dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). Cache side-channel attacks can recover cryptographic secrets, such as AES  (Irazoqui et al. 2014; Gülmezoglu et al. 2015)  and RSA  (Inci et al. 2015)  keys, across virtual machine boundaries. Intel introduced a new hardware extension SGX (Software Guard Extensions) (Intel Corporation 2016a) in their CPUs, starting with the Skylake microarchitecture. SGX is an isolation mechanism, aiming at protecting code and data from modification or disclosure even if all privileged software is malicious  (Costan and Devadas 2016). This protection uses special execution environments, so-called enclaves, which work on memory areas that are isolated from the operating system by the hardware. The memory area used by the enclaves is encrypted to protect the application's secrets from hardware attackers.
Moreover, SGX is a highly restrictive environment for implementing cache side-channel attacks. Both state-of-the-art malware and side-channel attacks rely on several primitives that are not available in SGX enclaves. Consequently, no enclave malware has been demonstrated on real hardware so far. In this paper, we show that it is very well possible for enclave malware to attack its hosting system. We demonstrate a cache attack from within a malicious enclave that is extracting secret keys from co-located enclaves. Our proof-of-concept malware can recover RSA keys by monitoring cache access patterns of an RSA signature process in a semi-synchronous attack. The malware code is entirely invisible to the operating system and cannot be analyzed due to the isolation provided by SGX. In order to build our attack, we present novel approaches to recover physical address bits, as well as to recover highly accurate timing in the absence of the timestamp counter, which is even more accurate than the native one.
Background Intel SGX in native and virtualized environments Intel Software Guard Extensions (SGX) are a new set of x86 instructions introduced with the Skylake microarchitecture. SGX allows protecting the execution of user programs in so-called enclaves. Only the enclave can access its own memory region, any other access to it is blocked by the CPU. As SGX enforces this policy in hardware, enclaves do not need to rely on the security of the operating system. In fact, with SGX, the operating system is generally not trusted. By doing sensitive computation inside an enclave, one can effectively protect against traditional malware, even if such malware has obtained kernel privileges. Furthermore, it allows running secret code in a cloud environment without trusting the cloud provider's hardware and operating system.
SGX comes in two versions. SGX1 specifies basic enclave operation. Moreover, all enclave memory pages have to be allocated at enclave creation. To account for limited memory resources, enclave pages can be swapped out and in at runtime. SGX2 extends SGX with dynamic memory management, allowing to allocate new enclave pages at runtime. However, we do not use SGX2 features and thus presume that our attack applies to SGX2 as well. At the time of writing, no hypervisor with SGX support was available to us. While there is an experimental version of KVM with SGX support (Intel Corporation 2016c), this is still under development and not readily usable. Moreover, this project has not been updated since the beginning of 2018. However, Docker (2016a) has support for Intel's SGX. Docker is an operating-system-level virtualization software that allows applications with all their dependencies to be packed into one container.
e. an eviction strategy). Gruss et al. (2016)  experimentally found efficient eviction strategies with high eviction rates and a small number of addresses. We use their eviction strategy on our Skylake test machine throughout the paper. Figure  1  shows the eviction set access pattern of this eviction strategy. A more powerful cache attack technique is Flush+Reload by  Yarom and Falkner (2014). For a Flush+Reload attack, attacker and victim need to share memory (i. e. a shared library or page deduplication). The attacker flushes a shared memory line from the cache to then measure the amount of time it takes to reload the cache line. This reveals whether or not another program reloaded this exact cache line. Although Flush+Reload attacks have been studied extensively  (Irazoqui et al. 2015; Gruss et al.
The attacker tries to stealthily extract secrets from this victim enclave. Both the attacker and the victim use Intel's SGX feature and are therefore subdivided into two parts, the enclave and loader, i. e. the main program that instantiates the enclave. The attack is a multi-step process that can be divided into an online and offline phase. "Extracting private key information" section describes the online phase, in which the attacker first locates the victim's cache sets that contain the secret-dependent data of the RSA private key. The attacker then monitors the identified cache sets while triggering a signature computation. "Recovering the private key" section gives a detailed explanation of the offline.
