Miracle-dz/UniFAR-FTUs huggingface dataset:
NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

—Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by perturbing correctly classiﬁed inputs to cause DNN models to misbehave (e.g., misclas-siﬁcation). This can potentially lead to disastrous consequences especially in security-sensitive applications. Existing defense and detection techniques work well for speciﬁc attacks under various assumptions (e.g., the set of possible attacks are known beforehand). However, they are not sufﬁciently general to protect against a broader range of attacks. In this paper, we analyze the internals of DNN models under various attacks and identify two common exploitation channels: the provenance channel and the activation value distribution channel. We then propose a novel technique to extract DNN invariants and use them to perform runtime adversarial sample detection. Our experimental results of 11 different kinds of attacks on popular datasets including ImageNet and 13 models show that our technique can effectively detect all these attacks (over 90% accuracy) with limited false positives. We also compare it with three state-of-the-art techniques including the Local Intrinsic Dimensionality (LID) based method, denoiser based methods (i.e., MagNet and HGD), and the prediction inconsistency based approach (i.e., feature squeezing). Our experiments show promising results.
Searching for statistically significant regulatory modules

MOTIVATION The regulatory machinery controlling gene expression is complex, frequently requiring multiple, simultaneous DNA-protein interactions. The rate at which a gene is transcribed may depend upon the presence or absence of a collection of transcription factors bound to the DNA near the gene. Locating transcription factor binding sites in genomic DNA is difficult because the individual sites are small and tend to occur frequently by chance. True binding sites may be identified by their tendency to occur in clusters, sometimes known as regulatory modules. RESULTS We describe an algorithm for detecting occurrences of regulatory modules in genomic DNA. The algorithm, called mcast, takes as input a DNA database and a collection of binding site motifs that are known to operate in concert. mcast uses a motif-based hidden Markov model with several novel features. The model incorporates motif-specific p-values, thereby allowing scores from motifs of different widths and specificities to be compared directly. The p-value scoring also allows mcast to only accept motif occurrences with significance below a user-specified threshold, while still assigning better scores to motif occurrences with lower p-values. mcast can search long DNA sequences, modeling length distributions between motifs within a regulatory module, but ignoring length distributions between modules. The algorithm produces a list of predicted regulatory modules, ranked by E-value. We validate the algorithm using simulated data as well as real data sets from fruitfly and human. AVAILABILITY http://meme.sdsc. edu/MCAST/paper
Failure prediction in the internet of things due to memory exhaustion

We present a technique to predict failures resulting from memory exhaustion in devices built for the modern Internet of Things (IoT). These devices can run general-purpose applications on the network edge for local data processing to reduce latency, bandwidth and infrastructure costs, and to address data safety and privacy concerns. Applications are, however, not optimized for all devices and could result in sudden and unexpected memory exhaustion failures because of limited available memory on those IoT devices. Proactive prediction of such failures, with sufficient lead time, allows for adaptation of the application or its safe termination. Our memory failure prediction technique for applications running on IoT devices uses k-Nearest-Neighbor (kNN) based machine learning models. We have evaluated our technique using two third-party applications and a real-world IoT simulation application on two different IoT platforms and on an Amazon EC2 t2.micro instance for both single and multitenancy use cases. Our results indicate that our technique significantly outperforms simpler threshold-based techniques: in our test applications, with 180 seconds of lead time, failures were accurately predicted with 88% recall at 74% precision for a single application failure and 76% recall at 71% precision for multitenancy failure.
The effect of gamification on software architecture knowledge management: a student experiment and focus group study

Both scientists and practitioners agree on the importance of software architecture knowledge (AK) in software development, but AK is often not recorded. This is especially problematic with large and complex software systems, as AK easily evaporates over time if it is not documented. A major problem is the lack of the motivation of architects and developers to document AK. Since gamification is a means of increasing motivation and work morale in general, we investigated its suitability for improving the capturing of AK. To investigate the effects of gamification and identify potential problems, we conducted an experiment and an associated focus group with graduate students in computer science. While we found some minor differences between groups using gamification and not using gamification, none of them was statistically significant. The focus group interviews, however, provided some explanation why we could not observe any significant differences as well as why it may still be worthwhile to further investigate the use of gamification in this area.
Enabling Technologies for Cell-Based Clinical Translation Extracellular Vesicles Improve Post-Stroke Neuroregeneration and Prevent Postischemic Immunosuppression

