Quality Trade-off Research Articles

An ECC/DCT-Based Robust Video Steganography Algorithm for Secure Data Communication

Nowadays, the science of information hiding has gained tremendous significance due to advances in information and communication technology. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic methods. In this paper, we propose a novel video steganography method in discrete cosine transform (DCT) domain based on error correcting codes (ECC). To improve the security of the proposed algorithm, a secret message is first encrypted and encoded by using Hamming and BCH codes. Then, it is embedded into the DCT coefficients of video frames. The hidden message is embedded into DCT coefficients of each Y, U, and V planes excluding DC coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast moving objects. The experiential results of the proposed algorithm are compared with three existing methods. The comparison results show that our proposed algorithm outperformed other algorithms. The hidden ratio of the proposed algorithm is approximately 27.53%, which is considered as a high hiding capacity with a minimal tradeoff of the visual quality. The robustness of the proposed algorithm was tested under different attacks.

Temporary Employment Boom in Poland: A Job Quality vs. Quantity Trade-Off?

Between 2002 and 2015, temporary employment in Poland more than doubled. Poland became the country with the highest share of temporary jobs in the EU. In this paper we study this process from the job quality vs. job quantity perspective. We analyse gaps between temporary and permanent workers in six dimensions of jobs quality, adopting measures proposed by the OECD and Eurofound. The gaps in earnings quality, job security and work scheduling quality were the most pronounced. Job quality has improved for both groups of workers but the gaps have not closed completely. Firms in Poland prefer temporary contracts because of lower firing cost, tax wedge and wages. We use a stylised labour demand model to quantify the upper bound of a potential job creation effect due to lower labour costs incurred by temporary contracts. We find that this effect doesn’t exceed 4% of dependent employment in 2015. We cannot rule out that the net employment effect was 0. Our findings show that even if cheaper, temporary contracts might have induced some job creation, workers suffered from lower job quality in several dimensions.

Social Information on Fear and Food Drives Animal Grouping and Fitness

Empirical studies in select systems suggest that social information-the incidental or deliberate information produced by animals and available to other animals-can fundamentally shape animal grouping behavior. However, to understand the role of social information in animal behavior and fitness, we must establish general theory that quantifies effects of social information across ecological contexts and generates expectations that can be applied across systems. Here we used dynamic state variable modeling to isolate effects of social information about food and predators on grouping behavior and fitness. We characterized optimal behavior from a set of strategies that included grouping with different numbers of conspecifics or heterospecifics and the option to forage or be vigilant over the course of a day. We show that the use of social information alone increases grouping behavior but constrains group size to limit competition, ultimately increasing individual fitness substantially across various ecological contexts. We also found that across various contexts, foraging in mixed-species groups is generally better than foraging in conspecific groups, supporting recent theory on competition-information quality trade-offs. Our findings suggest that multiple forms of social information shape animal grouping and fitness, which are sensitive to resource availability and predation pressure that determine information usefulness.

The Taxation of Environmental Pollution: A Model for Tax Revenue-Environmental Quality Tradeoffs

The paper analyzes the Willamette River in Oregon. Here a model (combining the least-cost model and the constraint method of multi-objective programming) is used to determine the appropriate tax rate on environmental externalities, incorporating both revenue and environmental quality objectives. The study finds the following. (1) By using the optimal tax rate, the appropriate tax revenue is determined. (2) The efficient solution set (including tax revenue and water quality considerations) is found by using differing optimal tax rates. (3) The optimal point (solution) in the efficient solution set is chosen by the geometrical argument approach and trade-off analysis approach.

Enhanced Dynamic Spectrum Access in Multiband Cognitive Radio Networks via Optimized Resource Allocation

In this paper, we address the constrained resource allocation problems arising in the context of spectrum sharing in cognitive radio networks utilizing a multi-dimensional formulation. Given the activity of the primary users (PUs), we consider multiple objectives and constraints, viz., sum rate, fairness, number of active secondary users (SUs), power consumption, and quality of service requirements (of both PUs and SUs). The three dimensions for the optimization task are the assignment of power, frequency, and antenna directionality to various SUs. Efficient heuristic algorithms are developed for five variations of the NP-hard optimization problems. Solution quality tradeoffs are shown for three algorithms, viz., convex relaxation with tree pruning, convex relaxation with gradual removal, and a genetic algorithm (GA); results show that the GA provides a reasonable balance between solution quality and computational effort. The multi-objective problems are solved using a modification of the NSGA-II evolutionary algorithm, obtaining a set of Pareto-optimal solutions under computational constraints.

