Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling information flow properties with UML

Providing guarantees regarding the privacy of sensitive information in a distributed system consisting of mobile apps and services is a challenging task. Our IFlow approach allows the model-driven development of such systems, as well as the automatic generation of code and a formal model. In this paper, we introduce modeling guidelines for the design of intuitive, flexible and expressive information flow properties with UML. Further, we show how these properties can be guaranteed using a combination of automatic language-based information flow control and model-based interactive verification.
Surface heterogeneity in KTaO 3 ( 001 )

Surface heterogeneity in Ar ion bombarded samples of (001) oriented KTaO3 was studied using (in-situ) electron microscopy and atomic force microscopy. Ion bombardment results in surface damage as well as in offstoichiometry of K and Ta due to the large differences in atomic radii of the two constituent elements and hence different dry etching rates. Subsequent annealing of such samples reduces the potassium content via vaporization and results in the formation of tantalum-rich secondary phases on the surface. The phase that segregates on the surface is dependent on the oxygen partial pressure, annealing temperature as well as K:Ta stoichiometry. However, samples etched with chemical means to remove lattice damage and surface disorder prior to annealing exhibit little to no surface heterogeneity.
Class-specific factor analysis for object recognition

We propose a new method which utilizes the factor analysis in a class-specific framework. This method can be favored in the general object recognition task. We conducted several experiments to show the capability of the method to handle different types of variations in the same framework. Results are very promising for each variation. The main advantage of the proposed method is its discriminative robustness against the increasing number of classes.
Toward Efficient Detection of Semantic Exceptions in Context-Aware Systems

Semantic exceptions mean undesirable contexts with respect to application semantics in context aware systems. Description of such semantic exceptions is based on regular expressions, which entails high complexity to detect exceptions. In this paper we devise a high level language with light-weight exception description features specific to context-aware systems. For more enhanced implementation, we also provide an intermediate language to bridge the gap between high level programming languages and event stream processing engines. We expect this approach to mitigate the complexity as well as to allow further opportunities to optimize exception detection process.
A Survey of RFID Tags

The Radio Frequency Identification System (RFID) has become a popular system and its applications has reached in most of the fields like toll bridge, supply chain management and defense sector (8). The RFID has also entered into the field of medical sciences (4, 7). Everyday, RFID tags are becoming very small and their dimensions are also reducing to 0.002 inches (RFID powder tag) developed by Hitachi (10,11). There are many different types of RFID tags available in the market for different types of applications. Due to reduction in the cost of production, RFID systems are being deployed everywhere in large number. Therefore, it becomes important to understand the tag technology and their applicability. In this paper, we provide a comprehensive survey of various RFID tag technologies reported in the literatures and classify them into different categories. Index Terms—RFID, Active tag, Passive tag, Semi-Passive tag, Digestive tag, EPC
Optimal Sleep – Wake Scheduling for Quickest Intrusion Detection using Sensor Networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: 1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, 2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which 3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.
City Children’s Nature Knowledge and Contact: It Is Not Just About Biodiversity Provision

Much attention has been directed at the perceived decline in city children’s contact with nature. We used a child-centric approach to assess neighborhood nature knowledge in 187 children aged 9 to 11 years, from different socioeconomic and ethnic groups in three New Zealand cities. We evaluated the relative importance of social (independence, gender, social connections, deprivation, age) and environmental factors (biodiversity) in explaining variation in knowledge at a scale relevant to each child’s independent movements. Our biodiversity evaluation reflected the natural dimensions of the habitats where children interacted with nature. Generalized linear modeling identified ethnicity as having the strongest association with nature knowledge. Within each ethnic group, social factors were most important (independence, social connections, deprivation) except for Pākehā/NZ European children, where local biodiversity was most important. Enhancing biodiversity values of private green spaces (yards) would be effective in facilitating opportunities to experience nature, which is fundamental to supporting nature contact.
Annotating the meaning of discourse connectives by looking at their translation: The translation-spotting technique

