Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Reuse Economics in Closed‐Loop Supply Chain Research

This paper provides a critical review of analytic research on the business economics of product reuse inspired by industrial practice. Insights and critical assumptions are provided for each paper. We further classify the research into four streams: industrial engineering/operations research, design, strategy, and behavioral, and present a framework linking these streams. We find that some modeling assumptions risk being institutionalized, and suggest a renewed exploration of industrial practice. Future research should also include empirical work on consumer behavior, product diffusion, and valuation of returns.
Transfer across Completely Different Feature Spaces via Spectral Embedding

In many applications, it is very expensive or time consuming to obtain a lot of labeled examples. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have 1) different feature spaces (e.g., image versus text data), 2) different data distributions, and 3) different output spaces? This paper proposes a solution and discusses the conditions where this is highly likely to produce better results. It first unifies the feature spaces of the target and source data sets by spectral embedding, even when they are with completely different feature spaces. The principle is to devise an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. A linear projection model, as well as a nonlinear approach are derived on the basis of this principle with closed forms. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 20 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space; documents are used to help image classification, etc. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as 50 percent, compared with the methods using only the examples from the target task.
Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter

This paper describes our sentiment analysis systems which have been built for SemEval2015 Task 10 Subtask B and E. For subtask B, a Logistic Regression classifier has been trained after extracting several groups of features including lexical, syntactic, lexiconbased, Z score and semantic features. A weighting schema has been adapted for positive and negative labels in order to take into account the unbalanced distribution of tweets between the positive and negative classes. This system is ranked third over 40 participants, it achieves average F1 64.27 on Twitter data set 2015 just 0.57% less than the first system. We also present our participation in Subtask E in which our system has got the second rank with Kendall metric but the first one with Spearman for ranking twitter terms according to their association with the positive sentiment.
Motivational Effectiveness a Case Study: Acme Company Introduction, Analysis, and Importance of Studying the Problem

In today's ultra-competitive business environment, service companies that rely on unskilled labor must have the proper skills and attitude when it comes to hiring, retaining, and motivating workers. This fact is extremely important when a shortage of labor exists. " ACME Company, " located in Madison, currently experiences the effects of a tight labor market where the unemployment rate fluctuates around 1.2% and the current wage rate for the most basic job is three to four dollars over the legal minimum wage. To remain competitive, companies must create new innovative solutions and initiatives to retain and motivate workers. This project focuses on management's skills and attitudes towards motivating their employees and addresses the implications of applying reinforcement theory as a way to create those new innovative solutions and initiatives. The study is based on a family owned multi-division service company. The division studied is the household goods relocation division. ACME Company is an agent for a larger national van line and is responsible for the domestic and international relocation of household goods for both residential and corporate clients. The company has been in business for over 110 years and relies on past performance records, methodologies, and management styles to guide the company into the future. The existence of a stringent top down management style makes it difficult for grass roots workers to be heard. Management feels they are aware of the problems plaguing the company, however, little has been done to solve these issues. The success of ACME Company depends on its labor force, quality of work, and the extent to which they can solve the problems detailed below. 3 It should be noted that ACME Company historically has not made the issue of employee motivation a high priority and as a result problems have developed which affect the company's performance. The main problem is the high number of damage claims initiated by customers. Claims result from damage to customer's property during the relocation process. More specifically, the damage depends largely on the quality of the packing performed by the employees. The number of claims filed by customers is quite high while at the same time the motivation level of the workers, as described by management, is low. The methodology employed to research this problem included interviews with management and employees, and the administration of a survey (created by our team) given to the employees (Appendix 1). The results of …
Sharp Analysis of a Simple Model for Random Forests

