Miracle-dz/UniFAR-FTUs huggingface dataset:
Proving full-system security properties under multiple attacker models on capability machines

Assembly-level protection mechanisms (virtual mem-ory, trusted execution environments, virtualization) make it possible to guarantee security properties of a full system in the presence of arbitrary attacker provided code. However, they typically only support a single trust boundary: code is either trusted or untrusted, and protection cannot be nested. Capability machines provide protection mechanisms that are more fine-grained and that do support arbitrary nesting of protection. We show in this paper how this enables the formal verification of full-system security properties under multiple attacker models: differ-ent security objectives of the full system can be verified under a different choice of trust boundary (i.e. under a different attacker model). The verification approach we propose is modular, and is robust: code outside the trust boundary for a given security objective can be arbitrary, unverified attacker-provided code. It is based on the use of universal contracts for untrusted adversarial code: sound, conservative contracts which can be combined with manual verification of trusted components in a compositional program logic. Compositionality of the program logic also allows us to reuse common parts in the analyses for different attacker models. We instantiate the approach concretely by extending an existing capability machine model with support for memory-mapped 1/0 and we obtain full system, machine-verified security properties about external effect traces while limiting the manual verification effort to a small trusted computing base relevant for the specific property under study.
M3G: Learning Urban Neighborhood Representation from Multi-Modal Multi-Graph

Recent urbanization has coincided with the enrichment of geo-tagged data, such as street view and point-of-interest (POI). Region embedding enhanced by the richer data modalities has enabled researchers and city administrators to understand the built environment, socioeconomics, and the dynamics of cities better. While some efforts have been made to simultaneously use multi-modal inputs, existing methods can be improved by incorporating different measures of “proximity” in the same embedding space — leveraging not only the data that characterizes the regions (e.g., street view, local businesses pattern) but also those that depict the relationship between regions (e.g., trips, road network). To this end, we propose a novel approach to integrate multi-modal geotagged inputs as either node or edge features of a multi-graph based on their relations with the neighborhood region (e.g., tiles, census block, ZIP code region, etc.). We then learn the neighborhood representation based on a contrastive-sampling scheme from the multi-graph. Specifically, we use street view images and POI features to characterize neighborhoods (nodes) and use human mobility to characterize the relationship between neighborhoods (directed edges). We show the effectiveness of the proposed methods with quantitative down-stream tasks as well as qualitative analysis of the embedding space: The embedding we trained outperforms the ones using only uni-modal data as regional inputs.
Comment on acp-2020-1237

NordstroÌm and SeppaÌlaÌ (2021) used the MERRA-2 reanalysis to study the possible impact of the Quasi Biennial Oscillation and the Semiannual Oscillation on the SH SSWs in 2002 and 2019. They proposed that the interaction between QBO and SAO can improve the predictability of the SH SSW. However, such a link between the QBO-SAO combined interaction and the predictability of the SH SSW is not stable and not applicable to other year with similar QBO-SAO configuration in the upper stratosphere and mesosphere. Most of the results are fairly descriptive and different SH stratospheric responses to the QBOSAO in different years are not well explained. Therefore, I would possibly recommend publication of the paper after a major revision is performed with the following comments considered.
Internet of Vehicles Your next connection

lthough the IoV is an emerging concept, some nascent forms ex i s t today. Inte l l igent Transport Systems (ITS) in Europe and Japan have adopted certain forms of IoV technology. In New Delhi, all 55,000 licensed rickshaws have been fitted w i t h G P S d e v i c e s s o that drivers can be held accountable for their questionable route selection. China’s Ministry of Transport (MOT) has ordered that GPS systems be installed and connected on all long-haul buses and hazmat vehicles by the end of 2011 to ensure good driving habits and reduce the risk for accidents and traffic jams. The Brazilian government has set a goal for all cars in circulation to be fitted with electronic ID chips from its National Automated Vehicle Identification System (Siniav). The launch of the U.S. National Strategy for Trusted Identities in Cyberspace (NSTIC) is a milestone for IoV. It requires that “security chips” be embedded in all online devices, including those in vehicles. Clearly, the IoV is no longer a matter of IT applications in the automotive industry; instead, it has become a national security concern. What is the IoV?
Rational Assembly of Hollow Microporous Carbon Spheres as P Hosts for Long‐Life Sodium‐Ion Batteries

