Miracle-dz/UniFAR-FTUs huggingface dataset:
Normal background concentrations (NBCs) of contaminants in English soils : final project report

The British Geological Survey (BGS) has been commissioned by the Department for Environment, Food and Rural Affairs (Defra) to give guidance on what are normal levels of contaminants in English soils in support of the Part 2A Contaminated Land Statutory Guidance. This has initially been done by studying the distribution of four contaminants – arsenic, lead, benzo[a]pyrene (BaP) and asbestos – in topsoils from England. This work was extended to a further four contaminants (cadmium, copper, nickel and mercury) which enabled methodologies developed to be tested on a larger range of contaminants. The first phase of the Project gathered data sets that were: nationally extensive; systematically collected so a broad range of land uses were represented; and collected and analysed to demonstrably and acceptable levels of quality. Information on the soil contaminant concentrations in urban areas was of particular importance as the normal background is considered to be a combination of both natural and diffuse anthropogenic contributions to the soil. Issues of soil quality are most important in areas where these affect most people, namely, the urban environment. The two principal data sets used in this work are the BGS Geochemical Baseline Survey of the Environment (G-BASE) rural and urban topsoils (37,269 samples) and the English NSI (National Soil Inventory) topsoils (4,864 samples) reanalysed at the BGS laboratories by X-ray fluorescence spectrometry (XRFS) so both data sets were highly compatible. These two data sets provide results for most inorganic element contaminants, though results explored for mercury and BaP are drawn from a variety of different and much less extensive data sets.
Cultural Conflicts and Knowledge Sharing: an Exploratory Analysis of Sino-Austrian Projects

This paper provides an exploratory analysis of cultural conflicts that potentially hinder knowledge sharing in the context of cross-national and knowledge-intensive projects including Austrian and Chinese project members. We firstly give an overview of central cultural concepts from a national viewpoint, highlight typical conflicts in projects, and discuss key factors that stimulate knowledge sharing. Next, we provide results gained from a series of interviews with practitioners and academics, and an exploratory workshop on global knowledge sharing held in Hong Kong. From the qualitative analysis, we posit six central cultural conflicts that emerged over task responsibilities, attitudes, work execution, power, communication, and time-orientation. We consequently classify these conflicts, discuss their causal placements within Hofstede’s cultural dimensions and suggest implications for knowledge sharing. Our research findings provide more groundwork for better understanding cultural conflicts and implications for knowledge sharing in the context of Sino-Austrian projects.
ERIM: Secure and Efficient In-process Isolation with Memory Protection Keys

Many applications can benefit from isolating sensitive data in a secure library. Examples include protecting cryptographic keys behind a narrow cryptography API to defend against vulnerabilities like OpenSSL's Heartbleed bug. When such a library is called relatively infrequently, page-based hardware isolation can be used, because the cost of kernel-mediated domain switching is tolerable. However, some applications require very frequent domain switching, such as isolating code pointers to prevent control flow hijack attacks in code-pointer integrity (CPI). These applications have relied on weaker isolation techniques like address-space layout randomization (ASLR), which allow efficient switching but have proved vulnerable to attack. In this paper, we present ERIM, a novel technique that combines the security of hardware-enforced isolation with a switching performance near that of ASRL. ERIM can support sensitive data access up to a million times per CPU core a second with low overhead. The key idea is to combine memory protection keys (MPKs), a feature recently added to Intel CPUs, with binary rewriting to prevent circumvention. ERIM provides three primitives: isolation, call gates, and syscall mediation. We show how to apply ERIM to isolate frequently accessed session keys (not just the long-term keys) in nginx, a high performance web server, and how to isolate sensitive data in CPI. Our measurements indicate a negligible degradation in performance, even with very high rates of switching between the untrusted application and the secure library.
DNN Explanation for Safety Analysis: an Empirical Evaluation of Clustering-based Approaches

