Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture of malware detector for obfuscated code inspection

Signature-based malware detection is a very fundamental technique that detects malware by generating signatures. The detection however, is unable to detect obfuscated malware unless pre-generated signature is stored in the database. In this paper, we propose a combination of known packer detection, unpacking module, and heuristic scanning techniques to find and block a malicious program before it manages to be executed locally. Unpacking is the process of stripping packer layers and restoring the original contents. This module contains self-decryption script bodies that are devised to detect and extract the hidden-code bodies of obfuscated malware. Hence, the scanning process only deals with real malware body but not junk block or junk subroutine code. This paper also draws up the implementation and the evaluation of our virus scanning mechanisms. Finally, we present experimental results of our proposed techniques and the results show that our test set is highly accurate.
Near Capacity Signaling over Fading Channels using Coherent Turbo Coded OFDM and Massive MIMO

The minimum average signal-to-noise ratio (SNR) per bit required for error-free transmission over a fading channel is derived, and is shown to be equal to that of the additive white Gaussian noise (AWGN) channel, which is $-1.6$ dB. Discrete-time algorithms are presented for timing and carrier synchronization, as well as channel estimation, for turbo coded multiple input multiple output (MIMO) orthogonal frequency division multiplexed (OFDM) systems. Simulation results show that it is possible to achieve a bit error rate of $10^{-5}$ at an average SNR per bit of 5.5 dB, using two transmit and two receive antennas. We then propose a near-capacity signaling method in which each transmit antenna uses a different carrier frequency. Using the near-capacity approach, we show that it is possible to achieve a BER of $2\times 10^{-5}$ at an average SNR per bit of just 2.5 dB, with one receive antenna for each transmit antenna. When the number of receive antennas for each transmit antenna is increased to 128, then a BER of $2\times 10^{-5}$ is attained at an average SNR per bit of 1.25 dB. In all cases, the number of transmit antennas is two and the spectral efficiency is 1 bit/transmission or 1 bit/sec/Hz. In other words, each transmit antenna sends 0.5 bit/transmission. It is possible to obtain higher spectral efficiency by increasing the number of transmit antennas, with no loss in BER performance, as long as each transmit antenna uses a different carrier frequency. The transmitted signal spectrum for the near-capacity approach can be restricted by pulse-shaping. In all the simulations, a four-state turbo code is used. The corresponding turbo decoder uses eight iterations. The algorithms can be implemented on programmable hardware and there is a large scope for parallel processing.
The Relationship Between Sleep Problems and Daytime Behavior in Children of Different Ages With Autism Spectrum Disorders

BACKGROUND The purpose of the current study was to evaluate the relationships among sleep problems and daytime behaviors in a large, well-defined cohort of children with autism spectrum disorder (ASD). METHODS Out of a registry population of 3452 children with ASDs, a subset of 1193 children aged 4 to 10 years of age from 14 centers across the country was used to evaluate the relationship between varying levels of sleep problems and daytime behavior. Measures included Children’s Sleep Habits Questionnaire, Vineland Adaptive Behavior Scales, Survey Interview Form, Second Edition, and Child Behavior Checklist. Multiple analysis of covariance was used to assess the association between sleep and behavior. RESULTS Results suggest that sleep problems, as identified by parent report by use of the Children’s Sleep Habits Questionnaire, have a negative relationship with daytime behavior. More specifically, children with ASDs and sleep problems had more internalizing and externalizing behavior problems, as measured by the Child Behavior Checklist, and poorer adaptive skill development, as measured by the Vineland Adaptive Behavior Scales, than children with ASDs and no sleep problems. Children with moderate to severe sleep problems had greater behavior difficulties, but not necessarily poorer adaptive functioning, than children with mild to moderate sleep problems. Both preschool- and school-aged children demonstrated a negative relationship between behavior and sleep, whereas the relationship between sleep and adaptive functioning was much more variable. CONCLUSIONS These results suggest that, although sleep has a negative relationship with internalizing and externalizing behavior, it may have a different relationship with the acquisition of adaptive skills.
Bioremediation of soil contaminated with crude oil using fresh and decomposed animal manure

