Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction-Aware Actual Causation: A Building Block for Accountability in Security Protocols

Protocols involving multiple agents and their interactions are ubiquitous. Protocols for tasks such as authentication, electronic voting, and secure multi-party computation ensure desirable security properties if participating agents follow their prescribed programs. However, if some agents choose to deviate from their prescribed programs and a security property is violated, it is important to hold agents accountable, i.e. assign blame for their choices and actions, and to x de ciencies in the protocol design. Prior work in accountability has focused primarily on detecting or punishing deviations. This dissertation proposes a novel interaction-aware approach to actual causation (i.e., the identi cation of particular agents’ choices to deviate, and interactions which caused a speci c violation). We propose this approach as a useful building block for accountability in interacting multi-agent systems, including but not limited to security protocols. The de nitions of actual cause in this dissertation are inspired by prior work on actual causation in philosophy, law, and computer science. However, prior frameworks do not account for the program dynamics that arise in protocol-based settings and do not naturally capture agent interactions and agents’ choices to deviate. Motivated by these applications and challenges, we make two main contributions. First, we propose a theory of actual causation with choice and interaction as key components. Speci cally, we de ne in an interacting program model, what it means for a sequence of program expressions (modeling choices, actions and interactions) to be an actual cause of a violation. We demonstrate that our theory signi cantly advances the state-of-the-art in the research area of actual causation by combining process-oriented and counterfactual-based viewpoints in prior work. A careful treatment of interaction and choice enables us to cleanly deal with a known set of issues that plague extant theories, including expressing concise interaction models and over-permissive counterfactual-based de nitions. Second, we demonstrate the value of this theory in the domain of security and privacy protocols, by proving that violations of a speci c class of safety properties always have an actual cause. We also present a sound technique for establishing program actions as actual causes. Additionally, we provide a causal analysis of a representative protocol, designed to address weaknesses in the current public key certi cation infrastructure. Our theory clearly distinguishes between deviances and actual causes which is important from the standpoint of accountability.
The Justification of Logical Theories based on Data Compression

Non-demonstrative or inductive reasoning is a crucial component in the skills of a learner. A leading candidate for this form of reasoning involves the automatic formation of hypotheses. Initial successes in the construction of propositional theories have now been followed by algorithms that attempt to generalise sentences in the predicate calculus. An important defect in these new-generation systems is the lack of a clear model for theory justiication. In this paper we describe a method of evaluating the signiicance of a hypothesis based on the degree to which it allows compression of the observed data with respect to prior knowledge. This can be measured by comparing the lengths of the input and output tapes of a reference Turing machine which will generate the examples from the hypothesis and a set of derivational proofs. The model extends an earlier approach of Muggleton by allowing for noise. The truth values of noisy instances are switched by making use of correction codes. The utility of compression as a signiicance measure is evaluated empirically in three independent domains. In particular, the results show that the existence of compression distinguishes a larger number of signiicant clauses than other signiicance tests. The method also appears to distinguish noise as incompressible data.
A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images

Now days, Skin cancer is life threatening disease which causes human death. Abnormal growth of melanocytic cells causes a skin cancer. Due to malignancy feature skin cancer is also known as melanoma. Melanoma appears on the skin due to exposure of ultraviolet radiation and genetic factors. So melanoma lesion appears as black or brown in colour. Early detection of melanoma can cure completely. Biopsy is a traditional method for detecting skin cancer. This method is painful and invasive. This method requires laboratory testing so it is time consuming. Therefore, in order to solve the above stated issues computer aided diagnosis for skin cancer is needed. Computer aided diagnosis uses Dermoscopy for capturing the skin image. In this paper first pre-processing of the skin image is done. After pre-processing lesion part is segmented by using image segmentation technique which is followed by feature extraction in which unique features are extracted from segmented lesion. After feature extraction, classification by using support vector machine is performed for classifying the skin image as normal skin and melanoma skin cancer. The proposed system results shows that support vector machine with linear kernel gives optimum accuracy.
Interactions between Sox9 and β-catenin control chondrocyte differentiation

