Miracle-dz/UniFAR-FTUs huggingface dataset:
EASE: Energy‐efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

Continuously growing network traffic has become a major technical bottleneck of the cloud service to develop the Internet of Things (IoTs) and mobile applications. Edge computing as a promising computing pattern deployed close to service users is expected to improve the quality of service (QoS). To fully utilize the capabilities of edge devices, a Device‐to‐Device (D2D)–based computing resource sharing and aggregation framework is proposed. Under this framework, this paper exploits the Beta distributions to characterize the uncertain communication rate and processing capability of the edge environment. The reliability and energy consumption of local computing and shared computing under uncertain conditions are, respectively, studied. We model the task scheduling as an Integer Programming problem, whose objective is to minimize the energy consumption while ensuring the reliability. Based on that, a heuristic task scheduling algorithm named EASE is proposed. Through a lot of simulation experiments, the performance of EASE is effectively evaluated under the static and dynamic environments. Compared with three comparison algorithms, EASE shows many advantages in terms of reliability, adaptability, and energy saving.
Definition of a “Sport-Health” Semantic Space

Product success depends on its capacity to meet users’ expectations. Human Centred Design approach helps to reach this success by focussing on users’ needs in the design process. These needs are as well functional as hedonic. Designing products requires then to design hedonic properties affecting users’ perception. For sport products, people wants to improve their performances while maintaining their health. Sport products are then considered not only “sporty” but also “healthy”. Thus, integrating both health and sport expectations into the design process are necessary.Last decades, Affective Engineering was developed to integrate perception into the design process. Applying this approach for sport products may allow defining and mixing sport and health perceptual characteristics all along the design process. However, defining these characterisitics into requirements implies to translate them into semantic terms. If we observe semantic descriptors for sport products and for health products, they seem opposite. In this paper, we aim defining a semantic space representative and respectful of both domains, sport and health, while they oppose.
Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

In this article, a method of multi-connection load compensation and load information calculation for an upper-limb exoskeleton is proposed based on a six-axis force/torque sensor installed between the exoskeleton and the end effector. The proposed load compensation method uses a mounted sensor to measure the force and torque between the exoskeleton and load of different connections and adds a compensator to the controller to compensate the component caused by the load in the human–robot interaction force, so that the human–robot interaction force is only used to operate the exoskeleton. Therefore, the operator can manipulate the exoskeleton with the same interaction force to lift loads of different weights with a passive or fixed connection, and the human–robot interaction force is minimized. Moreover, the proposed load information calculation method can calculate the weight of the load and the position of its center of gravity relative to the exoskeleton and end effector accurately, which is necessary for acquiring the upper-limb exoskeleton center of gravity and stability control of whole-body exoskeleton. In order to verify the effectiveness of the proposed method, we performed load handling and operational stability experiments. The experimental results showed that the proposed method realized the expected function.
Improving Service Quality With the Fuzzy TOPSIS Method: A Case Study of the Beijing Rail Transit System

Rail transit (RT) has been favored by passengers because it effectively alleviates the problems of dense population, housing shortages, small natural areas, and serious air pollution in urban centers. In this paper, we propose a framework that combines statistical analysis, fuzzy theory, and the technique for order preference by similarity to an ideal solution (TOPSIS) to evaluate the service quality of RT. First, the passenger perception of service quality is modeled as trapezoidal fuzzy numbers from the fuzzy theory, which solves the uncertainty problem of passenger perception that how factors affect service quality. Next, a case study that evaluates the service quality of the Beijing metro system is proposed using the fuzzy TOPSIS method. During the evaluation process, 8011 surveys are collected from 16 metro lines operated by Beijing Metro Operating Company Ltd. The evaluation results show that transfers, in-vehicle experience, and ticket purchases or recharges are the three factors that passengers find most unsatisfactory about metro travel and that need to be greatly improved in the future construction and management of metros. Furthermore, we analyze the stableness of the fuzzy TOPSIS method by the ranking change of service quality for a line from different comparison sets of metro lines. Finally, we provide suggestions and guidance for the optimization of RT infrastructure and investment.
TRECVID 2018: Benchmarking Video Activity Detection, Video Captioning and Matching, Video Storytelling Linking and Video Search

The TREC Video Retrieval Evaluation (TRECVID) 2018 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of contentbased exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last eighteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID is funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addi-
Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

