Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Privacy Preserving Group Nearest Neighbor Search

Group <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq1-2930696.gif"/></alternatives></inline-formula>-nearest neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq2-2930696.gif"/></alternatives></inline-formula>GNN) search allows a group of <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq3-2930696.gif"/></alternatives></inline-formula> mobile users to jointly retrieve <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq4-2930696.gif"/></alternatives></inline-formula> points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq5-2930696.gif"/></alternatives></inline-formula>GNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq6-2930696.gif"/></alternatives></inline-formula>GNN search in the full user collusion environment, where any <inline-formula><tex-math notation="LaTeX">$n-1$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wu-ieq7-2930696.gif"/></alternatives></inline-formula> users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq8-2930696.gif"/></alternatives></inline-formula>GNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wu-ieq9-2930696.gif"/></alternatives></inline-formula>GNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.
Statistical Inference of Diffusion Networks

To infer structures in diffusion networks, existing approaches mostly need to know not only the final infection statuses of network nodes, but also the exact times when infections occur. In contrast, in many real-world settings, such as disease propagation, monitoring exact infection times is often infeasible due to a high cost. We investigate the problem of how to learn diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing sequences of timestamps to determine potential parent-child influence relationships between nodes, we propose to find influence relationships with high statistical significance. To this end, we design a probabilistic generative model of the final infection statuses to quantitatively measure the likelihood of potential structures of the objective diffusion network, taking into account network complexity. Based on this model, we can infer an appropriate number of most probable parent nodes for each node in the network. Furthermore, to reduce redundant inference computations, we are able to preclude insignificant candidate parent nodes from being considered during inferencing, if their infections have little correlation with the infections of the corresponding child nodes. Extensive experiments on both synthetic and real-world networks offer evidence that the proposed approach is effective and efficient.
Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning

or Framework Our abstractor is a pointer-generator model that is trained using Self-Critic Policy-Gradient to create paraphrases for each sentence. Similar to the extractor, which uses attention over sentences, our abstractor attends over words in a sentence to generate the final output. This is an LSTM-LSTM framework in which the encoder reads a single sentence and the decoder will generate the paraphrase of that sentence. We use a bidirectional encoder and each encoder reads the word representation of xi and generate a forward and backward hidden state −−→ h i and ←−− h i ( −−→ h 0 = 0 and ←−− h Te = 0) which we concatenate them and represent it with 48 Chapter 3. Actor-Critic Model for Text Summarization h i = ( −−→ h i ⊕ ←−− h i ). During decoding, the decoder receives the last state of the backward and forward encoder states (z 0 = ( ←−− h 0 ⊕ −−→ h Te )) along with the ground-truth word yj and generate the output as follows: z j = LSTM(yj, z abs j−1) a ij = v1tanh(W1h abs i +W2z abs j ) α j = softmax(a abs j ) c j = ∑ i α abs ij W1h abs i b ij = v2tanh(W4h abs i +W5c abs j ) p j = softmax(b abs j ) (3.6) where p j is the probability distribution over all words in our dictionary and all W ’s and v’s are model parameters. Once we reach either the End-of-Sentence token or maximum decoding length T , we will calculate the loss using cross-entropy (CE) as follows:
Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms

A method for constructing the empirical models of complex processes has been developed on the basis of genetic algorithms which, compared to the inductive method of self-organization of models, significantly reduces computer time for their implementation. An approach has been used that allows a complex model to be considered as a composition of three components, i.e. a linear trend, an oscillatory component with non-multiple frequencies and a regression equation which simplifies the process of building complex models. To implement the proposed method, algorithms and software have been developed based on a specific example of the dependence of the water level in the river. The Dniester River weather conditions show that a model built on the basis of the proposed method describes the behavior of complex processes with sufficient accuracy. The resulting empirical model can be used to predict the water level depending on weather conditions.
Prevenção de Violações de Atomicidade usando Contractos

