Reviewerly/exHarmony huggingface dataset:
Hypotheses Elicitation in Early-Stage Software Startups Based on Cognitive Mapping

Software startups develop innovative products for which there are typically no customers to refer to elicit requirements. Often, these companies develop a set of features without a better understanding of customer needs. An experiment-based approach to validate hypotheses about the customer and market could increase their chance of success or, at least, accelerate their realization of the product worthlessness. The first step of an experiment-based approach is to elicit hypotheses to guide experiments. Software startups base their products on business assumptions, but there is a lack of understanding of how these assumptions are formed and how teams could elicit hypotheses systematically. To fill this gap, we performed an empirical study consisted of two steps. First, we explored based on which assumptions startups define their products using a multiple case study. The results indicate that these companies developed their products based on founders' assumptions derived from their previous experience. Second, we investigated cognitive mapping as a tool to elicit hypotheses systematically with two software startups. The results indicate that this approach can serve as the basis of a method to elicit hypotheses in early-stage software startups.
Approximate Temporal Conditioned Autoencoder and Regressor for Sales Prediction

Lost sales can be caused by out-of-stock products, bad customer-care and over-pricing and is a major source of business failure in retail market. Predicting demand for products and customers' purchasing behaviour can help the businesses avoid these type of failures. Such predictions are usualy done using matrix factorization techniques. These are not suited to handle temporal variations in data which is often the case in retail world. Beyond factorization, deep learning methods including autoencoders and regressor are proposed without considering temporal information. Extending them with deep learning components to handle temporal data is not straightforward as these type of models requires a lot of data with variations in items and customers as well as in time. Variations in item and customer is easily achieved but variation in time is usually limited. We therefore propose a method which approximates a recurrent neural network to handle a few samples in temporal dimension. To this end we start with an existing algorithm DCAR [1] and enhance it to handle a few samples of temporal data resulting a predictor that beats the performance of classical matrix factorization and deep factorization methods as well as DCAR. The method simultaneously trains multiple autoencoders and regressors with an unfolded recurrent neural network.
A Decentralized Autonomous Ecosystem for Peer-To-Peer Clean Energy Trade

One of the visions of our United Arab Emirates founding father, the late Sheikh Zayed bin Sultan Al Nahyan was to transform UAE into a sustainable country. Sustainability is not only environment related as popularly known, but also the economy and the people of the society. The UAE as a country has been focusing on sustainability from different aspects, mainly clean energy generation. UAE's ambitious road to sustainability is centralized on clean energy generation. The proposed Decentralized Autonomous Digital Ecosystem is an autonomous Blockchain based application framework that aims to accomplish UAE's target of reaching up to 25% clean energy generation by 2030. The digital ecosystem is an autonomous Blockchain based peer-to-peer clean energy trading platform between peers, where the excessive energy from small-scale decentralized energy resources including homes, office buildings and factories with capacity to generate clean energy using PV systems.
A Modeling Method for Inter-Satellite Transmission Tasks Planning in Collaborative Network based on PDDL

Various satellite systems has been actively developing laser inter-satellite link on the basis of the traditional microwave one. In the satellite collaborative network where microwave and laser links coexist, the planning of inter-satellite transmission tasks are difficult to solve with traditional mathematical model methods. PDDL (Problem Domain Definition Language) is well adapted to the modeling of inter-satellite transmission tasks planning because of its strong logical expression, clear structure and excellent versatility. This paper deeply analyzes the constraints of inter-satellite transmission tasks planning in collaborative networks, and uses PDDL to describe the constraints, resources, states and actions in the domain file. Take the collaborative network transmission tasks of 10 satellites as an example for establishing a problem file and the feasibility and effectiveness of the model was verified.
Attention model with multi-layer supervision for text Classification

Text classification is a classic topic in natural language processing. In this study, we propose an attention model with multi-layer supervision for this task. In our model, the previous context vector is directly used as attention to select the required features, and multi-layer supervision is used for text classification, i.e., the prediction losses are combined across all layers in the global cost function. The main contribution of our model is that the context vector is not only used as attention but also as a representation of an input text for classification at each layer. We conducted experiments based on five benchmark text classification data sets and the results indicate that our model can improve classification performance when applied to most of the data sets.
Segmentation of Breast Density Using K-Means Clustering Algorithm