Chondrogenesis is a multistep process that is essential for endochondral bone formation. Previous results have indicated a role for β-catenin and Wnt signaling in this pathway. Here we show the existence of physical and functional interactions between β-catenin and Sox9, a transcription factor that is required in successive steps of chondrogenesis. In vivo, either overexpression of Sox9 or inactivation of β-catenin in chondrocytes of mouse embryos produces a similar phenotype of dwarfism with decreased chondrocyte proliferation, delayed hypertrophic chondrocyte differentiation, and endochondral bone formation. Furthermore, either inactivation of Sox9 or stabilization of β-catenin in chondrocytes also produces a similar phenotype of severe chondrodysplasia. Sox9 markedly inhibits activation of β-catenin-dependent promoters and stimulates degradation of β-catenin by the ubiquitination/proteasome pathway. Likewise, Sox9 inhibits β-catenin-mediated secondary axis induction in Xenopus embryos. β-Catenin physically interacts through its Armadillo repeats with the C-terminal transactivation domain of Sox9. We hypothesize that the inhibitory activity of Sox9 is caused by its ability to compete with Tcf/Lef for binding to β-catenin, followed by degradation of β-catenin. Our results strongly suggest that chondrogenesis is controlled by interactions between Sox9 and the Wnt/β-catenin signaling pathway.
Vectorization of gridded urban land use data

In the digital entertainment industry, cities are one of the largest artifacts modeled by artists. One alternative to modeling an entire city by hand is to use an urban simulation. Often, those simulations use a gridded terrain representation. Translating gridded simulation results into a more continuous, realistic representation useful in games and film can often be difficult. Our vectorization process transforms gridded urban land use data into a representation useful in entertainment pipelines and many GIS or online mapping tools. The process has three major phases. In the first phase, the raster data is analyzed and the transportation layer is abstracted and filtered. Next, the city blocks are constructed from the raster data. Third, the blocks are subdivided and land use and density are assigned to each constructed parcel. The results are much smoother than the gridded input, but maintain the land use patterns of that input. We output these results in a GIS format readable by a wide range of modeling tools.
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

Ionic floating-gate memories Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low-power alternative, but most devices cannot update the synaptic weights uniformly or scale to large array sizes. Fuller et al. developed an integrated device, ionic floating-gate memory, that has the gate terminal of a redox transistor electrically connected to a diffusive memristor. This low-power device enabled linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 109 write-read cycles. Science, this issue p. 570 A low-power artificial neural network is enabled by crossbar arrays of redox-transistor memories and diffusive memristors. Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.
Sacral Neuromodulation Outcomes in Patients With Urge Urinary Incontinence and Concomitant Urge Fecal Incontinence

Objectives: To study the efficacy and safety of sacral neuromodulation (SNM) in women with dual urge incontinence (DUI). Methods: Women with simultaneous urge urinary and urge fecal incontinence (FI) were prospectively identified and tested to determine eligibility for SNM. Those who experienced at least 50% improvement in their urinary symptoms were offered a full implant. Changes in FI were not considered. Validated questionnaires were administered at baseline and during follow-up. Preimplant urodynamic data were collected. Adverse events were recorded. Results: A total of 11 women had DUI. All had greater than 50% improvement in their urinary symptoms and underwent full implantation. Average age was 76 years. Median follow-up was 14 months. There were statistically significant improvements in validated questionnaire scores. No women were able to achieve complete simultaneous urinary and fecal continence. Six women (54%) were able to achieve at least a 50% improvement simultaneously in both their urinary and fecal symptoms. Two women (18%) only experienced a significant improvement in their urinary symptoms. One (9%) only experienced significant improvement in her FI. Two (18%) experienced no significant improvement in either their urinary or fecal symptoms. The urodynamic findings did not correlate with outcome. Two patients had transient implant pain. One implant was removed due to lack of efficacy at patient's request. Conclusions: SNM is a safe and effective treatment for DUI. In patients with DUI, complete continence rates may be lower than with either type of incontinence alone. DUI may be the end-organ manifestations of a common neurologic pathway—overactive stimulation or underactive inhibition.
Fuzzy Clustering for TV Program Classification

In order to achieve TV program group recommendation, an approach based on fuzzy clustering is proposed for program classification in this paper. This paper firstly describes the XML based program description metadata representation, in which both textual and symbolic information is included. Secondly it presents the program feature extraction and presentation method. A program is defined as two vectors, one is based on term statistics implying what the program is about, and the other reflects broadcasting characteristics of the program. Then the classifying approach based on fuzzy clustering is proposed. The approach goes: normalizing original data, building fuzzy similarity matrix, and then clustering. The final fuzzy similarity matrix is constructed by combining two fuzzy similarity matrices calculated from two different aspects.
Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results

