Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Edge Intelligence

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Learning Implicit Surface Light Fields

Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing representations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.
Dynamical dark sectors and neutrino masses and abundances

We investigate generalized interacting dark matter--dark energy scenarios with a time-dependent coupling parameter, allowing also for freedom in the neutrino sector. The models are tested in the phantom and quintessence regimes, characterized by equations of state, ${w}_{x}l\ensuremath{-}1$ and ${w}_{x}g\ensuremath{-}1$, respectively. Our analyses show that for some of the scenarios, the existing tensions on the Hubble constant ${H}_{0}$ and on the clustering parameter ${S}_{8}$ can be significantly alleviated. The relief is either due to (a) a dark energy component which lies within the phantom region or (b) the presence of a dynamical coupling in quintessence scenarios. The inclusion of massive neutrinos into the interaction schemes does not affect either the constraints on the cosmological parameters or the bounds on the total number or relativistic degrees of freedom ${N}_{\mathrm{eff}}$, which are found to be extremely robust and, in general, strongly consistent with the canonical prediction ${N}_{\mathrm{eff}}=3.045$. The most stringent bound on the total neutrino mass ${M}_{\ensuremath{\nu}}$ is ${M}_{\ensuremath{\nu}}l0.116\text{ }\text{ }\mathrm{eV}$ and it is obtained within a quintessence scenario in which the matter mass-energy density is only mildly affected by the presence of a dynamical dark sector coupling.
Correlation functions for strongly confined wormlike chains

Polymer models describing the statistics of biomolecules under confinement have applications to a wide range of single molecule experimental techniques and give insight into biologically relevant processes in vivo. In this paper, I determine the transverse position and bending correlation functions for a wormlike chain confined within slits and cylinders (with one and two confined dimensions, respectively) using a mean field approach that enforces rigid constraints on average. I show the theoretical predictions accurately capture the statistics of a wormlike chain from Configurational Bias Monte Carlo simulations in both confining geometries for both weak and strong confinement. I also show that the longitudinal correlation function is accurately computed for a chain confined to a slit, and leverage the accuracy of the model to suggest an experimental technique to infer the (often unobservable) transverse statistics from the (directly observable) longitudinal end-to-end distance.
Democratic Value and Money for Decentralized Digital Society

Classical monetary systems regularly subject the most vulnerable majority of the world's population to debilitating financial shocks, and have manifestly allowed uncontrolled global inequality over the long term. Given these basic failures, how can we avoid asking whether mainstream macroeconomic principles are actually compatible with democratic principles such as equality or the protection of human rights and dignity? This idea paper takes a constructive look at this question, by exploring how alternate monetary principles might result in a form of money more compatible with democratic principles -- dare we call it "democratic money"? In this alternative macroeconomic philosophy, both the supply of and the demand for money must be rooted in people, so as to give all people both equal opportunities for economic participation. Money must be designed around equality, not only across all people alive at a given moment, but also across past and future generations of people, guaranteeing that our descendants cannot be enslaved by their ancestors' economic luck or misfortune. Democratic money must reliably give all people a means to enable everyday commerce, investment, and value creation in good times and bad, and must impose hard limits on financial inequality. Democratic money must itself be governed democratically, and must economically facilitate the needs of citizens in a democracy for trustworthy and unbiased information with which to make wise collective decisions. An intriguing approach to implementing and deploying democratic money is via a cryptocurrency built on a proof-of-personhood foundation, giving each opt-in human participant one equal unit of stake. Such a cryptocurrency would have both interesting similarities to, and important differences from, a Universal Basic Income (UBI) denominated in an existing currency.
Transmission of coronavirus by nebulizer: a serious, underappreciated risk

This letter is in response to the news article by Wendy Glauser. [1][1] The current pandemic of coronavirus disease 2019 (COVID-19) cases demands greater infection control precautions. Nebulizers generate aerosol particles in the size of 1–5 μm, which can carry bacteria and viruses into the deep
Successful Treatment of Compartment Syndrome Following Animal Envenomation by Fasciotomy through Small Incisions, Case Reports

