Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight Framework for Cold Boot Based Forensics on Mobile Devices

Mobile devices, like tablets and smartphones, are common place in everyday life. Thus, the degree of security these devices can provide against digital forensics is of particular interest. A common method to access arbitrary data in main memory is the cold boot attack. The cold boot attack exploits theremanence effect that causes data in DRAM modules not to lose the content immediately in case of a power cut-off. This makes it possible to restart a device and extract the data in main memory. In this paper, we present a novel framework for cold boot based data acquisition with a minimal bare metal application on a mobile device. In contrast to other cold boot approaches, our forensics tool overwrites only a minimal amount of data in main memory. This tool requires no more than five kilobytes of constant data in the kernel code section. We hence sustain all of the data relevant for the analysis of the previously running system. This makes it possible to analyze the memory with data acquisition tools. For this purpose, we extend the memory forensics tool Volatility in order to request parts of the main memory dynamically from our bare metal application. We show the feasibility of our approach by comparing it to a traditional memory dump based analysis using the Samsung Galaxy S4 mobile device.
Chicken adenovirus (CELO virus) particles augment receptor-mediated DNA delivery to mammalian cells and yield exceptional levels of stable transformants

Delivery of genes via receptor-mediated endocytosis is severely limited by the poor exit of endocytosed DNA from the endosome. A large enhancement in delivery efficiency has been obtained by including human adenovirus particles in the delivery system. This enhancement is probably a function of the natural adenovirus entry mechanism, which must include passage through or disruption of the endosomal membrane. In an effort to identify safer virus particles useful in this application, we have tested the chicken adenovirus CELO virus for its ability to augment receptor-mediated gene delivery. We report here that CELO virus possesses pH-dependent, liposome disruption activity similar to that of human adenovirus type 5. Furthermore, the chicken adenovirus can be used to augment receptor-mediated gene delivery to levels comparable to those found for the human adenovirus when it is physically linked to polylysine ligand-condensed DNA particles. The chicken adenovirus has the advantage of being produced inexpensively in embryonated eggs, and the virus is naturally replication defective in mammalian cells, even in the presence of wild-type human adenovirus.
Understanding learners preferences for learning environments in higher education

The higher education institutions (HEIs) are engaging with multi modal delivery for its courses and diversifying teaching and learning strategies. The reasons for multi modal delivery range from desire to increase enrolment to providing educational access to learners far and wide. In the university of the South Pacific (USP), learners constitute a diverse demographic and equally diverse is the learners’ geographical context. However, there is a need to listen to learners’ voice in light of their changing learning needs. The study examines learners’ preference for learning environments with the aim to understand the reasons for their preference for a particular learning environment. The study has implications for all HEIs especially if they wish to engage learners from diverse backgrounds.
Speech Enhancement Based on Adaptive Noise Power Estimation Using Spectral Difference

SUMMARY In this paper, we propose a spectral difference approach for noise power estimation in speech enhancement. The noise power estimate is given by recursively averaging past spectral power values using a smoothing parameter based on the current observation. The smoothing parameter in time and frequency is adjusted by the spectral difference between consecutive frames that can efficiently characterize noise variation. Specifically, we propose an effective technique based on a sigmoid-type function in order to adaptively determine the smoothing parameter based on the spectral difference. Compared to a conventional method, the proposed noise estimate is computationally efficient and able to effectively follow noise changes under various noise conditions.
Turbo DFE algorithm with imperfect decision feedback

In this letter, we propose a new turbo decision feedback equalizer (DFE) algorithm that takes into account decision feedback error propagation. We derive the exact mathematical expression for the probability density function (pdf) of soft decision feedback symbols, assuming that soft outputs from channel decoder are independent identically distributed Gaussian random variables with known mean and variance. We find a new set of "imperfect" turbo minimum mean-square error DFE coefficients based on the derived pdf. The proposed turbo detector outperforms other turbo equalization algorithms of similar computational complexity in terms of bit-error rate. The achieved improvement is up to 3.8 dB for severe frequency-selective channels.
The study of characteristics of the hybrid particle swarm algorithm in solution of the global optimization problem

The present paper considers convergence characteristics of the particle swarm algorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and Grid Search algorithm. Comparison of quality indices of the particle swarm algorithm and the steepest descent algorithm has been carried out for evaluation of advantages of the PSO algorithm in comparison with classical optimization algorithms.
Identifying Student Success at a Land Grant Institution