This paper reports the rational assembly of novel hollow porous carbon nanospheres (HPCNSs) as the hosts of phosphorous (P) active materials for high‐performance sodium‐ion batteries (SIBs). The vaporization‐condensation process is employed to synthesize P/C composites, which is elucidated by both theories and experiments to achieve optimized designs. The combined molecular dynamics simulations and density functional theory calculations indicate that the morphologies of polymeric P4 and the P loading in the P/C composites depend mainly on the pore size and surface condition of carbon supports. Micropores of 1–2 nm in diameter and oxygenated functional groups attached on carbon surface are essential for achieving high P loading and excellent structural stability. In light of these findings, HPCNS/amorphous red phosphorus composites with enhanced structural/functional features are synthesized, which present an extremely low volume expansion of ≈67.3% during cycles, much smaller than the commercial red P's theoretical value of ≈300%. The composite anodes deliver an exceptional sodium storage capacity and remarkable long‐life cyclic stability with capacity retention over 76% after 1000 cycles. This work signifies the importance of rational design of electrode materials based on accurate theoretical predictions and sheds light on future development of cost‐effective P/C composite anodes for commercially viable SIBs.
Single index Fréchet regression

Single index models provide an effective dimension reduction tool in regression, especially for high dimensional data, by projecting a general multivariate predictor onto a direction vector. We propose a novel single-index model for regression models where metric space-valued random object responses are coupled with multivariate Euclidean predictors. The responses in this regression model include complex, non-Euclidean data that lie in abstract metric spaces, including covariance matrices, graph Laplacians of networks, and univariate probability distribution functions. While Fr\'echet regression has proved useful for modeling the conditional mean of such random objects given multivariate Euclidean vectors, it does not provide for regression parameters such as slopes or intercepts, since the metric space-valued responses are not amenable to linear operations. As a consequence, distributional results for Fr\'echet regression have been elusive. We show here that for the case of multivariate Euclidean predictors, the parameters that define a single index and projection vector can be used to substitute for the inherent absence of parameters in Fr\'echet regression. Specifically, we derive the asymptotic distribution of suitable estimates of these parameters, which then can be utilized to test linear hypotheses for the parameters, subject to an identifiability condition. We demonstrate the finite sample performance of estimation and inference for the proposed single index Fr\'echet regression model through simulation studies. The method is illustrated for resting-state functional Magnetic Resonance Imaging (fMRI) data from the ADNI study.
Consumers’ Consumption and Saving Plans – Are They Following an Euler Equation?

Evaluating a new survey dataset of German consumers, we test whether individual consumption and saving plans are formed according to an Euler equation derived from consumption life-cycle models. The results imply that socio-demographic factors impact consumption and saving patterns mostly in line with life-cycle theories, but macroeconomic expectations are more related to actual consumption than to saving. Estimating both consumption and saving Euler equations, the results show that consumers’ current consumption behaves mostly in line with the theory: We find evidence of consumption smoothing, since current and planned consumption are positively correlated, while the effect of real interest rates seems to work mainly via a positive link between short-run inflation expectations and current consumption. Nominal interest rate expectations are negatively correlated with current consumption, but the effect is only significant with quantitative expectations. As expected, the effect of perceived real interest rates is most pronounced for consumers’ who are active on financial markets. Consumers also smooth their saving patterns, but we find no evidence that either inflation or nominal interest expectations affect current saving. Finally, economic news observed by the consumer generally exacerbate the effects of their interest rate and inflation expectations on current consumption and saving.
Fine-tuned Predictive Model for Verifying POI Data

Mapping websites and geo portals are playing a vital role in daily life due to the availability of geo-tagged data. From booking a cab to search a place, getting traffic information, review of the place, searching for a doctor or best school available in the locality, we are heavily dependent on the map services and geo portals available for finding such information. There is voluminous data available on these sources and it is getting increasing every moment. These data are majorly collected through crowdsourcing methods where people are contributing. As a basic principle of Garbage in garbage out, the quality of this data impacts the quality of the services based on this data. Therefore, it is highly desired to have a model which can predict the quality/accuracy of the geotagged Point of interest data. We propose a novel Fine-Tuned Predictive Model to check the accuracy of this data using the best suitable supervised machine learning approach. This work focuses on the complete life cycle of the model building, starting from the data collection to the fine-tuning of the hyperparameters. We covered the challenges particularly to the geotagged POI data and remedies to resolve the issues to make it suitable for predictive modeling for classifying the data based on their accuracy. This is a unique work that considers multiple sources including ground truth data to verify the geotagged data using a machine learning approach. After exhaustive experiments, we obtained the best values for hyperparameters for the selected predictive model built on the real data set prepared specifically to target the proposed solution. This work provides a way to develop a robust pipeline for predicting the accuracy of crowdsourced geotagged data. Keywords—Crowdsourced; fine-tuning; geotagged data; hyperparameters; predictive model
Customer perspective on dynamic marketing capability in international hotels of Indonesia during Covid-19: Confirmatory factor analysis