Victim The victim is an unprivileged program that uses SGX to protect an RSA signing application from both software and hardware attackers. Both the RSA implementation and the private key reside inside the enclave, as suggested by Intel (Intel Corporation 2016b). Thus, they can never be accessed by system software or malware on the same host. Moreover, information leakage from the enclave should not be possible due to hardware isolation and memory encryption. The victim uses the RSA implementation of the widely deployed mbedTLS library that relies on constant-time Montgomery multiplications. The victim application provides an API to compute a signature for the provided data.
Attacker The attacker runs an unprivileged program on the same host machine as the victim. The goal of the attacker is to stealthily extract the private key from the victim enclave. Therefore, the attacker uses the API provided by the victim to trigger signature computations. The attacker targets the exponentiation step of the RSA implementation. To perform the exponentiation in RSA, mbedTLS uses a windowed square-and-multiply algorithm in the Montgomery domain. The window size is fixed to 1, as suggested by the official knowledge base (ARMmbed 2016). If successful, the attack can be extended to arbitrary window sizes  (Liu et al. 2015). To prevent information leakage from function calls, mbedTLS uses the same function (mpi_montmul) for both the square and the multiply operation (see Algorithm 1).
The mpi_montmul takes two parameters that are multiplied together. For the square operation, the function is called with the current buffer as both arguments. For the multiply operation, the current buffer is multiplied with a buffer holding the multiplier. This buffer is allocated in the calling function mbedtls_mpi_exp_mod using calloc. Due to the deterministic behavior of the tlibc's calloc implementation, the used buffers always have the same virtual and physical addresses. Thus the buffers are always in the same cache sets. The attacker can, therefore, mount a Prime+Probe attack on the cache sets containing the buffer. In order to remain stealthy, all parts of the malware that contain attack code reside inside an SGX enclave. The enclave can protect the encrypted real attack code by only decrypting it after a successful remote attestation, after which the enclave receives the decryption key.
On our test machine, the code from Listing 1 achieves one increment every 4. 7cycles, which is an improvement of approximately 2% over the assembly code generated by gcc on the highest optimization level (-O3). We can improve the performance-and thus the resolution-further, by exploiting the fact that only the counting thread is writing to the counter variable. Reading the counter variable from memory is, therefore, never necessary as the value will not be changed by any other thread. To gain a higher performance from this observation, we have to eliminate the CPU's read access to the counter variable. Executing arithmetic operations directly on the memory location is thus not an option anymore, and it is necessary to perform any operation with data dependency on a CPU register. Therefore, we introduce a "shadow counter variable" which is always held in a CPU register.
This new method might open new possibilities of side-channel attacks that leak information through timing on a sub-rdtsc level. Figure  3  shows the performance comparison of the C version, the assembly version, the optimized assembly version, and the native timestamp counter as a baseline. Although the method with the shadow counter has the most instructions in the loop body, and an increase of 100% in code size compared to Listing 1, it has the best performance. Due to multiple execution units, pipelining, and the absence of memory dependencies, one increment can be carried out in less than 1cycle on the Skylake microarchitecture even though each instruction has a latency of 1cycle  (Fog 2016).
However, the method of  Oren et al. (2015)  uses a slow pointer-chasing approach and needs to find an eviction set without any assumptions, consuming more time. The method by  Gruss et al. (2016)  has the overhead of finding an eviction strategy and eviction set without any Fig. 3  Comparison of different variants for the counter thread to Intel's native timestamp counter as the baseline assumptions. Thus, while these approaches work, applying them for our purposes would consume multiple hours on average before even starting the actual Prime+Probe attack. We propose a new method to recover the cache set from a virtual address without relying on large pages. The method requires that an array within an SGX enclave is backed by physically contiguous pages. We verified that we have contiguous pages by inspecting Intel's SGX driver for Linux (Intel Corporation 2016a).
