Reviewerly/exHarmony huggingface dataset:
Shading atlas streaming demonstration

Streaming high quality rendering for virtual reality applications requires minimizing perceived latency. Shading Atlas Streaming (SAS) [Mueller et al. 2018] is a novel object-space rendering framework suitable for streaming virtual reality content. SAS decouples server-side shading from client-side rendering, allowing the client to perform framerate upsampling and latency compensation autonomously for short periods of time. The shading information created by the server in object space is temporally coherent and can be efficiently compressed using standard MPEG encoding. SAS compares favorably to previous methods for remote image-based rendering in terms of image quality and network bandwidth efficiency. SAS allows highly efficient parallel allocation in a virtualized-texture-like memory hierarchy, solving a common efficiency problem of object-space shading. With SAS, untethered virtual reality headsets can benefit from high quality rendering without paying in increased latency. Visitors will be able to try SAS by roaming the exhibit area wearing a Snapdragon 845 based headset connected via consumer WiFi.
Deep Learning and Sub-Word-Unit Approach in Written Art Generation

Automatic poetry generation is novel and interesting application of natural language processing research. It became more popular during the last few years due to the rapid development of technology and neural computing power. This line of research can be applied to the study of linguistics and literature, for social science experiments, or simply for entertainment. The most effective known method of artificial poem generation uses recurrent neural networks (RNN). We also used RNNs to generate poems in the style of Adam Mickiewicz. Our network was trained on the ‘Sir Thaddeus’ poem. For data pre-processing, we used a specialized stemming tool, which is one of the major innovations and contributions of this work. Our experiment was conducted on the source text, divided into sub-word units (at a level of resolution close to syllables). This approach is novel and is not often employed in the published literature. The sub-words units seem to be a natural choice for analysis of the Polish language, as the language is morphologically rich due to cases, gender forms and a large vocabulary. Moreover, ‘Sir Thaddeus’ contains rhymes, so the analysis of syllables can be meaningful. We verified our model with different settings for the temperature parameter, which controls the randomness of the generated text. We also compared our results with similar models trained on the same text but divided into characters (which is the most common approach alongside the use of full word units). The differences were tremendous. Our solution generated much better poems that were able to follow the metre and vocabulary of the source data text.
An Interpretable Recommendations Approach Based on User Preferences and Knowledge Graph

In recent years, how to accurately recommend and solve the opacity of the recommendation system has received more and more attention. Some researchers introduced some auxiliary information into the recommendation system, such as Knowledge Graph (KG). This method improves the accuracy of the recommendation, but there is still the problem of opacity of the recommendation system. In this paper, we propose a Knowledge-aware Path Model Based On User Preferences (KPUP), using knowledge map paths for interpretative recommendations. KPUP forms a preference distribution of users relative to candidates by exploring chain links in the KG. The KPUP can generate path representations by combining the semantics of entities and relationships. By using sequential dependencies in paths, we allow valid reasoning of paths to infer the interaction principle of user items and can be used to predict the final click probability (CTR). In addition, we set up a weighted pool operation to distinguish the contribution points of different paths to the preferences, so that the recommendations are better interpreted. We obtained two data sets, which reflected people’s preferences for movies and books, and conducted a lot of experiments. Compared with the most advanced Baseline, our results are better.
Knock Boredom for Six with Summer Activities Booklet

Ribble Valley youngsters are invited to ‘knock boredom for six’ with Ribble Valley Borough Council’s 2019 summer activities booklet.
Barcode Representation of Face Image Combining LGFA and Windowing Technique

This paper represents a novel work for standard type rectilinear barcode from illumination invariant confront pictures. This method finds out the qualification in slopes of picture sparkle utilizing LGFA and Windowing technique, at that point, it requires discovering normal of the angles into a limited number of interims utilizing standardization. After this, the aftereffect of quantization is changed over into the breaking points of decimal digits from zero to nine and the chart is converted into an extreme straight standardized identification. Surely, the present strategy creates the quality sort direct EAN-8 standardized tag of the best piece of face picture. In any case, in the present work, a system which is proposed to extract edge-based information utilizing LGFA and window method. LGFA helps us to generate gradient information from illumination invariant face image and by using the windowing technique; we scan the input image horizontally and take the upper 75% and 70% of the gradients. The barcode for these two cases are extracted from face images of five datasets, and it is found that the present technique is useful to account, discovery, acknowledgment and look for people in a group.
Um estudo de caso envolvendo os pilares de segurança da informação em uma organização privada