Many higher education institutions use admission criteria to match students with the educational requirements of the institution, thereby increasing the level of success of their students and allocating limited enrollment space in some cases. This study uses two different approaches to identify the affect students’ background characteristics have on first year cumulative GPA, and whether differences exist in the impact of high school grades on success in their first year in college between high schools in the state of Washington. Results show that students’ particular high schools systematically perform better or worse than the model predicts, holding the other characteristics of the students constant including their high school GPA. This suggests the same GPA from different schools is indicating different levels of preparedness, either reflecting different curriculum available or taken by a student, or grade inflations differences across schools.
DNA Hypomethylation Perturbs the Function and Survival of CNS Neurons in Postnatal Animals

DNA methyltransferase I (Dnmt1), the maintenance enzyme for DNA cytosine methylation, is expressed at high levels in the CNS during embryogenesis and after birth. Because embryos deficient for Dnmt1 die at gastrulation, the role of Dnmt1 in the development and function of the nervous system could not be studied by using this mutation. We therefore used the cre/loxP system to produce conditional mutants that lack Dnmt1 in neuroblasts of embryonic day 12 embryos or in postmitotic neurons of the postnatal animal. Conditional deletion of theDnmt1 gene resulted in rapid depletion of Dnmt1 proteins, indicating that the enzyme in postmitotic neurons turns over quickly. Dnmt1 deficiency in postmitotic neurons neither affected levels of global DNA methylation nor influenced cell survival during postnatal life. In contrast, Dnmt1 deficiency in mitotic CNS precursor cells resulted in DNA hypomethylation in daughter cells. Whereas mutant embryos carrying 95% hypomethylated cells in the brain died immediately after birth because of respiratory distress, mosaic animals with 30% hypomethylated CNS cells were viable into adulthood. However, these mutant cells were eliminated quickly from the brain within 3 weeks of postnatal life. Thus, hypomethylated CNS neurons were impaired functionally and were selected against at postnatal stages.
Intelligent Information Retrieval : KONTERM-Automatic Representation of Context Related Terms within a Knowledge Base for a Legal Expert System

Knowledge acquisition constitutes the bottleneck for the creation of legal expert systems. A certain degree of formalism of legal language is an inevitable prerequisite. Our prototype KONTERM deals with that problem by supporting the process of creating a selective thesaurus for a legal information system which can be used for automatic indexing and document classification. This selectivity is obtained by distinguishing between precise legal terms and words with fuzzy meanings. The resulting thesaurus can thus be seen as an important step to overcome the "untidiness" of natural language and to represent automatically the expert knowledge of a lawyer about legal terminology. Therefore, KONTERM can be used as a tool for the semi-automatic creation of a legal knowledge base.
A method of shading and shadowing in image-based rendering

This paper presents a method to generate dynamic shading of image-based objects without geometric models. Since conventional rendering techniques cannot be used to render the shading of this type of objects, image processing operation, such as interpolation or combination of image data, is performed. However, the variation of the shading of objects is not linear. Therefore, complicated processing is required to generate the desired shading, and they may not be compatible with real-time systems. Then, to perform the variation of the shading of image-based objects in a real-time system, we adopted a very simple method. From a set of image-based data with several different light conditions we select the image-based data with the light condition that is the closest to that of the rendering time, and use it to reconstruct the image. However, as it is necessary to save a large number of image data in this method, the volume of data is huge. Then we developed a method to reduce the data volume while maintaining real-time rendering. In this paper, we present not only our method, but also its application in the real-time system CyberMirage.
Bacterial Community Associated with Black Band Disease in Corals

