highest dimension and number of participants achieved to date. Their work brings quantum communication across a network of nodes closer to fruition.
Seismotectonics of the Ionian-Akarnania Block (IAB) and Western Greece deduced from a local seismic deployment.

&amp;lt;p&amp;gt;From July 2016 to May 2017, we deployed a local seismic network composed of 15 short-period seismic stations to investigate the ongoing seismotectonic deformation of Western Greece with emphasis on the region between Ambrakikos Gulf (to the north) and Kyparissia (to the south). The network was deployed to investigate the behavior of key crustal blocks in western Greece, such as the Ionian-Akarnania Block (IAB).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;After applying automatic P- and S- wave phase picking we located 1200 local earthquakes using HypoInverse and constrained five 1D velocity model by applying the error minimization technique. Events were relocated using HypoDD and 76&amp;amp;#160; focal mechanisms were computed for events with magnitudes down to M&amp;lt;sub&amp;gt;L&amp;lt;/sub&amp;gt; 2.3 using first motion polarities.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We combined the calculated focal mechanisms and the relocated seismicity to shed light on the IAB block boundaries. Three boundaries highlighted by previous studies were also evidenced :&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;-The north-west margin of the block, the Cephalonia Transform Fault, Europe&amp;amp;#8216;s most active fault. NW-striking dextral strike-slip motion was recognized for this fault near the Gulf of Myrtos and the town of Fiskardo.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;- The south-east margin is the Movri-Amaliada right-lateral Fault Zone, activated during the Movri Mt. M&amp;lt;sub&amp;gt;w&amp;lt;/sub&amp;gt; 6.4 earthquake sequence.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;- The Ambrakikos Gulf (a young E-W rift) and the NW-striking left-lateral Katouna-Stamna Fault zone depict the north and north-eastern margins of the IAB block.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Seismicity lineaments and focal mechanisms define theKyllini-Cephalonia left-lateral fault, which is also highlighted by bathymetry data. We interpret this fault as the south-western margin of IAB separating an aseismic area observed between Cephalonia and Akarnania from a seismogenic zone north of Zakynthos Island and bridging NW Peloponnese with Cephalonia.&amp;lt;/p&amp;gt;
Interest Mining Model of Micro-blog Users by Using Multi-modal Semantics and Interest Decay Model

In order to meet the personalized needs of users and provide better recommendations, how to analyse the user interest accurately has become the focus of research currently. Due to the short content of micro-blog data, the sufficient semantic information it is difficult to get, which leads to the difficulty in accurately mining user interest. Traditional methods mainly use social relations to mine user interest, solving the problem of sparse data to a certain extent. But for users with single social relations, there is a cold start problem, which makes it unable to establish an effective user interest model. In addition, user interest will change over time which results in deviations when using traditional feature extraction methods. In order to solve the problems, we present an interest mining model of micro-blog users by using multi-modal semantics and interest decay model. It builds a connection among semantic relations in multidimensional features. It can solve the data-sparse problem, as well as the cold start problem. The fusion of multiple data semantic represents user interest features more comprehensively. To solve the problem of user interest migration, we propose an interest decay model to assist mining user interest better. In this paper, experiments are carried out on the dataset of 2, 938 user information extracted from micro-blog. The experimental results show that the method proposed in this paper significantly improves the accuracy of user interest mining compared with the existing methods.
Saliency Segmentation with Fourier-space Diffractive Deep Neural Networks

We propose to apply diffractive deep neural networks (D 2 NN) for solving advanced computer vision tasks and demonstrate the successful application of Fourier-space D 2 NN for all-optical saliency segmentation of both microscopic samples and macroscopic scenes.
STUDIEREA PROGRAMĂRII ÎN ȘCOLI

Starting with a brief historical incursion of the introduction of computer science into the school from the author's perspective, the article outlines some theoretical aspects regarding the modern trends in the formation and development of programming skills of students and their involvement in computer competitions.
Devil's in the Detail: Graph-based Key-point Alignment and Embedding for Person Re-ID

Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-point and similar clothing still bring great challenges. Besides overall visual features, matching and comparing detailed local information is also essential for tackling these challenges. This paper proposes two key recognition patterns to better utilize the local information of pedestrian images. From the spatial perspective, the model should be able to select and align key-points from the image pairs for comparison (i.e. key-points alignment). From the perspective of feature channels, the feature of a query image should be dynamically adjusted based on the gallery image it needs to match (i.e. conditional feature embedding). Most of the existing methods are unable to satisfy both key-point alignment and conditional feature embedding. By introducing novel techniques including correspondence attention module and discrepancy-based GCN, we propose an end-to-end ReID method that integrates both patterns into a unified framework, called Siamese-GCN. The experiments show that Siamese-GCN achieves state-of-the-art performance on three public datasets.
Privacy-Preserving Service Composition with Enhanced Flexibility and Efficiency