A cell-free Massive multiple-input multiple-output (MIMO) uplink is considered, where the access points (APs) are connected to a central processing unit (CPU) through limited-capacity wireless microwave links. The quantized version of the weighted signals are available at the CPU, by exploiting the Bussgang decomposition to model the effect of quantization. A closed-form expression for spectral efficiency is derived taking into account the effects of channel estimation error and quantization distortion. The energy efficiency maximization problem is considered with per-user power, backhaul capacity and throughput requirement constraints. To solve this non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design, and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas a successive convex approximation (SCA) and a heuristic sub-optimal scheme are exploited to convert the power allocation problem into a standard geometric programming (GP) problem. An iterative algorithm is proposed to alternately solve each sub-problem. Complexity analysis and convergence of the proposed schemes are investigated. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation.
Efficient training of neural networks with interval uncertainty

In this paper we attempt to build upon past work on Interval Neural Networks, and provide a robust way to train and quantify the uncertainty of Deep Neural Networks. Speciﬁcally, we propose a back propagation algorithm for Neural Networks with constant width predictions. In order to maintain numerical stability we propose minimising the maximum of the batch of errors at each step. Our approach can accommodate incertitude in the training data, and therefore adversarial examples from a commonly used attack model can be trivially accounted for. We present preliminary results on a test function example. The reliability of the predictions of these networks are guaranteed by the non-convex Scenario approach to chance constrained optimisation. A key result is that, by using minibatches of size M , the complexity of our approach scales as O ( MN iter ), and does not depend upon the number of training data points as with other Interval Predictor Model methods.
Estimating whole-body mechanical power in running by means of simulated inertial sensor signals

The purpose of this study was to identify the potential of inertial sensor information to estimate whole-body mechanical power (WBP) in running. We recorded three-dimensional (3D) whole-body kinematic and kinetic data of eleven male subjects by means of optoelectronic motion capturing and an instrumented treadmill at speeds between 2.0 and 3.5 m/s. We simulated 3D acceleration and gyroscope signals for 15 segments of the whole body from marker trajectory data. We calculated one statistical model for each subject to estimate WBP from a set of 279 predictor variables derived from simulated sensor signals. Overall, WBP was estimated with root mean square errors between 4% and 20%. This highlights the potential of inertial sensor signals to estimate WBP. Nonetheless, in its current form, the method requires too many sensors for practical applications.
Measuring usability and purchase intention for online travel booking: A case study

User experience is vital in usability evaluation to understand all aspects of user interactions with a product or system. The usability includes user satisfaction, efficiency, and effectiveness to achieve certain goals. On the other hand, to support the sustainability of the business, it is necessary to know the factors that significantly affect purchase intention through online travel booking application. The main aim of this paper is to investigate the usability of online travel booking system and consumer-level factors of purchase intention in online travel booking application. The research attempt to find whether product diagnosticity influences perceived quality and product trust and generally impacts purchase intention. In this study, we select an existing online travel booking application named Traveloka. The system usability scales (SUS) were used to evaluate the usability for web applications. A total of 1,225 respondents participate in online questionnaire using Google form. Most of the respondents recruited are based in Jakarta, Indonesia. The overall SUS score is only 56.13 from 100. The results show that intention for purchase is influenced by product trust and perceived quality. Moreover, the product diagnosticity (PD) supported perceived quality (PQ) and product trust (PT), and variable of product trust (PT) also supported perceived quality (PQ).
An efficient combination between Berlekamp-Massey and Hartmann Rudolph algorithms to decode BCH codes

In digital communication and storage systems, the exchange of data is achieved using a communication channel which is not completely reliable. Therefore, detection and correction of possible errors are required by adding redundant bits to information data. Several algebraic and heuristic decoders were designed to detect and correct errors. The Hartmann Rudolph (HR) algorithm enables to decode a sequence symbol by symbol. The HR algorithm has a high complexity, that's why we suggest using it partially with the algebraic hard decision decoder Berlekamp-Massey (BM). In this work, we propose a concatenation of Partial Hartmann Rudolph (PHR) algorithm and Berlekamp-Massey decoder to decode BCH (Bose-Chaudhuri-Hocquenghem) codes. Very satisfying results are obtained. For example, we have used only 0.54% of the dual space size for the BCH code (63,39,9) while maintaining very good decoding quality. To judge our results, we compare them with other decoders.
The optimal upgrade frequency and lifecycle under maintenance for a single-product rollover

