Miracle-dz/UniFAR-FTUs huggingface dataset:
POLYTECHNIC OF TURIN

Nowadays, Deep Neural Networks (DNNs) are employed in many ﬁelds to perform diﬀerent tasks, such as autonomous driving, always listening, augmented reality, computer vision and many others. Some of the listed tasks, as the autonomous driving, are safety-critical. Aware of that, ensuring the reliability of this technology is very important since, an error of assessment could endanger human lives. DNNs are known to be intrinsically error resilient [1] since, their topology exploits data redundancy. Therefore, even if an error occurs the DNNs might be still capable to correctly predict the result. However, this may change whenever the DNNs are implemented in Hardware (HW) for the reason that, the target HW have limited processing resources in which multiple neurons are mapped and potentially be corruptible by a single fault. The aim of this master thesis is to study novels Hardware Description Level (HDL) fault injectors able to reduce the expensive time cost of the state-of-art HDL fault injectors. The attention was drawn onto permanent and transient fault models. Indeed, the thesis will provide a pipelined fault injector for permanent fault and an optimized fault injector for transient fault. Both fault injectors were tested in fault injections campaign to asses their eﬀectiveness. Such frameworks make feasible the reliability analysis at the HW level of systems based on neural network, reducing the required simulation time of at least 60%.
Effect of Temperature on the Thermal Conductivity of Rocks and Its Implication for In Situ Correction

College of Geosciences, China University of Petroleum, Beijing, Beijing 102249, China State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, Beijing 102249, China Tarim Oilfield Company, PetroChina, Korla, Xinjiang 841000, China Exploration and Development Research Institute, PetroChina Southwest Oil & Gasfield Company, Chengdu, Sichuan 610041, China PetroChina Hangzhou Institute of Geology, Hangzhou 310023, China
The Costs of Burn Victim Hospital Care around the World: A Systematic Review

Background: Burn injuries are very common and fire-related burns account for over 300,000 deaths per year globally. The costs of the treatment of these patients change around the world. The aim of the present study was to conduct a systematic literature review to identify the costs related to hospital stays of burn victims in countries with different Human Development Index (HDIs). Methods: PubMed, CINAHL and BVIS databases were searched using the following terms: “burn,” treatment” and “costs”. The review included articles that presented cost studies or economic assessments of burn victims in which the costs were reported, and published between 2012 and 2019. The quality of the evidence was assessed using the Consensus on Health Economic Criteria. This review presents register in Prospero (CRD42019137580). Results: The review included 19 economic studies conducted in 13 countries, most with a very high HDIs. Most studies estimated direct acute burn care costs through bottom-up costing and institutional data. Total hospital care costs ranged from US$ 10.58 to US$ 125,597.86 per patient, the cost of 1% of total body surface area burned ranged from US$ 2.65 to US$ 11,245.04, and the cost of hospital care per day, from US$ 24.23 to US$ 4,125.50. Conclusion: The costs are high and show wide discrepancies among countries. Medical costs and other losses caused by fatal and non-fatal burn injuries differ considerably among demographic groups, care protocols, and country HDIs.
Facultad de Ciencias Económicas, Universidad Nacional de Tucumán

The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we study the behaviour of the gross national product of Argentina in the long run and provide a method for modelling this behaviour, which in turn is equivalent to establishing the stylized facts associated with these time series. We analyse the annual data of the gross national product and the gross national product per capita of Argentina from 1875 to 1999. The study of these variables allows, among other things, to know the changes that have been taking place in the economy. The evolution of these variables in time tells us, roughly, if there was progress or not.
Influence of market structures and market regulation on the carbon market