Three cases of compartment syndrome caused by Habu-Snake bite and stone fish sting are treated by fasciotomies through small skin incisions. In Okinawa, the most southern prefecture in Japan, many suffer animal envenomation including 140 cases of Habu-Snake bites. Some of them develop compartment syndrome but usually recover soon after the fasciotomy and take uneventful course if diagnosis is not delayed. Different from compartment syndrome caused by trauma, ischemia, or others, those caused by water-soluble toxins following animal envenomation were expected to be treated successfully with small incisions for fasciotomy. The consecutive three cases of compartment syndrome following Habu-Snake bite and stone fish sting had fasciotomies through 1.5 to 3 cm skin incisions, instead of long skin incisions which are usually made in the same length as the underlying compartment, under following close observations with high index of suspicion of recurrence. Through small skin incisions, compartment syndrome could be released completely and the wounds could be maintained uneventfully with negative pressure therapy without recurrence, infection, or contracture. This method should be considered as an option to treat compartment syndrome caused by animal envenomation and may allow victims to avoid large scars that they should carry for the rest of their life.
Impact of Dopamine and Hydrogen Peroxide on Physiological Stress Parameters of Sarcophaga surcoufi (Diptera: Sarcophagidae)

Abstract Natural phenolics and reactive oxygen chemical species can have metabolic and fitness costs in insects and thus represent potential insecticidal agents. The response levels of total protein, phenoloxidase, proteases, chitinases, and total antioxidant activities to dopamine and hydrogen peroxide (H2O2) injected into third-instar flesh fly, Sarcophaga surcoufi Villeneuve (Diptera: Sarcophagidae), larvae were measured. In comparison to control groups, total protein initially increased following treatment with H2O2, but decreased at later sampling intervals. Treatment with dopamine did not affect total protein or protease activity for up to 12 h after treatment, but a slight decrease was observed in both at 24 and 36 h after treatment. Treatment with H2O2 decreased protease activity at all sampling intervals. H2O2 treatment increased phenoloxidase activity at each sampling interval, while treatment with dopamine decreased the activity at each interval. Total antioxidant activity increased substantially 12 h after treatment with H2O2 and increased only slightly 24 h after treatment before decreasing at 36 h, while dopamine increased antioxidant activity at all sampling intervals. Chitinase activity increased 12 h after H2O2 treatment, but then decreased at 24 and 36 h, but dopamine had no effect on chitinase activity. Antioxidants, phenoloxidase, and chitinase are important metabolic defense components in insects, and increases or decreases in each impacts the physiological integrity of the insect exposed to stressors that increase or decrease their quantity and activity.
Injection of automatically selected DBpedia subjects in electronic medical records to boost hospitalization prediction

Although there are many medical standard vocabularies available, it remains challenging to properly identify domain concepts in electronic medical records. Variations in the annotations of these texts in terms of coverage and abstraction may be due to the chosen annotation methods and the knowledge graphs, and may lead to very different performances in the automated processing of these annotations. We propose a semi-supervised approach based on DBpedia to extract medical subjects from EMRs and evaluate the impact of augmenting the features used to represent EMRs with these subjects in the task of predicting hospitalization. We compare the impact of subjects selected by experts vs. by machine learning methods through feature selection. Our approach was experimented on data from the database PRIMEGE PACA that contains more than 600,000 consultations carried out by 17 general practitioners (GPs).
DEMETER: hardware-assisted database checkpointing

For the past four decades, database checkpointing has long been a major system activity triggering heavy I/O and leading to performance fluctuations. This is unpleasing to database systems pursuing good and steady performance on high-end computing platforms. Hence, commercial and open-source databases usually spawn massive page flushers to complete the required tasks with system disruptions being minimized. In this work, to reduce the hefty cost, we propose Demeter, designs for hardware-assisted database checkpointing that unbundles checkpointing by offloading the task of building new pages from physical changes onto storage devices equipped with near-data processing (NDP) capability. Demeter consists of two parts; a software component designed for generating physical updates without affecting foreground transactions and a hardware part constructing new pages from the changes. For evaluation, we have built a prototype system based on the Biscuit framework and integrated Demeter with MariaDB. Experimental results demonstrated that the Demeter-enabled engine substantially reduces the amount of I/O and its foreground transaction processing gains more throughput than the vanilla system under the OLTP workloads modifying a large volume of data pages.
Analysis of label noise in graph-based semi-supervised learning