Abstract As technology improves and demand changes, products gradually become out of date over time, so firms can introduce new versions to make profits. Meanwhile some products come with an after-sale maintenance service which is included in the product purchase. Firms can affect the customer purchase and replacement decisions by adjusting their upgrade strategy and maintenance time simultaneously, so it is important for firms to coordinate the upgrade strategy and maintenance time. In this article, we study the interaction between upgrade frequency and maintenance time, and find that when the gap between customers’ willingness to pay is small, firms should provide a short maintenance time and reduce the upgrade frequency. When the gap between customers’ willingness to pay is large, firms should provide a long maintenance time and increase the upgrade frequency. In this case, firms implement a “price discrimination” policy by influencing customers’ replacement frequency to earn higher profits. Usually, two hidden costs exist in the product upgrade process: the fragmentation costs and customer switching costs. The existence of fragmentation costs and switching costs is equivalent to the reduction of product quality and the increase of development cost respectively. Ignoring these two costs can lead firms to provide a longer maintenance time than optimal.
Building explicit hybridization networks using the maximum likelihood and Neighbor-Joining approaches

Tree topologies are the simplest structures which can be used to represent the evolution of species. Over the two last decades more complex structures, called phylogenetic networks, have been introduced to take into account the mechanisms of reticulate evolution, such as species hybridization and horizontal gene transfer among bacteria and viruses. Several algorithms and software have been developed in this context, but most of them yield as output only an implicit network, which can be difficult to interpret. In this paper, we introduce a new algorithm for inferring explicit hybridization networks from binary data. In order to build our explicit hybridization networks, we use a maximum likelihood approach applied to Neighbor-Joining tree configurations.
Channel Magnitude-Based MIMO With Energy Detection for Internet of Things Applications

In this article, three low complexity multiple-input multiple-output (MIMO) receivers using energy detection and multilevel amplitude-shift keying (ASK) for Internet of Things (IoT) applications are described. To circumvent the inherent nonlinearity of the noncoherent MIMO system, an equivalent linear system model for the channel is introduced. This allows the proposed receivers to be configured analogously to coherent-based approaches. The tradeoff is that increased numbers of receive antennas need to be used but the overall complexity is decreased compared to existing noncoherent approaches. The receivers can also easily be integrated with energy harvesting systems. The three receiver structures considered are zero forcing (ZF), ZF with ordered successive interference cancellation (ZF-OSIC), and weighted envelope detection (WENVD), and their performance in flat Rayleigh fading channels with multilevel ASK is investigated. For the WENVD receiver, the error performance under the condition of high signal-to-noise ratio is also analyzed, and closed form approximations of the conditional (conditioned on the channel matrix) symbol vector error probability (SEP) and the average of the conditional SEP are derived. It is found that the ZF receiver has the lowest complexity and the WENVD receiver the highest; however, error performance comparison through numerical results reveals that the WENVD receiver has the best performance and the ZF-OSIC receiver has the next best. When combined with multiport energy harvesting technology the receivers have significant potential for use in IoT.
Experimental Investigation on the Thermal Performance of a Sintered Flexible Woven Heat Sink

A novel flexible woven heat sink (FWHS) composed of sintered copper braided belts was proposed here to meet the heat dissipation requirements of the flexible radiator installed in a narrow space. The sintering treatment was employed to ameliorate the tensile and bending properties as well as the thermal conductivity of the FWHS. The thermal resistance network model and the temperature field analysis in cases of natural convection and forced convection were presented to evaluate the thermal performance of the FWHS. The results show that the FWHS sintered at 850 °C with the expanded shape at a compressed height of 55 mm can reduce the temperature of heat source by 17.1% compared to the uncompressed FWHS, which demonstrates its outstanding advantage of maintaining high efficiency of heat dissipation under compressive deformation.
Automatic home video editing on music

A lot of videos are recorded during events (such as symposia), trips or other special occasions (like a wedding). Watching all the raw content is boring and takes a lot of time. Edited videos can summarize the experience in a fun and interesting way, but movie-editing still requires a lot of manual work and expertise. To solve this issue, we propose a method to automatically select important parts of photos and videos, analyze music for beats and use this to generate a short movie with transitions in rhythm. Automatic selection of important parts is based on motion detection, infidelity and simplicity. Combining the chosen song with the photos and videos is a constrained optimization problem and two solutions are given.
The Evolution of Teachers' Instructional Beliefs and Practices in High-Access-to-Technology Classrooms.

