Ergonomic evaluation of work table for waste sorting tasks using digital human modelling

Waste sorting facilities rely heavily on plentiful human labour, and the lack of adequately designed work systems leads to musculoskeletal disorders among the workers. The present research explores the ergonomic design aspects of a cost-effective intervention, a work table for waste sorting in developing countries. This study evaluates the appropriate range of work height for the sorting tasks and proper location of hoppers to drop the sorted items. The ergonomic assessment was conducted by simulating postures involved in sorting tasks in a virtual environment and performing a biomechanical evaluation of the static postures by digital human modelling. Thirty male subjects from Indian population simulated the postures of sorting tasks. The compressive force at L4/L5 intervertebral disc, shoulder flexion moment, shoulder abduction moment, and elbow flexion moment were taken as the indicative parameters of the subject's physical workload. The appropriate table height was marginally lower than the elbow height of the subject and within 4 cm. The preferred hopper position was on the floor adjacent to the worker compared with the hopper on the top of the table.     

ASC: an associative-computing paradigm

Today's increased computing speeds allow conventional sequential machines to effectively emulate associative computing techniques. We present a parallel programming paradigm called ASC (ASsociative Computing), designed for a wide range of computing engines. Our paradigm has an efficient associative-based, dynamic memory-allocation mechanism that does not use pointers. It incorporates data parallelism at the base level, so that programmers do not have to specify low-level sequential tasks such as sorting, looping and parallelization. Our paradigm supports all of the standard data-parallel and massively parallel computing algorithms. It combines numerical computation (such as convolution, matrix multiplication, and graphics) with nonnumerical computing (such as compilation, graph algorithms, rule-based systems, and language interpreters). This article focuses on the nonnumerical aspects of ASC     

Dense and dynamic 3D selection for game-based virtual environments

3D object selection is more demanding when, 1) objects densly surround the target object, 2) the target object is significantly occluded, and 3) when the target object is dynamically changing location. Most 3D selection techniques and guidelines were developed and tested on static or mostly sparse environments. In contrast, games tend to incorporate densly packed and dynamic objects as part of their typical interaction. With the increasing popularity of 3D selection in games using hand gestures or motion controllers, our current understanding of 3D selection needs revision. We present a study that compared four different selection techniques under five different scenarios based on varying object density and motion dynamics. We utilized two existing techniques, Raycasting and SQUAD, and developed two variations of them, Zoom and Expand, using iterative design. Our results indicate that while Raycasting and SQUAD both have weaknesses in terms of speed and accuracy in dense and dynamic environments, by making small modifications to them (i.e., flavoring), we can achieve significant performance increases.     

A Multistrategy Approach to Classifier Learning from Time Series

We present an approach to inductive concept learning using multiple models for time series. Our objective is to improve the efficiency and accuracy of concept learning by decomposing learning tasks that admit multiple types of learning architectures and mixture estimation methods. The decomposition method adapts attribute subset selection and constructive induction (cluster definition) to define new subproblems. To these problem definitions, we can apply metric-based model selection to select from a database of learning components, thereby producing a specification for supervised learning using a mixture model. We report positive learning results using temporal artificial neural networks (ANNs), on a synthetic, multiattribute learning problem and on a real-world time series monitoring application.     

Reordering columns for smaller indexes

Column-oriented indexes—such as projection or bitmap indexes—are compressed by run-length encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. Unfortunately, determining the best column order is NP-hard. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Experimentally, sorting based on Hilbert space-filling curves is poor at minimizing the number of runs.     

Mining human movement evolution for complex action recognition

In this paper, a novel and efficient system is proposed to capture human movement evolution for complex action recognition. First, camera movement compensation is introduced to extract foreground object movement. Secondly, a mid-level feature representation called trajectory sheaf is proposed to capture the temporal structural information among low-level trajectory features based on key frames selection. Thirdly, the final video representation is obtained by training a sorting model with each key frame in the video clip. At last, the hierarchical version of video representation is proposed to describe the entire video with higher level representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on UCF Sports, and comparable results on several challenge benchmarks, such as Hollywood2 and HMDB51 dataset.     

An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information

In many cases of practical multiattribute project portfolio selection problems, it is hard to obtain accurate measurements of attributes and precise preference information. Even after a long and costly information gathering, the attribute measurements and the preference information can still be uncertain or inaccurate. Considerable cost saving will be obtained if the selection of an optimal project portfolio can be done using rank‐level information based on some or all the attributes, without knowing the preference information. In this paper, we propose a stochastic multiattribute acceptability analysis‐based method that can deal with mixed rank and cardinal attribute measurements and uses little or no weight information. In the proposed stochastic multiattribute acceptability analysis‐based method, the decision makers need not to express their preferences explicitly or implicitly, so it is particularly useful when no weight information is available at all. A numerical example involving selection of photovoltaic plants in an industrial province in Eastern China is provided to demonstrate the proposed method.     