Resumo A programação concorrente obriga o programador a sincronizar os acessos concorrentes a regiões de memória partilhada, contudo esta abordagem não é suficiente para evitar todas as anomalias que podem ocorrer num cenário concorrente. Executar uma sequência de operações atómicas pode causar violações de atomicidade se existir uma correlação entre essas operações, devendo o programador garantir que toda a sequência de operações é executada atomicamente. Este problema é especialmente comum quando se usam operações de pacotes ou módulos de terceiros, pois o programador pode identificar incorretamente o âmbito das regiões de código que precisam de ser atómicas para garantir o correto comportamento do programa. Para evitar este problema o programador do módulo pode criar um contrato que especifica quais as sequências de operações do módulo que devem ser sempre executadas de forma atómica. Este trabalho apresenta uma análise estática para verificação destes contratos.
The impact of collective action and market prices

Purpose The purpose of this paper is to investigate whether the information sharing in an online discussion forum, over an agricultural market characterized by a large number of small-scale farmers, has an impact on the market prices. Design/methodology/approach All the comments posted by farmers and traders on four storable items (potato, onion, lemon and apple) in an online discussion forum over 2013–2017 are collected. By using text mining techniques and regression analysis, words characterizing the actions and expectations of farmers and traders on the course of the market price are identified. Then, summary indicators pointing to positive and negative views on prices are calculated. Finally, the relation between these indicators and market prices is analyzed. Findings The results point to economically significant impacts, as one standard deviation increase in the share of net positive comments is associated with 20, 22, 13 and 10 percent increase in the consumer prices of potato, onion, lemon and apple, respectively within three months. Originality/value Overall, this study provides an evidence for the link between information sharing of farmers on online domain and their collaboration in the physical domain. Thus, the study implies that the information synthesized from online discussion forums may actually contain valuable information for researchers and policy makers regarding the behavior of agents even on traditional domains such as agriculture.
Effect of Antenna Deployment on Achievable Rate in Cooperative Magnetic Induction Communication

Magnetic Induction (MI) communication can be applied in some through-the-earth scenarios such as mines and underground rivers. To increase the transmission rate of MI communications, we propose a cooperative MI (CMI) scheme with an amplify-and-forward (AF) relay. Different from existing studies, we mainly focus on the relay with arbitrary antenna position and orientation (antenna deployment, AD). We derive the closed-form expression of CMI achievable data rate gain (CMG) for the relay and the closed-form expression of CMI channel bandwidth. Simulations reveal that a relay with appropriate AD could yield a significant increase in the achievable rate for MI systems.
MUSDB18 - a corpus for music separation

The sigsep musdb18 data set consists of a total of 150 full-track songs of different styles and includes both the stereo mixtures and the original sources, divided between a training subset and a test subset. Its purpose is to serve as a reference database for the design and the evaluation of source separation algorithms. The objective of such signal processing methods is to estimate one or more sources from a set of mixtures, e.g. for karaoke applications. It has been used as the official dataset in the professionally-produced music recordings task for SiSEC 2018, which is the international campaign for the evaluation of source separation algorithms.
Steering the Growth of Adaptive Self-Preserving Dissipative Structures

In the 1950s, the famous cyberneticists Gordon Pask and Stafford Beer conducted a series of remarkable electrochemical deposition experiments. By applying an electric potential across electrodes submerged in an acidic solution of ferrous sulfate, they could bias the growth of electrochemical deposition so as to form functional structures including sensory structures capable of distinguishing between different sounds. Unfortunately, the details of their apparatus and methods are unavailable. As a consequence, their experiment has not been replicated, and the precise mechanisms underlying their results remain unknown. As preliminary steps toward recreating their remarkable results, this paper presents a new computational model that simulates the growth and decay of dendritic structures similar to those investigated by Beer & Pask. We use this model to demonstrate a plausible mechanism through which an electrochemical system of this kind could respond to a reinforcement signal. More specifically, we investigate three strategies for varying the applied electrical current so as to guide the formation of structures into target forms. Each presented strategy succeeds at influencing the growth of the structure, with the most successful strategy involving a ‘constant-current’ feedback mechanism combined with an externally driven oscillation. In the discussion, we compare the adaptation of these structures with various biological adaptive processes, including evolution and metabolism-based adaptive behaviour.
Petri Net Recommender System to Model Metabolic Pathway of Polyhydroxyalkanoates

