Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Legal Proceedings Status: an Approach Based on Sequential Text Data

Machine learning applications in the legal field are numerous and diverse. In order to make contribution to both the machine learning community and the legal community, we have made efforts to create a model compatible with the classification of text sequences, valuing the interpretability of the results. The purpose of this paper is to classify legal proceedings in three possible status classes, which are (i) archived proceedings, (ii) active proceedings and (iii) suspended proceedings. Our approach is composed by natural language processing, supervised and unsupervised deep learning models and performed remarkably well in the classification task. Furthermore we had some insights regarding the patterns learned by the neural network applying tools to make the results more interpretable.
Tourism Value Added Creation Through a User-Centric Context-Aware Digital System

This paper seeks to focus on the idea of a user-centric context-aware digital system and its rationale in a process of tourism value creation. The purpose of the study is to offer an understanding of the context-aware applications in order to analyse their usefulness in tourism marketing. This is achieved through a literature review of existing research and the demonstration of several applications in travel and hospitality which might enrich the set of marketing tools adopted by tourism regions and enterprises. Keywords: context-aware system, mobile applications in tourism, digital tourism, tourism value added.
Improved LAS Detector for MIMO Systems with Imperfect Channel State Information

The performance of coherent symbol detection in multiple-input multiple-output (MIMO) systems is tightly related to the channel knowledge at the receiver side. For MIMO detection, achieving near-optimal performance with a low computational complexity has been a topic of several recent researches. In this paper, we propose a robust detector for uncoded MIMO systems with imperfect CSI (ICSI). The proposed detector is based on the likelihood ascent search (LAS) algorithm which achieves near maximum likelihood (ML) performance with a reduced complexity. To take into account the channel estimation error, we propose to use a modified likelihood metric function. Moreover, to enhance the system performance, we suggest to perform the channel estimation and the data detection in an iterative manner. Simulation results show that the proposed scheme outperforms the conventional LAS detector.
Snow crystal and ice nuclei concentrations in orographic snowfall

Concentrations of snow crystals and ice nuclei within orographic cloud systems have not as yet been simultaneously monitored. Ground observations of these parameters however have been simultaneously obtained near Climax in the Central Colorado Rockies. The objective of this report is to determine the relationship between snow crystal and ice nuclei concentrations for similar crystal formation and nuclei activation temperatures. In this investigation, it is assumed that the ice nuclei and snow crystal concentrations measured in a volume of air at the ground can be used to represent the ice nuclei and snow crystal concentrations in a similar volume of air in the cloud. A continuous snowfall replicator is described which measures the necessary snow crystal concentrations. In the two unseeded orographic snowfall periods investigated, the average snow crystal and ice nuclei concentrations measured at the ground were approximately equal for the same crystal formation and nuclei activation temperatures. This determination considers an adjustment of the nuclei concentration by an average factor of 3.5 in the Bigg-Warner expansion-type ice nuclei counter. This factor is an average of the values presented by Warner and Newnham (1958) and Fletcher (1958).
A TEXT-TO-SPEECH SYNTHESIS FOR MARATHI LANGUAGE USING FESTIVAL & FESTVOX

This research paper describes the Impalement of the first, usable, Marathi Text to Speech system for Maharashtra Marathi using the open source Festival TTS engine. Besides that, this research paper also discusses a few practical applications that use this system. This system is developed using di-phone concatenation approach in its waveform generation phase. Construction of a di-phone database and implementation of the natural language processing modules are described. Natural language processing modules include text processing, tokenizing and grapheme to phoneme (G2P) conversion that were written in Festival's format. Finally, a test was conducted to evaluate the intelligibility of the synthesized speech.
A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records

Abstract Machine learning (ML) is a widely unexplored field in dendroclimatology, but it is a powerful tool that might improve the accuracy of climate reconstructions. In this paper, different ML algorithms are compared to climate reconstruction from tree-ring proxies. The algorithms considered are multiple linear regression (MLR), artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT), and random forests of regression trees (RF). April-May mean temperature at a Quercus robur stand in Slovenia is predicted with mean vessel area (MVA, correlation coefficient with April-May mean temperature, r = 0.70, p < 0.001) and earlywood width (EW, r = –0.28, p < 0.05). Similarly, June-August mean temperature is predicted with stable carbon isotope (δ13C, r = 0.72, p < 0.001), stable oxygen isotope (δ18O, r = 0.32, p < 0.05) and tree-ring width (TRW, r = 0.11, p > 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. In both spring and summer temperature models, BMT performed best respectively in 62% and 52% of the 100 repetitions. The second-best method was ANN. Although BMT gave the best validation results, the differences in the models’ performances were minor. We therefore recommend always comparing different ML regression techniques and selecting the optimal one for applications in dendroclimatology.
Testing evolutionary hypotheses in the classroom with MacClade software