Synthetic Aperture Radar (SAR) provides detailed information of Ocean's surface and man-made floating structures. Advances in SAR technology and deployment of new SAR satellites have contributed to an increasing number of remote sensing data available. Handle this large amount of data with human operators is infeasible. Therefore, the use of automated tools to process remote sensing images, identify regions of interest, and select relevant information are needed. The use of neural networks to solve SAR image classification problems is well known. A typical architecture consists of a shallow feed-forward neural network with an input layer, a hidden layer, and an output layer. This type of neural network, combined with back-propagation and a gradient-based training algorithm, is able to solve complex problems in SAR image analysis. However, this architecture is unable to take advantage of unlabeled data during its training process, and in many cases the input features need to be carefully tuned in order to reduce the overall network complexity. This paper proposes the application of Deep Neural Networks (DNN) to perform oceanographic-object classification.
Parsing the SynTagRus Treebank of Russian

We present the first results on parsing the SynTagRus treebank of Russian with a data-driven dependency parser, achieving a labeled attachment score of over 82% and an unlabeled attachment score of 89%. A feature analysis shows that high parsing accuracy is crucially dependent on the use of both lexical and morphological features. We conjecture that the latter result can be generalized to richly inflected languages in general, provided that sufficient amounts of training data are available.
A Dense Medial Descriptor for Image Analysis

We present dense medial descriptors, a new technique which generalizes the well-known medial axes to encode and manipulate whole 2D grayvalue images, rather than binary shapes. To compute our descriptors, we first reduce an image to a set of threshold-sets in luminance space. Next, we compute a simplified representation of each threshold-set using a noise-resistant medial axis transform. Finally, we use these medial axis transforms to perform a range of operations on the input image, from perfect reconstruction to segmentation, simplification, and artistic effects. Our pipeline can robustly handle any 2D grayscale image, is easy to use, and allows an efficient CPU or GPU-based implementation. We demonstrate our dense medial descriptors with several image-processing applications.
Router Designs for an Asynchronous Time-Division-Multiplexed Network-on-Chip

In this paper we explore the design of an asynchronous router for a time-division-multiplexed (TDM) network-on-chip (NOC) that is being developed for a multi-processor platform for hard real-time systems. TDM inherently requires a common time reference, and existing TDM-based NOC designs are either synchronous or mesochronous, but both approaches have their limitations: a globally synchronous NOC is no longer feasible in today's sub micron technologies and a mesochronous NOC requires special FIFO-based synchronizers in all input ports of all routers in order to accommodate for clock phase differences. This adds hardware complexity and increases area and power consumption. We propose to use asynchronous routers in order to achieve a simpler, more robust and globally-asynchronous NOC, and this represents an unexplored point in the design space. The paper presents a range of alternative router designs. All routers have been synthesized for a 65nm CMOS technology, and the paper reports post-layout figures for area, speed and energy and compares the asynchronous designs with an existing mesochronous clocked router. The results show that an asynchronous router is 2 times smaller, marginally slower and with roughly the same energy consumption, while offering a robust solution to the clock distribution problem. The paper further explores "clock-gating" of the individual pipeline stages in the asynchronous routers, and shows that this can lead to significant power savings.
Genome‐wide high resolution DNA profiling of hairy cell leukaemia

Lucia Prezioso Elena Rossetti Cecilia Caramatti Franco Aversa Marco Vitale Department of Clinical and Experimental Medicine, Haematology and Bone Marrow Transplantation Unit, University of Parma, Department of Biomedical, Biotechnological & Translational Sciences (S.Bi.Bi.T.), Unit of Human Anatomy & Histology, University of Parma, Parma, and Laboratory of Cytogenetic and Molecular Genetics, Haematology Unit, University of Perugia, Perugia, Italy E-mail: franco.aversa@unipr.it
ICT in Collaborative Learning in the Classroom of Elementary and Secondary Education