Detecting malware One of the core ideas of SGX is to remove the cloud provider from the root of trust. If the enclave is encrypted and only decrypted after successful remote attestation, the cloud provider has no way to access the secret code inside the enclave. However, eliminating this core feature of SGX could mitigate malicious enclaves in practice as the enclave binary or source code could be read by the cloud provider and scanned for malicious activities. Heuristic methods, such as behavior-based detection, are not applicable, as the malicious enclave does not rely on API calls or user interaction. Furthermore, for encrypted enclave code, a signature-based virus scanner has no access to the code, and the malware can easily change its signature by either re-encryption or modification of the plaintext.
In countries with extremely outdated census data, modelled gridded population estimates have instead been used to sample clusters  (2). A complete enumeration of all households is then conducted in the selected clusters using foot travel and hand-drawn sketch mapping. Finally, households are randomly selected from within the enumerated clusters for inclusion in the study. While multi-stage cluster samples are widely considered to optimize statistical efficiency and fieldwork effort, sampling from either the available 2002 Guatemala census, or a gridded population dataset derived from this outdated census, were considered poor options. Multi-stage sampling with random-walk initiation. Other traditional methods, including the World Health Organization's former Expanded Programme on Immunization (EPI) survey design, use two stage cluster samples(3).
This method was not suitable because it would require use of an outdated census simple frame and would not permit calculation of sample weights needed to produce unbiased estimates and confidence intervals of disease prevalence. Simple random sample of enumerated households. The most statistically efficient survey design would be a simple random sample of households from the entire population if an updated, complete household sample frame were available. Surveys with smaller coverage areas have selected simple random samples of households, for example in China, using electronic listings of households in two districts  (6) , and Democratic Republic of Congo (7) using civil registry data for household listings of small catchment areas. However, resource-limited countries like Guatemala rarely have a unique home address system or database of household GPS locations.
The digitization process is quite time-consuming and relies on the two-step process of first enumeration followed by selection. The Geo-Sampler method does not require digitizing all structures, and allows for simultaneous enumeration and selection, at a very rapid pace. Because each point in the polygon has an equal chance of being selected, and the user has the ability to select only points with a structure contained within the buffer as part of the sample, statistical correction for clustering is not required. In this lower-resource context, the absence of both 1)a recent national census and the corresponding updated sampling frame and 2) an updated accurate gridded population dataset, Geo-Sampler allowed us to select a simple random sample of households from a random sample of structures, rather than a multi-stage clustered sample, which allowed us to reduce our sample size and conduct the study with a reduced budget.
Some of the structures selected were in areas determined to be too dangerous to the safety of data collectors to enroll. Thus, there remains a group of community residents whose health indicators, perspectives and risks are invisible and undocumented. Further research on ways to include these most vulnerable groups in population studies is needed. the entire CKDu study will be made available at the Principal Investigator's Harvard University's Dataverse following publication of final study results. acknowledge the support of the INCAP laboratories, and Dr. Joaquin Barnoya for productive conversations. We gratefully acknowledge the work of Serge Balandine from Epicentre on design of Geo-Sampler and Epicentre for their cooperation in allowing us to use this tool. We especially thank the study participants, without whom this would not be possible.
This review gains interest at a time when a large number of universities, colleges and other institutions are converting to distance learning due to restrictive crowding measures (coronavirus disease -COVID-19). The current study conducts a literature review based on research published in periodicals indexed to Web of Science (WOS). Two distinct search terms are applied to this database, returning a final group of 115 papers. To analyse the content of these papers, the current study resorts to a text mining technique called topic modelling to find latent topics on VR and gamification in higher education. Recently, topic modelling has been used to uncover correlated discussions in text  (Loureiro et al. , 2019c; Loureiro et al. , 2020). The TCCM framework used in prior reviews  (Terjesen et al.
