Miracle-dz/UniFAR-FTUs huggingface dataset:
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions

In Visual Question Answering, most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image. Next, a reasoning module utilizes these explanations in place of the image to infer an answer. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some insights for the predicted answer; (2) these intermediate results can help identify the inabilities of the image understanding or the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and our system achieves comparable performance with the baselines, yet with added benefits of explanability and the inherent ability to further improve with higher quality explanations.
SP 800-22 Rev. 1a. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications

This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may be used in many cryptographic applications, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must be unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may be useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. However, no set of statistical tests can absolutely certify a generator as appropriate for usage in a particular application, i.e., statistical testing cannot serve as a substitute for cryptanalysis. The design and cryptanalysis of generators is outside the scope of this paper.
Progression of Carotid Intima-Media Thickness as Predictor of Vascular Events: Results from the IMPROVE Study

Objective—To investigate whether several different measures of carotid intima-media thickness (IMT) progression are associated with subsequent vascular events and whether such associations are independent of baseline carotid atherosclerotic profile and Framingham risk factors. Approach and Results—A longitudinal cohort study (the Carotid Intima Media Thickness [IMT] and IMT-Progression as Predictors of Vascular Events in a High Risk European Population study) was performed in 7 centers in 5 European countries (Finland, France, Italy, the Netherlands, and Sweden). Three thousand four hundred eighty-two subjects (median age 64.1 years; 47.8% men) with ≥3 vascular risk factors were recruited and monitored for a postprogression median follow-up of 21.5 months, during which time 129 subjects experienced a first vascular event (incidence of 20.4 per 1000 person-years). The 15th month progression of mean and maximum carotid IMT of the left and right common carotids, bifurcations, internal carotid arteries, and their composite measures, as well as the fastest IMTmax progression (Fastest-IMTmax-progr) detected in the whole carotid tree regardless of location, were used in statistical analyses. All carotid IMT measures showed significant progression during the first 15 months (P<0.001), but only the Fastest-IMTmax-progr was significantly associated with the risk of subsequent vascular events. The Fastest-IMTmax-progr association persisted after Bonferroni correction for multiple comparisons and after adjustments for Framingham risk factors and pharmacological treatments (all P<0.005). The use of Framingham Risk Score in place of Framingham risk factors provided almost identical results (P=0.003). Conclusions—The Fastest-IMTmax-progr, a novel approach to assess carotid IMT progression, identifies focal increases of carotid IMT and, in contrast to other progression variables, is associated with cardiovascular risk.
Achieving Content-Oriented Anonymity with CRISP

As a popular realization of Information-Centric Network (ICN), Named Data Networking (NDN) greatly improves the efficiency of Internet content-distribution. A feature of NDN is that it improves privacy, as no addresses are needed for either the content consumer or publisher. However, NDN packets contain content names, and hence a well-motivated adversary can still deduce what content the user is requesting once it can link the packets and users. How to provide privacy in NDN, given its unique data retrieval mode, is an open problem. In this paper, we explore a specific content-oriented anonymity model called content-user unlinkability, which breaks the relationship between the content and the requesting user. We argue that achieving content-user unlinkability efficiently is a non-trivial task, since existing tunnel-based approaches will largely dismiss content caching of NDN, resulting in large content retrieval delay. To this end, we propose CRISP, namely Cooperative Random IntereSt P ropagation. In CRISP, routers cooperate to form full-meshed groups, within which content requests are randomly propagated before they are forwarded to content producers. We show CRISP can achieve probable content-user unlinkability with probabilistic models. Extensive simulations demonstrate that CRISP outperforms existing solutions including ANDANA and Crowds, in terms of both content retrieval latency and data throughput.
Anaerobic Consumers of Monosaccharides in a Moderately Acidic Fen