Vast majority of rural dwellers stands to bear the brunt of oil spillage and soil pollution, hence the need to develop remediation options using materials that are cheap and readily available. A laboratory incubation study was conducted to evaluate the bioremediation potentials of different types of animal manure on soil contaminated with crude oil (Forcados). Treatments consisting of 20 t ha -1 each of poultry dropping (PD), swine manure(SM) and beef cattle manure (BCM) both in fresh (f) and partially decomposed (p) forms were applied to soils contaminated with (5 % w/w) the crude oil. Their effects were evaluated on total hydrocarbon content (THC), microbial respiration (MR) based on amount of CO 2 evolved and pH in situ during a period of 8 weeks of remediation. There was an improvement in the degree of remediation offered by the various bio-stimulants over time. At 4 weeks after amendment (WAA) the level of degradation of THC was in the order: BCMf> control > SMf = PDf > PDp > SMp>BCMp with 17.2, 16.4, 9.6, 9.6, 9.2 and 5.6>4 % of THC degraded respectively. At 8 WAA, % degradation was in the order: BCMf >PDp = PDf >SMf > SMp > BCMp > control with 66, 65.2, 65.2, 64.4, 63.6,62.8 and 61.6 % degradation, respectively. Control was the least while BCMf was the most efficient. Microbial respiration consistently reduced with time in un-remediated soils but BCMf enhanced it over time. This further validates BCMf to be the most efficient in terms of soil remediation. It is, therefore, concluded that fresh beef cattle manure applied at 20 t ha -1 could be very efficient in remediating crude oil polluted soil. Key words: Total hydrocarbon content, microbial respiration, remediation, biostimulant.
Optimal technology-oriented design of parallel robots for high-speed machining applications

In this paper, a new methodology for the optimal design of parallel kinematic machine tools is proposed. This approach is based on the concept of the maximal inscribed parallelepiped and uses technology-oriented constraints that are motivated by particular applications. This methodology is applied on two translational parallel robots with three degrees-of-freedom (DOF): the Y-STAR and the UraneSX. An analysis of the size of their workspace as a function of the design constraints is made. It is shown that, for identical workspaces with similar properties, the size of the legs of the UraneSX are greater than for the Y-STAR, thus leading to larger deformations. However, the footprint surface needed in order to install the Y-STAR is about two times bigger than for the UraneSX. Therefore, it may be interested to use the UraneSX in order to save some place on ground in manufacturing centres.
Channel estimation based on path separation for DVB-T in long delay situations

A least square (LS) channel estimation with pilots uses interpolation to obtain the channel information for the positions of data subcarriers in orthogonal frequency division multiplexing (OFDM) systems. However, the interpolation introduces estimation errors in long delay channels that usually happen in single frequency networks (SFNs). In this paper, we propose an algorithm that estimates the accurate channel information by separating channel paths. The performance is compared with conventional algorithms for DVB-T in SFN conditions1.
Matroid Bandits: Practical Large-Scale Combinatorial Bandits

A matroid is a notion of independence that is closely related to computational efficiency in combinatorial optimization. In this work, we bring together the ideas of matroids and multi-armed bandits, and propose a new class of stochastic combinatorial bandits, matroid bandits. A key characteristic of this class is that matroid bandits can be solved both computationally and sample efficiently. We propose a practical algorithm for our problem and bound its regret. The regret scales favorably with all quantities of interest. We evaluate our approach on the problem of learning routing networks for Internet service providers. Our results clearly show that the approach is practical.
CORC Tech Report TR-2002-07 Robust dynamic programming ∗

In this paper we propose a robust formulation for discrete time dynamic programming (DP). The objective of the robust formulation is to systematically mitigate the sensitivity of the DP optimal policy to ambiguity in the underlying transition probabilities. The ambiguity is modeled by associating a set of conditional measures with each state-action pair. Consequently, in the robust formulation each policy has a set of measures associated with it. We prove that when this set of measures has a certain “Rectangularity” property all the main results for finite and infinite horizon DP extend to natural robust counterparts. We identify families of sets of conditional measures for which the computational complexity of solving the robust DP is only modestly larger than solving the DP, typically logarithmic in the size of the state space. These families of sets are constructed from the confidence regions associated with density estimation, and therefore, can be chosen to guarantee any desired level of confidence in the robust optimal policy. Moreover, the sets can be easily parameterized from historical data. We contrast the performance of robust and non-robust DP on small numerical examples.
An Eight-Parent Multiparent Advanced Generation Inter-Cross Population for Winter-Sown Wheat: Creation, Properties, and Validation