, 2003)  an extensive search was conducted on WOS. The assessment of research based on the journal in which it is published is a widely adopted practice  (Chavarro et al. , 2018; Loureiro et al. , 2019c). WOS is a renowned electronic database, the content of the WOS collections is selective and consistent and an independent and detailed editorial process ensures journal quality  (Clarivate, 2019). The research process was undertaken by a research team comprising all the authors of the present paper, who looked for all publications in scholarly journals published until the end of 2019 in the WOS database. To locate the most relevant studies addressing the purpose of this review, the researchers made two queries using the keywords "VR" and "gamification". The use of these two keywords is central to the focus of our research.
(2008)  Experience and e-learning Case study  Bouras and Tsiatsos (2006)  Experience and e-learning Case study  Saxena (2006)  Experience and e-learning Case study  Mikropoulos et al. (2003)  Experience, virtual environments, presence and interaction Survey  Parkinson and Hudson (2002)  Experience and interactive learning environments Case study  Riva (2000)  Experience Case study  Thrush and Bodary (2000)  Experience, military and game applications Case study  Economou et al. (2000)  Virtual learning environments Case study  Vouk et al. (1999)  Quality of service and e-learning Virtual reality and gamification latent topics in scholarly documents  (Loureiro et al. , 2019c; Loureiro et al. , 2020). Helping researchers to implement this technique is text-mining software (MeaningCloud), in which a text clustering technique is performed.
PUBLICATION YEAR FOR ARTICLES Virtual reality and gamification results and findings as an initial list of different scenarios to help them decide when to use VR or gamification, for what purpose and what technology to use. Due to the nature of this literature review, and its selection and filtering process, this work is not without its limitations. Firstly, it was not possible to perform a meta-analysis due to the large degree of heterogeneity in the studies included in this review. Secondly, it is important to identify the possibility of publication biases, as the search was limited to English and peer-reviewed papers available on a single database, which may limit the number of potential papers identified and included in this review. Nevertheless, none of these limitations detracts from the value of the present research.
Biallelic variants in RNU4ATAC, a non-coding gene transcribed into the minor spliceosome component U4atac snRNA, are responsible for three rare recessive developmental diseases, namely Taybi-Linder/MOPD1, Roifman and Lowry-Wood syndromes. Next-generation sequencing of clinically heterogeneous cohorts (children with either a suspected genetic disorder or a congenital microcephaly) recently identified mutations in this gene, illustrating how profoundly these technologies are modifying genetic testing and assessment. As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. In order to facilitate and improve clinical diagnostic assessment and genetic counseling, we present i) an update of the previously reported RNU4ATAC mutations and an analysis of the genetic variations affecting this gene using the Genome Aggregation Database (gnomAD) resource; ii) the pathogenicity prediction performances of scores computed based on an RNA structure prediction tool and of those produced by the Combined Annotation Dependent Depletion tool for the 285 RNU4ATAC variants identified in patients or in large-scale sequencing projects; iii) a method, based on a cellular assay, that allows to measure the effect of RNU4ATAC variants on splicing efficiency of a minor (U12-type) reporter intron.
The generalisation of the use of exome and whole-genome sequencing to diagnose disorders with a suspected genetic cause implies that new RNU4ATAC variants will undoubtedly be identified in the future in laboratories without expertise on this gene. Yet, the classification of RNU4ATAC variants to provide accurate genetic counselling is difficult because the criteria used to predict the impact of variants in coding genes are partially inappropriate. Furthermore, functional assays are lacking and no published guidelines are yet available. In order to facilitate variant interpretation for diagnostic laboratories, we first reviewed the literature and compiled the RNU4ATAC variants reported as pathogenic in patients at the homozygous or compound heterozygous state. We next analysed the extent of genetic variations found in this gene using the Genome Aggregation Database (gnomAD) resource.
