Miracle-dz/UniFAR-FTUs huggingface dataset:
Steganographic Timing Channels

This paper describes steganographic timing channels that use cryptographic primitives to hide the presence of covert channels in the timing of network traffic. We have identified two key properties for steganographic timing channels: (1) the parameters of the scheme should be cryptographically keyed, and (2) the distribution of input timings should be indistinguishable from output timings. These properties are necessary (although we make no claim they are sufficient) for the undetectability of a steganographic timing channel. Without them, the contents of the channel can be read and observed by unauthorized persons, and the presence of the channel is trivially exposed by noticing large changes in timing distributions – a previously proposed methodology for covert channel detection. Our steganographic timing scheme meets the secrecy requirement by employing cryptographic keys, and we achieve a restricted form of input/output distribution parity. Under certain distributions, our schemes conforms to a uniformness property; input timings that are uniformly distributed modulo a timing window are indistinguishable from output timings, measured under the same modulo. We also demonstrate that our scheme is practical under real network conditions, and finally present an empirical study of its covertness using the firstorder entropy metric, as suggested by Gianvecchio and Wang [8], which is currently the best published practical detection heuristic for timing channels. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-11-18. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/957 Steganographic Timing Channels Adam J. Aviv, Gaurav Shah and Matt Blaze University of Pennsylvania {aviv, gauravsh, blaze}@cis.upenn. edu
RowCore: A Processing-Near-Memory Architecture for Big Data Machine Learning

The technology-push of die stacking and application-pull of Big Data machine learning (BDML) have created a unique opportunity for processing-near-memory (PNM). This paper makes four contributions: (1) While previous PNM work explores general MapReduce workloads, we identify three workload characteristics: (a) irregular-and-compute-light (i.e., perform only a few operations per input word which include data-dependent branches and indirect memory accesses); (b) compact (i.e., the computation has a small intermediate live data and uses only a small amount of contiguous input data); and (c) memory-row-dense (i.e., process the input data without skipping over many bytes). We show that BDMLs have or can be transformed to have these characteristics which, except for irregularity, are necessary for bandwidthand energyefficient PNM, irrespective of the architecture. (2) Based on these characteristics, we propose RowCore, a row-oriented PNM architecture, which (pre)fetches and operates on entire memory rows to exploit BDMLs’ row-density. Instead of this row-centric access and compute-schedule, traditional architectures opportunistically improve row locality while fetching and operating on cache blocks. (3) RowCore employs well-known MIMD execution to handle BDMLs’ irregularity, and sequential prefetch of input data to hide memory latency. In RowCore, however, one corelet prefetches a row for all the corelets which may stray far from each other due to their MIMD execution. Consequently, a leading corelet may prematurely evict the prefetched data before a lagging corelet has consumed the data. RowCore employs novel cross-corelet flow-control to prevent such eviction. (4) RowCore further exploits its flow-controlled prefetch for frequency scaling based on novel coarse-grain compute-memory rate-matching which decreases (increases) the processor clock speed when the prefetch buffers are empty (full). Using simulations, we show that RowCore improves performance and energy, by 135% and 20% over a GPGPU with prefetch, and by 35% and 34% over a multicore with prefetch, when all three architectures use the same resources (i.e., number of cores, and on-processor-die memory) and identical diestacking (i.e., GPGPUs/multicores/RowCore and DRAM).
The search for the laws of automatic random testing

Can one estimate the number of remaining faults in a software system? A credible estimation technique would be immensely useful to project managers as well as customers. It would also be of theoretical interest, as a general law of software engineering. We investigate possible answers in the context of automated random testing, a method that is increasingly accepted as an effective way to discover faults. Our experimental results, derived from best-fit analysis of a variety of mathematical functions, based on a large number of automated tests of library code equipped with automated oracles in the form of contracts, suggest a poly-logarithmic law. Although further confirmation remains necessary on different code bases and testing techniques, we argue that understanding the laws of testing may bring significant benefits for estimating the number of detectable faults and comparing different projects and practices.
FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.
Multi-Objective Learning via Genetic Algorithms

Genetic algorithms (GAs) are powerful, general purpose adaptive search techniques which have been used successfully in a variety of learning systems. In the standard formulation, GAs maintain a set of alternative knowledge structures for the task to be learned, and improved knowledge structures are formed through a combination of competition and knowledge sharing among the alternative knowledge structures. In this paper, we extend the GA paradigm by allowing multidimensional feedback concerning the performance of the alternative structures. The modified GA is shown to solve a multiclass pattern discrimination task which could not be solved by the unmodified GA.
HapTwist: Creating Interactive Haptic Proxies in Virtual Reality Using Low-cost Twistable Artefacts