Although the initial conceptsof stemcell therapy aimedat replacing lost tissue,more recent evidence has suggested that stem and progenitor cells alike promote postischemic neurological recovery by secreted factors that restore the injured brain’s capacity to reshape. Specifically, extracellular vesicles (EVs) derived from stem cells such as exosomes have recently been suggested to mediate restorative stem cell effects. In order to define whether EVs indeed improve postischemic neurological impairment andbrain remodeling,we systematically compared the effects ofmesenchymal stem cell (MSC)-derived EVs (MSC-EVs)withMSCs thatwere i.v. delivered tomice on days 1, 3, and 5 (MSCEVs) or on day 1 (MSCs) after focal cerebral ischemia in C57BL6 mice. For as long as 28 days after stroke, motor coordination deficits, histological brain injury, immune responses in the peripheral blood and brain, and cerebral angiogenesis and neurogenesis were analyzed. Improved neurological impairment and long-term neuroprotection associated with enhanced angioneurogenesis were noticed in stroke mice receiving EVs from two different bone marrow-derived MSC lineages. MSC-EV administration closely resembled responses to MSCs and persisted throughout the observation period. Although cerebral immune cell infiltration was not affected by MSC-EVs, postischemic immunosuppression (i.e., B-cell, natural killer cell, and T-cell lymphopenia) was attenuated in the peripheral blood at 6 days after ischemia, providing an appropriate external milieu for successful brain remodeling. Because MSC-EVs have recently been shown to be apparently safe in humans, the present study provides clinically relevant evidence warranting rapid proof-of-concept studies in stroke patients. STEM CELLS TRANSLATIONAL MEDICINE 2015;4:1131–1143
Combining on-line and off-line bidirectional long short-term memory networks for handwritten text line recognition

In this paper we present a multiple classifier system (MCS) for on-line handwriting recognition. The MCS combines several individual recognition systems based on bidirectional long short-term memory networks. To obtain diverse recognizers, we use different feature sets based on on-line and off-line features. Furthermore, we generate a number of different recognizers by changing the initialization of the networks. To combine the word sequences output by the recognizers, we incrementally align these sequences using the recognizer output voting error reduction framework (ROVER). For deriving the final decision, different voting strategies are applied. The best combination ensemble has a recognition rate of 83.64 %, which is significantly higher than the 81.26 % achieved by the best individual classifier.
Systemic Risk in the Interbank Market with Overlapping Portfolios

The increasing frequency and scope of the financial crisis have attracted more attention in the research of the systemic risk of banking system. A new model for the interbank market with overlapping portfolios is proposed to simulate a banking system in this work. The proposed model uses a bipartite network of banks and their assets to analyze the impact of bank investment on the stability of the banking system. In addition, this model introduces investment risk and allows banks to make up for liquidity by selling devaluated assets, which reflects the operating rules of the banking system more realistically. The results show that allowing banks to sell devaluated assets to make up for liquidity can improve the stability of the banking system and the interbank market can also improve the stability of the banking system. For the investment of banks, the investment risk is an uncertain factor that affects the stability of the banking system. The proposed model further analyzes the impact of average investment interest rate, savings interest rate, deposit reserve ratio, and investment asset diversity on the stability of the banking system. The model provides a tool for policy-makers and supervision agencies to prevent the systemic risk of banking system.
Changes of symptom and EEG in mal de debarquement syndrome patients after repetitive transcranial magnetic stimulation over bilateral prefrontal cortex: A pilot study

Mal de debarquement syndrome (MdDS) is a chronic disorder of imbalance characterized by a feeling of rocking and swaying. The medical treatment for MdDS is still limited. Motivated by our previous pilot study that demonstrates the promising clinical efficacy of repetitive transcranial stimulation (rTMS) in MdDS patients, a novel rTMS paradigm, i.e., 1 Hz stimulation over ipsilateral dorsal lateral prefrontal cortex (DLPFC) with respect to the dominant hand followed by 10 Hz stimulation over contralateral DLPFC, was proposed and conducted in MdDS in the present study. To evaluate the potential efficacy, we examined the changes before and after rTMS in both subjective reported symptom using visual analogue scale (VAS) and direct brain activity in resting state electroencephalography (rsEEG). To disentangle activity from distinct brain substrates and/or local networks in rsEEG signals, a group-wise independent component analysis was employed and the corresponding spectral power changes were examined in the identified components. In general, reduction in rocking sensation was reported in five of ten subjects (with dramatic reductions (changes > 30) in three subjects) after rTMS using the present paradigm, while no changes and slight increases in rocking sensation were reported in the remaining subjects. In rsEEG, significant elevated spectral powers in low frequency bands (i.e., theta and alpha) over broad areas of occipital, parietal, motor, and prefrontal cortices were induced by rTMS, reflecting the enhancement of cortical inhibition over these areas. Meanwhile, the significant correlations between changes in rsEEG and VAS scores were detected in the high frequency bands (i.e., high alpha and beta) over posterior parietal and left visual areas, reflecting the suppression of spatial information processing. Therefore, the present findings demonstrate the promising clinical efficacy of a new rTMS paradigm for MdDS, and suggest its merit for further studies in more patients.
Unveiling your keystrokes: A Cache-based Side-channel Attack on Graphics Libraries