MAGIC populations represent one of a new generation of crop genetic mapping resources combining high genetic recombination and diversity. We describe the creation and validation of an eight-parent MAGIC population consisting of 1091 F7 lines of winter-sown wheat (Triticum aestivum L.). Analyses based on genotypes from a 90,000-single nucleotide polymorphism (SNP) array find the population to be well-suited as a platform for fine-mapping quantitative trait loci (QTL) and gene isolation. Patterns of linkage disequilibrium (LD) show the population to be highly recombined; genetic marker diversity among the founders was 74% of that captured in a larger set of 64 wheat varieties, and 54% of SNPs segregating among the 64 lines also segregated among the eight founder lines. In contrast, a commonly used reference bi-parental population had only 54% of the diversity of the 64 varieties with 27% of SNPs segregating. We demonstrate the potential of this MAGIC resource by identifying a highly diagnostic marker for the morphological character "awn presence/absence" and independently validate it in an association-mapping panel. These analyses show this large, diverse, and highly recombined MAGIC population to be a powerful resource for the genetic dissection of target traits in wheat, and it is well-placed to efficiently exploit ongoing advances in phenomics and genomics. Genetic marker and trait data, together with instructions for access to seed, are available at http://www.niab. com/MAGIC/.
Application of change-point analysis to abnormal wind power data detection

The abnormal data of wind power could be caused by many on-site situations, such as meteorological conditions, control strategies and communication environments, which must be detected before put into work. This paper presents data detection methods by taking the abnormal data as the change points in wind power, which means an unknown moment when there are some changes appearing to the studied system abruptly. Due to the fluctuate nature and auto-regressive features of wind power at different time scales, change-point analysis in this paper is discussed on perspectives of cumulative probability distribution, changes on regression modeling characteristics and hypothesis testing about influences brought by some special key factors. Historical data of a wind farm in western China are studied to explain and verify the proposed detection methods, which could help catch the abnormal moment or period of wind power effectively and clarify the causes of such data.
Pessimistic Uplift Modeling

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the problem of uplift modeling, we demonstrate how disturbance in data can affect uplift measurement. We propose a new approach, we call it Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared our approach with the existing uplift methods, on simulated and real data-sets. The experiments show that our approach outperforms the existing approaches, especially in the case of high noise data environment.
High Liver Enzyme Concentrations are Associated with Higher Glycemia, but not with Glycemic Variability, in Individuals without Diabetes Mellitus

Background Elevated concentrations of liver enzymes have been associated with an increased risk of developing type 2 diabetes mellitus. However, it remains unclear to which specific aspects of diurnal glucose metabolism these associate most. We aimed to investigate the associations between liver enzyme concentrations and 24 h-glucose trajectories in individuals without diabetes mellitus from three independent cohorts. Methods This cross-sectional study included 436 participants without diabetes mellitus from the Active and Healthy Aging Study, the Switchbox Study, and the Growing Old Together Study. Fasting blood samples were drawn to measure gamma-glutamyltransferase (GGT), alanine transaminase, and aspartate transaminase. Measures of glycemia (e.g., nocturnal and diurnal mean glucose levels) and glycemic variability (e.g., mean amplitude of glucose excursions) were derived from continuous glucose monitoring. Analyses were performed separately for the three cohorts; derived estimates were additionally meta-analyzed. Results After meta-analyses of the three cohorts, elevated liver enzyme concentrations, and specifically elevated GGT concentrations, were associated with higher glycemia. More specific, participants in the highest GGT tertile (GGT ≥37.9 U/L) had a 0.39 mmol/L (95% confidence interval: 0.23, 0.56) higher mean nocturnal glucose (3:00 to 6:00 a.m.) and a 0.23 mmol/L (0.10, 0.36) higher diurnal glucose (6:00 to 0:00 a.m.) than participants in the lowest GGT tertile (GGT <21.23 U/L). However, elevated liver enzyme concentrations were not associated with a higher glycemic variability. Conclusion Though elevated liver enzyme concentrations did not associate with higher glycemic variability in participants without diabetes mellitus, specifically, elevated GGT concentrations associated with higher glycemia.
A Multilevel Cell for STT-MRAM Realized by Capping Layer Adjustment