The adoption of deep neural networks (DNNs) in safety-critical contexts is often prevented by the lack of effective means to explain their results, especially when they are erroneous. In our previous work, we proposed a white-box approach (HUDD) and a black-box approach (SAFE) to automatically characterize DNN failures. They both identify clusters of similar images from a potentially large set of images leading to DNN failures. However, the analysis pipelines for HUDD and SAFE were instantiated in specific ways according to common practices, deferring the analysis of other pipelines to future work. In this paper, we report on an empirical evaluation of 99 different pipelines for root cause analysis of DNN failures. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. Our results show that the best pipeline combines transfer learning, DBSCAN, and UMAP. It leads to clusters almost exclusively capturing images of the same failure scenario, thus facilitating root cause analysis. Further, it generates distinct clusters for each root cause of failure, thus enabling engineers to detect all the unsafe scenarios. Interestingly, these results hold even for failure scenarios that are only observed in a small percentage of the failing images.
Journal Pre-proof Rapid diagnosis of COVID-19 using disposal paper capacitive sensor

: Accurate and rapid detection and isolation become indispensable to restrict the spread of COVID-19. Since the start of COVID-19 pandemic in December 2019, many indisposal diagnostic tools are being developed incessantly . Out of all presently used tools, the gold standard rRT- PCR tool having very high sensitivity and specificity is a time consuming complicated molecular technique having requirements of special expensive equipment. Here, the main focus of this work is to develop rapid disposal paper capacitance sensor having simple and easy detection. We discovered a strong interaction between limonin and Spike-glycoprotein of SARS-COV-2 in comparison to its interaction with other similar viruses such as HCOV-OC43, HCOV-NL63, HCOV-HKU1, Influenza B and A viruses. The antibody free capacitive sensor having comb electrode structure was fabricated on whatman paper with drop coating of limonin (extracted using green method from pomelo seeds) and calibrated with known swab samples. The Blind test with unknown swab samples shows high sensitivity of 91.5% and high specificity of 88.37%. Requiring low sample volume and detection time and using biodegradable materials in the sensor fabrication assure the potential application as a point of care disposal diagnostic tool .
Using Eigenvoices and Nearest-Neighbors in HMM-Based Cross-Lingual Speaker Adaptation With Limited Data

Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish–English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built with specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. The traditional offline evaluation protocol however does not take this into account. To address this limitation, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We also propose to extend ranking metrics to the two-dimensional carousel setting in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen. Finally, we describe and evaluate two strategies for the ranking of carousels in a scenario where the technique used to generate the two-dimensional layout is agnostic on the algorithms used to generate each carousel. We report experiments on publicly available datasets in the movie domain to show how the relative effectiveness of several recommendation models compares. Our results indicate that under a carousel setting the ranking of the algorithms changes sometimes significantly. Furthermore, when selecting the optimal carousel layout accounting for the two dimensional layout of the user interface leads to very different selections.
Detecting Label Errors using Pre-Trained Language Models

We show that large pre-trained language models are extremely capable of identifying label errors in datasets: simply verifying data points in descending order of out-of-distribution loss signiﬁcantly outperforms more complex mechanisms for detecting label errors on natural language datasets. We contribute a novel method to produce highly realistic, human-originated label noise from crowdsourced data, and demonstrate the effectiveness of this method on TweetNLP, providing an otherwise difﬁcult to obtain measure of realistic recall. Finally, we use crowdsourced veriﬁcation to evaluate performance on IMDB and Amazon Reviews, and ﬁnd that pre-trained models detect errors with 15–28% higher absolute precision than existing methods.
Price Competition, Business Hours and Shopping Time Flexibility

We analyse retail industries with two-stage competition in opening hours and prices. We explore the effects of consumers' shopping time flexibility by comparing bi-directional consumers with forward- or backward-oriented consumers, who can either postpone or advance their shopping, but not both. We demonstrate that retailers with longer opening hours charge higher prices and that opening hour differentiation softens price competition. We show that competition does not create incentives for retailers to expand their business hours beyond social optimum. In this respect our model does not justify restrictions on shopping hours. Copyright (C) The Author(s). Journal compilation (C) Royal Economic Society 2008.
Post-Carboniferous tectonics in the Anadarko Basin, Oklahoma: Evidence from side-looking radar imagery