Due to the complexity of the metabolic pathways, their modeling has been a great challenge for the researchers. Various mathematical models have been developed and are continuing to be developed to model and study metabolic pathways. In this article, the authors have described Petri nets (PNs) as a recommender system to model one of the metabolic pathways of Polyhydroxyalkanoates. Recommender systems have become an integral part of today's world. Their applications lie in the fields of e-commerce, bioinformatics and many more. Petri nets are one of the promising mathematical tools which can be treated as a recommender system to model and analyze the complex metabolic pathways of various organisms because of the representation of these pathways as graphs. The PN toolbox validates the obtained Petri net model. Polyhydroxyalkanoates, commonly known as PHAs, are biodegradable microbial polyesters and have properties quite similar to commonly used non-biodegradable plastics. Due to their biodegradability, biocompatibility, and eco-friendly nature, they are of biological significance and are used in various applications in wide range of sectors like medical sector, drug delivery, tissue engineering, and many more. The obtained PN model of Polyhydroxyalkanoates has been validated using PN toolbox.
Healthcare decision-making capacity in old age: A qualitative study

Objectives Research about decision-making capacity has been growing in the last decades. That relates to more concerns regarding patients’ autonomy, and an increase in diseases that can negatively impact capacity. This research aims to: explore perceptions, legal aspects, and assessment procedures related to healthcare decision-making capacity in older adults with cognitive impairment; and study the first version of a new assessment instrument of this capacity. Method Nine focus groups were conducted, including healthcare, law and justice, nursing home professionals, institutionalized older adults, and dwelling older adults. Focus group discussions followed semi-structured interview scripts, specifically developed for each group. After group discussions, the assessment instrument was presented, and participants were asked to evaluate each item relevance and comprehensibility. Qualitative coding of the transcriptions was performed with resource to MAXQDA, using direct content analysis. Results Six primary themes emerged from the qualitative analysis: Decision-making capacity features; Abilities implied in decision-making; Factors influencing decision-making; Obstacles to decision-making; Legal aspects; and Assessment procedures. Discussion Results corroborate previous theoretical formulations of capacity. Generally, research results have implications for clinical and assessment practices, as well as preventive strategies that can improve older adult’s decision-making capacity. Assessment procedures of capacity should include a thorough protocol for the assessment of cognition, functionality, depressive symptoms, and decision-making abilities. In this respect, the need for an assessment tool that can provide valid information during evaluation processes is highlighted. Concerning the strategies to promote decision-making capacity, these rely on improving older adult’s health literacy and healthcare providers communication skills, as well as conduct actions to reduce stigma toward people with dementia.
CHOReOS Governance V&V policies and rules (D4.1)

This document presents an initial view of the framework under development for CHOReOS governance and V&V. The focus is on specifying policies and rules on which the framework will rely. After discussing the ULS-FI challenges which are more strictly related to WP 4 goals, we overview the preliminary architecture which will support governance and V&V. We classify policies supporting governance, and start discussing more relevant ones, concerning choreography roles and life-cycle. We also propose approaches for modeling and handling SLA-related requirements. We devote special attention to V&V-related governance aspects, and identify some policies to govern online testing, service ranking and scalability. The framework is still preliminary, in that governance is a transversal concern, and it certainly needs to be harmonized with the components and processes undergoing parallel investigation in the other CHOReOS WPs.
ESOT Consensus Platform for Organ Transplantation: Setting the Stage for a Rigorous, Regularly Updated Development Process

The European Society for Organ Transplantation (ESOT) has created a platform for the development of rigorous and regularly updated evidence based guidelines for clinical practice in the transplantation field. A dedicated Guideline Taskforce, including ESOT-council members, a representative from the Centre for Evidence in Transplantation, editors of the journal Transplant International has developed transparent procedures to guide the development of guidelines, recommendations, and consensus statements. During ESOT’s first Consensus Conference in November 2022, leading experts will present in-depth evidence based reviews of nine themes and will propose recommendations aimed at reaching a consensus after public discussion and assessment by an independent jury. All recommendations and consensus statements produced for the nine selected topics will be published including the entire evidence-based consensus-finding process. An extensive literature review of each topic was conducted to provide final evidence and/or expert opinion.
Joint appearance and motion model for multi-class multi-object tracking