Variant information extraction from gnomAD RNU4ATAC variant information was extracted from the Genome Aggregation Database v2. 1 (gnomAD, http://gnomad. broadinstitute. org/) [17]. We kept the variants whose coordinates were comprised in the following interval: chr2:122,288,456-122,288,585 (GrCh37). Due to the misannotation of the RNU4ATAC sequence in gnomAD, we had to correct the names of the variants by adding 1 to the coordinate of the RNU4ATAC nucleotide (e. g. n. 50G>A was transformed into n. 51G>A).
Bioinformatics model for large-scale U4atac/U6atac secondary structure predictions Three nucleotide substitutions found in patients but not present in gnomAD were added to the list, leading to a total number of 285 different RNU4ATAC variants for the 130 nucleotides of the RNU4ATAC gene. The bioinformatics pipeline that we set-up follows three steps. First, we generated the mutated RNU4ATAC sequences for the 285 variants. Then, for all the obtained sequences, we used the bifold function of the RNAstructure package (version 6. 1)  [18]  to predict the secondary structure of the bimolecule formed by wild type U6atac and mutant U4atac. This tool predicts the bimolecular structures of two sequences folded into their lowest hybrid free energy conformation. All default parameters were used to run bifold except the ''-p" parameter that we set to 2.
RNU4ATAC variants identified in large-scale sequencing projects To gain insight into the extent of RNU4ATAC genetic variability, we took advantage of the Genome Aggregation Database (gnomAD) resource (http://gnomad. broadinstitute. org/), exploiting the data present in the v2. 1. 1 version derived from 15,708 whole-genome and 65,258 exome sequences  [17, 27]. Of note, RNU4ATAC is half as much covered by exome sequencing as most coding genes because some commercially available exome capture kits target only a fraction of all non-coding genes. We listed 282 variants in RNU4ATAC: 239 substitutions concerning 123 of its 130 nucleotide-long genomic sequence, as well as 19 insertions, 16 duplications and 8 deletions whose size range from 1 to 107 (S2 Table ). Nearly half of these variants (129/282; 46%) were found in only one or two of the ~81.
93G>A) or variable importance (n. 87C>T, n. 91dupT and n. 110delT); n. 58C>T lies between the 5' Stem-Loop and Stem I, and n. 45A>G is in the loop of the 5' Stem-Loop, both regions also considered to be of limited or null importance for splicing  [22]. Among the 30 variants reported as pathogenic, 27 were identified in large-scale sequencing projects and have allele frequency ranging from 0. 0008% to 0. 04% for the most frequent pathogenic variant, i. e. n. 51G>A. Noteworthily, n. 51G>A is the 20th most frequent RNU4ATAC variant among all those present in gnomAD and it has been identified in all sub-populations but one (Ashkenazi Jewish) at frequencies ranging from 0. 03% to 0. 06% (S2 Table ). In summary, population data showed that although the extent of variations in RNU4ATAC is extremely large, all of them classify as rare variants.
Bioinformatics predictions RNU4ATAC is a single exon non-coding gene and the functional consequences of its variants cannot be predicted using traditional tools which focus on amino acid substitutions or splicing variants. It is however possible to predict the impact of the variants on the secondary structure of the U4atac/U6atac bimolecule using a dedicated software, RNAstructure  [18] , the only tool predicting the correct structure, to date. Using the bifold function of RNAstructure (http:// rna. urmc. rochester. edu/RNAstructure. html) in an automated way which we set up in house (Fig 2A ), we modelled the effect of the 282 variants identified in large-scale sequencing projects as well as the three pathogenic variants not present in gnomAD, namely n. 8C>T, n. 120T>G and n. 16_100dup. To assess the structural modifications of the U4atac/U6atac bimolecule resulting from U4atac variants, we defined a scoring system that takes into account the number of base-pairing changes and the importance for splicing of the U4atac snRNA domain where they occur (Fig  2B ).