The various meanings of discourse connectives like while and however are difficult to identify and annotate, even for trained human annotators. This problem is all the more important that connectives are salient textual markers of cohesion and need to be correctly interpreted for many NLP applications. In this paper, we suggest an alternative route to reach a reliable annotation of connectives, by making use of the information provided by their translation in large parallel corpora. This method thus replaces the difficult explicit reasoning involved in traditional sense annotation by an empirical clustering of the senses emerging from the translations. We argue that this method has the advantage of providing more reliable reference data than traditional sense annotation. In addition, its simplicity allows for the rapid constitution of large annotated datasets.
HoverLink: Joint Interactions using Hover Sensing Capability

Users often connect two mobile devices at close range to transfer files such as pictures and movies from one device to another. In this paper, we present HoverLink, a new form of joint interactions on two mobile touchscreen devices such as smartphones and tablets using hover sensing capability. HoverLink allows users to connect two devices, manipulate the data on the devices, and disconnect them in a simple and continuous manner. We also describe HoverLink's applications: transferring data, and a menu interface.
Ambiguity in text mining

Text Mining tasks include text categorization, text clustering, concept/entity extraction, document summarization, and entity relation modeling. In this paper, the focus is given to concept/entity extraction only. The major challenging issue in extracting concept/entity from texts is natural language words are always ambiguous. Up to now, not much research in text mining especially in concept/entity extraction has focused on the ambiguity problem. This paper addresses ambiguity issues in natural language texts, and presents a new technique for resolving ambiguity problem in extracting concept/entity from texts. The technique is developed by applying possibility theory, fuzzy set, and knowledge about the context to lexical semantics.
Advocacy for gender minority students: recommendations for school counsellors

Abstract Research shows that teachers’ and educators’ responses to gender diversity issues in schools either improves or limits the experiences of students. The school counsellor has an important role to play in this process by working closely with other stakeholders to advocate for transgender, intersex and genderqueer (TIG) students. Following a review of recent developments in the USA, recommendations are made and resources identified to assist school counsellors in validating TIG students, and improving school systems in pursuit of their academic, social and emotional success.
Exploring the impact of motivations on the attraction of innovation roles in open innovation web-based platforms

Recently, a large number of new innovative enterprises engaged in different competitive markets by promoting innovation both in products and in services through open innovation web-based platforms, which allow for the collaboration of individual users and companies and for so-called crowdsourcing. A key issue concerns the identification of the most important motivations that could attract users with a specific role to proactively participate and contribute to the open and collaborative innovation processes. This article, after highlighting the theoretical background, grounded on large body of psychology and sociology literature on the motivation behind collaboration, discusses the results of the analysis of 26 open innovation web-based platforms based on a multiple research methodology (qualitative empirical study, factor analysis and multi-dimensional scaling). It also discusses the effects of ‘motivational systems’ and platform models on the attraction of the individuals characterised by different innovation roles.
Hot Gas and Physical Structure of 30 Dor

Recent ROSAT and ASCA X-ray observations as well as HST images have provided new insights into the structure and evolution of the 30 Dor nebula. I review hot gas properties of the nebula and discuss related physical processes. The structure of the nebula can be understood as outflows of hot and HII gases from the parent giant molecular cloud of R136. The dynamic mixing between the two gas phases is likely a key mass loading process of the hot gas, providing a natural explanation of both temperature and density structure of the nebula.
Power of Randomization in Automata on Infinite Strings

Probabilistic Buchi Automata (PBA) are randomized, finite state automata that process input strings of infinite length. Based on the threshold chosen for the acceptance probability, different classes of languages can be defined. In this paper, we present a number of results that clarify the power of such machines and properties of the languages they define. The broad themes we focus on are as follows. We precisely characterize the complexity of the emptiness, universality, and language containment problems for such machines, answering canonical questions central to the use of these models in formal verification. Next, we characterize the languages recognized by PBAs topologically, demonstrating that though general PBAs can recognize languages that are not regular, topologically the languages are as simple as *** -regular languages. Finally, we introduce Hierarchical PBAs, which are syntactically restricted forms of PBAs that are tractable and capture exactly the class of *** -regular languages.
Mechanical properties of a bio-inspired robotic knifefish with an undulatory propulsor

