Reviewerly/exHarmony huggingface dataset:
The impact of requirements on systems development speed: a multiple-case study in automotive

Automotive manufacturers have historically adopted rigid requirements engineering processes. This allowed them to meet safety-critical requirements when producing a highly complex and differentiated product out of the integration of thousands of physical and software components. Nowadays, few software-related domains are as rapidly changing as the automotive industry. In particular, the needs of improving development speed are increasingly pushing companies in this domain toward new ways of developing software. In this paper, we investigate how the goal to increase development speed impacts how requirements are managed in the automotive domain. We start from a manager perspective, which we then complement with a more general perspective. We used a qualitative multiple-case study, organized in two steps. In the first step, we had 20 semi-structured interviews, at two automotive manufacturers. Our sampling strategy focuses on manager roles, complemented with technical specialists. In the second step, we validated our results with 12 more interviews, covering nine additional respondents and three recurring from the first step. In addition to validating our qualitative model, the second step of interviews broadens our perspective with technical experts and change managers. Our respondents indicate and rank six aspects of the current requirements engineering approach that impact development speed. These aspects include the negative impact of a requirements style dominated by safety concerns as well as decomposition of requirements over many levels of abstraction. Furthermore, the use of requirements as part of legal contracts with suppliers is seen as hindering fast collaboration. Six additional suggestions for potential improvements include domain-specific tooling, model-based requirements, test automation, and a combination of lightweight upfront requirements engineering preceding development with precise specifications post-development. Out of these 12 aspects, seven can likely be addressed as part of an ongoing agile transformation. We offer an empirical account of expectations and needs for new requirements engineering approaches in the automotive domain, necessary to coordinate hundreds of collaborating organizations developing software-intensive and potentially safety-critical systems.
An improved wavelet-based signal-denoising architecture with less hardware consumption

In this paper, we propose the VLSI implementation of a wavelet packet transform-based architecture. Moreover, the proposed architecture is compared to the wavelet transform architecture in terms of their signal denoising capabilities, hardware resources used, maximum operating frequency and maximum combination path delay. The Wavelet packet transform architecture is designed and implemented for db2 and db3 wavelet types. For the proposed real-time VLSI implementation, the Xilinx Spartan-6 series FPGA platform is used. Simulation results show that the proposed architecture implementation results in an improved performance for ECG signals taken from the Fantasia Database as well as from the self-recorded database, as compared to the WT wavelet architectures which result in distorted outputs. Moreover, from the synthesis results, it is concluded that the proposed architecture implementation requires significantly less hardware as compared to the WT architectures considered. In terms of LUTs, the proposed architecture consumes 38.20 percent and 84.96 percent less resources than the WT-db2 and WT-db5 architectures, respectively. Similarly, in terms of slice registers, the proposed architecture consumes, respectively, 21.39 percent and 75.61 percent less resources than the WT-db2 and WT-db5 architectures. Furthermore, the maximum operating frequency of the proposed architecture is 180.278 MHz as compared to that of 195.496 MHz and 121.038 MHz for the WT-db2 and WT-db5 architectures respectively. The maximum combinational path delay of the proposed architecture is 48.109 ns, as compared to that of 13.784 ns and 15.202 ns for the WT-db2 and WT-db5 architectures, respectively.
R-RNN: Extracting User Recent Behavior Sequence for Click-Through Rate Prediction

Click-through rate (CTR) prediction is of great importance in such web applications as recommender systems, web search, and online advertising.Exploring the interest of a user through feature interactions behind massive behaviors of users is essential for CTR prediction.Recently, deep learning-based models have been proposed, which follow a similar embedding and multi-layer perception (MLP) paradigm and achieve great success in both business and research fields.However, existing studies ignore the sequential characteristics of click-through behaviors of users.To overcome this limitation, we propose a novel model named recent recurrent neural network (R-RNN) to adaptively learn the representation of the interest of a user from his or her overall historical click-through behaviors.R-RNN puts an emphasis on recent click-through behaviors via a novel neural network architecture.Compared to the state-of-the-art methods including the deep interest network (DIN) model recently proposed by Alibaba, R-RNN not only applies the attention mechanism to help capture the representation of the main interest of a user but also incorporates a long short-term memory (LSTM) unit for exploring the trend of the change of the interest of a user behind his or her recent click-through behaviors.Comprehensive experiments are conducted to demonstrate the effectiveness of R-RNN for CTR prediction on a benchmark data set.The results show that R-RNN significantly outperforms existing deep learning models.The results also demonstrate that the length of the recent click-through behavior sequence has an important effect on the prediction performance of the model.
Robust fusion steady‐state filtering for multisensor networked systems with one‐step random delay, missing measurements, and uncertain‐variance multiplicative and additive white noises

Summary The robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, one‐step random delay, missing measurements, and uncertain noise variances, the phenomena of one‐step random delay and missing measurements occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, derandomization approach, and fictitious noise approach, the original multisensor system under study is converted into a multimodel multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. In addition, by using the proposed model transformation approach, the centralized fusion system is obtained, furthermore the robust centralized fusion steady‐state Kalman estimators are proposed. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of nonnegative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.
NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator

Biogeography-based optimization (BBO) is a novel evolutionary algorithm, which is proposed with inspiration from the science of biogeography, to solve global optimization problems. To overcome the inability of BBO to make a good balance between its exploration and exploitation abilities, this paper introduces a new variant of BBO, which is called NBBO. The framework of NBBO considers two or more sub-iterations in an iteration of the algorithm to perform the evolution process. In each sub-iteration, a sample (sub-population) is selected from the input population of the iteration, based on a triangular probability distribution, to choose emigrating habitats (solutions) from. On the other hand, a novel two-phase migration operator is used in the framework of NBBO to make the algorithm effectively explore a search space. By making a good balance between its exploration and exploitation abilities, which is conducted by its new framework, NBBO can escape from local optima. Quantitative evaluations, based on extensive experiments on a set of 23 benchmark functions with diverse complexities, reveal that NBBO achieves favorable results which are quite superior to the results of other relevant state-of-the-art swarm intelligence-based and evolutionary algorithms.
Image captioning via semantic element embedding

Image caption approaches that use the global Convolutional Neural Network (CNN) features are not able to represent and describe all the important elements in complex scenes. In this paper, we propose to enrich the semantic representations of images and update the language model by proposing semantic element embedding. For the semantic element discovery, an object detection module is used to predict regions of the image, and a captioning model, Long Short-Term Memory (LSTM), is employed to generate local descriptions for these regions. The predicted descriptions and categories are used to generate the semantic feature, which not only contains detailed information but also shares a word space with descriptions, and thus bridges the modality gap between visual images and semantic captions. We further integrate the CNN feature with the semantic feature into the proposed Element Embedding LSTM (EE-LSTM) model to predict a language description. Experiments on MS COCO datasets demonstrate that the proposed approach outperforms conventional caption methods and is flexible to combine with baseline models to achieve superior performance.
Review on Natural Language Processing Trends and Techniques Using NLTK

In modern age of information explosion, every day millions of gigabytes of data are generated in the form of documents, web pages, e-mail, social media text, blogs etc., so importance of effective and efficient Natural Language Processing techniques become crucial for an information retrieval system, text summarization, sentiment analysis, information extraction, named entity recognition, relationship extraction, social media monitoring, text mining, language translation program, and question answering system. Natural Language Processing is a computational technique applies different levels of linguistic analysis for representing natural language into a useful representation for further processing. NLP is recognized as a challenging task in computer science and artificial intelligence because understanding human natural language is not only depends on the words but how those words are linked together to form precise meaning is also considered. Regardless of language being one of the easiest concepts for human to learn, but for training computers to understand natural language is a difficult task due to the ambiguity of language syntax and semantics. Natural Language processing techniques involves processing documents or text which reduces storage space and also reduces the size of index and understanding the given information which satisfies user’s need. NLP techniques improve the performance of the information retrieval efficiency and effective documentation processes. Common dialect handling procedures incorporates tokenization, stop word expulsion, stemming, lemmatization, parts of discourse labeling, lumping and named substance recognizer which enhances execution of NLP applications. The Natural Language Toolkit is the best possible solution for learning the ropes of NLP domain. NLTK, a collection of application packages which encourage researchers and learners in natural language processing, computational linguistics and artificial intelligence.
Learning Generative Socially Aware Models of Pedestrian Motion

Among the biggest challenges faced when integrating robots and intelligent vehicles in crowded environments is the difficulty encountered by artificial agents in anticipating people's moves, which is indeed a critical machine reasoning task in order to enable safe and comfortable interaction with humans. In general terms, this is a multi-agent prediction problem, whose solution, from a Bayesian perspective, involves on one hand inferring the joint latent dynamical state of all agents conditioned on observed data and on the other hand learning the corresponding system dynamics. Among the advantages of such a probabilistic approach is the fact that it naturally allows to account for the intrinsic stochasticity of both human behavior and sensor data in a principled manner. In this letter, we propose a novel Bayesian generative model, which generalizes a popular class of deterministic multiagent motion models and is capable of encoding traffic agents' interactions in a latent probabilistic space. We demonstrate, using real pedestrian data-sets, how it can be exploited for inference, prediction, and trained for learning of agent interaction dynamics. Our experimental evaluations indicate that the proposed approach outperforms both probabilistic latent variable models that neglect multi-agent interactions as well as deterministic physics-inspired models of human motion in crowds.
Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors' experience, a framework proposal for creating valuable and aggregated knowledge is depicted.
V‐Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models

Abstract The usage of deep learning models for tagging input data has increased over the past years because of their accuracy and high‐performance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still mis classifications that prevent doctors from properly diagnosing sleep‐related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real‐life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real‐life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case.
Optimization of energy consumption of green data center in e-commerce