MacClade is a Macintosh-based software package that uses the techniques of cladistic analysis to explore evolutionary patterns. Although designed for the Macintosh platform, MacClade delivers excellent results onPCs when used with MacOs emulators. This paper describes a novel and effective exercise that allows under-graduate biology majors to test a hypothesis about behavioural evolution in insects. The exercise is derived from a real data set in the literature and provides students with the opportunity to learn firsthand how scientists evaluate hypotheses about patterns of adaptive change. The exercise can be modified to fit the needs of particular classes. In general, MacClade offers the life science teacher a wide variety of instructional possibilities.
Investigating the sources and atmospheric processing of fine particles from Asia and the Northwestern United States measured during INTEX B

During the National Aeronautics and Space Administration (NASA) Intercontinental Chemical Transport Experiment, Phase B (INTEX-B), in the spring of 2006, airborne measurements were made in the United States Pacific Northwest of the major inorganic ions and the water-soluble organic carbon (WSOC) of submicron (PM1.0) aerosol. An atmospheric trajectory (HYSPLIT) and a Lagrangian particle dispersion model (Flexpart) quantifying source contributions for carbon monoxide (CO) were used to segregate air masses into those of primarily Asian influence (>75% Asian CO) or North American influence (>75% North American CO). Of the measured compounds, fine particle mass mostly consisted of water-soluble organic carbon and sulfate, with median sulfate and WSOC concentrations in two to four times higher, respectively, in North American air masses versus transported Asian air masses. The fraction of WSOC to sulfate in transported Asian air masses was significantly lower than one at altitudes above 3 km due to depleted organic aerosol, opposite to what has been observed closer to Asia and in the northeastern United States, where organic components were at higher concentrations than sulfate in the free troposphere. The observations could be explained by loss of sulfate and organic aerosol by precipitation scavenging, with reformation of mainly sulfate during advection from Asia to North America. In contrast to free tropospheric measurements, for all air masses below approximately 2 km altitude median WSOC-sulfate ratios were consistently between one and two. WSOC sources were investigated by multivariate linear regression analyses of WSOC and volatile organic compounds (VOCs). In Asian air masses, of the WSOC variability that could be explained (49%), most was related to fossil fuel combustion VOCs, compared to North American air masses, where 75% of the WSOC variability was explained through a nearly equal combination of fossil fuel combustion and biogenic VOCs. Distinct WSOC plumes encountered during the experiment were also studied. A plume observed near the California Central Valley at 0.6 km altitude was related to both fossil fuel combustion and biogenic VOCs. Another Central Valley plume observed over Nevada at 3 to 5 km, in a region of cloud detrainment, was mostly related to biogenic VOCs.
Least-Squares Temporal Di erence Learning

TD( ) is a popular family of algorithms for approximate policy evaluation in large MDPs. TD( ) works by incrementally updating the value function after each observed transition. It has two major drawbacks: it makes ine cient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto (Bradtke and Barto, 1996) eliminates all stepsize parameters and improves data e ciency. This paper extends Bradtke and Barto's work in three signi cant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from = 0 to arbitrary values of ; at the extreme of = 1, the resulting algorithm is shown to be a practical formulation of supervised linear regression. Third, it presents a novel, intuitive interpretation of LSTD as a model-based reinforcement learning technique.
in Computer

— Image Classification is a vital errand inside the field of PC vision. Image classification alludes to the marking of images into one of various predefined classifications. Classification incorporates image sensors, image pre-handling, protest discovery, question division, include extraction and question order. Numerous arrangement procedures have been produced for image classification. As of late direct spatial coordinating (LSM) part has been very fruitful in image classification. In spite of its prevalence, these nonlinear have a multifaceted nature O(n 2 ~n 3 ) in preparing and O(n) in testing, where n is the preparation estimate, suggesting that it is nontrivial to scale-up the calculations to handle more than a large number of preparing images. In this paper we build up an expansion of the LSM strategy, by summing up vector quantization to inadequate coding took after by multi-scale spatial max pooling, and ace represent a direct LSM bit in light of SIFT meager codes. This new approach astoundingly lessens the many-sided quality of SVMs to O(n) in preparing and a consistent in testing with more exactness than past techniques. In various image arrangement tests, we discover order precision, the proposed straight SM in light of meager coding of SIFT descriptors dependably altogether outflanks the LSM portion on histograms, and is stunningly better than the nonlinear LSM pieces.
UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Ant Colony Optimization and the Minimum Spanning Tree Problem