In the contemporary world, breast tumor is the most consistently spotted neoplasm among women, that is, instigating women’s transience at a higher rate. Radiologists prefer computer-aided mammography in order to detect breast cancer. Mammograms consist of many artifacts like labels, pectoral muscles whose removal is a daunting task. The presence of glandular tissues in digital mammograms plays a pivotal role in the early finding of breast cancer. This is necessary to find the radiation risk linked with screening and also to find the irregularity between right and left breasts. The proposed method focuses on pre-processing and breast density segmentation. In this method, both binarization and the modified region growing method are applied for removing the labels, background and pectoral muscles. For improving the contrast of the images bereft of the pectoral muscles, it uses the contrast limited adaptive histogram equalization with Rayleigh distribution. Next use is made of K-means clustering to segment the digital mammograms into different density regions. The proposed method is applied on a publicly accessible mini-MIAS database. Its results are validated by the expert radiologists and these compete with those of the state of the art methods.
Comparison of HEVC-based OMAF-compliant 6K effective packings for viewport-dependent 360-degree video streaming

Omnidirectional MediA Format (OMAF), a recent standard for representation of 360° video content, supports only equirectangular projection (ERP) and cubemap projection (CMP) and their region-wise packing with a 4K decoding-capability constraint on the end-user devices. Streaming of 4K content allows only a limited viewport resolution, which is lower than the resolution of many current head-mounted displays (HMDs). Two 6K-effective multi-resolution packing schemes for ERP and CMP are recommended by the OMAF standard, which take full advantages of HMDs with quad HD resolution while sticking with 4K-decoding constraint. This paper studies and compares the performance of these two packings in the context of viewport-adaptive streaming using the High Efficiency Video Coding (HEVC) standard and Zonal-PSNR test methodology. Overall, the experimental results show that rate-distortion performance of both packings sequence wise mixed but similar in average.
Comparison of Random Weight Initialization to New Weight Initialization CONEXP

Training in feedforward networks is adaptation of weights of network which makes weight initialization an important factor in determining the training speed in network. This work aims at proposing a new weight initialization technique, wherein the weights are initialized in the useful range in threshold function. It further initializes weights to each hidden node in a region which alternately contracts and expands statistically. The proposed weight initialization is compared to the conventional random weight initialization. The proposed weight initialization is expected to perform better than the random weight initialization.
HTTP adaptive streaming over multiple network interfaces

Enhancing user experience in streaming applications is an important problem. Delivering the best quality possible for the given network conditions is not an easy task. In the case of a streaming client running in a multi-homed network, this problem becomes more complicated. In the simplest form, one network can be picked randomly or based on some criteria, and the streaming client solely uses that network. In another form, multiple networks can be simultaneously used by the streaming client and due to the aggregation, doing so may deliver better and more stable quality, and at a lower latency than using either of the networks individually. However, using multiple networks simultaneously is not trivial in certain scenarios. In this demo, we present a gateway-based solution where the gateway is connected to two different networks and dynamically decides which network(s) to use for streaming while hiding all the decision complexity from the streaming clients behind it. In other words, our solution is transparent to the streaming clients, which means any existing client can benefit from this solution without any changes.
Visualizing Byzantine Fault Tolerance and Compilers

Objectives:The investigation of B-trees has saddled RPCs, and current patterns recommend that the doubtful unification of various leveled databases and RAID will before long rise.In this study, we affirm the investigation of compose ahead logging, which typifies the specialized standards of electrical designing.In our re-seek we check that neural systems and ex-saucy frameworks are to a great extent contradictory.Methods/Statistical Analysis: We accept that mimicked toughening and blockage control are consistently contrary.We trust that every part of our framework keeps running in O (2n) time, free of every other segment.Findings: In this study we disconfirmed that replication and the area personality split can consent to address this deterrent.Our engineering for empowering join level affirmations is disastrously helpful.Application: On a similar note, Heaving should successfully improve many write -back cache sat once.Thus, our vision for the future of cyber informatics certainly includes ourheuristic.
Automatic double region of interest selection for spotting micro-expression from long videos