ABSTRACT 16S rRNA-based stable isotope probing identified active xylose- and glucose-fermenting Bacteria and active Archaea, including methanogens, in anoxic slurries of material obtained from a moderately acidic, CH4-emitting fen. Xylose and glucose were converted to fatty acids, CO2, H2, and CH4 under moderately acidic, anoxic conditions, indicating that the fen harbors moderately acid-tolerant xylose- and glucose-using fermenters, as well as moderately acid-tolerant methanogens. Organisms of the families Acidaminococcaceae, Aeromonadaceae, Clostridiaceae, Enterobacteriaceae, and Pseudomonadaceae and the order Actinomycetales, including hitherto unknown organisms, utilized xylose- or glucose-derived carbon, suggesting that highly diverse facultative aerobes and obligate anaerobes contribute to the flow of carbon in the fen under anoxic conditions. Uncultured Euryarchaeota (i.e., Methanosarcinaceae and Methanobacteriaceae) and Crenarchaeota species were identified by 16S rRNA analysis of anoxic slurries, demonstrating that the acidic fen harbors novel methanogens and Crenarchaeota organisms capable of anaerobiosis. Fermentation-derived molecules are conceived to be the primary drivers of methanogenesis when electron acceptors other than CO2 are absent, and the collective findings of this study indicate that fen soils harbor diverse, acid-tolerant, and novel xylose-utilizing as well as glucose-utilizing facultative aerobes and obligate anaerobes that form trophic links to novel moderately acid-tolerant methanogens.
One-press control: a tactile input method for pressure-sensitive computer keyboards

This work presents One-press control, a tactile input method for pressure-sensitive keyboards based on the detection and classification of pressing movements on the already held-down key. To seamlessly integrate the added control input with existing practices for ordinary computer keyboards, the redefined notion of virtual modifier keys is introduced. A number of application examples are given, especially to point out a potential for simplifying existing interactions by replacing modifier key combinations with single key presses. Also, a new class of interaction scenarios employing the technique is proposed, based on an interaction model named "What You Touch Is What You Get (WYTIWYG)". Here, the proposed tactile input method is used to navigate interaction options, get full previews of potential outcomes, and then either commit to one or abort altogether -- all in the space of one key depress / release cycle. The results of user testing indicate some remaining implementation issues, as well as that the technique can be learned within about a quarter of an hour of hands-on operating practice time.
Summarising text with a genetic algorithm-based sentence extraction

Automatic text summarisation has long been studied and used. The growth in the amount of information on the web results in more demands for automatic methods for text summarisation. Designing a system to produce human-quality summaries is difficult and therefore, many researchers have focused on sentence or paragraph extraction, which is a kind of summarisation. In this paper, we introduce a new method to make such extracts. Genetic Algorithm (GA)-based sentence selection is used to make a summary, and once the summary is created, it is evaluated using a fitness function. The fitness function is based on three following factors: Readability Factor (RF), Cohesion Factor (CF) and Topic-Relation Factor (TRF). In this paper, we introduce these factors and discuss the Genetic Algorithm with the specific fitness function. Evaluation results are also shown and discussed in the paper.
A Nuclear Actin Function Regulates Neuronal Motility by Serum Response Factor-Dependent Gene Transcription

Neuronal motility relies on actin treadmilling. In addition to regulating cytoskeletal dynamics in the cytoplasm, actin modulates nuclear gene expression. We present a hitherto unappreciated cross talk of actin signaling with gene expression governing neuronal motility. Toward this end, we used a novel approach using mutant actins either favoring (G15S) or inhibiting (R62D) F-actin assembly. Overexpressing these mutant actins in mouse hippocampal neurons not only modulated growth-cone function but also neurite elongation, which was ambiguous by traditional pharmacological interference. G15S actin enhanced neurite outgrowth and filopodia number. In contrast, R62D reduced neurite length and impaired growth-cone filopodia formation. Growth-cone collapse induced by ephrin-As, a family of repulsive axon guidance molecules, is impaired upon R62D expression, resulting in perseverance of ring-shaped F-actin filaments. R62D-induced phenotypes strongly resemble neurons lacking SRF (Serum Response Factor). SRF controls gene transcription of various actin isoforms (e.g., Actb, Acta1) and actin-binding proteins (e.g., Gsn) and is the archetypical transcription factor to study actin interplay with transcription. We show that neuronal motility evoked by these actin mutants requires SRF activity. Further, constitutively active SRF partially rescues R62D-induced phenotypes. Conversely, actin signaling regulates neuronal SRF-mediated gene expression. Notably, a nucleus-resident actin (R62DNLS) also regulates SRF's transcriptional activity. Moreover, R62DNLS decreases neuronal motility similar to the cytoplasmic R62D actin mutant although R62DNLS has no access to cytoplasmic actin dynamics. Thus, herein we provide first evidence that neuronal motility not only depends on cytoplasmic actin dynamics but also on the availability of actin to modulate nuclear functions such as gene transcription.
Long-term exposure to air pollution and incidence of cardiovascular events in women.