Ant Colony Optimization (ACO) is a kind of randomized search heuristic that has become very popular for solving problems from combinatorial optimization. Solutions for a given problem are constructed by a random walk on a so-called construction graph. This random walk can be influenced by heuristic information about the problem. In contrast to many successful applications, the theoretical foundation of this kind of randomized search heuristic is rather weak. Theoretical investigations with respect to the runtime behavior of ACO algorithms have been started only recently for the optimization of pseudo-boolean functions. We present the first comprehensive rigorous analysis of a simple ACO algorithms for a combinatorial optimization problem. In our investigations we consider the minimum spanning tree problem and examine the effect of two construction graphs with respect to the runtime behavior. The choice of the construction graph in an ACO algorithm seems to be crucial for the success of such an algorithm. First, we take the input graph itself as the construction graph and analyze the use of a construction procedure that is similar to Broder’s algorithm [1] for choosing a spanning tree uniformly at random. After that, a more incremental construction procedure is analyzed. It turns out that this procedure is superior to the Broder-based algorithm and produces additionally in a constant number of iterations a minimum spanning tree if the influence of the heuristic information is large enough. Financial support by the Deutsche Forschungsgemeinschaft (SFB) in terms of the Collaborative Research Center “Computational Intelligence” (SFB 531) is gratefully acknowledged.
Drought Analysis using SPI for Selangor River Basin in Malaysia

Drought has affected large areas of Peninsular Malaysia and drought monitoring plays an important role in managing water resource systems in the country. In this paper, the use of standardized precipitation index(SPI) as a tool to identify and monitor drought occurrence in Selangor river basin, is presented. Long term monthly rainfall data for nine stations in the Selangor basin have been used for this study. The SPI values were computed by fitting the Gamma probability density function to the frequency distribution of the monthly rainfall records of each station for durations of 3 to 24 months. Regional drought atlas were presented for the Selangor river basin using the SPI obtained from the notable drought events occurring in 1963, 1976-77 and 1989-90.
Ontology-based Concept Weighting for Text Documents

Documents clustering become an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering technique play core topics. Text clustering or shortly clustering is about discovering semantically related groups in an unstructured collection of documents. Clustering has been very popular for a long time because it provides unique ways of digesting and generalizing large amounts of information. One of the issues of clustering is to extract proper feature (concept) of a problem domain. The existing clustering technology mainly focuses on term weight calculation. To achieve more accurate document clustering, more informative features including concept weight are important. Feature Selection is important for clustering process because some of the irrelevant or redundant feature may misguide the clustering results. To counteract this issue, the proposed system presents the concept weight for text clustering system developed based on a k-means algorithm in accordance with the principles of ontology so that the important of words of a cluster can be identified by the weight values. To a certain extent, it has resolved the semantic problem in specific areas. Keywords—Clustering, Concept Weight, Document clustering, Feature Selection, Ontology
SINGLE IMAGE SUPER-RESOLUTION

Single Image Super-Resolution (SISR), which refers to an important technique of image processing, aims to enhance the resolution of a image or video in computer vision. With the help and support of deep learning, a remarkable progress has been made especially in the last decade. Various types of super-resolution deep networks has been innovated in order to achieve higher-resolution images with more details. In this project, we choose two successful and representative networks among them as our models, which are Super-Resolution Convolutional Neural Network (SR-CNN) and Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR). Public datasets for super-resolution benchmarks, T91, Set5 and Set14 are demonstrated to train those two networks.
Modelling soil water fluxes in a Norway spruce {Picea abies (L.) Karst.} stand at Ballyhooly, Co. Cork.

Solute fluxes within the soil can contribute greatly to the understanding of biogeochemical processes. They are typically estimated from measured concentrations and simulated soil water fluxes. Models used to calculate soil water fluxes generally require water retention characteristics, hydraulic conductivity characteristics and root distribution as inputs. Using measured precipitation and throughfall volume in combination with modelled soil water fluxes, estimated using the FORHYD model, the six-year (1989-1994) mean annual hydrological balance for a Norway spruce stand at Ballyhooly, Co. Cork, has been estimated. Precipitation was 1141 mm yr while evaporation from tree surfaces was 497 mm yr-1. The vertical soil-water flux, estimated by simulation, was 322 mm yr at 1m depth.
Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria

We analyze the problem of how to optimally bid for ad spaces in online ad auctions. For this we consider the general case of multiple ad campaigns with overlapping targeting criteria. In our analysis we first characterize the structure of an optimal bidding strategy. In particular, we show that an optimal bidding strategies decomposes the problem into disjoint sets of campaigns and targeting groups. In addition, we show that pure bidding strategies that use only a single bid value for each campaign are not optimal when the supply curves are not continuous. For this case, we derive a lower-bound on the optimal cost of any bidding strategy, as well as mixed bidding strategies that either achieve the lower-bound, or can get arbitrarily close to it.
Re-weighting of Vector-weighted Mechanisms for Utility Maximization under Differential Privacy