South American electric knifefish are a leading model system within neurobiology. Recent efforts have focused on understanding their biomechanics and relating this to their neural processing strategies. Knifefish swim by means of an undulatory fin that runs most of the length of their body, affixed to the belly. Propelling themselves with this fin enables them to keep their body relatively straight while swimming, enabling straightforward robotic implementation with a rigid hull. In this study, we examined the basic properties of undulatory swimming through use of a robot that was similar in some key respects to the knifefish. As we varied critical fin kinematic variables such as frequency, amplitude, and wavelength of sinusoidal traveling waves, we measured the force generated by the robot when it swam against a stationary sensor, and its velocity while swimming freely within a flow tunnel system. Our results show that there is an optimal operational region in the fin's kinematic parameter space. The optimal actuation parameters found for the robotic knifefish are similar to previously observed parameters for the black ghost knifefish, Apteronotus albifrons. Finally, we used our experimental results to show how the force generated by the robotic fin can be decomposed into thrust and drag terms. Our findings are useful for future bio-inspired underwater vehicles as well as for understanding the mechanics of knifefish swimming.
Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. The use of computational models allows predictions to be made over a wider parameter range and with more complete descriptions of AN responses than in analytical models. Performance based on AN discharge times (all-information) is compared to performance based only on discharge counts (rate-place). After the method is verified over the range of parameters for which previous analytical models are applicable, the parameter space is then extended. For example, a computational model of AN activity that extends to high frequencies is used to explore the common belief that rate-place information is responsible for frequency encoding at high frequencies due to the rolloff in AN phase locking above 2 kHz. This rolloff is thought to eliminate temporal information at high frequencies. Contrary to this belief, results of this analysis show that rate-place predictions for frequency discrimination are inconsistent with human performance in the dependence on frequency for high frequencies and that there is significant temporal information in the AN up to at least 10 kHz. In fact, the all-information predictions match the functional dependence of human performance on frequency, although optimal performance is much better than human performance. The use of computational AN models in this study provides new constraints on hypotheses of neural encoding of frequency in the auditory system; however, the method is limited to simple tasks with deterministic stimuli. A companion article in this issue (Evaluating Auditory Performance Limits: II) describes an extension of this approach to more complex tasks that include random variation of one parameter, for example, random-level variation, which is often used in psychophysics to test neural encoding hypotheses.
A Parallel Corpus of Italian/German Legal Texts

This paper presents the creation of a parallel corpus of Italian and German legal documents which are translations of one another. Thecorpus, which contains approximately 5 mio. words, is primarily intended as a resource for (semi-)automatic terminology acquisition.The guidelines of the Corpus Encoding Standard have been applied for encoding structural information, segmentation information, andsentence alignment. Since the parallel texts have a one-to-one correspondence on the sentence level, building a perfect sentence alignmentis rather straightforward. As a result of this the corpus constitutes also a valuable testbed for the evaluation of alignment algorithms. Thepaper discusses the intended use of the corpus, the various phases of corpus compilation, and basic statistics.
Supervised classification and visualization of social networks based on a probabilistic latent space model

Graph-structured networks are widely used to represent relationships between persons in organizations or communities. Recently, the need of classifying and visualizing such data has suddenly grown due to the emergence of a large number of social network websites. We propose in this paper two supervised approaches for learning a latent space model of the network taking into account both the observed class labels and the graph structure. The first proposed approach introduces the class information through the conditional model of the link existence between two nodes whereas the second one considers the class labels as new observed variables. The learned models are then used to project and classify new nodes.
Decision by Sampling 1 Running Head : DECISION BY SAMPLING Decision by Sampling

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision's context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.
Building Domain-Speci c Search Engines with Machine Learning Techniques

Domain-speci c search engines are growing in popularity because they o er increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch. com allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-speci c search engines are di cult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-speci c search engines. We describe new research in reinforcement learning, information extraction and text classi cation that enables e cient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora. justresearch.com.
Intracranial Self-Administration of Ethanol within the Ventral Tegmental Area of Male Wistar Rats: Evidence for Involvement of Dopamine Neurons