Digital technologies offer new opportunities for learning in an increasingly connected society, in which learning to work with others and collaborate has become an extremely important skill. This article presents the results of a research study into the conceptions and practices of working teachers regarding collaborative learning methodologies mediated by information and communication technologies (ICT) in Elementary and Secondary Education schools. We analyze data concerning ICT contributions to collaborative work processes in the classroom, from the point of view of teachers at schools accredited with a high level of ICT by the Regional Government of Castile and Leon. We furthermore take into account the limitations of these tools, as well as teachers’ conceptions of collaborative learning strategies. The methodology is based on the content analysis of interviews of teachers from a representative sample of schools. The results indicate that teachers think that ICT have great potential for enhancing collaborative activities among students and for developing highly relevant generic skills, although they are also aware of the difficulties that both students and teachers face in educational practice.
Optimal resampling for the noisy OneMax problem

The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noisy OneMax Problem showed that it is important to choose a good value for the resampling number to make a careful trade off between taking more samples in order to reduce noise, and taking fewer samples to reduce the total computational cost. This paper extends that observation, by deriving an analytical expression for the running time of the RMHC algorithm with resampling applied to the noisy OneMax problem, and showing both theoretically and empirically that the optimal resampling number increases with the number of dimensions in the search space.
Performance comparison for Raman+EDFA and EDFA+Raman hybrid amplifiers using recycled multiple pump lasers for WDM systems

This work extends the optimization of the gain profile for hybrid amplifiers by evaluating the performance of the configurations Raman+EDFA and EDFA+Raman considering multiple input channels simultaneously. An approximated technique to optimize the gain profile of multiple Raman pump lasers in order to obtain broadband hybrid amplifiers under residual pump recycling is applied to hybrid amplifiers with two stages, EDFA and Raman, for WDM systems. Multiple input channels allowed the gain characterization of the hybrid amplifiers of the two proposed configurations in terms of global gain, ripple, and noise figure. Results have presented global gain and noise figure values of approximately 40 dB and 4 dB, respectively, whereas obtained ripple values approximately of 0.8 dB, considering the EDFA+Raman hybrid amplifier configuration.
Wireless telemetry system for implantable cardiac monitoring in small animal subjects using pressure-volume sensors

We present the discrete prototype version of our design of a wireless telemetry system architecture, which is suitable for custom IC implementation and intended to eventually retrieve blood pressure and volume (PV) data from small animal subjects (e.g., mice, rats). The architecture consists of four system level blocks that are stacked in 2.475cm3 volume and it weights 4.01g. The current prototype's size is well suited for commercial implementation inside medium sized animal subjects as, for instance, rabbits and larger rats.
Frame-Frame Matching for Realtime Consistent Visual Mapping

Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery, matching visual frames with large numbers of point features. The relationship between frames is kept as a nonlinear measurement, and can be used to solve large loop closures quickly. Both monocular (bearing-only) and binocular vision can be used to generate matches. Other advantages of our system are that no special landmark initialization is required, and large loops can be solved very quickly.
Impacts of 25 years of groundwater extraction on subsidence in the Mekong delta, Vietnam

Many major river deltas in the world are subsiding and consequently become increasingly vulnerable to flooding and storm surges, salinization and permanent inundation. For the Mekong Delta, annual subsidence rates up to several centimetres have been reported. Excessive groundwater extraction is suggested as the main driver. As groundwater levels drop, subsidence is induced through aquifer compaction. Over the past 25 years, groundwater exploitation has increased dramatically, transforming the delta from an almost undisturbed hydrogeological state to a situation with increasing aquifer depletion. Yet the exact contribution of groundwater exploitation to subsidence in the Mekong delta has remained unknown. In this study we deployed a delta-wide modelling approach, comprising a 3D hydrogeological model with an integrated subsidence module. This provides a quantitative spatially-explicit assessment of groundwater extraction-induced subsidence for the entire Mekong delta since the start of widespread overexploitation of the groundwater reserves. We find that subsidence related to groundwater extraction has gradually increased in the past decades with highest sinking rates at present. During the past 25 years, the delta sank on average ∼18 cm as a consequence of groundwater withdrawal. Current average subsidence rates due to groundwater extraction in our best estimate model amount to 1.1 cm yr−1, with areas subsiding over 2.5 cm yr−1, outpacing global sea level rise almost by an order of magnitude. Given the increasing trends in groundwater demand in the delta, the current rates are likely to increase in the near future.