Miracle-dz/UniFAR-FTUs huggingface dataset:
Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition

In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained language model (LM). Our proposed fusion methods focus on the memory cell state and the hidden state in the seq2seq decoder long short-term memory (LSTM), and the memory cell state is updated by the LM unlike the prior studies. This means the memory retained by the main seq2seq would be adjusted by the external LM. These fusion methods have several variants depending on the architecture of this memory cell update and the use of memory cell and hidden states which directly affects the final label inference. We performed the experiments to show the effectiveness of the proposed methods in a mono-lingual ASR setup on the Librispeech corpus and in a transfer learning setup from a multilingual ASR (MLASR) base model to a low-resourced language. In Librispeech, our best model improved WER by 3.7%, 2.4% for test clean, test other relatively to the shallow fusion baseline, with multilevel decoding. In transfer learning from an MLASR base model to the IARPA Babel Swahili model, the best scheme improved the transferred model on eval set by 9.9%, 9.8% in CER, WER relatively to the 2-stage transfer baseline.
Packet Transactions: High-Level Programming for Line-Rate Switches

Many algorithms for congestion control, scheduling, network measurement, active queue management, and traffic engineering require custom processing of packets in the data plane of a network switch. To run at line rate, these data-plane algorithms must be implemented in hardware. With today's switch hardware, algorithms cannot be changed, nor new algorithms installed, after a switch has been built. This paper shows how to program data-plane algorithms in a high-level language and compile those programs into low-level microcode that can run on emerging programmable line-rate switching chips. The key challenge is that many data-plane algorithms create and modify algorithmic state. To achieve line-rate programmability for stateful algorithms, we introduce the notion of a packet transaction: a sequential packet-processing code block that is atomic and isolated from other such code blocks. We have developed this idea in Domino, a C-like imperative language to express data-plane algorithms. We show with many examples that Domino provides a convenient way to express sophisticated data-plane algorithms, and show that these algorithms can be run at line rate with modest estimated chip-area overhead.
Directed incremental symbolic execution

The last few years have seen a resurgence of interest in the use of symbolic execution -- a program analysis technique developed more than three decades ago to analyze program execution paths. Scaling symbolic execution and other path-sensitive analysis techniques to large systems remains challenging despite recent algorithmic and technological advances. An alternative to solving the problem of scalability is to reduce the scope of the analysis. One approach that is widely studied in the context of regression analysis is to analyze the differences between two related program versions. While such an approach is intuitive in theory, finding efficient and precise ways to identify program differences, and characterize their effects on how the program executes has proved challenging in practice. In this paper, we present Directed Incremental Symbolic Execution (DiSE), a novel technique for detecting and characterizing the effects of program changes. The novelty of DiSE is to combine the efficiencies of static analysis techniques to compute program difference information with the precision of symbolic execution to explore program execution paths and generate path conditions affected by the differences. DiSE is a complementary technique to other reduction or bounding techniques developed to improve symbolic execution. Furthermore, DiSE does not require analysis results to be carried forward as the software evolves -- only the source code for two related program versions is required. A case-study of our implementation of DiSE illustrates its effectiveness at detecting and characterizing the effects of program changes.
Critical variables for success in the technology adoption process in the framework of digital transformation

The objective of the research is to identify the most representative variables considered critical for success in adoption processes in technology applicable to projects based on information technology and communications (ICT) in the framework of the digital transformation of organizations. For its identification, a cause-and-effect analysis is applied where the relationships that exist between ICT projects emerge and a categorization of technology projects for digital transformation framed in a classic evolution of projects, it is believed that before undertaking projects of this type it is important to consult this article in order to know in advance the variables that will guarantee successful projects.
PageRank without hyperlinks: Structural reranking using links induced by language models