Beginning in 1985. Apple Computer, Inc., and several school districts began a collaboration to examine the impact of computer saturation cn instruction and learning in K-12 classrooms. The initial guiding cuestion was simply put: What happens when teachers and stnts have constant access to technology? To provide "constant access," each teacher and student in the project received two computers, one for the home and one for the classroom. This paper describes the program, Apple Classrooms of Tomorrow (ACOT), and reports on the instructional evolution that occurred in those classrooms. The personal struggles of teachers who came to confront the nature of learning and consequently, the efficacy of their own instructional practices, are examined in detail through individual journal entries. This paper also places the innovative ACOT program in a broader perspective on educational change and draws implications for the support and development of teachers engaged in significant reform projects. (67 references) (DB) ************************************X********-*********. *************** Reproductions supplied by EDRS are the best that can be made from the original document. ************************************************************ "**********
Los nuevos informes ampliados de auditoría. Caso: las empresas cotizadas españolas

La mayor complejidad del entorno regulatorio y de negocio ha llevado a los grupos de interés a exigir mayor confianza sobre la veracidad de la información financiera al auditor. En este contexto, el Consejo Internacional de Normas de Auditoría y Aseguramiento (IAASB) aprobó una nueva norma internacional que modificaba la estructura y los contenidos de los informes de auditoría. Uno de los principales cambios ha sido la obligación del auditor de describir en su informe las cuestiones clave de auditoría (KAM), relacionadas con riesgos significativos de la información financiera. El objetivo de este estudio es analizar el contenido del informe en el primer año de aplicación en España para 131 empresas cotizadas. Estas firmas reportan entre dos y cuatro KAM, destacando el reconocimiento de ingresos, el deterioro del fondo de comercio y la recuperación de impuestos diferidos. Este estudio tiene importantes implicaciones en otros países que van a afrontar esta reforma.
Task Selective and Comfort-Aware User Recruitment with Incentives in Mobile Crowd-Sensing

With the significant improvement in IoT technology and smart devices, data collection and distributed computation have led a foundation for Mobile crowd-sensing (MCS). MCS utilizes the capabilities of embedded sensors in smart devices for gathering data. MCS benefits both data provider (participant/user), and data requester, i.e. data providers via incentives/rewards, data requesters by delivering required data. Apart from the benefits gained through acquiring data, confronting challenges such as participant privacy, data trustworthiness, malicious attacks (from illegitimate users) need to be addressed to build robust and reliable data solicitation. In addition to that, it is necessary to consider user motivation and user preference, comfort during its engagement in crowd-sensing. User preferences/constraints can be due to privacy concerns in terms of location, the sensitivity of data or energy usage and many more. With this in mind, the main contributions of the thesis can be listed as follows. 1) We design user selective trustworthy data acquisition frameworks. We introduce a variety of user selection criteria to form participant communities based on participants reliability and income. To evaluate the trustworthiness of our selective reputation-based data acquisition, we consider malicious users in the environment and calculate the total rewards given to malicious users. Simulations results show that community formation based on the acquired income of participants ended up with a substantial loss to the cloud platform as well as participants. Contrary to that, reputation-based community formation has shown nearly equal platform utility (profit), negligible loss of user utility compared to benchmark Non-selective data acquisition with 7% malicious probability. 2) Moreover, we attempt to enable users to modify (allow/deny access to) their builtin sensor set according to their comfort levels. We formulate three comfort levels high (only allow access to sensors that would not directly reveal personal identity such as accelerometer, light sensor, etc.), moderate (obstruct access to sensitive data, e.g. camera), zero comfort (allow access to all users). We introduce Static modification, where users pre-arrange their sensor set before the start of data collection. Our feasibility study shows that pre-arrangement of the sensor set favours user comfort, user utility at the cost of loss in platform utility and performs better than selective reputation-based recruitment for the considered settings. 3) We apply Adaptive sensor modification on top of pre-arrangement of sensor set through which participants are authorized to re-arrange their sensor availability based on reliability scores. Simulation results show that the Adaptive comfort-aware approach performed better than static in terms of platform utility and achieved comparatively better user comfort with reasonable loss in user utility.