The objective of this study was to validate three independent variables, namely market sensing, learning, targeting and positioning, as to dimensions of dynamic marketing capability (DMC). By developing a theoretical framework, this study demonstrates the importance of three dimensions of DMC. This study collected 530 valid samples through an online questionnaire survey in major cities in Indonesia. Respondents are Indonesian citizens aged over 15 years who have stayed at three to five-star international hotels in Indonesia during the Covid-19 period in 2020. This study used a combination of Smart-PLS and SPSS to examine the outer model and inner model. The outcome of this study demonstrated that market sensing had a significant effect on DMC. Then, market learning also had a significant effect on DMC. Next, market targeting and positioning had a significant effect on DMC from the perspective of the customer in international hotels in Indonesia. The outcome of this study is to support the development of a conceptual framework and conduct an empirical evaluation of the relationship between market sensing, market learning, market targeting and positioning, which are DMC dimensions in global marketing. In general, this study contributes to the international hotel industry in developing the marketing area in the context of DMC.
Interactive comment on “Comparing SWAT with SWAT-MODFLOW hydrological simulations when assessing the impacts of groundwater abstractions for irrigation and drinking water” by Wei Liu et al

Aside from incorporating PEST to calibrate the coupled model, the manuscript does not support additional novelty. Note that in the publication “A model integration framework for linking SWAT and MODFLOW” the MODFLOW drainage package, SWAT autoirrigation, and the MODFLOW-NWT were already integrated into the SWAT-MODFLOW framework. However, using PEST to simultaneously calibrate both models may be of interest to the SWAT modeler’s community. The introduction needs extensive revision. I recommend that the authors refocus and simplify the objectives of the manuscript.
Modification and application of coking coal by alkali pretreatment in wastewater adsorption

ABSTRACT Coking coal has been employed for coking wastewater adsorption treatment. The modification and adsorption of coking coal pretreated by alkali solution is discussed and compared in this study. Results demonstrated OH- could react with -COOH, -OH and minerals, which resulted in coal surface variations. As the hydroxide concentration is increased, surface hydrophobility increased and the zeta potential became less negative. A coal sample pretreated by 0.1 mol/L NaOH solution had the highest surface area and pore volume. The adsorption ability of the samples was improved significantly as surface changes. The coal sample pretreated by 0.1 mol/L NaOH solution had the highest adsorption capacity, and followed pseudo-second-order kinetics.
Broadening the genetic basis of durum wheat

A large reservoir of genetic diversity is available in the Triticum turgidum sub-species. This diversity can be used in breeding programs but the method by which it could be introgressed in the elite durum pool is challenging once the objective is to cover the whole genome. T. turgidum accessions are usually classified in sub-species or taxa while evidence are accumulating that a more complex genetic structure can be revealed using no a priori approaches. Wild and cultivated emmers are the most diverse compartment and some cultivated emmers are strongly differentiated from naked wheats while other emmers are closer to the current durum. Recent and successive bottlenecks (in the XXth century) explain a large part of the structuration of the modern durum pool and the loss of molecular diversity. The use of T. turgidum germplasm in classical genealogical breeding may lead to some disappointments if a quick return is researched while some advances in breeding elite lines can be achieved with more recombination and selection. Evolutionary PreBreeding appears as a valuable alternative. Building and managing composite cross populations for a long period yields in innovative genitors, with an enriched allelic diversity and reduced long range linkage disequilibrium. found for SNPs located on different chromosomes. deeper investigations are needed to ascertain these linkage disequilibrium patterns but this preliminary data suggest that evolving composite cross such as IPBWDWP could have very good and interesting properties for detecting markers closely linked to causal polymorphim. They could then very good alternative to association panels.
A Comparative Study of Unimodal and Multimodal Interactions for Digital TV Remote Control Mobile Application among Elderly