This paper presents a case study on information security in a medium-sized organization, which analyzed the existence of a security policy and the basic principles of information security. The data collection and analysis process was performed directly in the organization, through direct observation, questionnaire and interview techniques. Regardless of the size of the company, they all have sensitive information that, if lost or stolen, could jeopardize the company's activities, such as its operations and operations. The technological advance has raised concerns about the risks of Information Security to many entrepreneurs, although others still do not have this awareness, not treating information as one of the main assets of the organization. The analyzes carried out in the case study present evaluations in accordance with the ABNT NBR ISO/IEC 27002:2005 standard, in which the problems encountered are displayed, and finally, it presents elaborated suggestions for the vulnerable points.
Toward an optimal quantum algorithm for polynomial factorization over finite fields

We present a randomized quantum algorithm for polynomial factorization over finite fields. For polynomials of degree n over a finite field F_q, the average-case complexity of our algorithm is an expected O(n^{1 + o(1)} \log^{2 + o(1)}q) bit operations. Only for a negligible subset of polynomials of degree $n$ our algorithm has a higher complexity of O(n^{4/3 + o(1)} \log^{2 + o(1)}q) bit operations. This breaks the classical 3/2-exponent barrier for polynomial factorization over finite fields \cite{guo2016alg}.
Role of Indomieâ??s â??Like No Otherâ? Campaign in Cultivating Pester Power in the Children of Eleko Community, Lagos

‘Pester power’ as a marketing strategy used in targeting kids has been a controversial topic for ages. Critics express concern over its negative results such as parent-child conflict, health hazards from unhealthy food consumption, unethical manipulation of children, etc. This births the question – who or what cultivates ‘pester power’ in children? While some scholars attribute the cultivation of pester power to advertisers, others suggest that the concept is a socially driven phenomena that has existed long before the coinage of the term. This study is an attempt to explore the concept of ‘pester power’ and determine the factors responsible for its cultivation in children by using Indomie’s “like no other” campaign as its focus. From the prisms of the Cognitive Development Theory and the AIDA Model, the study examined the processes of cultivating pester power in the early developmental stages of children in Eleko Community, Ibeju-Lekki, Lagos State. Exploratory research design comprising of both quantitative and qualitative methods was used for this investigation. Data was gathered from 244 questionnaires as well as interviews of five parents in the Eleko community. Results of the research revealed that Indomie “like no other” advertisement, parents and other identified factors played contributory roles in cultivating the children’s pester power. Based on these findings, the study recommends that parents should exercise better control over the exposure of their children to television and the communication patterns they create at home. The study makes the case for a stronger regulation of advertisement contents that target children as consumers and for further research on this sensitive subject matter.
Learning Spatial Transform for Video Frame Interpolation.

Video frame interpolation is one of the most challenging tasks in the videoprocessing area. Recently, many related studies based on deep learning havebeen suggested, which can be categorized into kernel estimation and flow mapestimation approaches. Most of the methods focus on finding the locations withuseful information to estimate each output pixel since the information neededto estimate an intermediate frame is fully contained in the two adjacentframes. However, we redefine the task as finding the spatial transform betweenadjacent frames and propose a new neural network architecture that combines thetwo abovementioned approaches, namely Adaptive Deformable Convolution. Ourmethod is able to estimate both kernel weights and offset vectors for eachoutput pixel, and the output frame is synthesized by the deformable convolutionoperation. The experimental results show that our method outperforms thestate-of-the-art methods on several datasets and that our proposed approachcontributes to performance enhancement.
Business in the Front, Crypto in the Back: How to Be a Blockchain Startup in Fintech