Supporting lock-free synchronization in Pfair-scheduled real-time systems

We consider the use of lock-free techniques for implementing shared objects in real-time Pfair-scheduled multiprocessor systems. Lock-free objects are more economical than locking techniques when implementing relatively simple objects such as buffers, stacks, queues, and lists. However, the use of such objects on real-time multiprocessors is generally considered impractical due to the need for analytical real-time guarantees. In this paper, we explain how the quantum-based nature of Pfair scheduling enables the effective use of such objects on real-time multiprocessors and present analysis specific to Pfair-scheduled systems. In addition, we show that analytical improvements can be obtained by using such objects in conjunction with group-based scheduling techniques. In this approach, a group of tasks is scheduled as a single entity (called a supertask in the Pfair literature). Such grouping prevents tasks from executing simultaneously, and hence from executing in parallel. Consequently, grouping tasks can improve the worst-case scenario with respect to object contention. Grouping also enables the use of less costly uniprocessor algorithms when all tasks sharing an object reside within the same group. We illustrate these optimizations with a case study that focuses on shared queues. Finally, we present and experimentally evaluate a simple heuristic for grouping tasks in order to reduce object contention. Though the analysis presented herein focuses specifically on Pfair-scheduled systems, the observations and techniques should be applicable to other quantum-scheduled systems as well.     

Managing accessibility in local e-government websites through end-user development: a case study

This work discusses accessibility problems concerning content creation and publication on e-government websites, and proposes an approach based on end-user development (EUD) techniques to overcome them. To deal with the huge and diverse amount of documentation to be published, content management systems (CMSs) are usually adopted in government institutions to support content creation by a large set of publishers, who typically have no competencies in information technology. This paper proposes the integration of EUD techniques in CMSs in order to limit CMS personalization and, at the same time, relieve publishers from managing the low-level details of content representation. To demonstrate the validity of the approach, a case study research has been performed by involving some employees of the Brescia Municipality in Italy. The results of the study confirm the positive impact of EUD techniques on content accessibility and publishers’ work practice, and suggest their possible use in more sophisticated tasks.     

Effects of perception of head-up display on the driving safety of experienced and inexperienced drivers

In this human subject study about interaction with head-up displays (HUDs), the impacts of three types of HUDs (flag, perspective, and flag and perspective) on experienced and inexperienced drivers were subjectively and objectively measured. The driving experiment divided 60 participants into two groups (experienced and inexperienced) and assessed their driving experiences in real high- and low-level driving tasks using the three types of HUDs. During the subjective performance evaluation, the participants executed secondary display tasks and performed a primary driving task with eye-tracking and physiological monitoring. The flag and perspective display (display FP) and the perspective display (display P) were more effective in helping drivers execute simple tasks and complex tasks, and both experienced and inexperienced drivers were HUD-dependent. Based on the study, we provided an optimized design strategy for the HUD interface. Also, we suggest the HUD design should give special consideration for inexperienced drivers.     

Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios

Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenarios. Two common approaches are often used, one based on low-level techniques to detect each object size, position and velocity, and other based on high-level techniques that deal with object appearance. None of these methods can deal with all possible problems in multiple-target tracking: environment occlusions, both total and partial, and collisions, such as grouping and splitting events. So one solution is to merge these techniques to improve their performance. Based on an existing hierarchical architecture, we present a novel technique that can deal with all the mentioned problems in multiple tracking targets. Blob detection, low-level tracking using adaptive filters, high-level tracking based on a fixed pool of histograms and an event management that can detect every collision event and performs occlusion recovery are used to be able to track every object during the time they appear within the scene. Experimental results show the performance of this technique under multiple situations, being able to track every object in the scene without losing their initial identification. The speed processing is higher than 50 frames, which allows it to be used under real-time scenarios.     

基于博弈论和启发式算法的超密集网络边缘计算卸载

超密集网络与边缘计算相结合时,高密度的基站分布可能会对同一用户重复覆盖,该用户选择不同基站进行卸载将会对系统性能产生不同影响,由此引出卸载对象选取问题。同时边缘计算可以将部分任务卸载到边缘服务器进行处理,选择合适的卸载比例能够显著降低所需的时延和能耗,由此引出卸载比例选取问题。提出一种超密集网络环境中基于博弈论和启发式算法的边缘计算卸载策略。针对卸载对象选取问题,根据边缘服务器到用户之间的距离和工作负载定义偏好度指标,各用户根据偏好度进行博弈后选择卸载对象,并对用户进行分组,将原问题分解为若干个并行的子问题。针对卸载比例选取问题,基于萤火虫群优化算法对各用户的卸载比例进行优化,得到适当的卸载比例。与全本地处理（ALP）策略、全卸载策略（AOS）和基于粒子群优化（PSO）算法的卸载策略进行对比,实验结果表明,ALP和AOS策略在总能耗和平均时延上具有一定的局限性,相比基于PSO的卸载策略,所提策略的时延降低22%,能耗降低20%,可以有效减少系统损失。     