ABSTRACT Black band disease (BBD) is a virulent polymicrobial disease primarily affecting massive-framework-building species of scleractinian corals. While it has been well established that the BBD bacterial mat is dominated by a cyanobacterium, the quantitative composition of the BBD bacterial mat community has not described previously. Terminal-restriction fragment length polymorphism (T-RFLP) analysis was used to characterize the infectious bacterial community of the bacterial mat causing BBD. These analyses revealed that the bacterial composition of the BBD mat does not vary between different coral species but does vary when different species of cyanobacteria are dominant within the mat. On the basis of the results of a new method developed to identify organisms detected by T-RFLP analysis, our data show that besides the cyanobacterium, five species of the division Firmicutes, two species of the Cytophaga-Flexibacter-Bacteroides (CFB) group, and one species of δ-proteobacteria are also consistently abundant within the infectious mat. Of these dominant taxa, six were consistently detected in healthy corals. However, four of the six were found in much higher numbers in BBD mats than in healthy corals. One species of the CFB group and one species of Firmicutes were not always associated with the bacterial communities present in healthy corals. Of the eight dominant bacteria identified, two species were previously found in clone libraries obtained from BBD samples; however, these were not previously recognized as important. Furthermore, despite having been described as an important component of the pathogenetic mat, a Beggiatoa species was not detected in any of the samples analyzed. These results will permit the dominant BBD bacteria to be targeted for isolation and culturing experiments aimed at deciphering the disease etiology.
Taxes, Spending, and Market Returns

How fiscal policy impacts equity and bond returns is an open question. Unlike previous studies, we address this issue in a way that decomposes current returns into news about cash flows and news about discount rates. Moreover, we use narrative methods to identify plausibly exogenous shocks to fiscal policy. Our main findings suggest that tax increases lead to lower cash flow news and lower discount rates. However, the discount rate news dominates so that higher taxes are associated with higher equity returns. We confirm these results with simulations from a standard New Keynesian DSGE model.
Segmentation of Large Images with Complex Networks

Image segmentation is still a challenging issue in pattern recognition. Among the various segmentation approaches are those based on graph partitioning, which present some drawbacks, one being high processing times. With the recent developments on complex networks theory, pattern recognition techniques based on graphs have improved considerably. The identification of cluster of vertices can be considered a process of community identification according to complex networks theory. Since data clustering is related with image segmentation, image segmentation can also be approached via complex networks. However, image segmentation based on complex networks poses a fundamental limitation which is the excessive numbers of nodes in the network. This paper presents a complex network approach for large image segmentation that is both accurate and fast. To that, we incorporate the concept of super pixels, to reduce the number of nodes in the network. We evaluate our method for both synthetic and real images. Results show that our method can outperform other graph-based methods both in accuracy and processing times.
Strategic Clarity on Different Prevention Levels of School Bullying and Violence: Rethinking Peer Defenders and Selected Prevention

ABSTRACT While there is growing recognition of the need to distinguish between universal, selected, and indicated prevention levels in school bullying and violence research, this more nuanced focus is far from being systematically applied in key research studies in this area internationally. This article raises concerns about the need for such a differentiated focus to address issues of bullying perpetrators’ different motivations to underpin critique of a peer defenders model. Such a peer defenders model conflates moderate risk (selected prevention) level of perpetrators’ motivations with those of chronic need (indicated prevention). A theoretical framework for selected prevention focusing on minority identity and discriminatory bullying is developed to incorporate a focus on inclusive systems and system blockage, building on social-ecological systems theory.
Online model-free sensor fault identification and dictionary learning in Cyber-Physical Systems

This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionary which is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionary is constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.
Enhanced nitrogen plasma immersion passivation method for high-K/Ge stack formation

In this paper, an enhanced Ge surface passivation method by nitrogen plasma immersion with adding RIE power is presented and experimentally demonstrated. With the acceleration effect resulting from electric field induced by proper RIE power, more nitrogen plasma will drift to Ge surface to passivate the dangling bonds. It is shown that nitrogen plasma immersion with RIE power is efficient in suppressing Ge suboxide growth during high-K dielectric deposition, reducing interface states and improving the C-V characteristic of both p-MOS and n-MOS capacitors in terms of flat-band voltage and hysteresis.
Estimating the Returns to Urban Boarding Schools: Evidence from Seed

The SEED schools, which combine a "No Excuses'' charter model with a five-day-a-week boarding program, are America's only urban public boarding schools for the poor. We provide the first causal estimate of the impact of attending SEED schools on academic achievement, with the goal of understanding whether changing a student's environment through boarding is a cost-effective strategy to increase achievement among the poor. Using admission lotteries, we show that attending a SEED school increases achievement by 0.198 standard deviations in reading and 0.230 standard deviations in math, per year of attendance. Despite these relatively large impacts, the return on investment in SEED is less than five percent due to the substantial costs of boarding. Similar "No Excuses'' charter schools -- without a boarding option -- have a return on investment of over eighteen percent.
RankMerging: Learning to Rank in Large-scale Social Networks