We present the journey by which Novera, a blockchain start-up, partnered with CompanyX, an SME investment firm actively looking for opportunities to leverage disruptive technologies. We believe that the key to this journey is expressed in Novera’s “Business in the front, crypto in the back” perspective. Finance is highly regulated and conservative. So, Novera would not have been able sell its concept for a novel bitcoin tracking fund that received over $1M in venture capital without its corporate ethos: Novera would be able to walk into any boardroom and clearly demonstrate compliance to regulations, and moreover show that their business practices would meet stringent expectations of large financial institutions. At the same time, it is Novera’s vision to innovate beyond the fund to a blockchain-based token platform that keeps its team of technologists motivated and forward-looking. We advise others weighing investment in blockchain, especially SME’s and start-ups in finance, to understand and manage this dichotomy.
Initial semantics for reduction rules

We give an algebraic characterization of the syntax and operational semanticsof a class of simply-typed languages, such as the language PCF: we characterizesimply-typed syntax with variable binding and equipped with reduction rules viaa universal property, namely as the initial object of some category of models.For this purpose, we employ techniques developed in two previous works: in thefirst work we model syntactic translations between languages over differentsets of types as initial morphisms in a category of models. In the second workwe characterize untyped syntax with reduction rules as initial object in acategory of models. In the present work, we combine the techniques used earlierin order to characterize simply-typed syntax with reduction rules as initialobject in a category. The universal property yields an operator which allows tospecify translations---that are semantically faithful by construction---betweenlanguages over possibly different sets of types. As an example, we upgrade a translation from PCF to the untyped lambdacalculus, given in previous work, to account for reduction in the source andtarget. Specifically, we specify a reduction semantics in the source and targetlanguage through suitable rules. By equipping the untyped lambda calculus withthe structure of a model of PCF, initiality yields a translation from PCF tothe lambda calculus, that is faithful with respect to the reduction semanticsspecified by the rules. This paper is an extended version of an article published in the proceedingsof WoLLIC 2012.
ASSORTED EXPRESSIVE CROSS-STANDARD BASED HASHING SCHEME

According to an investigation printed on eMarketer, approximately 75% of the content posted by Face book users contains photos. Appropriate data from various modalities will often have semantic correlations. Most existing works make use of a bag of words to model textual information. Since we propose to use a Fisher kernel structure to represent the textual information, we use it to add the SIFT descriptors of the images. It is advisable to include representations of continuous words to treat the semantic textual similarities and adopt them for the recovery of mixed media. Your network building block used at work may be the restricted Gaussian Boltzmann machine. However, Fisher vectors are often high-dimensional and dense. It limits the uses of PV for large-scale applications, where computational requirements must be studied. Finally, the Hamming distance can be used to determine the similarities between converted FV hash codes and other image hash codes. We evaluated the suggested SCMH method in three general purpose datasets. SCMH achieves better results than cutting-edge methods with several lengths of hash code. A Skip-gram template was used to create these 300-dimensional vectors for 3 million words and phrases. To generate Fisher vectors, we use the INRIA implementation. In this article, we compared the important duration of the suggested approach along with other hash learning methods. Although the off-line stage of the suggested structure requires a huge cost of computation, the computational complexity of the Internet stage is small or similar to other hashing methods.
Software Demonstrations and Training

Software product demonstrations and product trainingPYPR dimension Software demonstrations and training are frequently your product’s first impression to anyone outsideCustomer experience the product team. What’s the role of the product manager here? Usually you are not the one demonstrating your product, nor will you conduct the product training. You are the General Manager of YourProduct Inc., you are the product manager and that’s why you care that there are valuable software demos and relevant product trainings that underline the product position in relevant product trainings. This chapter explains the Software Demonstrations and Training PYPR dimension that represents “The Showcase Part of the Software Product Manager Role.” Due to its importance and according to my experience, that there are still many issues with software demonstrations, I consider Software Demonstrations and Training as being its own dimension and not part of the Go-to-MarketGo-to-Market dimension. We will examine in more detail the various software demonstrations and training types to improve the items of this PYPR dimension, and the essentials to be considered as well as some proven practices that worked for me in various situations over the last decade. The tools and templates provided are selected, proven practices that worked in my context. Although the dimension weights seem relatively low, I frequently considered this dimension to be the stepchild of product management and with that the limiting factor of product success.
Scientific Text Semantic Structure Modeling