The ad hoc retrieval task is to find documents in a corpus that are relevant to a query. Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural reranking approach to ad-hoc retrieval that applies to settings with no hyperlink information. We reorder the documents in an initially retrieved set by exploiting implicit asymmetric relationships among them. We consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another. We study a number of reranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks; the best resultant performance is comparable, and often superior, to that of a state-of-the-art pseudo-feedback-based retrieval approach. In addition, we demonstrate the merits of our language-model-based method for inducing interdocument links by comparing it to previously suggested notions of interdocument similarities (e.g., cosines within the vector-space model). We also show that ourmethods for inducing centrality are substantially more effective than approaches based on document-specific characteristics, several of which are novel to this study.
Characterization of changes in intrahepatic immune cell populations during HCV treatment with sofosbuvir and ribavirin

Treatment of chronic hepatitis C virus (HCV) infection with direct‐acting antivirals (DAAs) results in a sustained virologic response (SVR) in most patients. While highly efficacious, ~3%‐5% of patients do not achieve SVR despite having virus that appears susceptible. It is unclear whether host factors contribute to treatment failures, although innate and adaptive immunity may play a role. Previous studies showed that after DAA treatment, the composition of intrahepatic immune cells does not normalize relative to healthy volunteers, even in cases where SVR is achieved. We used paired pre‐ and post‐treatment liver biopsies from 13 patients treated with sofosbuvir and ribavirin, 4 of whom relapsed, to analyse intracellular immune changes during DAA treatment and explore correlations with inflammation and treatment outcome. We performed single marker immunohistochemistry followed by electronic image capture, manual annotation of parenchymal and non‐parenchymal regions, and quantitative image analysis. The predominant cellular change during treatment was a decrease in CD8+ cellular density in both parenchymal and non‐parenchymal regions. CD68+ Kupffer cell density correlated with hepatic inflammation (AST, ALT) pre‐treatment, but did not change during treatment. CD4+ cellular density decreased in non‐parenchymal regions and, intriguingly, was lower pre‐treatment in subjects who eventually relapsed. Other cellular markers (CD56, CD20), as well as markers of apoptosis (TIA‐1) and activated stellate cells, did not change significantly during treatment or differ by treatment outcome. The predominant intrahepatic cellular change during DAA treatment of chronic HCV infection is a reduction in CD8+ cellular density, but this did not correlate with treatment outcome.
Investigation of the molecular structure of 4-(3-methyl-3-phenylcyclobutyl)-2-[2-(3-methylbenzylidene)hydrazinyl]thiazole in the gas and solid phases.

In this study, the title Shiff base, C22H23N3S, was synthesized and examined by 1H and 13C NMR spectroscopy and X-ray analysis techniques. The crystal structure is stabilized by classical intermolecular N-H.N hydrogen bonding. The crystal packing is additionally stabilized by C-H. π interactions. It has been observed that the compound can exist in two different tautomeric forms, and experimental and theoretical studies were carried out on these tautomeric structures. For this purpose, the gas phase of the compound was optimized by density functional theory (DFT) using the B3LYP/6-311G(d) method, which allowed for the structural parameters (bond angles, bond lengths and dihedral angles), as well as the frontier molecular orbitals (FMO), to be examined. In addition, stable structures of the two tautomers in the solid phase were obtained using Quantum ESPRESSO under periodic boundary conditions (PBC).
Poster: Agent-based (BDI) modeling for automation of penetration testing

Traditional penetration testing relies on the domain expert knowledge and requires considerable human effort all of which incurs a high cost. In this paper, we propose an automated penetration testing approach based on the belief-desire-intention (BDI) agent model, which is central in the research on agentbased processing in that it deals interactively with dynamic, uncertain and complex environments. Penetration testing actions are defined as a series of BDI plans and the BDI reasoning cycle is used to represent the penetration testing process. The model is extensible and new plans can be added, once they have been elicited from the human experts. We report on the results of testing of proof of concept BDI-based penetration testing tool in the simulated environment.
Storage assignment to decrease code size

DSP architectures typically provide indirect addressing modes with auto-increment and decrement. In addition, indexing mode is not available, and there are usually few, if any, general-purpose registers. Hence, it is necessary to use address registers and perform address arithmetic to access automatic variables. Subsuming the address arithmetic into auto-increment and auto-decrement modes improves the size of the generated code. In this paper we present a formulation of the problem of optimal storage assignment such that explicit instructions for address arithmetic are minimized. We prove that for the case of a single address register the decision problem is NP-complete. We then generalize the problem to multiple address registers. For both cases heuristic algorithms are given. Our experimental results indicate an improvement of 3.
Towards Reconfigurable HPC Component Models