Operating systems use shared memory to improve performance. However, as shown in recent studies, attackers can exploit CPU cache side-channels associated with shared memory to extract sensitive information. The attacks that were previously attempted typically only detect the presence of a certain operation and require significant manual analysis to identify and evaluate their effectiveness. Moreover, very few of them target graphics libraries which are commonly used, but difficult to attack. In this paper, we consider the execution time of shared libraries as the side-channel, and showcase a completely automated technique to discover and select exploitable side-channels on shared graphics libraries. In essence, we first collect the cache lines accessed by a victim process during different key presses offline, and then use machine learning to infer the best cache lines (e.g., easily measurable, robust to noise, high information leakage) for a flush and reload attack. We are able to discover effective strategies to classify what keys have been pressed. Using this approach, we not only preclude the need for manual analyses of code and traces — the automated system discovered many previously unknown sidechannels of the type we are interested in, but also achieve high precision in terms of inferring the sensitive information entered on desktop and Android platforms. We show that our approach infers the passwords with lowercase letters and numbers 10,000 1,000,000 times faster than random guessing. For a large fraction of PINs consisting of 4 to 6 digits, we are able to infer them within 20 and 80 guesses respectively. Finally, we suggest ways to mitigate these attacks.
PeriScope: An Effective Probing and Fuzzing Framework for the Hardware-OS Boundary

Author(s): Song, Dokyung; Hetzelt, Felicitas; Das, Dipanjan; Spensky, Chad; Na, Yeoul; Volckaert, Stijn; Vigna, Giovanni; Kruegel, Christopher; Seifert, Jean-Pierre; Franz, Michael
Papillomaviral DNA sequences are not amplifiable from canine subungual squamous cell carcinomas

AIM: To determine if papillomaviral DNA is more frequently present within canine subungual squamous cell carcinomas (SCCs) than in non-SCC digit lesions. METHODS: Total DNA was extracted from 23 canine subungual SCCs and 23 non-SCC digit lesions. The presence of amplifiable DNA within each sample was confirmed by amplifying a section of the glyceraldehyde-3-phosphate dehydrogenase (GADPH) gene. Two different consensus PCR primer sets were used to amplify papillomaviral DNA from the samples. RESULTS: The consensus primers only amplified papillomaviral DNA from the positive control samples. None of the 46 canine digit samples contained DNA that was amplifiable by the consensus PCR primers. CONCLUSION: Papillomaviruses are unlikely to be a significant cause of canine subungual SCCs. CLINICAL RELEVANCE: While circumstantial evidence suggests that canine subungual SCCs could develop due to papillomaviral infection, this study did not reveal any evidence to support papillomaviral aetiology of these neoplasms.
Factors Supporting Implementation among CDSMP Organizations

Reaching individuals who can benefit from evidence-based health promotion and disability prevention programs is a goal of federal, state, and local agencies as well as researchers, providers, community agencies, and other stakeholders. Implementation effectiveness at the organizational level must be achieved in order to reach these individuals and sustain the program. This mixed methods study examined eight organizations within two states that successfully implemented the Chronic Disease Self-Management Program (CDSMP) and sustained it from 4 to 10 years. There were two types of organizations: aging services and health care. Internal and external implementation factors and influences were explored. Additional examination of state activities (as a key external agent supporting CDSMP implementation) was conducted. The examination found agreement among the eight organizations regarding why they had adopted the CDSMP – citing the alignment between the program and their organizations’ mission and purpose to improve health status and promote better self-care, and the demonstrated value (benefits) of the program. Organizations were also alike in that they described the importance of an internal champion and supportive senior leader. Organizations differed in how they experienced and valued peer support and collaborative networks. Organizations also differed in how they filled their CDSMP workshops. Internal drivers and capability were more often discussed as facilitating successful implementation than external factors. However, state activities and external support enabled successful adoption – particularly funding and training. The primary challenges identified by this set of organizations included difficulty in recruiting participants (filling workshops) and irregular or insufficient funding sources. These challenges were identified as significant and represented barriers to sustaining the program.
Using contemporary constraints to ensure data consistency