Micro expression is a short-lived expression made unconsciously when people try to hide emotion. The spotting of micro expression can help to reveal the real emotion. Micro expression has been used in national security, judicial system, medical and clinical fields, but it is very difficult to detect and recognize. This paper presents a method of automatic double region of interest selection to detect the time point of occurrence of micro expression. The method was tested on the CASME2, SMICE-HS, SMIC-E-NIR, and SMIC-E-VIS databases. LBP and MDMO are used as feature extractors to represent the texture and dynamic changes of each frame in the sequence. The experimental results show that the proposed method can improve the performance of spotting.
Gaze Data for Quality Assessment of Foveated Video

This paper presents current methodologies and challenges in the context of subjective quality assessment with a focus on adaptively encoded video streams.
A New Audio-Visual Gestural Instrument for Unlocking Creativity

The AirSticks are an audio-visual gestural instrument de- signed to inspire the improvisation and performance of real- time explorative electronic music through subtle and not-so- subtle physical movement. The instrument has been used professionally across the world. In doing so the designers have created hundreds of mappings of movement to sound, along with a real-time visualisation system that uses the same movement data from handheld motion controllers to unify the sound, graphics and movement. The instrument has shown to have a broad appeal beyond professional practice, which has led to hands-on demos with children through to older people. We invite visitors, for the first time, to play not only with the AirSticks, but also with the latest iteration of our audio-visual system, to inspire new collaborations in the field of computer-human interaction. We argue that we need more new tools to unlock people's creativity through moving, listening and music making.
Feature Analysis on Project Data of Teaching Reform of Higher Education

Based on the feature analysis on the project data of teaching reform in colleges and universities in X province in 2016, it is found that the total number of projects decline year by year, but the key or entrusted projects increase substantially; the quantitative distribution of projects in universities is unbalanced or centralized, while there are significant differences between ordinary colleges and vocational colleges; the subject words are biased towards curriculum or teaching research, which is consistent with social hot issues; the higher the professional titles of the topic host, the higher the level of the project type. It is suggested that try to treat the different types of universities differently in the approval of projects, and strengthen the approval of projects in orientation of running colleges and universities, and strengthen the training mode of talents. Further, we should strengthen the promotion and exchange of outstanding research results in colleges and universities so as to maximize the economic benefits of the achievements.
Improved algorithm for Phase Unwrapping with Continuous Submodular Minimization

The phase unwrapping is the process of attempting to reconstruct the true phase from modulo 2π phase values. This procedure requires that we have an important congruent constraint i.e. rewrapping unwrapped image should be identical to the original wrapped image. This constraint condition causes a discrete optimization problem. However, many methods have ignored this constraint condition to solve a continuous minimization problem directly, making it difficult to solve the solution correctly. We recently presented new continuous minimum norm method that is based on the Lovász extension. Our method can reach the optimal solution with the congruent constraint condition. Note that in our work, we have taken the subgradient method for minimization, but it is time consuming. In this paper, we introduce the incremental subgradient method to the minimization procedure, which is faster than the subgradient method. To solve the phase unwrapping, first, we also use the Lovász extension to transform the phase unwrapping problem to equivalent continuous minimization problem which consists of the sum of large numbers of component functions. Then we take the incremental subgradient method to solve the minimization problem in which operate on a single component at each iteration, rather than on the entire cost function. On the other hand, we also introduce new minimal function to deal with high lever noise. Several simulations show, compared with the subgradient method, the new algorithm only takes one quarter of (or less) the numbers of iteration for the convergence.
Cloud Native Efficient Solution for API Migration Across Environments For Agile Integration Enterprises

This paper describes an approach to minimize the effort and time in recreating Application programming interface artifacts and facilitate cross-collaboration and efficient migration across heterogeneous platforms environments without any significant post-migration changes and additional effort. The current software development evolution came up with the Agile concept of developing and delivering products in milestones based approach software development organizations are now maintaining separate environments for development, quality assurance, and production. These environments execute independently and have their deployments and own methods of traffic control. When moving Application programming interface artifacts from one environment to another, all the created APIs need to migrate across different environments to test the exact functionality and behavior of the application. Our motivation is to minimize the effort and time in recreating APIs and facilitate accurate and efficient migration across different environments without any significant post-migration changes and additional effort.
West Java tourism geographic information system design using adobe AIR