: Influence of market structures and market regulation on the Insights emissions trading systems This report provides an overview of the main outputs of the research project Influence of market structures and market regulation on the carbon market (2018-2021). The aim of the project, commissioned by the German Environment Agency, was to gain a better understanding of the impact of market structures and regulations on the carbon market, as well as the interactions between carbon and electricity markets. The project was divided into three stages. First, the project team identified and analysed the main interdependencies between ETSs and electricity sectors with a view to distilling the requisite conditions for cost-effective mitigation driven by the carbon price. Second, the project team applied the analytical framework of step 1 to a sample of seven diverse ETS jurisdictions spanning several continents, industrialised and emerging economies, mature and new ETSs, and differing electricity sectors in terms of market design, fuel mix, and sectoral policies. Namely, California (US), Mexico, Germany, Poland, Korea, Hubei (China), and Shenzhen (China). Third, the project findings were utilised to inform decision-makers and experts about pathways for effective carbon pricing to spur electricity sector decarbonisation. To this end, the project team organised six international virtual events over the course of the project. The results underline the high mitigation potential of ETSs when designed to deliver cost-reflective carbon prices. While this also applies to regulated sectors, interactions with electricity sector regulations and policies must be anticipated to safeguard the effectiveness of the instrument.
Meta4: Analyzing Internet Trafﬁc by Domain Name in the Data Plane

Associating network trafﬁc with human-readable domain names, instead of low-level identiﬁers like IP addresses, is helpful for network operators who might need to rate-limit trafﬁc by domain, conduct web-ﬁngerprinting, identify IoT devices, measure trafﬁc volume by domain name, and more. The problem is that most current methods of network monitoring require collecting and examining large amounts of network trafﬁc in a way that may compromise user privacy. The emergence of high-speed programmable switches makes it possible to implement monitoring programs that run in the data plane at line-rate without revealing user information to network operators. In this paper, we introduce Meta4 , a framework for implementing network monitoring by domain name in the data plane by extracting the client IP, server IP, and domain name from DNS response packets and using this information to identify the domain name associated with packets from the subsequent client-server session. A data-plane implementation has the beneﬁts of preserving the privacy of sensitive user information, running efﬁciently at line-rate, and allowing network operators to take action on network trafﬁc to rate-limit, block, or mark packets based on their associated domain. We implemented Meta4 on a Barefoot Toﬁno P4-programmable switch and deployed and assessed our implementation on the Princeton University campus.
Mixed precision low-rank approximations and their application to block low-rank LU factorization

We introduce a novel approach to exploit mixed precision arithmetic for low-rank approximations. Our approach is based on the observation that singular vectors associated with small singular values can be stored in lower precisions while preserving high accuracy overall. We provide an explicit criterion to determine which level of precision is needed for each singular vector. We apply this approach to block low-rank (BLR) matrices, most of whose off-diagonal blocks have low rank. We propose a new BLR LU factorization algorithm that exploits the mixed precision representation of the blocks. We carry out the rounding error analysis of this algorithm and prove that the use of mixed precision arithmetic does not compromise the numerical stability of the BLR LU factorization. Moreover, our analysis determines which level of precision is needed for each floating-point operation (flop), and therefore guides us toward an implementation that is both robust and efficient. We evaluate the potential of this new algorithm on a range of matrices coming from real-life problems in industrial and academic applications. We show that a large fraction of the entries in the LU factors and flops to perform the BLR LU factorization can be safely switched to lower precisions, leading to significant reductions of the storage and expected time costs, of up to a factor three using fp64, fp32, and bfloat16 arithmetics.
Perception Of Conventional And Electronic Cigarette Among Teenage Students

From years to years, the smoking behavior of teenagers in Indonesia seems to keep increasing. There are also various types of cigarette being consumed, the most popular of which are, beside conventional cigarettes, electronic cigarettes. This research aims at expounding the perception of conventional and electronic cigarettes on teenage students in on Sumedang Regency, West Java, Indonesia. The research method is quantitative-descriptive, involving 365 students using stratified cluster sampling. The research result shows that most teenagers have a high positive perception on the impact of conventional cigarettes (65.2%) and electronic cigarettes (59.8%). The tendency of respondents who have a high perception on the impact of conventional and electronic cigarettes are in grade X (tenth), are female, have a daily allowance, and are not smokers, both of conventional and electronic cigarettes.
EFFECTS OF BONE EROSION ON A TOTAL SHOULDER ARTHROPLASTY: FINITE ELEMENT ANALYSIS