High performance computing (HPC), as other domains, is facing growth of the complexity of both hardware and software. Hardware complexity can be observed in the diversification of computing units (multi-core, many-core, GPGPU, FPGA, etc.) and, more recently, in storage elements (SSD and then SVRAM). Many of these new technologies stem from frequency scaling limitations and energy consumption issues. Hence, not only do applications have to be continuously adapted to varied and changing hardware configurations, but also energy-related concerns encourage runtime adaptation to use as little resources as possible.
Dense Texture Flow Visualization using Data-Parallel Primitives

Recent trends in supercomputing towards massively threaded on-node processors to increase performance has also introduced fragmented software support. In response to this changing landscape, new scientiﬁc visualization packages have been developed to provide a portable framework to exploit this on-node parallelism with data parallel primitives, while also providing a single interface to multiple hardware backends. This necessitates adapting algorithms to the data parallel primitives paradigm. In numerous cases the algorithm is serial, but other times the technique is tied to hardware and needs to be generalized to broadly disseminate. Inthiswork, we present unsteady ﬂow line integral convolution (UFLIC) using only data parallel primitives. Line integral convolution (LIC) is a fundamental ﬂow visualization technique in scientiﬁc visualization. LIC and its texture-based variants, are used in ﬁelds such as meteorology and computational ﬂuid dynamics to aid practitioners because of its efﬁcient memory usage, strong, visual ﬂow characteristics, and efﬁcient performance. However, in practice performant implementations are GPU shader-based approaches, which limits deployment and adoption. By utilizing VTK-m, our approach is a performant, memory efﬁcient implementation, with the added beneﬁt of portability, with a single implementation across many architectures.
Scribble-Supervised Segmentation of Aerial Building Footprints Using Adversarial Learning

Aerial image segmentation usually requires a large amount of pixel-level masks in order to achieve quality performance. Obtaining these annotations can be both costly and time-consuming, limiting the amount of data available for training. In this paper, we present an approach for learning to segment aerial building footprints in the absence of fully annotated label masks. Instead, we exploit cheap and efficient scribble annotations to supervise deep convolutional neural networks for segmentation. Our proposed model is based on an adversarial architecture that jointly trains two networks to produce building footprint segmentations that resemble synthetic label masks. We present competitive segmentation results on the Massachusetts Buildings data set by using only scribble supervision signals. Further experiments show that our method effectively alleviates building instance separation issue and displays strong robustness towards different scribble instance levels. We believe our cost-effective approach has the potential to be adapted for other aerial image interpretation tasks.
The liberal media and right-wing conspiracies: using cocitation information to estimate political orientation in web documents

This paper introduces a simple method for estimating cultural orientation, the affiliation of online entities in a polarized field of discourse. In particular, cocitation information is used to estimate the political orientation of hypertext documents. A type of cultural orientation, the political orientation of a document is the degree to which it participates in traditionally left- or right-wing beliefs. Estimating documents' political orientation is of interest for personalized information retrieval and recommender systems. In its application to politics, the method uses a simple probabilistic model to estimate the strength of association between a document and left- and right-wing communities. The model estimates the likelihood of cocitation between a document of interest and a small number of documents of known orientation. The model is tested on three sets of data, 695 partisan web documents, 162 political weblogs, and 72 non-partisan documents. Accuracy above 90% is obtained from the cocitation model, outperforming lexically based classifiers at statistically significant levels.
What Makes You Stressed? Finding Reasons From Tweets

Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals. This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time. Three word-vector based methods are evaluated on collections of tweets about politics or airlines and are found to be more accurate than standard machine learning algorithms.
Combining Crowd and Machines for Multi-predicate Item Screening

