Reviewerly/exHarmony huggingface dataset:
A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements.In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed.However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed.The result is that, in many practical situations, analysts end up resorting to afterthe-fact reviews for sifting requirements from other material in a requirements specification.This is both tedious and time-consuming.We propose an automated approach for demarcating requirements in free-form requirements specifications.The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles.We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications.Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%.Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall.We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications.The results indicate that experts find our approach useful and efficient in practice.We developed a prototype tool, named DemaRQ, in support of our approach.To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data.
Privacy With Estimation Guarantees

We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data.In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT).Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy).We demonstrate how chi-square information captures the fundamental PUT in this case and provide bounds for the best PUT.We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known.We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution.We illustrate the proposed approach through two numerical experiments.
GeoGate: Correlating Geo-Temporal Datasets Using an Augmented Reality Space-Time Cube and Tangible Interactions

This paper introduces GeoGate, an Augmented Reality tabletop system that extends the Space-Time Cube and utilizes a ring-shaped tangible user interface to explore correlations between entities in multiple location datasets. We demonstrate GeoGate in the context of the maritime domain, where operators seek to find geo-temporal associations between trajectories recorded from a global positioning system, and light data extracted from night time satellite images. GeoGate utilizes a tabletop system displaying a traditional 2D map in conjunction with a Microsoft Hololens to present a single view of the data with a novel Augmented Reality extension of the Space-Time Cube. To validate GeoGate, we present the results of a user study comparing GeoGate with the existing 2D approach used in a normal desktop environment. The outcomes of the user study show that GeoGate's approach reduces mistakes in the interpretation of the correlations between various datasets, while the qualitative results show that such a system is preferable for the majority of geo-temporal maritime tasks compared.
Design thinking in practice: understanding manifestations of design thinking in software engineering

This industry case study explores where and how Design Thinking supports software development teams in their endeavour to create innovative software solutions. Design Thinking has found its way into software companies ranging from startups to SMEs and multinationals. It is mostly seen as a human centered innovation approach or a way to elicit requirements in a more agile fashion. However, research in Design Thinking suggests that being exposed to DT changes the mindset of employees. Thus this article aims to explore the wider use of DT within software companies through a case study in a multinational organization. Our results indicate, that once trained in DT, employees find various ways to implement it not only as a pre-phase to software development but throughout their projects even applying it to aspects of their surroundings such as the development process, team spaces and team work. Specifically we present a model of how DT manifests itself in a software development company.
Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks

We propose an end to end deep learning approach for generating real-time facial animation from just audio. Specifically, our deep architecture employs deep bidirectional long short-term memory network and attention mechanism to discover the latent representations of time-varying contextual information within the speech and recognize the significance of different information contributed to certain face status. Therefore, our model is able to drive different levels of facial movements at inference and automatically keep up with the corresponding pitch and latent speaking style in the input audio, with no assumption or further human intervention. Evaluation results show that our method could not only generate accurate lip movements from audio, but also successfully regress the speaker's time-varying facial movements.
Empirical Studies on the Visual Perception of Spatial Patterns in Choropleth Maps

An essential purpose of choropleth maps is the visual perception of spatial patterns (such as the detection of hot spots or extreme values). This requires an effective and as intuitive as possible comparison of color values between different regions. Accordingly, a number of design requirements must be considered. Due to the lack of empirical evidence regarding some elementary design aspects, an online study with 260 participants was conducted. Three closely related effects were examined: the “dark-is-more bias” (i.e., the intuitive ranking of color lightness), the “area-size bias” (i.e., the neglect of small areas, since these are less dominant in perception than larger ones) and the “data-classification effect” (i.e., attention to data classification when interpreting spatial patterns). For each hypothesis, one or more maps in connection with single or multiple choice questions were presented. Users should detect extreme values, central tendencies or homogeneities of values as well as comment on their task solving certainty. In general, the hypotheses regarding the mentioned effects could be confirmed by statistical analysis. The results are used to derive conclusions and topics for future research. In particular, further comparative empirical studies are recommended to determine the best possible map types for given applications, also considering alternatives to choropleth maps.
An Edge-Fog-Cloud Architecture of Streaming Analytics for Internet of Things Applications