Random forests have become an important tool for improving accuracy in regression and classification problems since their inception by Leo Breiman in 2001. In this paper, we revisit a historically important random forest model originally proposed by Breiman in 2004 and later studied by G\'erard Biau in 2012, where a feature is selected at random and the splits occurs at the midpoint of the node along the chosen feature. If the regression function is Lipschitz and depends only on a small subset of $ S $ out of $ d $ features, we show that, given access to $ n $ observations and properly tuned split probabilities, the mean-squared prediction error is $ O((n(\log n)^{(S-1)/2})^{-\frac{1}{S\log2+1}}) $. This positively answers an outstanding question of Biau about whether the rate of convergence for this random forest model could be improved. Furthermore, by a refined analysis of the approximation and estimation errors for linear models, we show that this rate cannot be improved in general. Finally, we generalize our analysis and improve extant prediction error bounds for another random forest model in which each tree is constructed from subsampled data and the splits are performed at the empirical median along a chosen feature.
Beamformer performance limits in monaural and binaural hearing aid applications

This paper investigates the performance limits of beamforming algorithms in monaural and binaural multi-microphone hearing aids. Directional microphones and minimum variance distortionless response (MVDR) beamformer designs are reviewed in the context of hearing aids. A general binaural filter-and-sum beamformer structure is described based on combining microphone signals from right and left hearing aids. The monaural and binaural algorithms are evaluated with respect to directivity pattern, directivity index, and noise gain. Simulation results show that for a frontal source signal, a binaural beamformer is capable of increasing the directivity by approximately 3 dB compared to a single-array beamformer, but with no increase in the noise gain.
Feature Selection Based Transfer Subspace Learning for Speech Emotion Recognition

Cross-corpus speech emotion recognition has recently received considerable attention due to the widespread existence of various emotional speech. It takes one corpus as the training data aiming to recognize emotions of another corpus, and generally involves two basic problems, i.e., feature matching and feature selection. Many previous works study these two problems independently, or just focus on solving the first problem. In this paper, we propose a novel algorithm, called feature selection based transfer subspace learning (FSTSL), to address these two problems. To deal with the first problem, a latent common subspace is learnt by reducing the difference of different corpora and preserving the important properties. Meanwhile, we adopt the <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>l</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="song-ieq1-2800046.gif"/></alternatives></inline-formula>-norm on the projection matrix to deal with the second problem. Besides, to guarantee the subspace to be robust and discriminative, the geometric information of data is exploited simultaneously in the proposed FSTSL framework. Empirical experiments on cross-corpus speech emotion recognition tasks demonstrate that our proposed method can achieve encouraging results in comparison with state-of-the-art algorithms.
It’s Out of My Hands! Grasping Capacity May Not Influence Perceived Object Size

Linkenauger, Witt, and Proffitt (2011) found that the perceived size of graspable objects was scaled by perceived grasping capacity. However, it is possible that this effect occurred because object size was estimated on the same trial as grasping capacity. This may have led to a conflation of estimates of perceived action capacity and spatial properties. In 5 experiments, we tested Linkenauger et al.’s claim that right-handed observers overestimate the grasping capacity of their right hand relative to their left hand, and that this, in turn, leads them to underestimate the size of objects to-be-grasped in their right hand relative to their left hand. We replicated the finding that right handers overestimate the size and grasping capacity of their right hand relative to their left hand. However, when estimates of object size and grasping capacity were made in separate tasks, objects grasped in the right hand were not underestimated relative to those grasped in the left hand. Further, when grasping capacity was physically restricted, observers appropriately recalibrated their perception of their maximum grasp but estimates of object size were unaffected. Our results suggest that changes in action capacity may not influence perceived object size if sources of conflation are controlled for.
1 High-Performance Computing on Clusters : The Distributed JVM Approach †

A Distributed Java Virtual Machine (DJVM) is a cluster-wide virtual machine that supports parallel execution of a multithreaded Java application on clusters, as if it was executed on a single machine but with improved computation power. The DJVM hides the physical boundaries between the cluster nodes and allows parallelly executed Java threads to access all cluster resources through a unified interface. It is a more user-friendly parallel environment than many other existing parallel languages [8], or libraries for parallel programming such as MPI [13], CORBA [16], and Java RMI [7]. The DJVM research is valuable for high-performance computing as Java has become the dominant language for building the server-side applications, such as enterprise information systems, Web services, and large-scale Grid computing systems, due to its platform independency and built-in multithreading support at language level. This chapter addresses the realization of a distributed Java virtual machine, named JESSICA2, on clusters. Section 1.1 describes Java, Java Virtual Machine, and the main programming paradigms using Java for high-performance computing. We then focus our study on the newly emerging distributed JVM research in Section 1.2. In Section 1.3, we introduce our JESSICA2 Distributed JVM. Section 1.4 gives the performance analysis of JESSICA2. Related work is given in Section 1.5. Section 1.6 concludes this chapter.
Oral hygiene status among orthodontic patients.