In this paper, we present a series of studies on using Rubik's Twist, a type of low-cost twistable artefact, to create haptic proxies for various hand-graspable VR objects. Our pilot studies validated the feasibility and effectiveness of Rubik's-Twist-based haptic proxies. The pilot results also revealed user challenges in the physical shape creation, motivating the development of the HapTwist toolkit. The toolkit consists of the shape-generation algorithm, the software interface for shape-construction guidance and interaction authoring, and the hardware modules for constructing interactive haptic proxies. The user studies showed that HapTwist was easy to learn and use, and it significantly improved user performance in creating interactive haptic proxies with Rubik's Twist. Furthermore, HapTwist-generated haptic proxies achieved similar VR experience as the real objects.
"They Don't Leave Us Alone Anywhere We Go": Gender and Digital Abuse in South Asia

South Asia faces one of the largest gender gaps online globally, and online safety is one of the main barriers to gender-equitable Internet access [GSMA, 2015]. To better understand the gendered risks and coping practices online in South Asia, we present a qualitative study of the online abuse experiences and coping practices of 199 people who identified as women and 6 NGO staff from India, Pakistan, and Bangladesh, using a feminist analysis. We found that a majority of our participants regularly contended with online abuse, experiencing three major abuse types: cyberstalking, impersonation, and personal content leakages. Consequences of abuse included emotional harm, reputation damage, and physical and sexual violence. Participants coped through informal channels rather than through technological protections or law enforcement. Altogether, our findings point to opportunities for designs, policies, and algorithms to improve women's safety online in South Asia.
Chemical and Physical Enhancers for Transdermal Drug Delivery

Jose Juan Escobar-Chavez1,*, Isabel Marlen Rodriguez-Cruz2 and Clara Luisa Dominguez-Delgado2 1Unidad de Investigacion Multidisciplinaria, Laboratorio 12: Materiales Nanoestructurados y Sistemas Transdermicos, Facultad de Estudios Superiores Cuautitlan-Universidad Nacional Autonoma de Mexico, Carretera Cuautitlan–Teoloyucan, San Sebastian Xhala, Cuautitlan Izcalli, Estado de Mexico, 2Departamento de Ingenieria y Tecnologia, Seccion de Tecnologia Farmaceutica, Facultad de Estudios Superiores Cuautitlan-Universidad Nacional Autonoma de Mexico, Cuautitlan Izcalli, Estado de Mexico, Mexico
From dirt to shovels: fully automatic tool generation from ad hoc data

An ad hoc data source is any semistructured data source for which useful data analysis and transformation tools are not readily available. Such data must be queried, transformed and displayed by systems administrators, computational biologists, financial analysts and hosts of others on a regular basis. In this paper, we demonstrate that it is possible to generate a suite of useful data processing tools, including a semi-structured query engine, several format converters, a statistical analyzer and data visualization routines directly from the ad hoc data itself, without any human intervention. The key technical contribution of the work is a multi-phase algorithm that automatically infers the structure of an ad hoc data source and produces a format specification in the PADS data description language. Programmers wishing to implement custom data analysis tools can use such descriptions to generate printing and parsing libraries for the data. Alternatively, our software infrastructure will push these descriptions through the PADS compiler, creating format-dependent modules that, when linked with format-independent algorithms for analysis and transformation, result infully functional tools. We evaluate the performance of our inference algorithm, showing it scales linearlyin the size of the training data - completing in seconds, as opposed to the hours or days it takes to write a description by hand. We also evaluate the correctness of the algorithm, demonstrating that generating accurate descriptions often requires less than 5% of theavailable data.
Slipping Past the Cordon : A Systematization of Internet Censorship Resistance

The Internet censorship conflict is like a game of cat-and-mouse, in which the censor discovers and acts on distinguishers that set the usage of censorship resistance systems (CRSs) apart from other Internet usage, and the CRSs’ attempts to remove and mitigate these distinguishers. Our systematization is based on a simple and intuitive model of the distinguisher-leveraging censorship apparatus, but one that is grounded in the inherent limitations of the censor and the unavoidable error rates of the apparatus used. We are then able to categorize CRS techniques and designs in terms of their impact on distinguishers, and thereby evaluate a broad range of systems with respect to their security and applicability for certain use cases. We present a taxonomy of censorship circumvention strategies and techniques and an evaluation of the systems that utilize them. Our evaluation leads us to identify gaps in the literature, question the assumptions at play, and formulate a plan of action to develop effective CRSs.
Psychosocial correlates of nutritional status of family caregivers of persons with dementia