The Anadarko Basin is a west-northwest–east-southeast elongated trough filled with 10+ km of Paleozoic sediments. Most models call for tectonic activity to end in Pennsylvanian time. NASA Shuttle Imaging Radar (SIR-A) has revealed a distinctive and very straight west-northwest–striking lineament set extending virtually the entire length of the Anadarko Basin. The lineaments cut across the relatively flat-lying Permian units exposed at the surface. The character of these lineaments is seen most obviously as a tonal variation. Between the lineaments there is a poorly reflecting “gray” zone; better reflectors are located south and particularly north of the lineaments. Analysis of stream drainage and topography suggests that the area between the lineaments is low in the west and high in the east. Major streams, including the Washita and Little Washita rivers, appear to be controlled by the location of the lineaments. Subsurface data indicate that the lineaments may be the updip expression of a buried major fault system, the Mountain View fault. This fault is characterized as southerly dipping; recent COCORP data suggest a shallow dip (30°–40°). Two principal conclusions arise from this analysis: (1) the complex Mountain View fault system appears to extend southeast to join the Reagan, Sulphur, and/or Mill Creek faults of the Arbuckle Mountains, and (2) this fault system has been reactivated in Permian or more recent time. We infer that minor reactivation of the Pennsylvanian faults has resulted in a subdued surficial expression of buried structures that largely control the location of several oil fields in the Anadarko Basin.
A new method to measure the semantic similarity of GO terms

MOTIVATION Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY The online tools are available at: http://bioinformatics.clemson. edu/G-SESAME. SUPPLEMENTARY INFORMATION http://bioinformatics.clemson. edu/Publication/Supplement/gsp.htm.
FocalGatedNet: A Novel Deep Learning Model for Accurate Knee Joint Angle Prediction

—Predicting knee joint angles accurately is critical for biomechanical analysis and rehabilitation. This paper introduces a new deep learning model called FocalGatedNet that incorporates Dynamic Contextual Focus (DCF) Attention and Gated Linear Units (GLU) to enhance feature dependencies and interactions. Our proposed model is evaluated on a large-scale dataset and compared to existing models such as Transformer, Autoformer, Informer, NLinear, DLinear, and LSTM in multi-step gait trajectory prediction. Our results demonstrate that Fo-calGatedNet outperforms other state-of-the-art models for long-term prediction lengths (60 ms, 80 ms, and 100 ms), achieving an average improvement of 13.66% in MAE and 8.13% in RMSE compared to the second-best performing model (Transformer). Furthermore, our model has a lower computational load than most equivalent deep learning models. These results highlight the effectiveness of our proposed model for knee joint angle prediction and the importance of our modifications for this specific application.
El Niño-Southern Oscillation variability during the Little Ice Age and medieval climate anomaly reconstructed from fossil coral geochemistry and pseudoproxy analysis

The El Nino-Southern Oscillation (ENSO) drives interannual climate variability, but evidence of its pre-instrumental expression is limited to proxy data and model inference. We develop a skill assessment technique using instrumental records of sea surface temperature (SST) and salinity (SSS), and apply it to a suite of 23 coral O records, a common ENSO proxy. We compare coral proxy skill with pseudoproxy records constructed from instrumental data to establish the expected proxy response to ENSO events and constrain proxy sensitivity. The central Pacific has a balanced response towards ENSO events, the Western Pacific Warm Pool (WPWP) favors El Nino expression, and the South Pacific Convergence Zone (SPCZ) preferentially records La Nina events. Eastern Pacific coral records display a surprisingly low ENSO sensitivity despite the presence of large ENSO-related SST anomalies, suggesting non-climatic influences on several coral records from this region. Networks of proxy data skill ratings
Optimal Restricted Isometry Condition for Exact Sparse Recovery with Orthogonal Least Squares

Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the OLS algorithm. Specifically, we show that OLS guarantees the exact reconstruction of any K-sparse vector in K iterations, provided that a sensing matrix has unit ℓ2-norm columns and satisfies the restricted isometry property (RIP) of order K +1 with\begin{equation*}{\delta _{K + 1}} < {C_K} = \begin{cases} {\frac{1}{{\sqrt K }},}&{K = 1,} \\ {\frac{1}{{\sqrt {K + \frac{1}{4}} }},}&{K = 2,} \\ {\frac{1}{{\sqrt {K + \frac{1}{{16}}} }},}&{K = 3,} \\ {\frac{1}{{\sqrt K }},}&{K \geq 4. } \end{cases} \end{equation*}Furthermore, we show that the proposed guarantee is optimal in the sense that if δK+1 ≥ CK, then there exists a counterexample for which OLS fails the recovery.
Inflação versus desemprego: novas evidências para o Brasil