1 Chapter 1: Introduction . 3 1.1 Shoulder anatomy . 3 1.1.1 Motions . 4 1.2 Total Shoulder Arthroplasty (TSA) . 5 1.3 Bone erosions . 7 1.4 Aim of the work . 8 Chapter 2: Glenoid components . 9 2.1 Glenoid classification . 9 2.1.1 All Polyethylene Components . 10 2.1.2 Metal-backed Components . 12 2.1.3 Cemented vs Uncemented implants . 13 2.2 Glenoid loosening . 15 Chapter 3: Finite Element Models . 17 3.1 Preliminary assumptions. 18 3.2 Scapula. 18 3.2.1 Glenoid orientation . 19 3.2.2 Scapular material properties . 21 3.3 Anatomical Shoulder Pegged and T.E. S.S. cementless components . 22 3.3.1 Implant’s material properties . 23 3.4 Shoulder erosions . 24 3.5 Implanted models . 27 3.6 Contact properties . 29 3.7 Boundary conditions . 30 3.8 Mesh . 32 Chapter 4: Results . 35 4.1 All PE Implant . 36 4.1.1 Cement stress . 36 4.1.2 Bone-Cement Interfacial Contact Pressure . 39 4.1.3 Bone Stress . 42 4.1.4 Bone Strain . 44 4.2 Cementless implant. 47 4.2.1 Relative Micromotions . 47 4.2.2 Medial displacements . 50 4.2. 3 Bone stress . 53
Survey Design, Order Effects, and Causal Mediation Analysis

Causal mediation analysis requires measurement of an outcome variable (O) with and without treatment, plus a set of mediator variables (M) that constitute possible pathways for the treatment effect. There is no consensus on whether surveys should measure potentially mediating variables before or after the outcome variables—MO or OM. We use a replication exercise to demonstrate how the order of mediator and outcome items can be consequential for the results from causal mediation analysis. Order can affect mediation conclusions, even if the treatment effect is similar across designs. As such, randomizing order is usually prudent, although best practice depends on the researcher’s contextual knowledge about her particular application.
Spectrum sharing: The way to finally realise the M2M vision

— For decades many have shared a vision of connected machines. Of having devices such as sensors, smart meters and healthcare monitors able to send messages back to a central network. Of having everything from cars to toothbrushes being part of the “Internet of Things”. This vision is often described as machine-to-machine (M2M) connectivity. Yet despite a compelling need, this vision has not yet come to pass, hampered by the lack of a low-cost ubiquitous wireless communications network which in turn was not viable within the constraints of conventional spectrum management and assignment. Recently, new approaches to spectrum access have been pioneered, using shared access in the UHF TV bands – often termed “white space spectrum”. This approach provides spectrum that is potentially low cost, globally available, relatively plentiful and has excellent propagation characteristics – exactly what is needed for M2M. However, using this spectrum is not simple, requiring a new standard optimised for TV white space and M2M. Such a standard, termed Weightless, is now being developed. This paper discusses its background, design and likely impact.
Graph-Driven Federated Data Management

Modern data analysis applications require the ability to provide on-demand integration of data sources while offering a flexible and user-friendly query interface. Traditional techniques for answering queries using views, focused on a rather static setting, fail to address such requirements. To overcome these issues, we propose a fully-fledged data integration approach based on graph-based constructs. The extensibility of graphs allows us to extend the traditional framework for data integration with view definitions. Furthermore, we also propose a query language based on subgraphs. We tackle query answering via a query rewriting algorithm based on well-known algorithms for answering queries using views. We experimentally show that the proposed method yields good performance and does not introduce a significant overhead.
The sound of silence: Breathing analysis for finding traces of trauma and depression in oral history archives