ABSTRACT Background: This exploratory study investigated the associations of individual characteristics of both persons with dementia and family caregivers with the nutritional status of caregivers. Methods: This cross-sectional study was conducted at home by psychogerontologist within the frame of a community gerontological center in rural areas of south west France. The study participants comprised 56 community-dwelling persons with dementia (mean 80.7 years, SD 6.5) and 56 family caregivers (mean 70.9 years, SD 11.0). Persons with dementia were assessed with Mini-Mental State Examination (MMSE), Basic Activities Of Daily Living (ADL), Instrumental ADL (IADL), and NeuroPsychiatric Inventory (NPI), and family caregivers with the Burden Interview (Zarit scale), the State-Trait Anxiety Inventory (STAI Y-B), the Center for Epidemiologic Studies Depression Scale (CES-D), the emotional impact measure of NPI and the Autonomy, Gerontology and Group Resources scale (AGGIR scale). For both, nutritional status was evaluated using the Mini Nutritional Assessment (MNA®). Results: Among family caregivers, 32.1% were at risk of malnutrition and 5.4% were malnourished, and among people with dementia, 58.9% and 23.2%, respectively. NPI severity score of apathy of persons with dementia (Beta = −0.342, p = 0.001), dependency on AGGIR scale (Beta = −0.336, p = 0.002), and CES-D score of caregivers (Beta = −0.365, p = 0.001) were associated with caregivers’ MNA score (Adjusted R2 = 0.480, p < 0.001). Conclusion: These preliminary findings emphasize the need for routine assessment of depressive symptoms, functional and nutritional status in dementia family caregivers, and confirm the value of investigating caregivers’ nutritional risk through an integrative view including psychosocial approach.
Fusion of transfer learning features and its application in image classification

Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning is a highly appreciated strategy in deep convolutional neural networks (DCNNs) due to its multifaceted benefits. It heartens us to explore the effect of fusing multiple transfer learning features of different DCNN architectures. Thus, in this work, we extract features of image statistics by exploiting three different pre-trained DCNNs through transfer learning. Then, we transform the features into a generalized subspace through a recently introduced Autoencoder network and fuse them to form intra-class invariant feature vector that is used to train a multi-class Support Vector Machine (SVM). The experimental results on various datasets, including object and action image statistics show that the fusion of multiple transfer learning features improves classification accuracy as compared to fusion of multiple hand-crafted features and usage of single component transfer learning features.
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition

A challenge in dialogue act recognition is the mapping from noisy user inputs to dialogue acts. In this paper we describe an approach for re-ranking dialogue act hypotheses based on Bayesian classifiers that incorporate dialogue history and Automatic Speech Recognition (ASR) N-best information. We report results based on the Let’s Go dialogue corpora that show (1) that including ASR N-best information results in improved dialogue act recognition performance (+7% accuracy), and (2) that competitive results can be obtained from as early as the first system dialogue act, reducing the need to wait for subsequent system dialogue acts.
Evaluation of the Mycobacterium bovis Restriction Fragment Length Polymorphism Probe pUCD, in Combination with the Direct Repeat Probe, for Molecular Typing of Mycobacterium tuberculosis Strains in Ireland

ABSTRACT A mycobacterial restriction fragment length polymorphism probe, pUCD, has recently been described which represents an effective tool for the strain typing of Mycobacterium bovis. The present study evaluated this probe, in combination with the direct repeat probe (DR), for the molecular typing of 90 strains of Mycobacterium tuberculosis from 87 patients, looking at a group (62 isolates) of nonselected samples to assess pUCD combined with DR as a general tool and a subset of 32 isolates with a common specific IS6110 strain type in Ireland. Within the group of 62 isolates, pUCD-DR identified 42 strains and was comparable to both IS6110 (41 strains) and polymorphic guanine-cytosine-rich sequence (PGRS) (37 strains) analysis. pUCD-DR was found to be comparable to IS6110 and PGRS in identifying four separate clusters of isolates which were confirmed to be clinically related. pUCD-DR divided the common IS6110 isolates into six distinct types and was comparable to PGRS (seven strain types). The usefulness of this probe as an epidemiological tool is discussed.
Highly sensitive optical microresonator sensors for photoacoustic imaging

