Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon Labelling and Low-Income Country Exports: A Review of the Development Issues

This article discusses the carbon accounting and carbon-labelling schemes being developed to address growing concerns over climate change. Its particular concern is their impact on small stakeholders, especially low-income countries. The popular belief that trade is by definition problematic is not true; carbon efficiencies elsewhere in the supply chain may more than offset emissions from transportation. Indeed, low-income countries may offer important opportunities for carbon emission reductions because of their favourable climatic conditions and use of low energy-intensive production techniques. However, their effective inclusion in labelling schemes will require innovative solutions to provide low-cost data collection and certification.
A short review and primer on the use of human voice in human computer interaction applications

The application of psychophysiologicy in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Human speech affords, alongside linguistic content, rich information in the intonation, voice quality, prosody, and rhythmic variation of utterances, allowing listeners to recognise numerous distinct emotional states in the speaker. Several types of factors affect speech, ranging from emotions to cognitive load and pathological conditions, providing a promising non-intrusive source for online understanding of context and psychophysiological state. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.
A NEW SYNTHESIS FOR NEW ECONOMY

Michael Woodford (2009) predicted convergence on economic theories in a New Synthesis. We examine in this issue some argument which could strengthen or weaken this statement. We take some example of economic politic after exogenous shocks, as natural disasters or subprime crisis in European countries. Development of organizations and new management is necessary to make face shocks supplies. Thus, the organizational dimension should be present in the New Synthesis, and we explore it.
Specifying Software Architectures Using a Formal-Based Approach

Architecture description languages (ADLs) describe the abstracted structure of a system. In this paper we describe a new ADL based on extension of the existing CARE language used informally specifying and implementing reusable software components. The main elements of this ADL are components and connectors, with functional and nonfunctional behaviours and interfaces defined. The ADL includes a configuration part, describing the connection between components and connectors, defined using a CSP-like notation. The ADL is amenable to the use of theorem proving techniques for establishing correctness of the architecture. The recursive architecture is also specified as apart of the communication. The design for the CARE ADL is incorporated with the plan to leverage existing tools for matching and adapting CARE components, to develop support for the detection and correction of architecture mismatches (i.e. where components do not interoperate correctly).
What do large commits tell us?: a taxonomical study of large commits

Research in the mining of software repositories has frequently ignored commits that include a large number of files (we call these large commits). The main goal of this paper is to understand the rationale behind large commits, and if there is anything we can learn from them. To address this goal we performed a case study that included the manual classification of large commits of nine open source projects. The contributions include a taxonomy of large commits, which are grouped according to their intention. We contrast large commits against small commits and show that large commits are more perfective while small commits are more corrective. These large commits provide us with a window on the development practices of maintenance teams.
Extract knowledge from semi-structured websites for search task simplification

Simplifying the key tasks of search engine users by directly retrieving to them structured knowledge according to their queries is attracting much attention from both industry and academia. A bottleneck of this challenging problem is how to extract the structured knowledge from the noisy and complex Web scale websites automatically. In this paper, we propose an unsupervised automatic wrapper induction algorithm, named as Scalable Knowledge Extractor from webSites (SKES). SKES induces the wrapper in a divide and conquer mode, i.e., it divides the general wrapper into several sub-wrappers to learn from the data independently. Moreover, through employing techniques such as tag path representation of Web pages, SKES is verified to be efficient and noise-tolerant by the experimental results. Furthermore, based on our automatically extracted knowledge, we also built a prototype to serve structured knowledge to end users for simplifying their key search tasks. Very positive feedbacks were received on the prototype.
Relational learning and transitive expression in aging and amnesia

Aging has been associated with a decline in relational memory, which is critically supported by the hippocampus. By adapting the transitivity paradigm (Bunsey and Eichenbaum (1996) Nature 379:255‐257), which traditionally has been used in nonhuman animal research, this work examined the extent to which aging is accompanied by deficits in relational learning and flexible expression of relational information. Older adults' performance was additionally contrasted with that of amnesic case DA to understand the critical contributions of the medial temporal lobe, and specifically, the hippocampus, which endures structural and functional changes in healthy aging. Participants were required to select the correct choice item (B versus Y) based on the presented sample item (e.g., A). Pairwise relations must be learned (A‐>B, B‐>C, C‐>D) so that ultimately, the correct relations can be inferred when presented with a novel probe item (A‐>C? Z? ). Participants completed four conditions of transitivity that varied in terms of the degree to which the stimuli and the relations among them were known pre‐experimentally. Younger adults, older adults, and DA performed similarly when the condition employed all pre‐experimentally known, semantic, relations. Older adults and DA were less accurate than younger adults when all to‐be‐learned relations were arbitrary. However, accuracy improved for older adults when they could use pre‐experimentally known pairwise relations to express understanding of arbitrary relations as indexed through inference judgments. DA could not learn arbitrary relations nor use existing knowledge to support novel inferences. These results suggest that while aging has often been associated with an emerging decline in hippocampal function, prior knowledge can be used to support novel inferences. However, in case DA, significant damage to the hippocampus likely impaired his ability to learn novel relations, while additional damage to ventromedial prefrontal and anterior temporal regions may have resulted in an inability to use prior knowledge to flexibly express indirect relational knowledge. © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc.
"Best Dinner Ever!!!": Automatic Generation of Restaurant Reviews with LSTM-RNN

