Miracle-dz/UniFAR-FTUs huggingface dataset:
EMSys : An Emotion Monitoring System for Call Center Agents

In this paper, a prosody-based emotion monitoring system for call centers is proposed. It aims to simplify the tracking and management of emotions extracted from call center agent-client conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Digital Signal Processing algorithms to extract the low-level features necessary for reliable emotion detection. A Support Vector Machines (SVM) classifier then detects the emotions contained in an utterance. The four emotions detected by the system are happiness, anger, boredom and neutral. The Emotion Analysis and Report Generation module performs computational analysis of the detected emotions during calls. This module also polishes the output of Emotion Detection module to provide a more presentable output of sequence of emotions of the agent and the caller. The Database Manager is responsible for the management of the database wherein it handles the creation, deletion and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of a stand alone application and a web application. The stand alone application was developed using Eclipse Rich Client Platform to maintain the modularity and flexibility of the system. It displays the detected previous and current emotions of both the caller and the agent. On the other hand, the web application was constructed using Google Web toolkit to maintain its modularity and abstraction. It provides reports and analysis of the emotions expressed by the agents during conversations. Using the Model View Controller (MVC) approach, the emotion monitoring system is scalable, reusable and modular.
Accounting for Improved Brick and Mortar Shopping Experiences : Explaining the Post-2002 Wholesale and Retail Slowdown

August 2018 Brick and mortar retailers spent $484 billion providing “free” shopping experiences in 2016. For example, vehicle dealerships provide “free” test drives, book stores provide “free” book signings and grocery stores provide “free” food samples. To capture the value of “free” shopping experiences, the paper models them as an implicit barter transaction of shopping experiences for sales attention. The paper then modifies previously created productivity accounts for the wholesale and retail sector (Jorgenson, Ho and Samuels 2016) to include shopping experiences as a new industry output and sales attention as a new industry input. Despite the rise of e-commerce, “free” brick and mortar shopping experiences grew faster than overall retail margins after 2002. Furthermore, brick and mortar stores have dramatically increased service speed since 2002. Between 2002 and 2014, better shopping experiences contributed $110 billion to real industry output growth and faster service speed subtracted $78 billion from real industry input growth. Furthermore, slower service speed between 1947 and 2002 increases real industry input growth and decreases productivity growth for that time period. Combining all these modifications together, the post-2002 wholesale and retail productivity slowdown shrinks from 0.98 percentage points per year to only 0.08 percentage points per year.  Rachel Soloveichik is a research economist at the U.S. Bureau of Economic Analysis; e-mail: rachel.soloveichik@bea.gov. The views expressed here are those of the author and do not necessarily represent the U.S. Bureau of Economic Analysis or the U.S. Department of Commerce.
Lobbying by firms to influence public decision : is it a legal or an illegal networking ?The cases of France and the United kingdom

Lobbying by businesses is a practice that has become increasingly widespread today, but hardly studied in Europe. This paper endeavours to determine whether corporate political operations differ significantly from one country to the other. To address this question in Europe, lobbying by French and UK firms is analysed by creating a database from a number of the biggest financial newspapers in both countries over the last years. Multivariate data analysis techniques are used to model corporate political actions in France and the UK. Our results suggest that there is only a few lobbying practices. Three types of lobbying practices can indeed be found in France as well as in the UK. This could suggest that firms' reaction to their political environment is less determined by country specific factors, although national specifics exist, than by the particular problem try to address.
On the Structure Groups of Decomposable Algebraic Curvature Tensors

This paper examines the action of GLN (R) on decomposable algebraic curvature tensors. The main result is that the structure group of a decomposable algebraic curvature tensor can only permute the subspaces into which the tensor decomposes: if the tensor decomposes into tensors Ri on Vi where V = ⊕k i=1 Vi, then for any matrix A in the structure group there exists a permutation σ such that A : Vi → Vσ(i). Restrictions are found for when σ(i) = j; for one, dim(Vi) must equal dim(Vj). We find A restricted to Vi, namely if we set W = Vi then the set of possible maps A|W : W → Vj is isomorphic to a particular left coset of the structure group of Ri.
SURVEY AND SUMMARY Splicing regulation by long noncoding RNAs

Massive high-throughput sequencing techniques allowed the identification of thousands of noncoding RNAs (ncRNAs) and a plethora of different mRNA processing events occurring in higher organisms. Long ncRNAs can act directly as long transcripts or can be processed into active small si/miRNAs. They can modulate mRNA cleavage, translational repression or the epigenetic landscape of their target genes. Recently, certain long ncRNAs have been shown to play a crucial role in the regulation of alternative splicing in response to several stimuli or during disease. In this review, we focus on recent discoveries linking gene regulation by alternative splicing and its modulation by long and small ncRNAs.
Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging

Accurate digital mapping of soil organic carbon (SOC) is important in understanding the global carbon cycle and its implications in mitigating climate change. Visible and near-infrared hyperspectral imaging technology provides an alternative for mapping SOC efficiently and accurately, especially at regional and global scales. However, there is a lack of understanding of the impacts of spatial resolution of hyperspectral images and spatial autocorrelation of spectral information on the accuracy of SOC retrievals. In this study, the hyperspectral images (380–1700 nm) with a spatial resolution of 1 m were acquired by Headwall Micro-Hyperspec airborne sensors. Then, hyperspectral images were resampled into three different spatial resolutions of 10 m, 30 m, and 60 m by near neighbor (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods. The geographically weighted regression (GWR) model was used to explore the role of spatial autocorrelation in predicting SOC contrast with the partial least squares regression (PLSR) model. Results showed that (1) the hyperspectral images can be used to predict SOC and the spatial autocorrelation can improve the prediction accuracy, as the ratio of performance to interquartile range (RPIQ) values of PLSR and GWR were 1.957 and 2.003; (2) The SOC prediction accuracy decreased with the degradation of spatial resolution, and the RPIQ values of PLSR were from 1.957 to 1.134, and of GWR were from 2.003 to 1.136; (3) Three resampling methods had a much weaker influence than spatial resolution on SOC predictions because the differences of RPIQ values of NN, BI, and CC resampling methods were 0.146, 0.175, and 0.025 in the spatial resolutions of 10 m, 30 m, and 60 m, respectively; (4) Finally, the Global Moran’s I and the Anselin Local Moran’s I proved the existence of the spatial autocorrelation in SOC maps. We hope that this study can offer valuable information for digital soil mapping by satellite hyperspectral images in the near future.
Combining Recursive Feature Elimination and Support Vector Machines for Stock Price Forecasting ⋆

Predicting the trend of stock price has been the dream of a large number of practitioners and theorists for a long time. As the financial time series are inherently non-stationary,the accuracy is always low. In this paper we combine recursive feature elimination method (RFE) with support vector machine (SVM) and use it to predict the trend of stock price. We use the technical indicators as the input vector, if the price rise the next day, label the vector as the positive class, if the price drop the next day, label it as the negative class. According to this principle, the SVM can construct a classification model. Then select some listed companies randomly to go on an empirical test. Results show that combining the RFE and SVM has a higher prediction accuracy than using the SVM alone.
Pinpoint SLAM: A hybrid of 2D and 3D simultaneous localization and mapping for RGB-D sensors

Conventional SLAM systems with an RGB-D sensor use depth measurements only in a limited depth range due to hardware limitation and noise of the sensor, ignoring regions that are too far or too close from the sensor. Such systems introduce registration errors especially in scenes with large depth variations. In this paper, we present a novel RGB-D SLAM system that makes use of both 2D and 3D measurements. Our system first extracts keypoints from RGB images and generates 2D and 3D point features from the keypoints with invalid and valid depth values, respectively. It then establishes 3D-to-3D, 2D-to-3D, and 2D-to-2D point correspondences among frames. For the 2D-to-3D point correspondences, we use the rays defined by the 2D point features to “pinpoint” the corresponding 3D point features, generating longer-range constraints than using only 3D-to-3D correspondences. For the 2D-to-2D point correspondences, we triangulate the rays to generate 3D points that are used as 3D point features in the subsequent process. We use the hybrid correspondences in both online SLAM and offline postprocessing: the online SLAM focuses more on the speed by computing correspondences among consecutive frames for real-time operations, while the offline postprocessing generates more correspondences among all the frames for higher accuracy. The results on RGB-D SLAM benchmarks show that the online SLAM provides higher accuracy than conventional SLAM systems, while the postprocessing further improves the accuracy.
Traversing the Cell: Agrobacterium T-DNA’s Journey to the Host Genome

The genus Agrobacterium is unique in its ability to conduct interkingdom genetic exchange. Virulent Agrobacterium strains transfer single-strand forms of T-DNA (T-strands) and several Virulence effector proteins through a bacterial type IV secretion system into plant host cells. T-strands must traverse the plant wall and plasma membrane, traffic through the cytoplasm, enter the nucleus, and ultimately target host chromatin for stable integration. Because any DNA sequence placed between T-DNA “borders” can be transferred to plants and integrated into the plant genome, the transfer and intracellular trafficking processes must be mediated by bacterial and host proteins that form complexes with T-strands. This review summarizes current knowledge of proteins that interact with T-strands in the plant cell, and discusses several models of T-complex (T-strand and associated proteins) trafficking. A detailed understanding of how these macromolecular complexes enter the host cell and traverse the plant cytoplasm will require development of novel technologies to follow molecules from their bacterial site of synthesis into the plant cell, and how these transferred molecules interact with host proteins and sub-cellular structures within the host cytoplasm and nucleus.
Dependable execution control for autonomous robots