Many people experience a traumatic event during their lifetime. In some extraordinary situations, such as natural disasters, war, massacres, terrorism or mass migration, the traumatic event is shared by a community and the effects go beyond those directly affected. Today, thanks to recorded interviews and testimonials, many archives and collections exist that are open to researchers of trauma studies, holocaust studies, historians among others. These archives act as vital testimonials for oral history, politics and human rights. As such, they are usually either transcribed, or meticulously indexed. In this work, we propose to look at the nonverbal signals emitted by victims of various traumatic events when they describe the trauma and we seek to render these for novel representations without taking into account the explicit verbal content. Our preliminary paralinguistic analysis on a manually annotated collection of testimonials from different archives, as well as on a corpus prepared for depression and posttraumatic stress disorder (PTSD) detection indicates a tentative connection between breathing and emotional states of speakers, which opens up new possibilities of exploring oral history archives.
An Improved AODV Multicast Routing for the Zigbee Heterogeneous Networks in 5G Environment on Wireless Sensor Network

: A modified AODV routing protocol is adopted as part of the combined routing strategy in the Zigbee networks. However, its routing overhead caused by the routing discovery and the routing table maintenance needs to be further reduced. Considering the deploying environment, the emerging 5G technology tends to be ubiquitous in the near future. Due to its native support to the smart devices and the M2M communication, the 5G terminals may be used as gateways for the Zigbee networks to improve the existing routing method. The Zigbee coordinator and the 5G terminals, which both have the accessibility to the internet, are defined as associated gateways (AG). Aiming to the higher efficiency and the shorter path, an improved Zigbee AODV (Z-AODV) routing protocol using associated gateways for the heterogeneous networks in the 5G environment is proposed in this paper. Besides the regular Zigbee function, by sharing the neighbour and routing information via IP network, the AGs are also responsible for collaboratively finding the optimum path and transmitting the packets to reduce the consumption for Zigbee devices. Moreover, an additional routing information collecting method is developed to further improve the routing performance. The proposed algorithm is evaluated based on simulation results. It is shown that our routing method outperforms the existing ones by higher picketer deliver ratio, shorter path length, lower latency, fewer packets sent per Zigbee node and lower routing overhead.
Isolation, Assessment of Antimicrobial Sensitivity of Bacterial Pathogens from Post- Cesarean section Infection of patients in Thi- Qar Province

Cesarean section is the most frequent surgical procedure in obstetrics. This study was carried out to isolating and identifying bacteria causing cesarean section inflammation from patients and antimicrobial susceptibility test to bacterial isolates; 30 samples were collected from patients with different aged of women that had suffering from Post-Cesarean section Infection in Bent-Al-Huda & Al-Habboubi hospital in in Thi-Qar province. The isolates were identified on the bases of their morphological characteristics, the Gram stain reaction, biochemical tests and confirmed by the Api 20 E test. The results revealed that the rate of bacterial isolates in patients with Post-Cesarean section Infection was (76.6%). out of these (34.8 %) were E . coli, (30.4%) were Pseudomonas aeroginosa, (13%) of Staphylococcus aurous, (8.6%) isolates of Acintobacter and Bacillus. Antimicrobial susceptibility testing to 23 bacterial isolates showed variable sensitivity to antibiotic, they were more sensitive to chloramphenicol and Imipenem also the results appeared increasing prevalence of multidrug resistant between isolates.
Towards a Lower Bound Founded Fixpoint Semantics: Working Abstract

A distinguishing feature of answer set programming (ASP; [2]) is that all atoms belonging to a stable model must be founded. This can be made precise by means of the constructive logic of here-and-there (HT; [9]), whose equilibrium models correspond to stable models [10]. Foundedness can be regarded by assigning the smallest Boolean truth value that can be proven, where false is smaller than true. In recent work
Detecting Adversarial Text Attacks via SHapley Additive exPlanations