A multilevel cell (MLC) allows gigabit high-density integration of a spin-transfer torque magnetic random-access memory (STT-MRAM). In this paper, we present an MLC structure based on MgO/CoFeB interfacial perpendicular magnetic anisotropy (PMA) and capping layer adjustment. The effect of capping layer toward interfacial PMA modulation has been investigated by first-principles calculations, which is consistent with the previous experimental results. Two methods have been proposed to realize this STT-MRAM MLC: 1) capping layer thickness and 2) material composition design. Using a physics-based compact model of perpendicular magnetic tunnel junctions, resistance-current curves with four-level resistance states are demonstrated, for which the threshold switching currents with clear separations prove the feasibility of this concept.
Head shop: generating animated head models with anatomical structure

We present a versatile construction and deformation method for head models with anatomical structure, suitable for real-time physics-based facial animation. The model is equipped with landmark data on skin and skull, which allows us to deform the head in anthropometrically meaningful ways. On any deformed model, the underlying muscle and bone structure is adapted as well, such that the model remains completely animatable using the same muscle contraction parameters. We employ this general technique to fit a generic head model to imperfect scan data, and to simulate head growth from early childhood to adult age.
An energy-efficient VLSI architecture for pattern recognition via deep embedding of computation in SRAM

In this paper, we propose the concept of compute memory, where computation is deeply embedded into the memory (SRAM). This deep embedding enables multi-row read access and analog signal processing. Compute memory exploits the relaxed precision and linearity requirements of pattern recognition applications. System-level simulations incorporating various deterministic errors from analog signal chain demonstrates the limited accuracy of analog processing does not significantly degrade the system performance, which means the probability of pattern detection is minimally impacted. The estimated energy saving is 63 % as compared to the conventional system with standard embedded memory and parallel processing architecture, for 256×256 target image.
Peripheral Design of Progressive Addition Lenses and the Lag of Accommodation in Myopes.

Purpose Insufficient accommodative response is assumed to result in myopia progression. We have investigated if the accommodative lag in myopes is different between a single vision lens (SVL) and the progressive addition lens PAL 2, clinically trialled for its ability to reduce progression of myopia, and if there exist differences in accommodative lag between PAL 2 and other PALs with the same addition power (+1.50 D). Methods The influence of spherical SVL and four different designs of PALs that differ in the near zone width (PAL 1) or that have different signs and magnitude of horizontal gradients of mean power adjacent to their near vision zones (PAL 3 and PAL 4) on the accommodative response was investigated for different near viewing distances (40, 33, and 25 cm) in 31 subjects, aged 18 to 25 years. Results The SVL correction resulted in insufficient accommodative response for the near object viewing distances tested. PAL 2 did significantly reduce accommodative lag for all near object distances tested. The PAL design with a more negative horizontal mean power gradient (PAL 4) provided a lower lag of accommodation when compared with PAL 2 at the shortest object distance of 25 cm (P = 0.03) and was able to reduce the lag of accommodation to a level below the depth of focus for the higher near working distances tested. Conclusions Designs of PAL with more negative horizontal mean power gradients are the most effective in lowering the lag of accommodation in myopes. This could make them good test candidates for myopia control applications.
Classification of genome-wide copy number variations and their associated SNP and gene networks analysis

Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. In this paper, we incorporate DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. By shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and find out the unique genes that those SNPs are located in. A gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their p-values. By using two real genome-wide data sets, we further demonstrate SNP and gene networks play a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to disease study.
Performance evaluation of replication-based DTN routing protocols in Intermittently Connected Mobile Networks

Intermittently Connected Mobile Networks (ICMNs) are sparse wireless networks where most of the time an end-to-end path does not exist from source to destination. These networks are characterized by opportunistic connectivity, long and variable delay, asymmetric data rate and high error rate. Hence, they can be appropriately modeled as Delay-Tolerant Networks (DTNs). In this paper, the performance of replication-based DTN routing protocols, namely Epidemic, PRoPHET, MaxProp, RAPID, Binary Spray-and-Wait, and Spray-and-Focus has been investigated in an ICMN scenario against varying message generation rates and number of mobile nodes, respectively. The performance is analyzed on the basis of three metrics: Delivery Probability, Average Latency, and Overhead Ratio. Opportunistic Network Environment (ONE) simulator is used as the simulation tool for evaluating these performance metrics. The outcome of this work shows that for the ICMN scenario and given simulation setting, the best DTN routing technique is Spray-and-Focus, whereas Epidemic routing exhibits the worst performance in terms of all the metrics considered here.