nils-herrmann/clean_scirepeval huggingface dataset:
Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction;  For the developed BPNN model of the early period after June drop, correlation coefficients (R2) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4)  Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.
Shakeout: A New Approach to Regularized Deep Neural Network Training

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines <inline-formula><tex-math notation="LaTeX">$L_{0}$</tex-math><alternatives> <inline-graphic xlink:href="kang-ieq1-2701831.gif"/></alternatives></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$L_{1}$</tex-math><alternatives><inline-graphic xlink:href="kang-ieq2-2701831.gif"/> </alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$L_{2}$</tex-math><alternatives> <inline-graphic xlink:href="kang-ieq3-2701831.gif"/></alternatives></inline-formula> regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.
An Ultra-Low Power Fully Integrated Energy Harvester Based on Self-Oscillating Switched-Capacitor Voltage Doubler

This paper presents a fully integrated energy harvester that maintains >35% end-to-end efficiency when harvesting from a 0.84 mm 2 solar cell in low light condition of 260 lux, converting 7 nW input power from 250 mV to 4 V. Newly proposed self-oscillating switched-capacitor (SC) DC-DC voltage doublers are cascaded to form a complete harvester, with configurable overall conversion ratio from 9× to 23×. In each voltage doubler, the oscillator is completely internalized within the SC network, eliminating clock generation and level shifting power overheads. A single doubler has >70% measured efficiency across 1 nA to 0.35 mA output current ( >10 5 range) with low idle power consumption of 170 pW. In the harvester, each doubler has independent frequency modulation to maintain its optimum conversion efficiency, enabling optimization of harvester overall conversion efficiency. A leakage-based delay element provides energy-efficient frequency control over a wide range, enabling low idle power consumption and a wide load range with optimum conversion efficiency. The harvester delivers 5 nW-5 μW output power with >40% efficiency and has an idle power consumption 3 nW, in test chip fabricated in 0.18 μm CMOS technology.
The roots of empathy: the shared manifold hypothesis and the neural basis of intersubjectivity.

Starting from a neurobiological standpoint, I will propose that our capacity to understand others as intentional agents, far from being exclusively dependent upon mentalistic/linguistic abilities, be deeply grounded in the relational nature of our interactions with the world. According to this hypothesis, an implicit, prereflexive form of understanding of other individuals is based on the strong sense of identity binding us to them. We share with our conspecifics a multiplicity of states that include actions, sensations and emotions. A new conceptual tool able to capture the richness of the experiences we share with others will be introduced: the shared manifold of intersubjectivity. I will posit that it is through this shared manifold that it is possible for us to recognize other human beings as similar to us. It is just because of this shared manifold that intersubjective communication and ascription of intentionality become possible. It will be argued that the same neural structures that are involved in processing and controlling executed actions, felt sensations and emotions are also active when the same actions, sensations and emotions are to be detected in others. It therefore appears that a whole range of different "mirror matching mechanisms" may be present in our brain. This matching mechanism, constituted by mirror neurons originally discovered and described in the domain of action, could well be a basic organizational feature of our brain, enabling our rich and diversified intersubjective experiences. This perspective is in a position to offer a global approach to the understanding of the vulnerability to major psychoses such as schizophrenia.
Simultaneous Feature Aggregating and Hashing for Large-Scale Image Search

In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization produces aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, we also propose a fast version of the recently-proposed Binary Autoencoder to be used in our proposed framework. We perform extensive retrieval experiments on several benchmark datasets with both SIFT and convolutional features. Our results suggest that the proposed framework achieves significant improvements over the state of the art.
Challenges and solutions in distributed software development project management: A systematic literature review

This paper presents a systematic literature review of the challenges, best practices, models, and tools in Distributed Software Development (DSD) Project Management. The objective is to collect and systematize reported knowledge in terms of what are the difficulties in managing DSD projects, what are the best practices to overcome these difficulties, and how existing models and tools support these practices. We found 54 works related to DSD project management, published between 1998 and 2009. Using the data systematically extracted from these works, we propose an evidence-based DSD project management improvement model. Our contention is that this model can support practitioners and researchers to better understand the landscape of DSD project challenges and devise more effective solutions to improve project management in a distributed setting.
Humanoid robot HRP-2

A development of humanoid robot HRP-2 is presented in this paper. HRP-2 is a humanoid robotics platform, which we developed in phase two of HRP. HRP was a humanoid robotics project, which had run by the Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with uneven surface, can walk at two third level of human speed, and can walk on a narrow path. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot's own self if HRP-2 tips over safely. In this paper, the appearance design, the mechanisms, the electrical systems, specifications, and features upgraded from its prototype are also introduced.
The collaborative filtering recommendation based on SOM cluster-indexing CBR

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model, which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference. q 2003
Cost-Efficient Data Redundancy in the Cloud