BACKGROUND Fine particulate air pollution has been linked to cardiovascular disease, but previous studies have assessed only mortality and differences in exposure between cities. We examined the association of long-term exposure to particulate matter of less than 2.5 microm in aerodynamic diameter (PM2.5) with cardiovascular events. METHODS We studied 65,893 postmenopausal women without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998, with a median follow-up of 6 years. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence. Hazard ratios were estimated for the first cardiovascular event, adjusting for age, race or ethnic group, smoking status, educational level, household income, body-mass index, and presence or absence of diabetes, hypertension, or hypercholesterolemia. RESULTS A total of 1816 women had one or more fatal or nonfatal cardiovascular events, as confirmed by a review of medical records, including death from coronary heart disease or cerebrovascular disease, coronary revascularization, myocardial infarction, and stroke. In 2000, levels of PM2.5 exposure varied from 3.4 to 28.3 microg per cubic meter (mean, 13.5). Each increase of 10 microg per cubic meter was associated with a 24% increase in the risk of a cardiovascular event (hazard ratio, 1.24; 95% confidence interval [CI], 1.09 to 1.41) and a 76% increase in the risk of death from cardiovascular disease (hazard ratio, 1.76; 95% CI, 1.25 to 2.47). For cardiovascular events, the between-city effect appeared to be smaller than the within-city effect. The risk of cerebrovascular events was also associated with increased levels of PM2.5 (hazard ratio, 1.35; 95% CI, 1.08 to 1.68). CONCLUSIONS Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease.
Cholesterol-lowering efficacy of Lactobacillus plantarum CECT 7527, 7528 and 7529 in hypercholesterolaemic adults

Previous studies have indicated that supplementation with probiotic bacteria may improve lipid metabolism. The present study was aimed at investigating the effects of a mixture of three strains of Lactobacillus plantarum (CECT 7527, CECT 7528 and CECT 7529) on cholesterol-lowering efficacy in hypercholesterolaemic patients. A total of sixty volunteers (thirty participants in the placebo group and thirty counterparts in the L. plantarum group), aged 18–65 years old, participated in a controlled, randomised, double-blind trial. The study group received one capsule daily containing 1·2 × 109 colony-forming units of Lactobacillus strains in a unique dose; the placebo group consumed the same product without bacteria for 12 weeks. A significant reduction of 13·6 % in plasma total cholesterol (TC) levels was observed after 12 weeks of consumption in the L. plantarum group when compared with the placebo group. The lipidic outcomes were also analysed based on TC values at baseline: low initial values (LIV, 2000–2500 mg/l) v. high initial values (HIV, 2510–3000 mg/l). In the HIV group, the L. plantarum treatment showed a reduction after 12 weeks of consumption compared with the placebo group in TC, LDL-cholesterol (LDL-C) and oxidised LDL-C (17·4, 17·6 and 15·6 %, respectively). In the LIV, the L. plantarum treatment only showed a reduction after 12 weeks of consumption when compared with the placebo group in TC (9·4 %). The present results showed that the biofunctionality of L. plantarum (CECT 7527, CECT 7528 and CECT 7529) is proportional to the cardiovascular risk of the patient, having a better effect in patients with higher levels of cholesterol.
Visual analytics methods for categoric spatio-temporal data

We focus on visual analysis of space- and time-referenced categorical data, which describe possible states of spatial (geographical) objects or locations and their changes over time. The analysis of these data is difficult as there are only limited possibilities to analyze the three aspects (location, time and category) simultaneously. We present a new approach which interactively combines (a) visualization of categorical changes over time; (b) various spatial data displays; (c) computational techniques for task-oriented selection of time steps. They provide an expressive visualization with regard to either the overall evolution over time or unusual changes. We apply our approach on two use cases demonstrating its usefulness for a wide variety of tasks. We analyze data from movement tracking and meteorologic areas. Using our approach, expected events could be detected and new insights were gained.
Incremental controller synthesis in probabilistic environments with temporal logic constraints