How Client Capabilities, Vendor Configuration, and Location Impact BPO Outcomes

ABSTRACTDespite the increasing use of onshore and offshore business process outsourcing (BPO), a comprehensive literature review [38] finds that there has been limited empirical research on BPO outcomes. This article responds to the call for research by developing and testing a conceptual model for BPO outcomes using data from 50 firms publicly traded in the U.S., including 38 firms in the Forbes Global 2000. We find that client firm capabilities, vendor configuration, and country location lead to interesting tradeoffs in the BPO quality, cost, and time outcomes. For example, while multi-sourcing offers advantages such as risk mitigation, client firms encounter reduced BPO time benefits when they use multiple vendors. While onshore BPO can lead to an improved quality, higher onshore labor costs result in lower BPO cost savings. And while offshore destinations such as India offer lower labor costs, time zone differences lead to reduced BPO time benefits.

Family size, birth order, and tests of the quantity–quality model

Recent empirical studies challenge the quantity–quality (Q–Q) trade-off of children modeled by Becker and Lewis. In the ordinary least squares (OLS) estimates, the effect of family size on child outcomes is frequently estimated with birth order controls. In a group of instrumental variable (IV) estimates, the family size effect is estimated only for low-parity children. We show that existing studies using the above two specifications do not identify the family size effect on average child quality and do not contradict the Becker–Lewis Q–Q theory.

Digitizing an Analog Radiography Teaching File Under Time Constraint: Trade-Offs in Efficiency and Image Quality.

We digitized the radiography teaching file at Black Lion Hospital (Addis Ababa, Ethiopia) during a recent trip, using a standard digital camera and a fluorescent light box. Our goal was to photograph every radiograph in the existing library while optimizing the final image size to the maximum resolution of a high quality tablet computer, preserving the contrast resolution of the radiographs, and minimizing total library file size. A secondary important goal was to minimize the cost and time required to take and process the images. Three workers were able to efficiently remove the radiographs from their storage folders, hang them on the light box, operate the camera, catalog the image, and repack the radiographs back to the storage folder. Zoom, focal length, and film speed were fixed, while aperture and shutter speed were manually adjusted for each image, allowing for efficiency and flexibility in image acquisition. Keeping zoom and focal length fixed, which kept the view box at the same relative position in all of the images acquired during a single photography session, allowed unused space to be batch-cropped, saving considerable time in post-processing, at the expense of final image resolution. We present an analysis of the trade-offs in workflow efficiency and final image quality, and demonstrate that a few people with minimal equipment can efficiently digitize a teaching file library.

Experimental low cost reflective type oximeter for wearable health systems

Abstract The advent of wearable technology is fundamental to the dissemination of wearable personal health monitoring devices. Recent developments of biomedical sensors have decreased the form factor and power consumption that can be worn on a permanent basis. This paper discusses a low cost reflective photoplethysmography (PPG) system using a dedicated integrated circuit (IC) solution as the core of a wearable health monitoring device. The measurement of two physiological indicators is performed, namely the pulse rate (HR) and the blood oxygen saturation (SpO 2 ). The paper analyses in depth the PPG signals sensing architecture, guaranteeing high resolution measurements due to a delta-sigma analog to digital conversion unit. Post-processing digital filter operations are implemented to enhance low noise PPGs acquisition for physiological signals extraction. A complete system design is presented and a detailed evaluation is made in a real-time processing scenario. The test platform is completed with a PC based graphics application for on-line and off-line data analysis. Minimizing power dissipation is the main challenge in a wearable design. However, it restrains PPG signal measurement sensitivity by lowering signal quality. Using the developed prototype power consumption, studies concerning the characterization of power consumption and signal quality over various working conditions are performed. Next, a performance merit figure is proposed as the main research contribution, which addresses the power consumption and signal quality trade-off subject. It aims to be used as an analysis for trade-offs between these two conflicting design criteria.

A Novel Embedded Interpolation Algorithm with Negative Squared Distance for Real-Time Endomicroscopy

Interpolation is the most executed operation and one of the main bottlenecks in embedded imaging, registration, and rendering systems. Existing methods either lack parallelization and scalability capabilities or are too computationally complex to execute efficiently. Acknowledging that improving execution time leads to degradation in image quality, we formulate a novel Negative Squared Distance (NSD) interpolation method that exhibits excellent performance by exploiting Look-Up Table (LUT) optimization for Field Programmable Gate Array (FPGA) speedup, with a balanced trade-off in quality in our embedded endomicroscopic imaging system. Quantitative analysis on performance and resource utilization of NSD against existing methods is reported through an implementation on a Xilinx ML605 platform. Functional validation using practical image resizing and rotation applications to compare qualitative performance against existing algorithms is performed and presented with visual and numerical results. Our method is shown to have a smaller design size and produces a maximum throughput of over twofold against trilinear interpolation with on-par image quality as the baseline method.