Nowadays, the usage of cloud storages to store data is a popular alternative to traditional local storage systems. However, besides the benefits such services can offer, there are also some downsides like vendor lock-in or unavailability. Furthermore, the large number of available providers and their different pricing models can turn the search for the best fitting provider into a tedious and cumbersome task. Furthermore, the optimal selection of a provider may change over time.In this paper, we formalize a system model that uses several cloud storages to offer a redundant storage for data. The according optimization problem considers historic data access patterns and predefined Quality of Service requirements for the selection of the best-fitting storages. Through extensive evaluations we show the benefits of our work and compare the novel approach against a baseline which follows a state-of-the-art approach.
A 60-GHz Millimeter-Wave CPW-Fed Yagi Antenna Fabricated by Using 0.18- $\mu\hbox{m}$ CMOS Technology

This letter presents a 60-GHz millimeter-wave on-chip Yagi antenna fabricated with a 0.18-mum CMOS process. A feeding network is designed in a coplanar waveguide (CPW) technology. The 0.18-mum six-metal-layer CMOS process allows the on-chip antenna to utilize a simple CPW-to-coplanar-stripline feed transition and the first metal layer to implement a reflector strip. The CMOS antenna chip size is 1.1 x 0.95 mm2 . A FEM-based 3D full-wave electromagnetic solver, the HFSS, is used for design simulation. The measured antenna input VSWR is less than two from 55 to 65 GHz. The front-to-back ratio of the Yagi antenna is about 9 dB. The measured maximum antenna power gain is about -10 dBi. The simulated antenna radiation efficiency is about 10%.
Bidirectional recurrent neural network language models for automatic speech recognition

Recurrent neural network language models have enjoyed great success in speech recognition, partially due to their ability to model longer-distance context than word n-gram models. In recurrent neural networks (RNNs), contextual information from past inputs is modeled with the help of recurrent connections at the hidden layer, while Long Short-Term Memory (LSTM) neural networks are RNNs that contain units that can store values for arbitrary amounts of time. While conventional unidirectional networks predict outputs from only past inputs, one can build bidirectional networks that also condition on future inputs. In this paper, we propose applying bidirectional RNNs and LSTM neural networks to language modeling for speech recognition. We discuss issues that arise when utilizing bidirectional models for speech, and compare unidirectional and bidirectional models on an English Broadcast News transcription task. We find that bidirectional RNNs significantly outperform unidirectional RNNs, but bidirectional LSTMs do not provide any further gain over their unidirectional counterparts.
Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems

The collection and combination of assessment data in trustworthiness evaluation of cloud service is challenging, notably because QoS value may be missing in offline evaluation situation due to the time-consuming and costly cloud service invocation. Considering the fact that many trustworthiness evaluation problems require not only objective measurement but also subjective perception, this paper designs a novel framework named CSTrust for conducting cloud service trustworthiness evaluation by combining QoS prediction and customer satisfaction estimation. The proposed framework considers how to improve the accuracy of QoS value prediction on quantitative trustworthy attributes, as well as how to estimate the customer satisfaction of target cloud service by taking advantages of the perception ratings on qualitative attributes. The proposed methods are validated through simulations, demonstrating that CSTrust can effectively predict assessment data and release evaluation results of trustworthiness.
Mixed methods research synthesis : definition , framework , and potential

Literature on the combination of qualitative and quantitative research components at the primary empirical study level has recently accumulated exponentially. However, this combination is only rarely discussed and applied at the research synthesis level. The purpose of this paper is to explore the possible contribution of mixed methods research to the integration of qualitative and quantitative research at the synthesis level. In order to contribute to the methodology and utilization of mixed methods at the synthesis level, we present a framework to perform mixed methods research syntheses (MMRS). The presented classification framework can help to inform researchers intending to carry out MMRS, and to provide ideas for conceptualizing and developing those syntheses. We illustrate the use of this framework by applying it to the planning of MMRS on effectiveness studies concerning interventions for challenging behavior in persons with intellectual disabilities, presenting two hypothetical examples. Finally, we discuss possible strengths of MMRS and note some remaining challenges concerning the implementation of these syntheses.
Strategic Information Systems Alignment: Alignment of IS/IT with Business Strategy

Using information systems effectively requires an understanding of the organisation, management, and the technology shaping the systems. All information systems can be described as organisational and management solutions to challenges posed by the environment. The advances in information systems have affect on our day-to day lives . As the technology is evolving immensely so are the opportunities in a healthy way to prepare the organisation in the competitive advantage environment In order to manage the IS/IT based systems, it is important to have an appropriate strategy that defines the systems and provide means to manage them. Strategic Information Systems Alignment (SISA) is an effective way of developing and maintaining the IS/IT systems that support the business operations. Alignment of the IS/IT plans and the business plans is essential for improved business performance, this research looks at the key features of SISA in the changing business circumstances in Saudi Arabia. Information Systems, Information Systems, Business Planning, Planning Strategy, IT/IS Alignment.
Energy Delay Trade-Off in Cloud Offloading for Mutli-Core Mobile Devices