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity, and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. As an illustration, we apply the method to the case of a cell phone service provider, which uses the network among its contractors as a learning set to discover links existing among users of its competitors. We show that our method substantially improves the performance of unsupervised metrics of classification. Finally, we discuss how it can be used with additional sources of data, including temporal or semantic information.
Association Analysis of Canonical Wnt Signalling Genes in Diabetic Nephropathy

Aims/Hypothesis Several studies have provided compelling evidence implicating the Wnt signalling pathway in the pathogenesis of diabetic nephropathy. Gene expression profiles associated with renal fibrosis have been attenuated through Wnt pathway modulation in model systems implicating Wnt pathway members as potential therapeutic targets for the treatment of diabetic nephropathy. We assessed tag and potentially functional single nucleotide polymorphisms (SNPs; n = 31) in four key Wnt pathway genes (CTNNB1, AXIN2, LRP5 and LRP6) for association with diabetic nephropathy using a case-control design. Methods SNPs were genotyped using Sequenom or Taqman technologies in 1351 individuals with type 1 diabetes (651 cases with nephropathy and 700 controls without nephropathy). Cases and controls were white and recruited from the UK and Ireland. Association analyses were performed using PLINK, to compare allele and haplotype frequencies in cases and controls. Adjustment for multiple testing was performed by permutation testing. Results Following logistic regression analysis adjusted by collection centre, duration of T1D, and average HbA1c as covariates, a single SNP in LRP6 (rs1337791) was significantly associated with DN (OR = 0.74; CI: 0.57–0.97; P = 0.028), although this was not maintained following correction for multiple testing. Three additional SNPs (rs2075241 in LRP6; rs3736228 and rs491347 both in LRP5) were marginally associated with diabetic nephropathy, but none of the associations were replicated in an independent dataset. Haplotype and subgroup analysis (according to duration of diabetes, and end-stage renal disease) also failed to reveal an association with diabetic nephropathy. Conclusions/Interpretation Our results suggest that analysed common variants in CTNNB1, AXIN2, LRP5 and LRP6 are not strongly associated with diabetic nephropathy in type 1 diabetes among white individuals. Our findings, however, cannot entirely exclude these genes or other members of the Wnt pathway, from involvement in the pathogenesis of diabetic nephropathy as our study had limited power to detect variants with small effect size.
An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT)

Indoor positioning system (IPS) allows an object to be located and tracked within an indoor environment. With the introduction of Internet of Things (IoT), the business interest in location-based application and services has also increased. Hence, the demand for accurate indoor localization services has become important. Until now, researches related to IPS are still being conducted with the objective to improve the performance of positioning techniques. Trilateration is one of the techniques available to determine the location of an object. This paper proposes an improved WiFi trilateration-based method for indoor positioning system. The improved model is based on the test results which was conducted by using Intel Galileo (Gen2) board as an access point. The signal blocking problem caused by obstacles existed inside the building is resolved by improving received signal strength measurement. The proposed model includes implementation of trilateration technique to determine the position of users and then using specific reference points to improve the positioning results.
Individual differences and animal personality

As illustrated in the above quote by Yerkes it is commonplace to regard individual animals as possessing distinct personalities. Yerkes went on to say in that same article, “One may as readily identify a familiar ape among many by its personality as mirrored in behavior as by its physical appearance” (1939, p. 111). I am guessing that anyone with extensive contact with almost any species, certainly with any primate species, would say something similar. As a consequence of that widespread opinion, while personality may not be a topic that presently attracts much attention within comparative cognition, the study of personality in nonhumans is in fact abundant. The study of personality in animals has attracted considerable empirical attention beginning with Pavlov’s identification of personality types in dogs. Subsequent work has revealed a number of similarities in personality between humans and nonhuman animals. A number of personality traits that are typically identified in studies of human personality, including aspects of neuroticism and extraversion, have also been isolated in studies of animal personality, predominantly in studies of nonhuman primates. Even traits that might appear to be uniquely human such as conscientiousness and psychopathy have nonhuman parallels. Moreover, a number of personality traits in humans that have distinctive neurobiological signatures, particularly aspects of neuroticism, are identifiable in nonhuman primates. These similarities include low basal serotonin levels and elevated cortisol levels in response to stress. It is argued that the inclusion of personality assessments in studies of comparative cognition will identify sources of variance that affect cognitive functioning, and will identify mutual influences between personality and cognition.