This paper presents a new approach to integrate real-time execution control in autonomous systems and how such an approach integrates in their software architecture. The use of decisional autonomy is becoming more widely accepted as a solution to the increasing need to deploy complex systems (robots, satellites, etc.) able to perform nontrivial tasks in various environments. We present an overview of the organization of such systems. Then we explain why the increasing complexity of functional components as well as the presence of autonomy components becomes an obstacle to system safety and dependability. To address this issue, we propose the integration of an execution control component in the software architecture. This component is synthesized from a model of the acceptable and dangerous state using model-checking techniques. The execution controller has a generic representation of system behavior and, according to some specified system constraints, acts as a "safety bag" allowing acceptable states and avoiding forbidden ones. The controller uses an OBDD like data structure which offers a bounded execution time, and which can be formally validated offline to check temporal properties. Real experimentations have been made on our autonomous mobile robots, and have confirmed it can catch in real-time design errors from the decisional components which would have lead to disastrous consequences.
Nutrient Interactions and Toxicity Liver Fat and Plasma Ethanol Are Sharply Lower in Rats Fed Ethanol in Conjunction with High Carbohydrate Compared with High Fat Diets 1

The effects of high fat and high carbohydrate diets on alcohol metabolism were studied on blood alcohol and liver fat concentration. In Experiment 1, rats consumed an alcohol-containing liquid diet. Blood was collected for ethanol, glucose and lactate analyses and livers were excised for lipid determination. Blood ethanol and liver fat were lower when rats consumed the high carbohydrate diet. Glucose concentrations were lower in rats fed the high fat diet compared with those fed the high carbohydrate diet when ethanol was consumed. In Experiment 2, rats consumed a high fat, ethanol-containing diet for 13 d. Half of the rats were switched to a high carbohydrate, ethanol-containing diet for an additional 11 d. The same analyses were carried out as for Experiment 1. Switching the high fat–fed rats to the high carbohydrate diet reversed the high blood ethanol and high liver fat values, even though the rats consumed significantly more alcohol with the high carbohydrate diet. In Experiment 3 the same high fat and high carbohydrate diets without ethanol were consumed for 2 wk, at which time ethanol was administered acutely, intraperitoneally, at 2 g/kg. Blood was analyzed for ethanol, glucose and lactate 30, 60 and 120 min after injection. Rats fed the high carbohydrate diet had lower blood ethanol but higher lactate at 120 min compared with those fed the high fat diet. The results suggest that the rate of ethanol elimination is slower in rats fed high fat than in those fed high carbohydrate diets, resulting in elevated blood ethanol and liver fat levels for the former. J. Nutr. 132: 2732–2736, 2002.
Input Price Shocks and Investment : Evidence from OEMs I

In a competitive market, a producer who experiences an increase in input prices cannot pass-through one-hundred percent of that increase to consumers. In the presence of market frictions, what effect does the internalization of an increase in input price have on firm investment? Using a novel setting, this paper finds that firms facing a shock to internal capital because of an increase in input prices do not uniformly cut back on all types of investment, but allocate capital away from investments with uncertain returns. I use the setting of the 1999 Taiwan earthquake, which disrupted the global semiconductor supply chain and increased production costs for a subset of original equipment manufacturers (OEMs) in the US high-technology industry, causing a drain on internal capital. I find that firms negatively impacted by the shock did not cutback on capital expenditures, but reduced R&D investment. I thank seminar participants at the University of Chicago Booth School of Business, and Washington University’s Olin Business School for helpful feedback. I am grateful to Phil Berger, Steve Kaplan, Christian Leuz, Haresh Sapra, Chad Syverson, and Anastasia Zakolyukina for helpful comments and suggestions. I thank Alice Chung and Yidan Wu for excellent research assistance. This work is supported by the Kathryn and Grant Swick Faculty Research Fund at the University of Chicago Booth School of Business. Email address: Rimmy.Tomy@chicagobooth.edu (Rimmy E. Tomy)
Meta-Modeling Relevance Feedback process with ATOM 3 Diseño de Software basado en modelado y simulación

In the field of Information Retrieval one of the biggest problems is to know which is the goal of the user, her information needs, but sometimes she does not even know which are these needs. This is because a lot of reasons, for example the user has little knowledge of the collection or the retrieval system, perhaps she does not know how to utilize the query language used by the system (natural language versus bag of words), furthermore query ambiguity does exist because of synonymy and hyponymy (i.e., python may refers to the snake or to the programming language). From all this one conclusion arises: creating good search queries is hard. A possible solution to this problem is the query reformulation, a manual process which requires quite a lot of intellectual effort. The relevance feedback process comes from making the query reformulation a controlled and automatic process. In the next section we will explain in detail this process, how we have modeled it using the ATOM tool and some advantages we have obtained from this (mainly simulation and code generation).
A Complexity Based Forensic Analysis of the Trojan Horse Defence