Concern about the propagation of fake news and data grow every day. At the same time, the analysis and mining of large data sets have become essential in the decision-making processes, in which incorrect data bring misleading insights. However, there is no general-purpose, automatic mechanism to effectively ensure the consistency of temporal data in Linked Data sources such as Wikidata, Wikimedia's free knowledge base. In this paper, an approach based on cross-comparing date values to discover inconsistent data is proposed. Besides, the concept of contemporary constraint, on which this approach is based, is defined and formalized in order to show how to find inconsistencies in a wide range of data sources. Our experimental results show that contemporary constraints are effective and can be used with multiple purposes for data curation and data quality analysis. As a success story, the contemporary constraint has been implemented in Wikidata.
Reevaluation of T Cell Receptor Excision Circles as a Measure of Human Recent Thymic Emigrants1

The human thymus exports newly generated T cells to the periphery. As no markers have been identified for these recent thymic emigrants (RTE), it is presently impossible to measure human thymic output. T cell receptor excision circles (TREC) have been recently used to assess thymic output during both health and disease. Using a mathematical model, we quantify age-dependent changes both in the number of RTE generated per day and in TREC concentration during an 80-year lifespan. Through analyses, we demonstrate that RTE and peripheral T cell division have the same potential to affect TREC concentration at any age in healthy people. T cell death also influences TREC concentration, but to a lesser extent. During aging, our results indicate that thymic involution primarily induces an age-dependent decline in TREC concentrations within both CD4+ and CD8+ T cell populations. We further apply this model for studying TREC concentration during HIV-1 infection. Our analyses reveal that a decrease in thymic output is the major contributor to the decline in TREC concentration within CD4+ T cells, whereas both increased peripheral T cell division and decreased thymic output induce the decline in TREC concentration within CD8+ T cells. Therefore, we suggest that T cell turnover should be examined together with TREC concentration as a measure of RTE. If peripheral T cell division remains relatively unchanged, then TREC concentration indeed reflects thymic output.
The ROP needle: hiding trigger-based injection vectors via code reuse

In recent years, researchers have come up with proof of concepts of seemingly benign applications such as InstaStock and Jekyll that remain dormant until triggered by an attacker-crafted condition, which activates a malicious behavior, eluding code review and signing mechanisms. In this paper, we make a step forward by describing a stealthy injection vector design approach based on Return Oriented Programming (ROP) code reuse that provides two main novel features: 1) the ability to defer the specification of the malicious behavior until the attack is struck, allowing fine-grained targeting of the malware and reuse of the same infection vector for delivering multiple payloads over time; 2) the ability to conceal the ROP chain that specifies the malicious behavior to an analyst by using encryption. We argue that such an infection vector might be a dangerous weapon in the hands of advanced persistent threat actors. As an additional contribution, we report on a preliminary experimental investigation that seems to suggest that ROP-encoded malicious payloads are likely to pass unnoticed by current security solutions, making ROP an effective malware design ingredient.
Revealing the History of Sheep Domestication Using Retrovirus Integrations

Sheep retroviruses can be used to map the selective preferences of early farmers and trace livestock movements across Europe. Not Just Dinner on Legs Several thousand years ago, human beings realized the virtues of domesticating wild animals as easy meat. Soon other possibilities became apparent, and as revealed in a series of papers in this issue, early pastoralists became selective about breeding for wool, leather, milk, and muscle power. In two papers, Gibbs et al. report on the bovine genome sequence (p. 522; see the cover, the Perspective by Lewin, and the Policy Forum by Roberts) and trace the diversity and genetic history of cattle (p. 528), while Chessa et al. (p. 532) survey the occurrence of endogenous retroviruses in sheep and map their distribution to historical waves of human selection and dispersal across Europe. Finally, Ludwig et al. (p. 485) note the origins of variation in the coat-color of horses and suggest that it is most likely to have been selected for by humans in need of good-looking transport. The domestication of livestock represented a crucial step in human history. By using endogenous retroviruses as genetic markers, we found that sheep differentiated on the basis of their “retrotype” and morphological traits dispersed across Eurasia and Africa via separate migratory episodes. Relicts of the first migrations include the Mouflon, as well as breeds previously recognized as “primitive” on the basis of their morphology, such as the Orkney, Soay, and the Nordic short-tailed sheep now confined to the periphery of northwest Europe. A later migratory episode, involving sheep with improved production traits, shaped the great majority of present-day breeds. The ability to differentiate genetically primitive sheep from more modern breeds provides valuable insights into the history of sheep domestication.