With the development of data center around the world, its energy consumption becomes an increasingly urgent issue, and green data center (GDC) emerges. Most modern data centers are operated in terms of peak load, with large scales. However, many servers and network devices are idle or light-loaded for a long time, which causes energy waste. Therefore, it is necessary to carry out energy saving in GDC. The work studied the optimization of energy consumption of switches, proposing an energy-saving strategy based on artificial bee colony (ABC) for Fat Tree. The simulation results showed the proposed algorithm has obvious effects on energy saving and completion time in large and small traffic, which embodies the superiority and effectiveness of the algorithm.
Multi-Task Cost-Sensitive-Convolutional Neural Network for Car Detection

This paper proposes a novel smart parking scheme for the parking lot. Automatic car detection is the core technology of the proposed scheme. However, new challenges arise in car detection in aerial views, such as a large number of tiny objects and complex backgrounds. In order to solve these issues, this paper proposes a car detection method based on multi-task cost-sensitive-convolutional neural network (MTCS-CNN). In the proposed network framework, multi-task partition layer which is composed of some sub-task selection units is first developed. The sub-task selection unit is constructed by introducing local mask and non-zero pooling, which can divide the complex detection task into many simple sub-tasks. To tackle the obtained sub-tasks, cost-sensitive sub-network is proposed based on faster R-CNN framework with the introduction of cost-sensitive loss. In the proposed Multi-task partition layer, the sub-task selection unit is used to capture the local map of the original aerial view image. In each local map, the scale and the number of objects are enlarged and decreased, respectively. Therefore, multi-task partition layer can divide a complex tiny objects detection task into many simple enlarged objects detection sub-tasks, which is helpful for performance improvement. In addition, the proposed cost-sensitive loss can effectively discount the effect of easy examples and focus attention on the hard examples, which may improve the detection performance on hard examples. Therefore, the model with incorporation of proposed cost-sensitive loss is robust to the complex background, further improving the performance. On our dataset, the proposed method obtained an mAP of 85.3%, outperformed state-of-the-art method.
InteractionFusion

Hand-object interaction is challenging to reconstruct but important for many applications like HCI, robotics and so on. Previous works focus on either the hand or the object while we jointly track the hand poses, fuse the 3D object model and reconstruct its rigid and nonrigid motions, and perform all these tasks in real time. To achieve this, we first use a DNN to segment the hand and object in the two input depth streams and predict the current hand pose based on the previous poses by a pre-trained LSTM network. With this information, a unified optimization framework is proposed to jointly track the hand poses and object motions. The optimization integrates the segmented depth maps, the predicted motion, a spatial-temporal varying rigidity regularizer and a real-time contact constraint. A nonrigid fusion technique is further involved to reconstruct the object model. Experiments demonstrate that our method can solve the ambiguity caused by heavy occlusions between hand and object, and generate accurate results for various objects and interacting motions.
Playing Atari with few neurons

We propose a new method for learning compact state representations and policies separately but simultaneously for policy approximation in vision-based applications such as Atari games. Approaches based on deep reinforcement learning typically map pixels directly to actions to enable end-to-end training. Internally, however, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it, two objectives which can be addressed independently. Separating the image processing from the action selection allows for a better understanding of either task individually, as well as potentially finding smaller policy representations which is inherently interesting. Our approach learns state representations using a compact encoder based on two novel algorithms: (i) Increasing Dictionary Vector Quantization builds a dictionary of state representations which grows in size over time, allowing our method to address new observations as they appear in an open-ended online-learning context; and (ii) Direct Residuals Sparse Coding encodes observations in function of the dictionary, aiming for highest information inclusion by disregarding reconstruction error and maximizing code sparsity. As the dictionary size increases, however, the encoder produces increasingly larger inputs for the neural network; this issue is addressed with a new variant of the Exponential Natural Evolution Strategies algorithm which adapts the dimensionality of its probability distribution along the run. We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on each game's controls). These are still capable of achieving results that are not much worse, and occasionally superior, to the state-of-the-art in direct policy search which uses two orders of magnitude more neurons.
A data-driven approximate dynamic programming approach based on association rule learning: Spacecraft autonomy as a case study

Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for formulating, modeling, and solving decision making problems under uncertainty. In real-world applications, the applicability of DP is limited by severe scalability issues. These issues can be addressed by Approximate Dynamic Programming (ADP) techniques. ADP methods are based on the assumption of having either a proper estimation of the underlying state transition probability distributions or a simulation mechanism with the capability of generating samples according to such probability distributions. In this paper, we present a data-driven ADP-based approach, which can offer an alternative in case such assumption cannot be guaranteed. In particular, when varying the set-up of the MDP state transition probability matrix, different policies can be calculated through exact DP or ADP methods. Such policies are then processed by an Apriori-based algorithm to find frequent association rules within them. A pruning procedure is used to select the most suitable association rules, and finally an Association Classifier infers the optimal policy in all the possible circumstances. We show a detailed application of the proposed approach for the calculation of a proper mission operations plan for spacecrafts with a high level of on-board autonomy.