The Operational Complexity Model (OCM) has been used to derive the complexities of the five most prevalent cyber-crimes occurring in southeast Asia, namely peer-to-peer (P2P) multimedia piracy, online auction fraud, online storage of offensive material, theft of online game weapons, and distributed denial of service (DDoS) attacks. In each case the complexity of the simplest Trojan horse process that might be invoked as an alternative explanation for the recovered digital evidence is also determined using the OCM, and the results are used to assess the relative plausibility of the two competing explanations in each case. Finally, the forensically determined circumstances under which a Trojan horse defence is most likely to be successful are outlined.
PSO based Deployment Algorithms in Hybrid Sensor Networks

Summary The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is supported by the several methods. Particle Swarm Optimization (PSO) algorithm is one these methods, however computation time required is a big bottleneck. This paper proposes three dynamic PSO-based deployment algorithms that reduce the computation time. First algorithm by the name of “PSO-LA” algorithm comprised of PSO algorithm and learning automata. In this algorithm, speed of particles is corrected by using the existing knowledge and the feedback from the actual implementation of the algorithm. Hence in this algorithm mobile nodes move more objectively than PSO and achieve the result with less number of repetitions. To improve performance of this algorithm, second algorithm by the name of “Improved PSO-LA” algorithm is introduced, regulating its movement without an impact from the movement of other mobile nodes and based on the result gained from its previous movement. The first and second algorithms require sensors to move iteratively, eventually reaching the final destination. In the third algorithm by the name of “Improved PSO-LA with logical movement” with the same round-by-round procedure of the second algorithm, sensors calculate their target locations, virtually move there. The real movement only happens at the last round after final destinations are determined. Simulation results show the effectiveness of our proposed algorithms against other common approaches like VF and PSO algorithms.
Real Time Simulation of Elastic Latex Hand Puppets

Children television productions have been using puppets for a long time. Since the early days of computer animation, computer puppet simulation has been researched intensively. Complex motion capture equipment allows nowadays the real time mapping of movement for virtual puppets (performance animation). However, the costs of capturing equipment are too high and the difference in the workflow make it difficult for small production teams to access and use such technology. This paper presents a system for the real time simulation of elastic latex hand puppets which are used in television productions. After an analysis of the production processes of real puppets and of the materials used for their production, the paper describes the components of the system simulating them. The system connects a high resolution visual mesh to a three-layered 3D mass spring mesh, which is used for the elastic simulation. Polygonal mesh decimation of the puppet surface model is used as a basis for generating the elastic mesh. From the decimated mesh a new metod is proposed for generating the internal layers of the mass-spring mesh. A data handglove is used for transmitting forces to the elastic mesh, indirectly moving the surface of the virtual puppet in real time. Dataglove interaction maps in a natural way the hand movements of a puppeteer to the computer model. The tradeoffs of the implementation on low cost hardware and its efficiency are also discussed.
“ InThe-Wild ” Observation and Evaluation of a Chinese Heritage VR Environment with HTC VIVE

Virtual Reality (VR) is an industry where heavy investments were made in recent years, it is also a revived research interest both internationally and within China due to the development of usable hardware by technology giants. In China, VR has seen an explosion of startups, and has attracted venture capital investments, but has also seen subsequent implosions and failures largely due to marketing hypes, mismanaged expectations, and definitely an unsustainable VR business model. There are presently over 100 types of VR devices, ranging from the low to the higher end equipment in the mass market which could potentially diversify application areas. However, a lack of developmental experience, usability studies, adjustments to socialcultural differences, and unsustainability in content generation and development may be seen as an issue within China’s market. These observed issues are broad and challenging, which perhaps the VR academic community may have a stronger foothold from which the industry could benefit from. One aspect which may require immediate attention and active investigation is the two fronts of VR’s core areas – user experience and content. In this paper, we conducted an ‘in-the-wild’ observation of a majority of participants experiencing VR for the first time in China. We observed more than 200 walk-in participants experiencing a virtual environment we reconstructed using the HTC VIVE during a four-day public exhibition at the 2017 China (Ningbo) Cultural Industries Fare. The observation outcomes are presented from three angles: user immersive experience; learnability; affective and emotional response. Our experience and findings demonstrated that VR application is effective in nurturing immersive experience and automated learning, establishing perception and intuition, and develop higher level of emotional engagement, which could benefit both the academia and the industry in heritage and cultural education. Keywords— VR, HTC VIVE, in-the-wild, user observation, user experience