Sort-sharing-aware query processing

Many database applications require sorting a table (or relation) over multiple sort orders. Some examples include creation of multiple indices on a relation, generation of multiple reports from a table, evaluation of a complex query that involves multiple instances of a relation, and batch processing of a set of queries. In this paper, we study how to optimize multiple sortings of a table. We investigate the correlation between sort orders and exploit sort-sharing techniques of reusing the (partial) work done to sort a table on a particular order for another order. Specifically, we introduce a novel and powerful evaluation technique, called cooperative sorting, that enables sort sharing between seemingly non-related sort orders. Subsequently, given a specific set of sort orders, we determine the best combination of various sort-sharing techniques so as to minimize the total processing cost. We also develop techniques to make a traditional query optimizer extensible so that it will not miss the truly cheapest execution plan with the sort-sharing (post-) optimization turned on. We demonstrate the efficiency of our ideas with a prototype implementation in PostgreSQL and evaluate the performance using both TPC-DS benchmark and synthetic data. Our experimental results show significant performance improvement over the traditional evaluation scheme.     

Goal directed multi-finger manipulation: Control policies and analysis

We present a method for one-handed, task-based manipulation of objects. Our approach uses a mid-level, multi-phase approach to organize the problem into three phases. This provides an appropriate control strategy for each phase and results in cyclic finger motions that, together, accomplish the task. The exact trajectory of the object is never specified since the goal is defined by the final orientation and position of the object. All motion is physically based and guided by a control policy that is learned through a series of offline simulations. We also discuss practical considerations for our learning method. Variations in the synthesized motions are possible by tuning a scalarized multi-objective optimization. We demonstrate our method with two manipulation tasks, discussing the performance and limitations. Additionally, we provide an analysis of the robustness of the low-level controllers used by our framework.     

Minimization of Boolean functions of any number of variables using decimal labels

This paper deals with the problem of minimization of a Boolean function, particularly when the number of variables is very large. Using the decimal labels of the minterms, a table is worked out which helps to determine all the prime implicants and the essential prime implicants. A systematic method of sorting out the dominating and the dominated columns is then given, which helps to reduce the number of successive cover tables considerably.     

Configural display design techniques considered at multiple levels of evaluation

Two studies were conducted to examine issues in the design and evaluation of configural displays. Four design techniques (bar graphs/extenders, scale markers/ scale grids, color coding/color layering/color separation, and annotation with digital values) were applied, alone and in combination, to a baseline configural display, forming 10 displays. Two qualitatively different evaluations assessed performance for (A) low-level data probes (quantitative estimates of individual variables) and (B) system control and fault detection tasks. Three of the four design techniques improved performance significantly for low-level data probes (color coding was the exception). A display with digital values only (i.e., no analog configural display) produced the poorest performance for control/fault detection tasks. When both levels of evaluation are considered, a composite display (configural display with all four techniques applied) was clearly the most effective. Overall, the findings obtained in the two experiments provide very limited evidence for the generalization of results between evaluations. The two levels of evaluation, the display manipulations, and the patterns of results are considered in terms of a cognitive systems engineering evaluation framework. General implications for the evaluation of displays and interfaces are discussed. Actual or potential applications include design techniques to improve graphical displays and methodological insights to focus and improve evaluation efforts.     

Evaluating the use of data transformation for information visualization

Data transformation, the process of preparing raw data for effective visualization, is one of the key challenges in information visualization. Although researchers have developed many data transformation techniques, there is little empirical study of the general impact of data transformation on visualization. Without such study, it is difficult to systematically decide when and which data transformation techniques are needed. We thus have designed and conducted a two-part empirical study that examines how the use of common data transformation techniques impacts visualization quality, which in turn affects user task performance. Our first experiment studies the impact of data transformation on user performance in single-step, typical visual analytic tasks. The second experiment assesses the impact of data transformation in multi-step analytic tasks. Our results quantify the benefits of data transformation in both experiments. More importantly, our analyses reveal that (1) the benefits of data transformation vary significantly by task and by visualization, and (2) the use of data transformation depends on a user’s interaction context. Based on our findings, we present a set of design recommendations that help guide the development and use of data transformation techniques.     

Learning Task-Specific Object Recognition and Scene Understanding

In this paper, we present an approach to object recognition and scene understanding which integrates low-level image processing and high-level knowledge-based components. A novel machine learning system is presented which is used to acquire knowledge relating to a specific task. Learned feedback from high-level to low-level processes is introduced as a means of achieving robust task-specific segmentation. The system has been implemented and trained on a number of scenarios with differing tasks from which results are presented and discussed.