In machine learning, one must acquire labels to help supervise a model that will be able to generalize to unseen data. However, the labeling process can be tedious, long, costly, and error-prone. It is often the case that most of our data is unlabeled. Semi-supervised learning (SSL) alleviates that by making strong assumptions about the relation between the labels and the input data distribution. This paradigm has been successful in practice, but most SSL algorithms end up fully trusting the few available labels. In real life, both humans and automated systems are prone to mistakes; it is essential that our algorithms are able to work with labels that are both few and also unreliable. Our work aims to perform an extensive empirical evaluation of existing graph-based semi-supervised algorithms, like Gaussian Fields and Harmonic Functions, Local and Global Consistency, Laplacian Eigenmaps, Graph Transduction Through Alternating Minimization. To do that, we compare the accuracy of classifiers while varying the amount of labeled data and label noise for many different samples. Our results show that, if the dataset is consistent with SSL assumptions, we are able to detect the noisiest instances, although this gets harder when the number of available labels decreases. Also, the Laplacian Eigenmaps algorithm performed better than label propagation when the data came from high-dimensional clusters.
Resource allocation for edge computing with multiple tenant configurations

Edge Computing (EC) consists in deploying computational resources, e.g., memory, CPUs, at the Edge of the network, e.g., base stations, access points, and run there a part of the computation currently running on the Cloud. This approach promises to reduce latency, inter-domain traffic and enhance user experience. Since resources at the Edge are scarce, resource allocation is crucial for EC. While most of the studies assume users interact directly with the Edge submitting a sequence of tasks, we instead consider that users will interact with different Service Providers (SPs), as they currently do in the Web. We therefore consider the case of a Network Operator (NO) that owns the resources at the Edge and must decide how much resource to allocate to the different tenants (SPs). We propose MORA, a polynomial time strategy which allows the NO to maximize its utility, which can be inter-domain traffic savings, improved users' QoE or other metrics of interest. The core of MORA is that (i) it exploits service elasticity, i.e., the fact that services can adapt to the resources allocated by the NO and rely on a remote Cloud for the excess of computation, (ii) it is suitable for micro-services architecture, which decomposes a single service in a set of components, which MORA places in the different computational nodes of the Edge and (iii) it copes with multi-dimensional resources, e.g., memory and CPUs. After analyzing the properties of the algorithm, we show numerically that it performs close to the optimum. To guarantee reproducibility, the numerical evaluation is performed on publicly available traces from Google and Alibaba clusters and in synthetic scenarios and our code is open source.
ACIDroid: a practical app cache integrity protection system on Android Runtime

To improve the execution performance of applications, Android introduced a new optimization technique using app cache. This new feature not only improves the performance of Android applications but exposes a new attack surface to be compromised. Attackers can eventually change the behavior of installed applications by modifying executable bytecode in their app cache files. We call this attack "app cache tampering attack". This attack would be difficult for device owners to recognize its existence because the modification in cache files does not require user's any explicit actions. To mitigate the risks of app cache tampering attack, we present an efficient App Cache Integrity protection solution on Android called "ACIDroid", which provides the secure management of hash values of the optimized executable bytecode in app cache files. To show the feasibility of ACIDroid, we performed app cache tampering attacks on 11 popular Android apps (Paypal, Bank of America, Outlook, lPassword, Dropbox, Azure Authenticator, Blizzard Authenticator, TexasHealthMyChart, Google Authenticator, Booking and Amazon Alexa) and tried to detect the changes in app cache files using ACIDroid. With the modified app cache files, ACIDroid is able to correctly detect all the (intentional) changes in the apps tested while maintaining an acceptable verification time overhead less than 2.69% (48.27ms) and 21.18% (155.54ms) of the launch time of each app on average for AOSP and PIXEL2, respectively, running Android version 8.
Truver: a blockchain for verifying credentials: poster