Exploring Internet of Things (IoT) data streams generated by smart cities means not only transforming data into better business decisions in a timely way but also generating long-term location intelligence for developing new forms of urban governance and organization policies. This paper proposes a new architecture based on the edge-fog-cloud continuum to analyze IoT data streams for delivering data-driven insights in a smart parking scenario.
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.
Mitigating long tail effect in recommendations using few shot learning technique

Recommender system has been established as an effective tool for users in providing personalized suggestions in many domains, especially in e-commerce. In these domains, recommendations are provided based on the feedback (ratings) given by the users. However, recommendations provided by the traditional approaches are biased towards the popular items (items that receive more number of ratings). As a result, unpopular items are left out and these items remain un-recommended and unsold. These unpopular items form a “long tail” in the product space, resulting in a huge loss to the e-commerce industry. However, diminishing this long tail effect is a highly challenging and non-trivial task due to limited available rating information. Recommending long tail items helps in improving the item liquidation and recommendation diversity as well. In this paper, we propose a novel framework to mitigate the long tail effect and overcome the limited ratings problem using few shot learning techniques. Siamese network, a type of few shot learning technique is found to be performing well in many domains with a limited number of instances in the recent past. In the proposed framework, vital statistics of each user are computed and this information is provided to deep siamese network. The trained siamese network is used to identify the long tail items that are similar to the liked items of each user. Finally, the identified long tail items are recommended to the appropriate users. We introduce three novel performance metrics to evaluate the long tail item recommendations. The proposed framework is evaluated on two real world datasets (MovieLens 1M and Netflix) and the results demonstrate that the proposed framework outperforms the traditional approaches and existing long-tail recommendation techniques.
A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier

With the maturity and popularity of dialogue systems, detecting user’s unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks since we can hardly get examples, prior knowledge or the exact numbers of unknown intents. In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers. Our method can be flexibly applied on top of any classifiers trained in deep neural networks without changing the model architecture. We calibrate the confidence of the softmax outputs to compute the calibrated confidence score (i.e., SofterMax) and use it to calculate the decision boundary for unknown intent detection. Furthermore, we feed the feature representations learned by the deep neural networks into traditional novelty detection algorithm to detect unknown intents from different perspectives. Finally, we combine the methods above to perform the joint prediction. Our method classifies examples that differ from known intents as unknown and does not require any examples or prior knowledge of it. We have conducted extensive experiments on three benchmark dialogue datasets. The results show that our method can yield significant improvements compared with the state-of-the-art baselines1 .
Disposition effect and herding behavior in the cryptocurrency market

This paper investigates two behavioral biases—the disposition effect and herding—using the Mt. Gox data between 2011–2013 in the bitcoin cryptocurrency market. Using trade round-trip and survival analysis, it shows the market exhibits a reverse disposition effect in bullish periods and the usual positive disposition effect in bearish periods. It finds evidence of herding in bearish as well as bullish periods using a return dispersion model. Additionally, it shows that herding moves along the market trend. Herding increases in both bullish and bearish periods when the bitcoin price increases and decreases, respectively.
Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization

In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where each event proposal consists of a set of short video segments, we extract visual feature (e.g., C3D feature) from each segment and use the existing image/video captioning approach to generate one sentence description for this segment. Considering that the generated sentences contain rich semantic descriptions about the whole event proposal, we formulate the dense video captioning task as a visual cue aided sentence summarization problem and propose a new two stage Long Short Term Memory (LSTM) approach equipped with a new hierarchical attention mechanism to summarize all generated sentences as one descriptive sentence with the aid of visual features. Specifically, the first-stage LSTM network takes all semantic words from the generated sentences and the visual features from all segments within one event proposal as the input, and acts as the encoder to effectively summarize both semantic and visual information related to this event proposal. The second-stage LSTM network takes the output from the first-stage LSTM network and the visual features from all video segments within one event proposal as the input, and acts as the decoder to generate one descriptive sentence for this event proposal. Our comprehensive experiments on the ActivityNet Captions dataset demonstrate the effectiveness of our newly proposed DaS framework for dense video captioning.
Efficient topview person detector using point based transformation and lookup table