Consumer reviews are an important information resource for people and a fundamental part of everyday decision-making. Product reviews have an economical relevance which may attract malicious people to commit a review fraud, by writing false reviews. In this work, we investigate the possibility of generating hundreds of false restaurant reviews automatically and very quickly. We propose and evaluate a method for automatic generation of restaurant reviews tailored to the desired rating and restaurant category. A key feature of our work is the experimental evaluation which involves human users. We assessed the ability of our method to actually deceive users by presenting to them sets of reviews including a mix of genuine reviews and of machine-generated reviews. Users were not aware of the aim of the evaluation and the existence of machine-generated reviews. As it turns out, it is feasible to automatically generate realistic reviews which can manipulate the opinion of the user.
Eigenelements of a General Aggregation-Fragmentation Model

We consider a linear integro-differential equation which arises to describe both aggregation-fragmentation processes and cell division. We prove the existence of a solution (λ, , ϕ) to the related eigenproblem. Such eigenelements are useful to study the long-time asymptotic behavior of solutions as well as the steady states when the equation is coupled with an ODE. Our study concerns a non-constant transport term that can vanish at x = 0, since it seems to be relevant to describe some biological processes like proteins aggregation. Non-lower-bounded transport terms bring difficulties to find a priori estimates. All the work of this paper is to solve this problem using weighted-norms.
The Second International Workshop on Mining Ubiquitous and Social Environments

Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis technology, relatively little attention has been paid to the spatial non-stationarity of sensed data which is an intrinsic property of the geographically distributed data. In this paper we deal with non-stationarity of geographically distributed data for the task of regression. At this purpose, we extend the Geographically Weighted Regression (GWR) method which permits the exploration of the geographical differences in the linear effect of one or more predictor variables upon a response variable. The parameters of this linear regression model are locally determined for every point of the space by processing a sample of weighted neighboring observations. Although the use of locally linear regression has proved appealing in the area of sensor data analysis, it also poses some problems. The parameters of the surface are locally estimated for every space point, but the form of the GWR regression surface is globally defined over the whole sample space. Moreover, the GWR estimation is founded on the assumption that all predictor variables are equally relevant in the regression surface, without dealing with spatially localized phenomena of collinearity. Our proposal overcomes these limitations with a novel tree-based approach which is adapted to the aim of recovering the functional form of a regression model only at the local level. A stepwise approach is then employed to determine the local form of each regression model by selecting only the most promising predictors and providing a mechanism to estimate parameters of these predictors at every point of the local area. Experiments with several geographically distributed datasets confirm that the tree based construction of GWR models improves both the local estimation of parameters of GWR and the global estimation of parameters performed by classical model trees.
Deterministic compressed-sensing matrix from grassmannian matrix: Application to speech processing

Reconstruction of a signal based on Compressed Sensing (CS) framework relies on the knowledge of the sparse basis & measurement matrix used for sensing. While most of the studies so far focus on the prominent random Gaussian, Bernoulli or Fourier matrices, we have proposed construction of efficient sensing matrix we call Grassgram Matrix using Grassmannian matrices. This work shows how to construct effective deterministic sensing matrices for any known sparse basis which can fulfill incoherence or RIP conditions with high probability. The performance of proposed approach is evaluated for speech signals. Our results shows that these deterministic matrices out performs other popular matrices.
Longitudinal associations between body mass index, physical activity, and healthy dietary behaviors in adults: A parallel latent growth curve modeling approach