Novel Chroma Subsampling Strategy Based on Mathematical Optimization for Compressing Mosaic Videos With Arbitrary RGB Color Filter Arrays in H.264/AVC and HEVC

To save manufacturing cost, most color digital video cameras employ a single-sensor technology with a red–green–blue (RGB) color filter array (CFA) to capture real-world scenes. Due to only one primary color measured at each pixel location, the captured videos are usually referred to as the mosaic videos. For the purposes of economical storage and transmission, it is very important to achieve a good tradeoff between the quality and bitrate when compressing mosaic videos with different RGB-CFA structures. In this paper, based on mathematical optimization technique, a novel chroma subsampling strategy is presented for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and High Efficiency Video Coding (HEVC). For each $2 \times 2$ YUV block to be subsampled with 4:2:0 format, the proposed strategy determines the proper sampled $U$ and $V$ components by minimizing, prior to compression, the quality distortion between the original colocated mosaic block and the mosaic block conversed from the current subsampled YUV block. Through the mathematical optimization formulated in the proposed strategy, the significance of the sampled $U$ and $V$ components for reconstructing R, G, and B pixels can be simultaneously taken into consideration. The experimental results demonstrate that the proposed chroma subsampling strategy has the best quality and bitrate tradeoff at a similar execution time requirement for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and HEVC compared with the state-of-the-art ones by Chen et al. and Yang et al. as well as the three commonly used ones.

Robust geometric accuracy allocation of machine tools to minimize manufacturing costs and quality loss

With increasing demands of machining accuracy, designing of machine tools for satisfactory performance using cost-effective geometric accuracy configurations is becoming a complex problem to the machine tool manufacturers. In this paper, a novel robust accuracy allocation method is proposed for multi-axis machine tools based on multi-objective quality and cost trade-offs. To model the volumetric accuracy of machine tool based on geometric errors, the multi-body system theory was introduced. A manufacturing cost model for the machine tool components with a significant effect on geometric errors was established based on the machining features. The quality loss of the machine tool was also integrated into a single optimization objective. After identifying the relationship between the accuracy grade parameters of the feeding components and the geometric errors, the maximum in the Euclidean norm of all the accuracy parameters was defined as another optimization objective. The robust accuracy allocation was performed using Isight software and the Non-Dominated Sorting Genetic Algorithm-II built in the MATLAB. The optimization results for a four-axis horizontal machining center showed that the proposed method can realize the optimization of geometric accuracy and can determine the optimal accuracy grade of the feeding components satisfying the machining accuracy requirements.

Constraint-based and SAT-based diagnosis of automotive configuration problems

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FPNP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.

Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

Energy savings have become an essential consideration in sustainable manufacturing projects due to the associated environmental impacts and constraints on carbon emissions. In the past, machining operations primarily examined technological consideration (e.g., machining quality) and neglected energy consumption. Therefore, this paper investigates an energy-efficient multi-pass turning operation problem and establishes a multi-objective multi-pass turning operations model. Energy consumption and machining quality are both considered in this problem. Although several models of this problem have considered these criteria, the objectives are usually combined into a single objective using a weighted sum approach, which results in poor non-dominated solutions. To obtain high quality trade-offs between the two challenging objectives, a novel multi-objective backtracking search algorithm is proposed to solve this multi-objective optimization problem. To verify the feasibility and validity of the proposed algorithm, it is compared with other classical multi-objective metaheuristics on multi-objective multi-pass turning operations. This study's experimental results demonstrate that the proposed algorithm significantly outperforms other algorithms for this optimization problem, which is a significant result regarding practical application.

“Reaching its limits”: industry perspectives on salt reduction

Purpose – The purpose of this paper is to explore the barriers to, and implications of, salt reduction initiatives within the UK food manufacturing industry. Design/methodology/approach – In total, 13 technical and new product development (NPD) managers were purposefully sampled from businesses supplying foods within the chilled convenience food sector. Data were generated using semi-structured interviews incorporating the critical incident technique. Thematic and comparative analyses identified similarities and differences in the challenges facing different product categories within the sector. Findings – Barriers to further salt reduction included: manufacturing limitations; NPD constraints; food safety, quality and shelf-life trade-offs; and organoleptic acceptance. No single barrier dominated industry concerns and many barriers were interlinked. Overarching issues of competitive inequality between signatories and non-participants to voluntary salt reduction agreements, and the experience of product reformulation having reached its limits were prevalent. Originality/value – This research provides a food industry perspective on the identified barriers faced by UK food processors and manufacturers in advancing salt reduction within the chilled convenience sector.