Cloud offloading is considered a promising approach to energy conservation and storage/computation enhancement for resource limited mobile devices. In this paper, we present a Lyapunov optimization based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental trade-off between the two optimization goals.
The State of the Art in Multiple Object Tracking Under Occlusion in Video Sequences

In this paper, we present a review of existing techniques and systems for tracking multiple occluding objects using one or more cameras. Following a formulation of the occlusion problem, we divide these techniques into two groups: mergesplit (MS) approaches and straight-through (ST) approaches. Then, we consider tracking in ball game applications, with emphasis on soccer. Based on this assessment of the state of the art, we identify what appear to be the most promising approaches for tracking in general and for soccer in
Multichannel Shopper Segments and Their Covariates

The proliferation of channels has created new challenges for research, including understanding how consumers may be segmented with respect o their information search and purchase behavior in multichannel environment. This research considers shopping a dynamic process that consists f search and purchase phases, in which the total utility of shopping process is determined by the perceived consumer utility toward channel use, hich is mainly driven by consumer characteristics. The authors (1) segment consumers on the basis of their attitudes toward multiple channels as earch and purchase alternatives; (2) investigate the association among psychological, economic, and sociodemographic covariates and segment embership; and (3) explore how multichannel behavior might differ across different product categories. Using survey data from 364 Dutch onsumers and Latent-Class Analyse, they identify three segments – multichannel enthusiasts, uninvolved shoppers, and store-focused consumers and covariates, such as shopping enjoyment, loyalty, and innovativeness that predict segment membership. The category-specific analysis suggests hat overall segment descriptions apply generally to a variety of categories, though some differences exist, including the impact of covariates, across ategories. The authors discuss implications for further research and practice.
Examining a multidimensional model of student motivation and engagement using a construct validation approach.

This study seeks to examine a multidimensional model of student motivation and engagement using within- and between-network construct validation approaches.



The study tests the first- and higher-order factor structure of the motivation and engagement wheel and its corresponding measurement tool, the Motivation and Engagement Scale - High School (MES-HS; formerly the Student Motivation and Engagement Scale).


SAMPLE
The study draws upon data from 12,237 high school students from 38 Australian high schools.



The hypothesized 11-factor first-order structure and the four-factor higher-order structure, their relationship with a set of between-network measures (class participation, enjoyment of school, educational aspirations), factor invariance across gender and year-level, and the effects of age and gender are examined using confirmatory factor analysis and structural equation modelling.



In terms of within-network validity, (1) the data confirm that the 11-factor and higher-order factor models of motivation and engagement are good fitting and (2) multigroup tests showed invariance across gender and year levels. In terms of between-network validity, (3) correlations with enjoyment of school, class participation and educational aspirations are in the hypothesized directions, and (4) girls reflect a more adaptive pattern of motivation and engagement, and year-level findings broadly confirm hypotheses that middle high school students seem to reflect a less adaptive pattern of motivation and engagement.



The first- and higher-order structures hold direct implications for educational practice and directions for future motivation and engagement research.
Fundamental limitations of phase noise on FMCW radar precision

This work presents an approach for estimating the effect of the fractional-N phase locked loop (Frac-N PLL) phase noise profile on frequency modulated continuous wave (FMCW) radar precision. Unlike previous approaches, the proposed modelling method takes the actual shape of the phase noise profile into account leading to insights on the main regions dominating the precision. Estimates from the proposed model are in very good agreement with statistical simulations and measurement results from an FMCW radar test chip fabricated on an IBM7WL BiCMOS 0.18 μm technology. At 5.8 GHz center frequency, a close-in phase noise of −75 dBc/Hz at 1 kHz offset is measured. A root mean squared (RMS) chirp nonlinearity error of 14.6 kHz and a ranging precision of 0.52 cm are achieved which competes with state-of-the-art FMCW secondary radars.
Unconstrained Face Alignment Without Face Detection

This paper introduces our submission to the 2nd Facial Landmark Localisation Competition. We present a deep architecture to directly detect facial landmarks without using face detection as an initialization. The architecture consists of two stages, a Basic Landmark Prediction Stage and a Whole Landmark Regression Stage. At the former stage, given an input image, the basic landmarks of all faces are detected by a sub-network of landmark heatmap and affinity field prediction. At the latter stage, the coarse canonical face and the pose can be generated by a Pose Splitting Layer based on the visible basic landmarks. According to its pose, each canonical state is distributed to the corresponding branch of the shape regression sub-networks for the whole landmark detection. Experimental results show that our method obtains promising results on the 300-W dataset, and achieves superior performances over the baselines of the semi-frontal and the profile categories in this competition.