Abstract Google can be maximized with applications that can facilitate the direct location of tourist destination. The introduction of problem is no West Java tourism application using Adobe Flash, so the researchers take the theme Designing West Java Tourism Geographic Information Systems Using Adobe AIR. The purpose of this study is to assist the government and people in Indonesia using mobile application to reduce time efficiency in finding tourist location maps and information about tourist attractions in West Java and as the places to help tourists. The application will increase the regional income to visit attractions / tourism in West Java. The research methods carried out in this study use the Rapid Application Development method. The most important process is planning, user design, construction, and cutting needs. The design steps to create a navigation menu consisting of maps of West Java that can be connected directly with GPS and google maps. The next stage is the development with a combination of action script 3 programming, images, and the export process to the apk file. The testing process is done using a cell phone that is connected to GPS and has been installed on Google Maps and Adobe AIR. Conclusions obtained by the application successfully designed with 100% test results successfully run on android-based mobile devices.
Summarization for online reviews based on hierarchical attention network

With the rapid growth of online reviews, the rapid and effective access to summary information is a hot topic of research. In this paper, an abstract sentence extraction model based on hierarchical attention network is proposed. The model has an encoder-decoder structure, which introduces a two-lay er attention mechanism. The sentence encoder uses attention mechanism to obtain the vectorized representation of the sentence by inputting the aspect words. The review document encoder uses the attention mechanism to realize the context association of the sentences before and after. When decoding, a sentence output component composed of the GRU network makes the choice whether the sentence should be selected as the candidate sentence. Greedy algorithm is used to eliminate the redundantly result, and then obtains the final summary based on the sentence sorting method. The experiment results show that the proposed model is better than the benchmark model.
Safecracker: Exploring Immersion Through Audio and Object-Based Controllers

We outline the ideation, development, and gameplay of Safecracker, an interactive heist game. The game provides immersion through a multisensory gameplay experience. Players are engaged in thematic, audio-based mechanics while interacting with a tangible controller. Players attempt to break into a life-sized safe by determining its 3-number combination. Their primary crime-committing tool is a stethoscope, which listens for audio cues as they rotate the safe's dial. Headphones embedded within the stethoscope play distinct sounds when the dial approaches each number in the sequence. Once the player has identified all 3 numbers in the combination, the safe unlocks and opens, revealing different treasures depending on the selected difficulty level.
Detection of Shocking Images as One-Class Classification Using Convolutional and Siamese Neural Networks

Not safe for work content automatic detection is a serious challenge for social media due to overwhelming growth of uploaded images, gifs and videos. This paper focuses on shocking images automatic detection by convolutional neural networks. It was considered that the correct recognition of the shocking class is more important than the non-shocking one. Binary classification by a convolutional network that training during operation has been used as a baseline solution. However, this solution has two drawbacks: the network highlights incorrect features of non-shocking images (infinite class) and tends to forget rare subclasses of shocking images, which is unacceptable. To eliminate the first drawback, we approach this problem as a one-class classification with having in mind that a “non-shocking” image can be defined only via contradiction with a shocking one. This method is based on using sparse autoencoders build on top of a pretrained convolutional neural network and is not trained during operation. To eliminate the second drawback, we memorized vectors of images that were incorrectly classified during operation. A trained siamese network during the prediction is used to search for similar images in the database. In the case of an incorrect prediction by the combined model, vectors of images are added to the database and the siamese network is trained on them. This method allows you to minimize the number of errors in rare subclasses identified only during the operation phase of the model.
Development of marine grade radiation cross-linked high density polyethylene (HDPE) floater pontoon material

Abstract Crosslinked High Density Polyethylene (HDPE) was developed using electron beam radiation techniques. The crosslinked HDPE can be used as marine grade cross-linked HDPE floater pontoon material. Thermoplastic resin, HDPE blended with Ethylene Vinyl Acetate (EVA) in various weight percentage compositions before being radiate with electron beams irradiation. These composites showed significant changes in mechanical properties and temperature stability after the irradiations. From this study, the optimum dose for HDPE and HDPE/EVA blends will be obtain. This study also will provide the optimum irradiation dose in order to have a good, high strength and impact for the production of pontoon material. The present of EVA in the blend will act as impact modifier also being investigated in this study. From the study, the result showed some significant changes in thermal and mechanical properties after being irradiated with electron beam. The comparison of the result between these composite composition of irradiated and non-irradiated HDPE composite used for floater pontoon material is presented in this paper