Towards statistical guarantees in controlling quality tradeoffs for approximate acceleration

Conventionally, an approximate accelerator replaces every invocation of a frequently executed region of code without considering the final quality degradation. However, there is a vast decision space in which each invocation can either be delegated to the accelerator---improving performance and efficiency--or run on the precise core---maintaining quality. In this paper we introduce M ithra , a co-designed hardware-software solution, that navigates these tradeoffs to deliver high performance and efficiency while lowering the final quality loss. M ithra seeks to identify whether each individual accelerator invocation will lead to an undesirable quality loss and, if so, directs the processor to run the original precise code. This identification is cast as a binary classification task that requires a cohesive co-design of hardware and software. The hardware component performs the classification at runtime and exposes a knob to the software mechanism to control quality tradeoffs. The software tunes this knob by solving a statistical optimization problem that maximizes benefits from approximation while providing statistical guarantees that final quality level will be met with high confidence. The software uses this knob to tune and train the hardware classifiers. We devise two distinct hardware classifiers, one table-based and one neural network based. To understand the efficacy of these mechanisms, we compare them with an ideal, but infeasible design, the oracle. Results show that, with 95% confidence the table-based design can restrict the final output quality loss to 5% for 90% of unseen input sets while providing 2.5× speedup and 2.6× energy efficiency. The neural design shows similar speedup however, improves the efficiency by 13%. Compared to the table-based design, the oracle improves speedup by 26% and efficiency by 36%. These results show that M ithra performs within a close range of the oracle and can effectively navigate the quality tradeoffs in approximate acceleration.

SU-F-R-11: Designing Quality and Safety Informatics Through Implementation of a CT Radiation Dose Monitoring Program

Purpose: Recent legislative and accreditation requirements have driven rapid development and implementation of CT radiation dose monitoring solutions. Institutions must determine how to improve quality, safety, and consistency of their clinical performance. The purpose of this work was to design a strategy and meaningful characterization of results from an in-house, clinically-deployed dose monitoring solution. Methods: A dose monitoring platform was designed by our imaging physics group that focused on extracting protocol parameters, dose metrics, and patient demographics and size. Compared to most commercial solutions, which focus on individual exam alerts and global thresholds, the program sought to characterize overall consistency and targeted thresholds based on eight analytic interrogations. Those were based on explicit questions related to protocol application, national benchmarks, protocol and size-specific dose targets, operational consistency, outliers, temporal trends, intra-system variability, and consistent use of electronic protocols. Using historical data since the start of 2013, 95% and 99% intervals were used to establish yellow and amber parameterized dose alert thresholds, respectively, as a function of protocol, scanner, and size. Results: Quarterly reports have been generated for three hospitals for 3 quarters of 2015 totaling 27880, 28502, 30631 exams, respectively. Four adult and two pediatric protocols were higher than external institutional benchmarks. Four protocol dose levels were being inconsistently applied as a function of patient size. For the three hospitals, the minimum and maximum amber outlier percentages were [1.53%,2.28%], [0.76%,1.8%], [0.94%,1.17%], respectively. Compared with the electronic protocols, 10 protocols were found to be used with some inconsistency. Conclusion: Dose monitoring can satisfy requirements with global alert thresholds and patient dose records, but the real value is in optimizing patient-specific protocols, balancing image quality trade-offs that dose-reduction strategies promise, and improving the performance and consistency of a clinical operation. Data plots that capture patient demographics and scanner performance demonstrate that value.

Scalable texture compression using the wavelet transform

2D texture data represent one of the main data sources in 3D graphics, requiring large amounts of memory and bandwidth. Texture compression is of critical importance in this context to cope with these bottlenecks. To improve upon the available supported texture compression systems, several transform-based solutions have been proposed. These solutions, however, are not suitable for real-time texture sampling or provide insufficient image quality at medium to low rates. We propose a new scalable texture codec based on the 2D wavelet transform suitable for real-time rendering and filtering, using a new subband coding technique. The codec offers superior compression performance compared to the state-of-the-art, resolution scalability coupled with a wide variety of quality versus rate trade-offs as well as complexity scalability supported by the use of different wavelet filters.

Quality versus efficiency in document scoring with learning-to-rank models

Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far.The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features.We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (Ωλ-MART), an algorithm that induces forests of oblivious regression trees.We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget.

Parents face quantity-quality trade-offs between reproduction and investment in offspring in Iceland.

How to optimally allocate time, energy and investment in an effort to maximize one's reproductive success is a fundamental problem faced by all organisms. This effort is complicated when the production of each additional offspring dilutes the total resources available for parental investment. Although a quantity–quality trade-off between producing and investing in offspring has long been assumed in evolutionary biology, testing it directly in humans is difficult, partly owing to the long generation time of our species. Using data from an Icelandic genealogy (Íslendingabók) over two centuries, I address this issue and analyse the quantity–quality trade-off in humans. I demonstrate that the primary impact of parents on the fitness of their children is the result of resources and or investment, but not genes. This effect changes significantly across time, in response to environmental conditions. Overall, increasing reproduction has negative fitness consequences on offspring, such that each additional sibling reduces an individual's average lifespan and lifetime reproductive success. This analysis provides insights into the evolutionary conflict between producing and investing in children while also shedding light on some of the causes of the demographic transition.