A research was conducted to study user interactions design for the TV remote control applications that are preferable among the elderly. Now-a-days smart home concept is widely accepted around the globe. Many applications were developed based on smart home concepts, such as smart remote-control applications for TVs and air conditioners. These applications were helpful in our daily life. However, the elderly tends not to use these applications because of the complexity of the processes and interaction design that is unfriendly. Therefore, this study was conducted to determine which interaction design is preferable for the elderly, enhancing the elderly experience in using the TV remote control application besides encouraging them to use one in daily life and keep up with new technologies. In this paper, the two types of new interaction designs – a touch-based only (unimodal) interaction and multimodal interaction prototypes and an existing TV Remote Control application were compared by conducting usability testing of these three applications on the elderly. Three parameters were considered to compare these three interaction designs: task completion time, error rate, and satisfaction. Also, using usability testing’s data collection, statistical analysis was conducted to find out which type of interaction is preferable by the elderly. Ten elderlies participated in the usability testing carried out. The results show a significant difference in these three interactions designs regarding task completion time and satisfaction, but not error rate. After considering usability testing and analyses conducted, the elderly prefers a unimodal interaction design in the TV Remote Control application. Nevertheless, the unimodal interaction was not the typical “tapping buttons” user interface in existing applications. Instead, the favourable interaction design was the one that involved swiping gestures to replace several features that were implemented using buttons on existing TV remote control applications. Keywords—HCI; usability testing; unimodal; multimodal; elderly
Persistent Memory

The recent rise of byte-addressable non-volatile memory technologies is blurring the dichotomy between memory and storage. In particular, they allow programmers to have direct access to persistent data instead of relying on traditional interfaces, such as file and database systems. However, they also bring new challenges, as a failure may render the program in an unrecoverable and inconsistent state. Consequently, a lot of effort has been put by both industry and academia into making the task of programming with such memories easier while, at the same time, efficient from the runtime perspective. This survey summarizes such a body of research, from the abstractions to the implementation level. As persistent memory is starting to appear commercially, the state-of-the-art research condensed here will help investigators to quickly stay up to date while also motivating others to pursue research in the field.
Understanding Human Intelligence in the Era of Artiﬁcial Intelligence

Humans accomplish complex sensory-motor and cognitive tasks with ease and grace. How does the brain accomplish this? For example, playing video games requires rapidly moving the eyes to perceive relevant information in a dynamic environment, memorizing important object locations, predicting the future state of the moving targets, and making decisions that maximize the expected reward. These cognitive abilities are fundamental for artiﬁcial intelligence (AI), but only recently have AI programs been able to play simple Atari games with the skill of a human player. Beyond these limited applications, AI is still far from handling many mundane tasks that we as humans can easily perform daily. My research lies at the intersection of computer science and cognitive psychology. I explore how human intelligence and machine intelligence are related to each other intending to build human-centered intelligent machines. Although biological brain and machine hardware are inherently different, the underlying mathematical principles, i.e., the software that deﬁnes general intelligence, may be similar in nature. Biologically inspired AI designs, through reverse-engineering the human brain, have given birth to at least three major breakthroughs in the ﬁeld of AI: artiﬁcial neural networks, imitation learning, and reinforcement learning. They belong to a research ﬁeld called machine learning, in which statistical learning algorithms allow machines to learn from past experiences. My research follows this trend and aims to discover the mechanism of intelligence by studying human brains and behaviors and applying these insights to the ﬁeld of AI. Models that seek to explain human intelligence inspire me to build intelligent machines. Through building AI systems, we can test these models and attempt to reproduce human-level intelligent behaviors, therefore machines can better assist humans in daily life. More importantly, designing machines to be human-like facilitate communication between humans and machines hence they can better assist humans in daily life.
Upside Down Facebook: Raising Awareness of Behavioral Advertising Through Artistic Provocation

Opaque corporate data disclosures, limited privacy controls, complex data ﬂows, and cognitive limitations prevent people from understanding how their personal data is used for behavioral advertising. Privacy tools seek to make data ﬂows transparent and actionable, but often fall short in clarifying systems and breaking through digital resignation. Using ideas from conceptual artwork investigating digital privacy issues, we propose an approach to raising awareness of behavioral advertising through artistic provocation. We developed Upside Down Facebook, a website that re-frames institutional privacy issues as social privacy issues by recasting an individual’s Facebook data, using Facebook’s “Information about you” data export, as deliberately “creepy” posts by a personiﬁed version of Facebook. Preliminary evaluation (n=7) of our functional prototype shows that most participants were provoked to learn more about how their data is used for behavioral advertising and formed an intent to change their Facebook privacy settings. Our work indicates the potential for artistic provocation to inform new perspectives on privacy tool design and research.
Deep Learning : Final Project

Section 1 introduces two approaches to tackle the problem of probability distribution learning and make a brief introduction to a model the GAN that leverages one of these approaches. Then, section 2 builds on the remarks made in the previous part and introduces the Wasserstein GAN. Section 3 and section 4 propose an in-depth review of two GANs and their implementations1, namely the DCGAN and the CycleGAN.