We present novel concave Fabry Perot (FP) sensor arrays for photoacoustic imaging which were fabricated using a high-precision inkjet printing approach to produce the cavity and employed physical vapor deposition to form the dielectric mirrors. Our concave FP cavity design provides excellent beam confinement within the cavity enabling high finesse and thus high sensitivity to be achieved. Two such concave sensors are evaluated in terms of their sensitivity and acoustic bandwidth. A 66 μm thick concave sensor is shown to provide a noise equivalent pressure (NEP) of 85 Pa and an acoustic bandwidth of 16 MHz, and can potentially be used as a sensitive broadband sensor for superficial imaging. A 250 μm thick sensor with an NEP of 12 Pa and acoustic bandwidth of 4 MHz was also developed for deep tissue imaging applications.
Characterization of a Novel Leucine-Rich Repeat Protein Antigen from Group B Streptococci That Elicits Protective Immunity

ABSTRACT Group B streptococci (GBS) usually behave as commensal organisms that asymptomatically colonize the gastrointestinal and urogenital tracts of adults. However, GBS are also pathogens and the leading bacterial cause of life-threatening invasive disease in neonates. While the events leading to transmission and disease in neonates remain unclear, GBS carriage and level of colonization in the mother have been shown to be significant risk factors associated with invasive infection. Surface antigens represent ideal vaccine targets for eliciting antibodies that can act as opsonins and/or inhibit colonization and invasion. Using a genetic screen for exported proteins in GBS, we identified a gene, designated lrrG, that encodes a novel LPXTG anchored surface antigen containing leucine-rich repeat (LRR) motifs found in bacterial invasins and other members of the LRR protein family. Southern blotting showed that lrrG was present in all GBS strains tested, representing the nine serotypes, and revealed the presence of an lrrG homologue in Streptococcus pyogenes. Recombinant LrrG protein was shown in vitro to adhere to epithelial cells in a dose-dependent manner, suggesting that it may function as an adhesion factor in GBS. More importantly, immunization with recombinant LrrG elicited a strong immunoglobulin G response in CBA/ca mice and protected against lethal challenge with virulent GBS. The data presented in this report suggest that this conserved protein is a highly promising candidate antigen for use in a GBS vaccine.
Protective Effect of Self-Compassion to Emotional Response among Students with Chronic Academic Stress

The literature has shown that self-compassion is a protective factor of an individual’s emotional response to chronic stress. However, this stress-buffering effect has not been completely analyzed in individuals who report significantly high academic stress. The present study explored the role of self-compassion in a group of undergraduate students who experience chronic academic stress. A total of 208 undergraduate students who were preparing for the Postgraduate Entrance Examination (PEE) were recruited and completed the Self-Compassion Scale, Adolescent Self-Rating Life Event Check List, and Positive and Negative Affect Schedule. Differences analysis confirmed that the participants reported significantly higher academic stress than their peers who were not preparing for PEE. Self-compassion positively related to positive affect but negatively related to negative affect and learning stress. Further analysis showed that self-compassion negatively mediated the relationship between chronic academic stress and negative affect. Findings imply that self-compassion-centered interventions can be developed in the educational context to assist students cope with chronic academic stress.
Complete Syntactic Analysis Bases on Multi-level Chunking

This paper describes a complete syntactic analysis system based on multi-level chunking. On the basis of the correct sequences of Chinese words provided by CLP2010, the system firstly has a Part-of-speech (POS) tagging with Conditional Random Fields (CRFs), and then does the base chunking and complex chunking with Maximum Entropy (ME), and finally generates a complete syntactic analysis tree. The system took part in the Complete Sentence Parsing Track of the Task 2 Chinese Parsing in CLP2010, achieved the F-1 measure of 63.25% on the overall analysis, ranked the sixth; POS accuracy rate of 89.62%, ranked the third.
Establishment of an Integrated Computational Workflow for Single Cell RNA-Seq Dataset

Single cell RNA-sequencing (scRNA-Seq) has emerged as a popular transcriptomic profiling approach to address long-standing questions on developmental biology and cancer biology. With the advent of increasing single-cell computational methods, it is not easy to determine which profiler to use. Here, we provide an integrated pipeline for both gene expression and genomic architecture analysis in single cells. Our pipeline reveals the global expression profile of the populations, and also identifies the changes in transcriptome/genome including alternative splicing (AS), single-nucleotide polymorphisms (SNPs), RNA editing and gene fusion.
Ad-Hoc vs. Standardized and Optimized Arthropod Diversity Sampling

The use of standardized and optimized protocols has been recently advocated for different arthropod taxa instead of ad-hoc sampling or sampling with protocols defined on a case-by-case basis. We present a comparison of both sampling approaches applied for spiders in a natural area of Portugal. Tests were made to their efficiency, over-collection of common species, singletons proportions, species abundance distributions, average specimen size, average taxonomic distinctness and behavior of richness estimators. The standardized protocol revealed three main advantages: (1) higher efficiency; (2) more reliable estimations of true richness; and (3) meaningful comparisons between undersampled areas.