This paper discusses how crowd and machine classifiers can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost. We further show how, given a new classification problem and a set of classifiers of unknown accuracy for the problem at hand, we can identify how to manage the cost-accuracy trade off by progressively determining if we should spend budget to obtain test data (to assess the accuracy of the given classifiers), or to train an ensemble of classifiers, or whether we should leverage the existing machine classifiers with the crowd, and in this case how to efficiently combine them based on their estimated characteristics to obtain the classification. We demonstrate that the techniques we propose obtain significant cost/accuracy improvements with respect to the leading classification algorithms.
Effectiveness of nursing interventions for breathlessness in people with chronic obstructive pulmonary disease: A systematic review and meta‐analysis

AIM To critically review and synthesize the findings of studies that evaluated the effectiveness of nursing interventions for improving breathlessness in adults with chronic obstructive pulmonary disease. BACKGROUND Systematic reviews of nursing interventions for breathlessness in people with chronic obstructive pulmonary disease have not been specifically addressed. DESIGN Systematic review with meta-analysis. DATA SOURCES A systematic search of Medline, CINAHL, PsycINFO and Embase was performed for studies published between January 2000 and June 2017. REVIEW METHODS Risk of bias, data extraction and meta-analysis were conducted using Cochrane methodology. The quality of evidence was assessed using the GRADE approach. RESULTS Twenty papers were included. A meta-analysis of interventions performed at home, including two trials, showed a significant effect in favour of experimental groups for the symptom score of the St. George Respiratory Questionnaire compared with controls. A meta-analysis of interventions performed in clinics with home follow-up showed a significant effect in favour of experimental groups for the mastery and fatigue scores of the Chronic Respiratory Questionnaire compared with controls. In this category of intervention, an additional meta-analysis showed a significant effect in favour of experimental groups for the symptom, activity and total scores of the St. George Respiratory Questionnaire compared with controls. The quality of evidence was assessed to be very low to moderate. CONCLUSION The results are equivocal as to whether nursing interventions performed at home and nursing interventions performed in hospital with follow-up improve breathlessness in people with chronic obstructive pulmonary disease.
BGP Communities: Even more Worms in the Routing Can

BGP communities are a mechanism widely used by operators to manage policy, mitigate attacks, and engineer traffic; e.g., to drop unwanted traffic, filter announcements, adjust local preference, and prepend paths to influence peer selection. Unfortunately, we show that BGP communities can be exploited by remote parties to influence routing in unintended ways. The BGP community-based vulnerabilities we expose are enabled by a combination of complex policies, error-prone configurations, a lack of cryptographic integrity and authenticity over communities, and the wide extent of community propagation. Due in part to their ill-defined semantics, BGP communities are often propagated far further than a single routing hop, even though their intended scope is typically limited to nearby ASes. Indeed, we find 14% of transit ASes forward received BGP communities onward. Given the rich inter-connectivity of transit ASes, this means that communities effectively propagate globally. As a consequence, remote adversaries can use BGP communities to trigger remote blackholing, steer traffic, and manipulate routes even without prefix hijacking. We highlight examples of these attacks via scenarios that we tested and measured both in the lab as well as in the wild. While we suggest what can be done to mitigate such ill effects, it is up to the Internet operations community whether to take up the suggestions.
Andro-Simnet: Android Malware Family Classification using Social Network Analysis

While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware’s attack behavior pattern and tactical characteristics. Also, we apply community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.
Spoken Conversational AI in Video Games: Emotional Dialogue Management Increases User Engagement

In a traditional role-playing game (RPG) conversing with a Non-Playable Character (NPC) typically appears somewhat unrealistic and can break immersion and user engagement. In commercial games, the player usually selects one of several possible predefined conversation options which are displayed as text or labels on the screen, to progress the conversation. In contrast, we first present a spoken conversational interface, built using a state-of-the-art open-domain social conversational AI developed for the Amazon Alexa Challenge, which was modified for use in a video game. This system is designed to keep users engaged in the conversation -- which we measure by time taken speaking with the character. In particular, we use emotion detection and emotional dialogue management to enhance the conversational experience. We then evaluate the contribution of emotion detection and conversational responses in a spoken dialogue system for a role-playing video game. In order to do this, two prototypes of the same game were created: one system using sentiment analysis and emotional modelling and the other system that does not detect or react to emotions. Both systems use a spoken conversational AI system where the user can freely talk to a Non-Playable-Character using unconstrained speech input.