AIM The aim of this study was to evaluate the oral hygiene status of patients with fixed orthodontic appliances. METHODS AND MATERIALS The following indices were used to evaluate the oral hygiene status of patients in orthodontic treatment: gingival bleeding index (GBI), plaque index (PI), and ortho-plaque index (OPI). A self-administrated questionnaire was prepared covering oral hygiene practice, oral hygiene cleaning aids, and number of visits to a dental hygienist. RESULTS Fifty patients (15-30 years old) were selected for the study from among the orthodontic patients treated at the King Saud University College of Dentistry, Riyadh, Saudi Arabia. Results showed that the PI and OPI were high with mean scores of 65.24 (SD 16.43) and 53.56 (SD 8.74) respectively, while the average GBI was a much lower value at 19.14 (SD 7.95). No significant difference was observed between male and female patients for the PI (p=0.925) and for the OPI (p=0.072), but a significant difference was observed for the GBI at the 5 percent significance level (p=0.033). The result of OPI showed that 20 (40 percent) of the patients had fair oral hygiene, whereas 30 (60 percent) had poor oral hygiene. Only 16 (32 percent) of the participants reported visiting the dental hygienist during their orthodontic treatment, while the remaining 34 (68 percent) did not. CONCLUSIONS The oral home care of the orthodontic patients surveyed was not at an optimal level, which indicated the need to establish an oral hygiene maintenance program. CLINICAL SIGNIFICANCE Inadequate oral home care among orthodontic patients may make them more prone to develop gingivitis during orthodontic treatment. It is, therefore, essential that oral hygiene instructions and a hygiene maintenance program not be overlooked during orthodontic treatment.
Modulation Power and Phase Spectrum of Natural Sounds Enhance Neural Encoding Performed by Single Auditory Neurons

We examined the neural encoding of synthetic and natural sounds by single neurons in the auditory system of male zebra finches by estimating the mutual information in the time-varying mean firing rate of the neuronal response. Using a novel parametric method for estimating mutual information with limited data, we tested the hypothesis that song and song-like synthetic sounds would be preferentially encoded relative to other complex, but non-song-like synthetic sounds. To test this hypothesis, we designed two synthetic stimuli: synthetic songs that matched the power of spectral-temporal modulations but lacked the modulation phase structure of zebra finch song and noise with uniform band-limited spectral-temporal modulations. By defining neural selectivity as relative mutual information, we found that the auditory system of songbirds showed selectivity for song-like sounds. This selectivity increased in a hierarchical manner along ascending processing stages in the auditory system. Midbrain neurons responded with highest information rates and efficiency to synthetic songs and thus were selective for the spectral-temporal modulations of song. Primary forebrain neurons showed increased information to zebra finch song and synthetic song equally over noise stimuli. Secondary forebrain neurons responded with the highest information to zebra finch song relative to other stimuli and thus were selective for its specific modulation phase relationships. We also assessed the relative contribution of three response properties to this selectivity: (1) spiking reliability, (2) rate distribution entropy, and (3) bandwidth. We found that rate distribution and bandwidth but not reliability were responsible for the higher average information rates found for song-like sounds.
A 1-V CMOS Current Reference With Temperature and Process Compensation

A 1-V current reference fabricated in a standard CMOS process is described. Temperature compensation is achieved from a bandgap reference core using a transimpedance amplifier in order to generate an intermediate voltage reference, VREF. This voltage applied to the gate of a carefully sized nMOS output transistor provides a reference drain current, IREF , nearly independent of temperature by mutual compensation of mobility and threshold voltage variations. The circuit topology allows for compensation of threshold voltage variation due to process parameters as well. The current reference has been fabricated in a standard 0.18-mum CMOS process. Results from nineteen samples measured over a temperature range of 0degC to 100degC , showed values of IREF of 144.3 muA plusmn 7% and VREF of 610.9 mV plusmn 2% due to the combined effect of temperature and process variations.
Cross-Validation and the Bootstrap : Estimating the Error Rate ofa Prediction