State-of-the-art machine learning models are 001 prone to adversarial attacks: maliciously 002 crafted inputs to fool the model into making a 003 wrong prediction, often with high conﬁdence. 004 While defense strategies have been extensively 005 explored in the computer vision domain, re-006 search in natural language processing still 007 lacks techniques to make models resilient to 008 adversarial text inputs. We propose an adver-009 sarial detector leveraging Shapley additive ex-010 planations against text attacks. Our approach 011 outperforms the current state-of-the-art detec-012 tor by around 19% F1-score on the IMDb and 013 14% on the SST-2 datasets while also show-014 ing competitive performance on AG_News and 015 Yelp Polarity . Furthermore, we prove the de-016 tector to only require a low amount of training 017 samples and, in some cases, to generalize to 018 different datasets without needing to retrain. 019
VISUALIZATION TECHNIQUES IN REPRESENTING TIME-VARYING SENTIMENT DATA

: Microblogging sites like Twitter contain a huge repository of user-generated sentiment about world-events, reviews and products. Sentiment analysis can be very useful if utilized properly. Sentiments cannot be 100% accurate, though it is a useful metric that can be used in relation with other metrics. Existing systems depict the sentiment as a data point over a varying time period. The data point is used to determine the context of the text. This paper proposes the use of a Gauge chart (speedometer) to determine the range of sentiment in web posts. The chart hints whether something is ‘on the whole positive’ or ‘on the whole negative’ without confining itself to a single data point.
[Re] Can gradient clipping mitigate label noise?

All the experiments described in the paper were fully re-implemented using NumPy, SciPy and PyTorch. The experiments on synthetic data were run on a CPU, while the deep learning experiments were run using a Nvidia RTX 2080 Ti GPU. Running the experimentationnecessary to gain some insight on someof the network architectures used and reproducing the real-world experiments required over 550 GPU hours.
Support Vector Machine Tuning for Improving Four-Quadrant Emotion Prediction in Virtual Reality (VR) using Wearable Electrodermography (EDG)

Electrodermography (EDG) / Galvanic Skin Response (GSR) indicates the psychophysiological of emotion, EDG is an emerging signal used in the field of emotion classification aside from Electroencephalography (EEG) and Electrocardiography (ECG). The Empatica E4 wearable device was used in collecting EDG signals and employed as the method in capturing the test subject’s physiological signal of their skin activity. This experiment had 10 participants that use a Virtual Reality (VR) headset for viewing video stimuli in 360 degrees while collecting the EDG signals. Python with Support Vector Machine (SVM) was used in processing the 10 subjects’ data. This paper aims to compare the accuracy of the SVM experiments with different parameters, different settings based on the data retrieved from the wearable. The emotions were classified into four distinct quadrants with inter-subject classifications yielding an accuracy of 54.3%, and intra-subject classification yielded an accuracy of 57.1% to 99.2%. The presented results show that it is possible to achieve results with higher accuracy when parameter tuning. Hence, promising results were demonstrated for emotion prediction in four quadrants using wearable EDG technology in virtual reality environments. This paper provides two contributions, the use of EDG signals in emotion prediction, and the parameter setting to increase the accuracy for SVM classification.
Flexible Antenna: A Review of Design, Materials, Fabrication, and Applications

The Flexible antenna is experiencing major growth recently because of the demand for wearable devices, military applications, health monitoring systems, communication devices, and global positioning systems (GPS). The choice of the flexible antenna relies on many factors such as materials used, substrate, antenna performance, processing technique, and the surrounding environment. Numerous antenna designs had been investigated by the researchers using conductive materials such as silver. Substrates such as cotton and elastomeric polymers with different fabrication techniques such as screen printing and inkjet printing had been implemented for various applications. This paper focuses on the previous work of flexible antenna design and miniaturization techniques such as electromagnetic bandgap (EBG) for button antenna. A summary of previous works on the flexible antenna is also highlighted.