In this paper, we consider automatic computation of optimal control strategies for a robot interacting with a set of independent uncontrollable agents in a graph-like environment. The mission specification is given as a syntactically co-safe Linear Temporal Logic formula over some properties that hold at the vertices of the environment. The robot is assumed to be a deterministic transition system, while the agents are probabilistic Markov models. The goal is to control the robot such that the probability of satisfying the mission specification is maximized. We propose a computationally efficient incremental algorithm based on the fact that temporal logic verification is computationally cheaper than synthesis. We present several case studies where we compare our approach to the classical non-incremental approach in terms of computation time and memory usage.
SLFiducials: 6DoF markers for tabletop interaction

In this paper we introduce a new method for 6DoF marker tracking, specially designed for Microsoft SecondLight or any camera-based tabletop interface that is able to see objects through the surface. Our method is based on topological region adjacency for the identification of the markers, which are fitted into a squared shape for properly track the marker pose in the real world. We also describe the constraints imposed by the system which will determine the size and ID range of the new markers, and we finally evaluate the system.
Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.
A study on influence of gender on speech emotion classification

Though a great deal of research has been done to recognize emotions automatically from human speech, low recognition rate is still a serious problem. In order to improve recognition performance, we used an improved ranked voting fusion algorithm to combine the decisions from eight hidden Markov model (HMM) classifiers which are based on different feature vectors respectively. On the other hand, in view of the severe influence to emotion recognition precision from the individual differences of acoustic character and gender is a main factor leading to acoustic difference, gender distinction method was adopted. The recognition results show that compared with the isolated HMM classifier, the recognition results of the classifier fusion system is more satisfying. Besides, gender distinction method can also improved recognition rate evidently.
Manipulation of stable matchings using minimal blacklists

Gale and Sotomayor [1985] have shown that in the Gale-Shapley matching algorithm [1962], the proposed-to side W (referred to as women there) can strategically force the W-optimal stable matching as the M-optimal one by truncating their preference lists, each woman possibly blacklisting all but one man. As Gusfield and Irving have already noted in 1989, no results are known regarding achieving this feat by means other than such preference-list truncation, i.e. by also permuting preference lists. We answer Gusfield and Irving's open question by providing tight upper bounds on the amount of blacklists and their combined size, that are required by the women to force a given matching as the M-optimal stable matching, or, more generally, as the unique stable matching. Our results show that the coalition of all women can strategically force any matching as the unique stable matching, using preference lists in which at most half of the women have nonempty blacklists, and in which the average blacklist size is less than 1. This allows the women to manipulate the market in a manner that is far more inconspicuous, in a sense, than previously realized. When there are less women than men, we show that in the absence of blacklists for men, the women can force any matching as the unique stable matching without blacklisting anyone, while when there are more women than men, each to-be-unmatched woman may have to blacklist as many as all men. Together, these results shed light on the question of how much, if at all, do given preferences for one side a priori impose limitations on the set of stable matchings under various conditions. All of the results in this paper are constructive, providing efficient algorithms for calculating the desired strategies.
Lifted Variable Elimination with Arbitrary Constraints

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more eciently. More specifically, they identify groups of interchangeable variables and perform inference once for each group, as opposed to once for each variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing approaches for exact lifted inference rely on (in)equality constraints. We show how inference methods can be generalized to work with arbitrary constraints. This allows them to capture a broader range of symmetries, leading to more opportunities for lifting. We empirically demonstrate that this improves inference eciency with orders of magnitude, allowing exact inference in cases where until now only approximate inference was feasible.
An efficient data mining technique for discovering interesting sequential patterns

Mining sequential patterns is used to discover sequential purchasing behaviors of most customers from a large amount of customer transactions. A data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the sequential patterns to be discovered. Also, an efficient data mining technique is proposed to extract sequential patterns according to user requests.