Previous work from our laboratory indicated that female Wistar rats will self-administer ethanol (EtOH) directly into the posterior ventral tegmental area (VTA). These results suggested that VTA dopamine (DA) neurons might be involved in mediating the reinforcing actions of EtOH within this region. The objectives of this study were to determine (1) the dose–response effects for the self-administration of EtOH into the VTA of male Wistar rats, and (2) the involvement of VTA DA neurons in the reinforcing actions of EtOH within the VTA. Adult male Wistar rats were implanted stereotaxically with guide cannulas aimed at the posterior or anterior VTA. After 1 week, rats were placed in standard two-lever (active and inactive) experimental chambers for a total of seven to eight sessions. The first experiment determined the intracranial self-administration of EtOH (0–400 mg%) into the posterior and anterior VTA. The second experiment examined the effects of coadministration of the D2/3 agonist quinpirole on the acquisition and maintenance of EtOH self-infusions into the posterior VTA. The final experiment determined the effects of a D2 antagonist (sulpiride) to reinstate self-administration behavior in rats given EtOH and quinpirole to coadminister. Male Wistar rats self-infused 100–300 mg% EtOH directly into the posterior, but not anterior, VTA. Coadministration of quinpirole prevented the acquisition and extinguished the maintenance of EtOH self-infusion into the posterior VTA, and addition of sulpiride reinstated EtOH self-administration. The results of this study indicate that EtOH is reinforcing within the posterior VTA of male Wistar rats and suggest that activation of VTA DA neurons is involved in this process.
A tree parent storage based on hashtable for XML construction

The emergence of the Web has increased interests in XML data. Labeling methods to extract and reconstruct the structural information of XML data, which are important for many applications such as XML IR, are becoming more attractive. To achieve efficient structural information extraction, in this paper we propose a tree parent storage based on hashtable, a novel dynamic-friendly encoding scheme, which has great advantages both in storage space and time of constructing label paths compared with prefix schema. We have conducted an experimental study on real-life XML data sets, on Berkeley DB to evaluate and compare the performance and storage consumption of the proposed method with prefix schema. The experimental results show that our schema save about 60%–70% space in structural information storing whereas having the same performance in executing time for a certain query compared with prefix schema. In conclusion,it is effective, and efficient when compared with existing proposals.
On the interpretation of the multicolour disc model for black hole candidates

We present a critical analysis of the usual interpretation of the multicolour disc model parameters for black hole candidates in terms of the inner radius and temperature of the accretion disc. Using a self-consistent model for the radiative transfer and the vertical temperature structure in a Shakura–Sunyaev disc, we simulate the observed disc spectra, taking into account Doppler blurring and gravitational redshift, and fit them with multicolour models. We show not only that such a model systematically underestimates the value of the inner-disc radius, but that when the accretion rate and/or the energy dissipated in the corona are allowed to change, the inner edge of the disc, as inferred from the multicolour model, appears to move even when it is in fact fixed at the innermost stable orbit.
Secure the Clones - Static Enforcement of Policies for Secure Object Copying

Exchanging mutable data objects with untrusted code is a delicate matter because of the risk of creating a data space that is accessible by an attacker. Consequently, secure programming guidelines for Java stress the importance of using defensive copying before accepting or handing out references to an internal mutable object. However, implementation of a copy method (like clone()) is entirely left to the programmer. It may not provide a sufficiently deep copy of an object and is subject to overriding by a malicious sub-class. Currently no language-based mechanism supports secure object cloning. This paper proposes a type-based annotation system for defining modular copy policies for class-based object-oriented programs. A copy policy specifies the maximally allowed sharing between an object and its clone. We present a static enforcement mechanism that will guarantee that all classes fulfill their copy policy, even in the presence of overriding of copy methods, and establish the semantic correctness of the overall approach in Coq. The mechanism has been implemented and experimentally evaluated on clone methods from several Java libraries.