The goal of this article is to estimate the New Keynesian Phillips Curve for Brazil economy. The robustness was checking using not only different proxies but also samples with distinct temporal dimension. The main achievements are the following. Firstly, the inflationary inertia and expectation of inflation are important variables for the dynamic of inflation although the relevance of expectation rise from 2002 onwards. Secondly, the effect of unemployment on inflation seems to be located in the short term. Finally, the relationship between the exchange rate and inflation is marked by a structural break. With data from 2002, the effect of exchange rate shock is negative. But, when one uses data from 1995, the effect on inflation is positive impact.
Vanish: Increasing Data Privacy with Self-Destructing Data

Today's technical and legal landscape presents formidable challenges to personal data privacy. First, our increasing reliance on Web services causes personal data to be cached, copied, and archived by third parties, often without our knowledge or control. Second, the disclosure of private data has become commonplace due to carelessness, theft, or legal actions. Our research seeks to protect the privacy of past, archived data -- such as copies of emails maintained by an email provider -- against accidental, malicious, and legal attacks. Specifically, we wish to ensure that all copies of certain data become unreadable after a userspecified time, without any specific action on the part of a user, and even if an attacker obtains both a cached copy of that data and the user's cryptographic keys and passwords. This paper presents Vanish, a system that meets this challenge through a novel integration of cryptographic techniques with global-scale, P2P, distributed hash tables (DHTs). We implemented a proof-of-concept Vanish prototype to use both the million-plus-node Vuze Bit-Torrent DHT and the restricted-membership OpenDHT. We evaluate experimentally and analytically the functionality, security, and performance properties of Vanish, demonstrating that it is practical to use and meets the privacy-preserving goals described above. We also describe two applications that we prototyped on Vanish: a Firefox plugin for Gmail and other Web sites and a Vanishing File application.
UNDRAINED RESPONSE OF LOOSELY COMPACTED SANDS DURING MONOTONIC AND CYCLIC COMPRESSION TESTS

Undrained monotonie compression tests were conducted on loose sand samples subjected to an initial isotropic stress state, and undrained cyclic compression tests with no shear stress reversal were performed on loose sand samples subjected to initial anisotropic stress, in order to establish the stress conditions needed for triggering contractive flow deformation and the corresponding steady-state characteristics. It was shown that the peak strength envelope was also the triggering strength envelope, which appears to be unique for samples prepared by the moist tamping method and independent of stress path. It was established that the failure envelope for a given sand is unique but that the steady-state strength was not unique and was dependent on void ratio and stress conditions at the onset of flow failure. The use of normalized stress space is very helpful in the analysis of undrained monotonie and cyclic compression tests and permits ready prediction of strain-softening and strain-hardening behaviour. A.
Security Engineering: a Guide to Building Dependable Distributed Systems Nuclear Command and Control

In Germany and Turkey they viewed scenes that were particularly distressing. On the runway stood a German (or Turkish) quick-reaction alert airplane loaded with nuclear weapons and with a foreign pilot in the cockpit. The airplane was ready to take off at the earliest warning, and the nuclear weapons were fully operational. The only evidence of U.S. control was a lonely 18-year-old sentry armed with a carbine and standing on the tarmac. When the sentry at the German airfield was asked how he intended to maintain control of the nuclear weapons should the pilot suddenly decide to scramble (either through personal caprice or through an order from the German command circumventing U.S. command), the sentry replied that he would shoot the pilot; Agnew directed him to shoot the bomb. The uniquely catasrophic harm that could result from the unauthorized use of a nuclear weapon, or from the proliferation of nuclear technology to unsuitable states or substate groups, has led the United States (and other nuclear states) to spend colossal amounts of money protecting not just nuclear warheads but also the supporting infrastructure, industry, and materials. Quite a lot of nuclear security know-how has been published. In fact, there are severe limits on how much could be kept secret, even if this was thought desirable. Many countries are capable of producing nuclear weapons, but have decided not to (Japan, Australia, Switzerland,. . .) and so maintain controls on nuclear materials in a civilian