During the job application process, applicants are asked to upload/send their resumes, certificates, recommendation letters, etc. However, how can employers ensure that the education and certifications are legitimate? Digital copies of physical documents cannot be trusted. The current verification process is inefficient, time consuming, and expensive, causing many delays and difficulties for the Human Resources. To address this problem, we propose Truver, a blockchain based platform which (a) provides a distributed and secure system on which every individual can store his/her verified credentials and share them with all potential employers, and (b) allows employers to verify an applicant's credentials instantaneously. The real world tests, using Ethereum blockchain, illustrates that Truver runs in acceptable cost and time. We formally prove the requirements of the proposed system against a strong realistic adversarial model, using an automated cryptographic protocol verifier.
A distributed approach for autonomous cooperative transportation in a dynamic multi-robot environment

This paper describes a distributed approach for autonomous cooperative transportation in a dynamic multi-robot environment. The proposed approach forms an optimal coalition at runtime for cooperative transportation and assigns a group of robots for the task. Explicit communication is used to acquire information as the robots do not have a global knowledge of the environment, i.e., no robot knows the location and state of another robot. For cooperative transportation, such information is essential as objects may arrive at any time and at any location. The proposed approach deals with on-demand missions, where the number of robots required to solve the problem is not known a priori. The applicability of the approach is demonstrated on a road clearance scenario in a realistic urban search and rescue simulation environment. The experimental results validate the correctness of the approach.
Empowering personalized feedback on hot water usage: a field study with shower meters

Digitalization enables an ever-increasing opportunity to promote resource conservation by providing timely consumption feedback to individuals. Yet, in multi-person households, appliances and fixtures are often shared, which makes it difficult to deliver person-specific feedback for each user. In this paper, we tackle this problem for household's most energy-intensive day-to-day activity - showering - by leveraging granular water consumption data. To this end, we collected labeled time series data of 691 shower events from 28 individuals, defined features, implemented and adjusted several classifiers, and analyzed the feasibility of our identification approach. Across all locations, the results - which were evaluated with stratified five-fold cross validations - provide robust evidence that the presented approach can indeed identify users reliably immediately after the end of a shower event. More specifically, the classifier with the best overall performance (Random Forest) achieved an average accuracy of 83.2% even for the most challenging environment in our field test (differentiating between five individuals in a company shower) and reached an average accuracy of 98.8% for a two-person household. Moreover, the approach requires only little training data for a satisfactory performance.
Representation learning for entity type ranking

The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context. In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from both the structural and probabilistic information of the entities. We propose a Representation Learning model for Type Ranking (RL-TRank) and the results of the structure embedding and the probabilistic embedding are combined to get the entity type ranking. Experimental results show that the accuracy of RL-TRank approaches outperform the state-of-the-art type ranking models while, at the same time, being more efficient and scalable.
GrCluster: a score function to model hierarchy in knowledge graph embeddings

Low-dimensional embeddings for knowledge graph entities and relations help preserve their latent semantics while enabling computation efficiency. These embeddings are often used to perform tasks such as knowledge graph completion, question answering and inference. Knowledge graph embedding methods aid in the representation of entities and relationships of a knowledge graph in continuous vector spaces. However, most existing techniques ignore the inherent hierarchical structure of entities present in the knowledge graph, defined by ontological relationships between entity types. This paper introduces a novel score function called GrCluster that helps fill that gap. GrCluster is a simple, intuitive and efficient scoring function that incorporates the entity hierarchical correlation into existing knowledge graph embeddings. The effectiveness of GrCluster is demonstrated by integrating it into several well known embedding models. The experimental results show consistent improvements across metrics and embedding models for the tasks of entity prediction and triplet classification.