Background Physical activity (PA) and healthy dietary behaviors (HDB) are two well-documented lifestyle factors influencing body mass index (BMI). This study examined 7-year longitudinal associations between changes in PA, HDB, and BMI among adults using a parallel latent growth curve modeling (LGCM). Methods We used prospective cohort data collected by a private company (SimplyWell LLC, Omaha, NE, USA) implementing a workplace health screening program. Data from a total of 2,579 adults who provided valid BMI, PA, and HDB information for at least 5 out of 7 follow-up years from the time they entered the program were analyzed. PA and HDB were subjectively measured during an annual online health survey. Height and weight measured during an annual onsite health screening were used to calculate BMI (kg·m2). The parallel LGCMs stratified by gender and baseline weight status (normal: BMI<25, overweight BMI 25–29.9, and obese: BMI>30) were fitted to examine the longitudinal associations of changes in PA and HDB with change in BMI over years. Results On average, BMI gradually increased over years, at rates ranging from 0.06 to 0.20 kg·m2·year, with larger increases observed among those of normal baseline weight status across genders. The increases in PA and HDB were independently associated with a smaller increase in BMI for obese males (b = -1.70 and -1.98, respectively), and overweight females (b = -1.85 and -2.46, respectively) and obese females (b = -2.78 and -3.08, respectively). However, no significant associations of baseline PA and HDB with changes in BMI were observed. Conclusions Our study suggests that gradual increases in PA and HDB are independently associated with smaller increases in BMI in overweight and obese adults, but not in normal weight individuals. Further study is warranted to address factors that check increases in BMI in normal weight adults.
Defining smallholder agriculture in Ghana: Who are smallholders, what do they do and how are they linked with markets?

""Smallholders in Ghana, as elsewhere, are widely considered to be the largest as well as the most vulnerable component of the rural sector. Ghana professes national development objectives of reducing rural poverty through the increased productivity and commercialization of smallholder agriculture. This work uses household survey data, district-level production data and a variety of mapped infrastructural and biophysical data to characterize the production environments and characteristics of smallholder agriculture. This paper explores the relevance of geographically-differentiated characteristics. Several key issues are highlighted: the less prevalent use of inputs, lower commercialization, and lower welfare rates of producers with smaller landholdings.
Stimulatory effects of calcium carbonate on butanol production by solventogenic Clostridium species.

Solventogenic Clostridium species are motile Gram positive anaerobic bacteri a used for the fermentative production of acetone-butanol-ethanol (ABE). The addition of CaCO 3 to semi-defined P2 medium was investigated to evaluate its impact on g rowth, amelioration of butanol toxicity, glucose utilization, and ABE production by solventogenic Clostridium species. Two Clostridium species commonly used in industrial production of fermentat ive-derived ABE were used in the study. Under culture conditions of the present study, CaCO 3 enhanced growth of and total ABE production by solventogenic Clostridium species ( C. acetobutylicum ATCC 824 and C. beijerinckii 260 produced more than 24 and 22 g/L ABE, respectively) by 31% t o 46%. Dramatically increased growth of solventogenic Clostridium species and fermentation rate resulted in greater ABE concentration and productivity for treatments containing CaCO 3 relative to the control with no CaCO 3 addition. The addition of CaCO3 into P2 medium, in addition, increased butanol tol erance by more than 40% indicating that elevated levels of ABE produced by solventogenic Clostridium species may have occurred due to enhanced tolerance of butanol in th e presence of CaCO 3.
Approximating the Expansion Profile and Almost Optimal Local Graph Clustering

Spectral partitioning is a simple, nearly-linear time, algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee of the quality of the approximation found by the algorithm. Local graph partitioning algorithms [1], [2], [3] run in time that is nearly linear in the size of the output set, and their approximation guarantee is worse than the guarantee provided by the Cheeger inequalities by a poly-logarithmic logΩ(1) n factor. It has been an open problem to design a local graph clustering algorithm with an approximation guarantee close to the guarantee of the Cheeger inequalities and with a running time nearly linear in the size of the output. In this paper we solve this problem; we design an algorithm with the same guarantee (up to a constant factor) as the Cheeger inequality, that runs in time slightly super linear in the size of the output. This is the first sublinear (in the size of the input) time algorithm with almost the same guarantee as the Cheeger's inequality. As a byproduct of our results, we prove a bicriteria approximation algorithm for the expansion profile of any graph. Let μ(S) = Σv∈S d(v) be the volume, and φ(S) := |E(S, S̅)|/μ(S), be the conductance of a set S of vertices. If there is a set of volume at most γ and conductance φ, we can find a set of volume at most γ1+ϵ and conductance V at most O(√φ/ϵ), for any ϵ >; 0. Our proof techniques also provide a simpler proof of the structural result of Arora, Barak, Steurer [4], that can be applied to irregular graphs. Our main technical tool is a lemma stating that, for any set S of vertices of a graph, a lazy t-step random walk started from a randomly chosen vertex of S, will remain entirely inside S with probability at least (1-φ(S)/2)t. The lemma also implies a new lower bound to the uniform mixing time of any finite states reversible markov chain.
Audio-visual classification of Swedish phonemes for pronun ciation training