Model-free tracking is a widely-accepted approach to track an arbitrary object in a video using a single frame annotation with no further prior knowledge about the object of interest. Extending this problem to track multiple objects is really challenging because: a) the tracker is not aware of the objects’ type while trying to distinguish them from background (detection task) , and b) The tracker needs to distinguish one object from other potentially similar objects (data association task) to generate stable trajectories. In order to track multiple arbitrary objects, most existing model-free tracking approaches rely on tracking each target individually by updating their appearance model independently. Therefore, in this scenario they often fail to perform well due to confusion between the appearance of similar objects, their sudden appearance changes and occlusion. To tackle this problem, we propose to use both appearance and motion models, and to learn them jointly using graphical models and deep neural networks features. We introduce an indicator variable to predict sudden appearance change and/or occlusion. When these happen, our model does not update the appearance model thus avoiding using the background and/or incorrect object to update the appearance of the object of interest mistakenly, and relies on our motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all targets simultaneously as a graphical model inference problem. We learn the joint parameters for both appearance model and motion model in an online fashion under the framework of LaRank. Experiment results show that our method outperforms the state-of-the-art.
Hunting down NLRP3 inflammasome: An executioner of radiation-induced injury

Radiotherapy is one of the mainstream treatment modalities for several malignancies. However, radiation-induced injury to surrounding normal tissues limits its efficacy. The NLRP3 inflammasome is an essential mechanism of innate immunity that reacts to challenges from endogenous danger signals and pathological microbes. A growing body of evidence has demonstrated a key role of NLRP3 inflammasome in the pathogenesis of radiation-induced tissue injury. Despite accumulating evidence, the potential value of the NLRP3 inflammasome in the management of radiation-induced tissue injury is not adequately recognized. We conducted a literature review to characterize the relationship between NLRP3 inflammasome and radiation injury. By analyzing recent evidence, we identify NLRP3 inflammasome as one of the executioners of radiation-induced injury, since it responds to the challenges of radiation, induces cell pyroptosis and tissue dysfunction, and initiates non-resolving inflammation and fibrosis. Based on these concepts, we propose early intervention/prevention strategies targeting NLRP3 inflammasome in a radiation context, which may help resolve imperative clinical problems.
Single-Bit Millimeter Wave Beam Alignment Using Error Control Sounding Strategies

Millimeter wave technology is an essential component of most solutions that address the coverage and throughput demands of next-generation cellular networks. To overcome the high propagation losses however, it is necessary to deploy large antenna arrays for spatial localization of energy by beamforming. This imposes a significant communication overhead, especially when channel reciprocity does not hold. In this work, we study the problem of successive one-bit feedback-assisted beam alignment. We exploit the sparse nature of the millimeter wave channel to pose the beamforming problem as a questioning strategy. We consider both adaptive (closed-loop) and non-adaptive (open-loop) channel sounding techniques which are robust to erroneous feedback signals caused by noisy quantization. In the adaptive case, we formulate new sounding signals by drawing a parallel with the well known Ulam's problem. In the non-adaptive case, the beams are designed in accordance to an open-loop code. We demonstrate that multiple paths can also be resolved by using ideas from group testing. Finally, we show the efficacy of our proposed techniques via simulations.
Research on JAVA Mobile Application Development Framework based on Ant Colony Optimization

The application framework is a reusable design component. It specifies the architecture of the application, clarifies the dependency relationship, responsibility allocation and control flow among the whole design, collaboration components. JAVA mobile application program. In order to deepen the readers' understanding of JAVA, this paper first introduces the technology, and develops a practical application for JAVA mobile application development. The registration mechanism is used to verify the validity. The problem is solved by an ant colony optimization algorithm. First, using state, decision making, and patterning of problems, it is easy to use ACO to solve. Research shows that the algorithm determines the stack allocation object by analyzing the Java source code at the compile stage, and identifies these objects with extension instructions. When the program runs, they will be directly assigned to Java. These objects are automatically added when the program leaves its scope. Released, other Java objects are still allocated to the heap and are recycled by the garbage collector.