A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. A particular bootstrap method, the 632+ rule, is shown to substantially outperform cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric, and apply to any possible prediction rule: however we only study classiication problems with 0-1 loss in detail. Our simulations include \smooth" prediction rules like Fisher's Linear Discriminant Function, and unsmooth ones like Nearest Neighbors.
Influence of dielectric layers on the transmission properties of FSS with cross slots

The influence of dielectric layers on the transmission properties of FSS with cross slots was studied by using the simulation method. The model of the FSS, the simulation results and the experimental results of the template are given. The relation between the resonance frequency and the thickness of the dielectric layers, the curves of the transmission vs frequency of the varying dielectric layers thickness are presented and analysed.
Data Combination Privacy Preservation Adjusting Mechanism for Software as a Service

In Software as a Service model, i.e. SaaS, tenants' sensitive data are stored and processed at the platform of untrusted service providers. Data privacy has become the biggest challenge hindering wider adoption of software as a service. Data combination privacy has been proposed to protect privacy of data combination through sensitive association hidden. However, this approach doesn't consider the scenario where tenants' requirements changed and customization happened. When tenants customize data schema or privacy requirements, there is a possibility that underlying physical data chunk schema collides with the privacy requirements of tenants. This paper proposed the data combination privacy preservation adjusting mechanism for data privacy leakage caused by on demand customization of software as a service. Three principles of privacy preservation adjusting mechanism are proposed. Based on the adjusting mechanism, there would no more privacy leakage than before customization during the adjusting process to the customized schema. Analysis and experiments demonstrate the corrective and effective of the data privacy preservation adjusting mechanism for software as a service.
On-Line Single Event Upset Detection and Correction in Field Programmable Gate Array Configuration Memories

Larger field programmable gate array (FPGA) configuration memories and shrinking design rules have raised concerns about single event upsets (SEUs), especially for highreliability, high-availability systems that use FPGAs. We present a design for the on-line detection and correction of SEUs in the configuration memory of Xilinx Virtex-4 and Virtex-5 FPGAs. The design corrects all single-bit errors and detects all double-bit errors in the configuration memory at maximum speed and with minimal overhead and power dissipation. A method for SEU emulation in the configuration memory of FPGAs is presented which enables the experimental verification of the approach. The results of SEU emulation in Xilinx FPGAs are discussed. 1
Classification and quality assessment of high quality digital photographs

We are interested in high quality photographs. This paper outlines our research proposal for the tasks of classification and quality assessment. We address these challenges by exploring the aesthetics from the combined perspectives of the artists and the photographers. We propose to use the aesthetic primitives of images for visualization as a guideline for high and low-level image feature extraction and to classify this high quality content into six creative exposure themes, which are commonly followed by the professional photographers. Then, we suggest to evaluate the quality of the photograph accordingly to these themes. We solve the problems using statistical modeling and learning approach.
FPGA-accelerated group-by aggregation using synchronizing caches

Recent trends in hardware have dramatically dropped the price of RAM and shifted focus from systems operating on disk-resident data to in-memory solutions. In this environment high memory access latency, also known as memory wall, becomes the biggest data processing bottleneck. Traditional CPU-based architectures solved this problem by introducing large cache hierarchies. However algorithms which experience poor locality can limit the benefits of caching. In turn, hardware multithreading provides a generic solution that does not rely on algorithm-specific locality properties. In this paper we present an FPGA-accelerated implementation of in-memory group-by hash aggregation. Our design relies on hardware multithreading to efficiently mask long memory access latency by implementing a custom operation datapath on FPGA. We propose using CAMs (Content Addressable Memories) as a mechanism of synchronization and local pre-aggregation. To the best of our knowledge this is the first work, which uses CAMs as a synchronizing cache. We evaluate aggregation throughput against the state-of-the-art multithreaded software implementations and demonstrate that the FPGA-accelerated approach significantly outperforms them on large grouping key cardinalities and yields speedup up to 10x.