We present a method for audio-visual classification of Swedi sh phonemes, to be used in computer-assisted pronunciation training. The probabilistic kernel-based method is applied to the audio signal and/or either a principal or an independent component (PCA or ICA) representation of the mouth region in video images. We investigate which representation (PCA or ICA) that may be most suitable and the number of components required in the base, in order to be able to automatically detect pronunciation errors in Swedish from audio-visual input. Experiments performed on one speaker show that the visual information help avoiding classification errors that would lead to gravely er roneous feedback to the user; that it is better to perform phoneme classification on audio and video seperately and then fuse th e results, rather than combining them before classification; and that PCA outperforms ICA for few components. Index Terms: audiovisual phoneme classification, pronunciation error detection, PCA, ICA
Performance analysis of PI and PI+CI compensation for an IPDT process

In this work, it is performed a comparison between the performance of PI and PI+CI compensators, in terms of a balanced index (servo/regulatory) based on the integrated absolute error (IAE). The plant is modelled by an integrating plus dead time (IPDT) process. The purporse of this work is to provide insight into the benefit of reset compensation both in servo and regulatory problems. This is accomplished by providing a tuning rule for the PI+CI, that allows to analyze the performance index in the compensator parameter space. The analysis done is independent of the process parameters.
The road data acquisition system MoSES – determination and accuracy of trajectory data gained with the Applanix POS /

System performance of the kinematic surveying system MoSES has been studied under excellent GPS coverage as well as with simulated GPS gaps of various lengths. The uncertainty of photogrammetric measurement alone has been proved to be less than 0.1 cm RMS. Finally, the accuracy of the survey system has been evaluated along a test loop on the university campus , representative of the typical Middle European environment, where GPS positions can be obtained for only 55% of the time travelled. The stereo cameras on the vehicle captured a large number of control points along the test loop. The resulting uncertainty for object determination is 0.3 m RMS for Northing and Easting and 0.5 m for height. The evaluation shows the quality of the trajectory obtained by an Applanix POS/LV and estimated with the Applanix POSPac software.
Designing a Parallel-Kinematic High-Speed Machine Tool

Parallel mechanisms have many features such as high payload, high speed, high acceleration, high rigidity (stiffness), and low inertia and non-accumulating error, these advantages makes it excellent for machine tool applications. In the present study, the analysis of a category of parallel manipulators for the purpose of designing machine tools is developed. First, the solutions of the inverse and forward kinematic problems of the present manipulator are developed. These solutions are then used to determine reachable workspace of the present manipulator at different orientations of the moving platform. It is found that the maximum reachable workspace occurs when the orientation of the moving platform is The Jacobian analysis of the proposed manipulator is introduced and the singular configurations are determined. Second, the dynamic model of the proposed manipulator is developed and is used to determine the actuation forces for different trajectories. Finally, simulation models built using ADAMS software are used to validate the developed kinematic and dynamic models. The simulation shows a good agreement between the results obtained using the developed models and those obtained using ADAMS models.
Enhanced Reweighted MRFs for Efficient Fashion Image Parsing

Previous image parsing methods usually model the problem in a conditional random field which describes a statistical model learned from a training dataset and then processes a query image using the conditional probability. However, for clothing images, fashion items have a large variety of layering and configuration, and it is hard to learn a certain statistical model of features that apply to general cases. In this article, we take fashion images as an example to show how Markov Random Fields (MRFs) can outperform Conditional Random Fields when the application does not follow a certain statistical model learned from the training data set. We propose a new method for automatically parsing fashion images in high processing efficiency with significantly less training time by applying a modification of MRFs, named reweighted MRF (RW-MRF), which resolves the problem of over smoothing infrequent labels. We further enhance RW-MRF with occlusion prior and background prior to resolve two other common problems in clothing parsing, occlusion, and background spill. Our experimental results indicate that our proposed clothing parsing method significantly improves processing time and training time over state-of-the-art methods, while ensuring comparable parsing accuracy and improving label recall rate.