Nowadays due to big data revolution, image analytics is seen as a potential solution to solve different real-life problems. In this regard, one of the applications could be a person detection system. The overhead mounted camera with a wide-angle lens gives more coverage and visibility in occluded and cluttered environments than a traditional or frontal view. Person detection from the top view is a challenging task because there is variation in position, orientation, poses, body articulation and appearance of a person depending upon the position in the scene. To handle these issues an efficient method is proposed that uses different geometric transformations, concepts of perspective geometry and homography matrix as a pre-processing step. The composite transformation matrix is then used with perspective transformations to standardized the shape of the image patch containing person. At this stage, the histogram of oriented features is extracted along with our proposed five additional spatial features. These features are then fed to a linear SVM classifier for training and testing and finally, a simple effective clustering process is used to accumulate the votes of SVM to render a decision for localization of the Person in the image. To reduce the computational cost of these points based geometric and perspective transformations a Lookup table structure is used which contains pre-calculated positions of different perspective points against spatial co-ordinates. The use of lookup table and point based operations significantly reduced processing time up to 50% as compared to the previous approach which uses RHOG algorithm with image-based rotations, transformations, interpolations, and no lookup structure. The proposed method is efficient both in terms of computation and accuracy. The performance of the proposed algorithm is tested using our newly recorded dataset having more wide coverage of the scene. The performance of the developed technique shows an accuracy of 98% TDR with 3% FDR.
Multiple Pyramids Based Image Inpainting Using Local Patch Statistics and Steering Kernel Feature

In this paper, we propose a novel multiple pyramids based image inpainting method using local patch statistics and geometric feature-based sparse representation to maintain texture consistency and structure coherence. First, we approximate each patch in the target region (region to be inpainted) by statistically dominant local candidate patches to preserve local consistency. Then each approximated patch is refined by a sparse representation of candidate patches based on local steering kernel (LSK) feature to retain texture quality. We also propose a multiple pyramids based approach to generate several inpainted versions of the input image, one for each of the pyramids. Finally, we combine the inpainted images by gradient-based weighted average to produce the final inpainted image. This approach helps to maintain structure coherence and to remove artifacts which may appear in the inpainted images due to different initial scales of the individual pyramids. The proposed method is tested on a wide range of natural images for scratch and blob/object removal. We have presented both quantitative and qualitative comparison with the existing methods to demonstrate the superiority of the proposed method.
Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing

The power demand (kW) and energy consumption (kWh) of data centers were augmented drastically due to the increased communication and computation needs of IT services. Leveraging demand and energy management within data centers is a necessity. Thanks to the automated ICT infrastructure empowered by the IoT technology, such types of management are becoming more feasible than ever. In this paper, we look at management from two different perspectives: (1) minimization of the overall energy consumption and (2) reduction of peak power demand during demand-response periods. Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustively reviewed the potential mechanisms in data centers that provided flexibilities together with flexible contracts such as green service level and supply-demand agreements. We extended state-of-the-art by introducing the methodological building blocks and foundations of management systems for the above mentioned two perspectives. We validated our results by conducting experiments on a lab-grade scale cloud computing data center at the premises of HPE in Milano. The obtained results support the theoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT: 33% of overall energy savings and 50% of power demand reduction during demand-response periods in the case of data center federation.
An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

Abstract Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVM-based convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.
Oracle Bone Inscription Detector Based on SSD

This paper introduces Oracle Bone Inscription Detector which based on Single Shot Multibox Detector, for segmenting and recognizing Oracle Bone Inscriptions from rubbing images. Oracle Bone Inscription which is the one of the oldest and most mysterious ancient characters, used about 3000 years ago in china, and lots of these literature are stored by rubbing images. Because that only few of specialists understand the Oracle Bone Inscriptions, lots of Oracle Bone Inscriptions are waiting for be understood for helping researchers know the history, culture, economy etc. Currently, deep learning method of single shot multibox detector achieves a good performance for segmentation and recognition, and may achieve a good performance for Oracle Bone Inscription detection. However, we fond the Single Shot Multibox Detector is weak at small object detection. This research equips and extends Single Shot Multibox Detector for Oracle Bone Inscription detection, and analyzes the mis-detection for achieving a better accuracy. The experimental results shows that Precision, Recall and F value achieve 0.95, 0.83 and 0.88 respectively, and proves the effectiveness of extended Single Shot Multibox Detector in Oracle Bone Inscription detection.