We present practical aspects of implementing a pseudo posterior synthesizer for microdata dissemination under a new re-weighting strategy for utility maximization that applies to any vector-weighting approach. We demonstrate on two different vector-weighted schemes. Both schemes target high-risk records by exponentiating each of their likelihood contributions with a record-level weight, $\alpha_i \in [0,1]$ for record $i \in (1,\ldots,n)$. The first vector-weighted synthesizing mechanism computes the maximum (Lipschitz) bound, $\Delta_{x_{i}}$, of each log-likelihood contribution over the space of parameter values, and sets the by-record weight $\alpha_{i} \propto 1 / \Delta_{x_{i}}$. The second vector-weighted synthesizer is based on constructing an identification disclosure risk probability, $IR_i$ of record $i$, and setting the by-record weight $\alpha_i \propto 1 / IR_i$. We compute the overall Lipschitz bound, $\Delta_{\boldsymbol{\alpha},\mathbf{x}}$, for the database $\mathbf{x}$, under each vector-weighted synthesizer such that both provide an $(\epsilon = 2 \Delta_{\boldsymbol{\alpha},\mathbf{x}})-$differential privacy (DP) formal guarantee. Our new vector re-weighting strategy maximizes the data utility given any privacy budget for the vector-weighted synthesizers by adjusting the by-record weights, $(\alpha_{i})_{i = 1}^{n}$, such that their individual Lipschitz bounds, $\Delta_{\boldsymbol{\alpha},x_{i}}$, approach the bound for the entire database, $\Delta_{\boldsymbol{\alpha},\mathbf{x}}$. We illustrate our methods using simulated count data with and without over-dispersion-induced skewness and compare the results to a scalar-weighted synthesizer under the Exponential Mechanism (EM). We further demonstrate our methods on a sample of the Survey of Doctorate Recipients.
MEME-LaB: motif analysis in clusters

Summary: Genome-wide expression analysis can result in large numbers of clusters of co-expressed genes. Although there are tools for ab initio discovery of transcription factor-binding sites, most do not provide a quick and easy way to study large numbers of clusters. To address this, we introduce a web tool called MEME-LaB. The tool wraps MEME (an ab initio motif finder), providing an interface for users to input multiple gene clusters, retrieve promoter sequences, run motif finding and then easily browse and condense the results, facilitating better interpretation of the results from large-scale datasets. Availability: MEME-LaB is freely accessible at: http://wsbc.warwick.ac.uk/wsbcToolsWebpage/. Contact: p.e. brown@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Software Architecture

Software architecture has received much attention in the past few years. This is not a block diagram that gives a rough functional decomposition of the system. It is a multiperspective, quality-based approach to ensuring that software is built to fit its purpose. In this edition of Strategic Software Engineering we will consider how software architecture provides strategic support to the organization.
Person Re-identification in Surveillance Videos using Multi-part Color Descriptor

Person re-identification is gaining significance in today’s world. In this paper, we present a novel method for reidentifying persons in surveillance videos. We propose a new color based image descriptor which is used to extract the local interest points by subdividing the pedestrian image into upper and lower parts. The descriptor is experimentally evaluated on two benchmark datasets. General Terms Pattern recognition, Video surveillance, computer vision.
Effect of Compiler Optimizations on DSP Processor Power and Energy Consumption

This paper examines the effect of compiler optimizations on the energy usage and power consumption of DSP processor, specifically to the Texas Instruments TMS320VC5510. The effects of different levels of general and specific optimization on the energy and power consumption are measured for this processor. Given the special characteristics of DSP programs, the benchmark routines were selected from DSPStone, and some typical DSP applications. Finally, Texas Instruments library routines are compared with the compiled versions. The paper provides an analysis of the results together with recommendations for improving performance. The binaries used in this study were generated using the Texas Instrument C/C++ Compiler, which allows control over the whole set of optimizations.
Title Effect of Compiler Optimizations on DSP Processor Power and Energy Consumption

This paper examines the effect of compiler optimizations on the energy usage and power consumption of DSP processor, specifically to the Texas Instruments TMS320VC5510. The effects of different levels of general and specific optimization on the energy and power consumption are measured for this processor. Given the special characteristics of DSP programs, the benchmark routines were selected from DSPStone, and some typical DSP applications. Finally, Texas Instruments library routines are compared with the compiled versions. The paper provides an analysis of the results together with recommendations for improving performance. The binaries used in this study were generated using the Texas Instrument C/C++ Compiler, which allows control over the whole set of optimizations.