Service compositions are implemented through the interplay between actors of different organizations. Many composition systems use a middleware, which coordinates the service calls according to specified workflows. These middlewares pose a certain privacy issue, since they may read all the exchanged data. Furthermore, service compositions may require that only selected subsets of data that was initially supplied by the user are disclosed to the receiving actors. Traditional public key encryption only allows encryption for a particular party and lack of the ability to efficiently define more expressive access controls for a one-to-many communication. Besides privacy-preserving requirements, it may be necessary for participants in service compositions to be able to verify which actor has modified or added data during a process to ensure accountability of performed actions. This paper introduces a concept for efficient, privacy-preserving service composition using attribute-based encryption in combination with outsourced decryption as well as collaborative key management. Our concept enables end-to-end confidentiality and integrity in a one-to-many communication using fine-grained access controls, while minimizing the decryption effort for devices with low calculation capacity, which enables to use smartphones at the client side. The feasibility of the proposed solution is demonstrated by an implemented proof-of-concept including a performance evaluation.
Cinematic Surrealism of the Interactive Virtual Space

SurrealismSurrealism has been a revolutionary art movement since the early 1920s. It transcended the boundaries of previous art movements by visualizing the uncanny subconscious mind and dream. SurrealismSurrealism was also very influential in the development of cinema through the creative collaboration of legendary artist Salvador Dalí and cinema masters such as Luis Buñuel, Alfred Hitchcock, and Walt Disney. Standing on the shoulders of these giants, this 4D4D project built on this collaborative tradition between surreal arts and cinematic arts by adding digital artsDigital art into this fusion of cinematic surrealismCinematic surrealism. Through the use of latest VR digital technologyDigital technology in surrealism, this project expanded the cinematic experience from the 2D screen into a 3D environment with interactivityInteractivity (3 + 1D) and invited users to be “active creative dreamers” in a virtual dream designed in the style of Salvador Dalí with reference to the works of René Magritte. “Passively active” interactivityInteractivity was introduced in the digital construction of Magritte’s dream world. This non-empirical chapter discusses the creative concepts and theories of visual perception that informed this project and explores the implications of this digital treatment of cinematic surrealismCinematic surrealism and the digital fusion between art and technology.
One Problem of the Sum of Weighted Convolution Differences Minimization, Induced by the Quasiperiodic Sequence Recognition Problem

We consider an unexplored discrete optimization problem of summing the elements of two numerical sequences. One of them belongs to the given set (alphabet) of sequences, while another one is given. We have to minimize the sum of M terms (M is unknown), each of them being the difference between the unweighted auto-convolution of the first sequence stretched to some length and the weighted convolution of this stretched sequence with the subsequence of the second one. We show that this problem is equivalent to the problem of recognizing a quasiperiodic sequence as a sequence induced by some sequence U from the given alphabet.We have constructed the algorithm which finds the exact solution to this problem in polynomial time. The numerical simulation demonstrates that this algorithm can be used to solve modeled applied problems of noise-proof processing of quasiperiodic signals.
Importance of Real-time PGV Shakemaps: Experience from 2018 ML 6.2 & 2019 ML 6.3 Hualien Earthquakes

&amp;lt;p&amp;gt;Two earthquakes having almost the same magnitude and focal mechanism occurred in Hualien County, Taiwan, in 2018 and 2019. The 2018 earthquake had a magnitude&amp;amp;#160; M&amp;lt;sub&amp;gt;L&amp;lt;/sub&amp;gt;6.2 produced severe destruction; however, the 2019 earthquake ( M&amp;lt;sub&amp;gt;L&amp;lt;/sub&amp;gt;=6.3) did not cause any significant damage. The P-Alert instrumentation network consisting of 711 instruments provided high-quality real-time peak ground acceleration (PGA) and peak ground velocity (PGV) shakemaps during both events. Considering recorded PGA, both events should cause substantial destruction. On the contrary, PGV shakemaps display a different pattern. The higher PGV values (more than 17 cm/s) are observed in the rupture zone during the 2018 earthquake (locations suffering building collapse) as compared to the 2019 earthquake, proving the fact that PGV is a better indicator of damage distribution. The PGV shakemap, currently only available for P-Alert network, provides crucial information that complements the PGA issued by the official agency in Taiwan&amp;lt;/p&amp;gt;
FERPA meets COVID: Contact tracing

I'm taking a slight U‐turn this month. Instead of continuing with the topic of school officials “messing up” by inappropriately disclosing education records, I thought it would be timely to discuss the disclosure of education records as a result of this COVID‐19 pandemic.
Is the chain unbroken : a pilot study of the local police use of IT forensic processes

This work is done in cooperation with the Halmstad local police. We examine how seizedphones are handled throughout the organisation. We investigate where and if there canoccur issues with the phys ...
Analyzing the Energy Consumption of Sequential and Parallel Metaheuristics

Real-life problems are usually time-consuming since they require solving large instances of NP-hard problems. Exact search methods in most of the cases cannot afford practical solutions for such problems. Metaheuristics arise as promising solvers for these problems, by obtaining acceptable solutions in terms of quality and computational cost in a reasonable time bound. Nowadays, energy efficiency is also taken into consideration during the design of new algorithms because of the million times that algorithms run on labs and computation centers. This work presents two novel experiments for investigating the numerical performance and energy efficiency of the sequential and parallel metaheuristics. The main aim of this study is to analyze the energy consumption of three well-known and commonly used metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their parallel versions. The discussions reveal the differences/similarities between the different sequential/parallel algorithms, which include trajectory-based and population-based metaheuristics so that this study is useful for the future design of energy-aware algorithms.