Authors proposed and substantiated the model of polytextual structural organization for a scientific text, taking into account its dependence on the content of the scientist's cognitive activity. The model is based on the concept of an epistemic situation in the unity of four aspects of knowledge: ontological, methodological, reflexive, and communicative-pragmatic. The concept of subtext is introduced as a structural-semantic unit of a scientific text. They considered the content of subtext new and old knowledge, evaluation, authorization, etc.
Significant Improvement in Classification Performance Metrics by Ensemble Approach

Today most of real time applications are source of big data and classification process over huge amount of this data is a challenge. The training of a single classifier with such type of large amount of data causes plasticity–stability problem. A single classifier is not able to preserve large amount of knowledge when it starts to learn new knowledge. This paper gives the introduction about the ensemble and techniques to generate ensemble which shows that how one can maintain stability between bias and variance to improve classification performance. Various classification performance metrics are elaborated and effect of ensemble size on different evaluation measures is also demonstrated.
Legitimacy, Government Regulations and the Emergence of the Market for Cryptocurrencies

Building on legitimacy in institutional theory research, we theorize how the perception of regulative legitimacy based on strong versus weak government regulations varies in conjunction with institutional voids, country level corruption and trust and its implications on the emergence of the market for cryptocurrencies (i.e. Bitcoin trading) from 2013 to 2017 in 45 countries. We find that strong regulations based on the declaration of Bitcoins as either legal or illegal makes Bitcoin trading in a country less attractive – even under high corruption. However, in the context of institutional voids strong regulations facilitate Bitcoin trading while in the context of high social trust, strong regulations hamper it. Our study contributes to a better understanding of new market emergence and the tension between (il)legality and (il)legitimacy in different institutional settings.
Predictability Analyzing: Deep Reinforcement Learning for Early Action Recognition

Early action recognition aims at inferring ongoing activities from partial videos as early as possible, whereas conventional action recognition relies on fully observed activities. Observations show that the predictability of different activity subsequences vary wildly, however most existing work failing to fully exploit this phenomenon. We define the predictability of activity subsequences as its capacity to perform recognition early and accurately. A predictability-based early action recognition framework(PEAR) is established to utilize predictability information to achieve early recognition. It consists of a predictability evaluator and a classifier. Due to lacking of fine-grained supervision, we develop a reinforcement-learning-based strategy to optimize the evaluator encouraged by a recognizability reward and an early reward. With the predictability estimated by the evaluator, the classifier learns discriminative representation of subsequences to perform early action recognition without sacrificing much accuracy. Experiments on two benchmark datasets demonstrate the proposed approach outperforms existing methods significantly.
Topology Sequences-based Fuzzy Track Initialization Method For Radars and ADS-B *

Radars and automatic dependent surveillance -broadcast (ADS-B) are two important surveillance technologies in aerial surveillance networks. For this situations with different systematic errors of 2D radars and their time-difference-arrival (TOA) measurements in the fusion center, a fuzzy track initialization method based on topology sequences (TS-FTI) for heterogeneous sensors is proposed. In this proposed method, topology sequences of various measurements from a radar and an ADS-B receiver are respectively constructed. Then the topology sequence method is utilized to classify these measurements. The measurement sets of candidate targets can be constructed by accumulating measurements in several successive sampling intervals. Finally, a fuzzy Hough transform (FHT) method is used to detect tracks. The performance of the TS-FTI method is evaluated by simulation experiments, and it is found to be better than that of the track initialization method based on standard Hough transform (HT-TI) in calculated complexity and correct initialization rate.
WSMR Dynamics Based DWA for Leader-Follower Formation Control

One of the critical problems in multi-robots formation is motion control. To achieve the fast formation in the leader-follower formation control, the paper proposes an improved dynamic window approach (DWA) to control the followers moving towards the virtual goals which are generated by the distance-orientation method. The proposed DWA is based on the dynamic model of the wheeled skid-steer mobile robot (WSMR) instead of the conventional kinematics model. First of all, the kinematic model and dynamic model of the WSMR are analyzed and built. After that, the DWA is introduced to achieve the faster following control by using the WSMR dynamics to predict the trajectories of the followers. Then, the motor-wheel model is described by an empirical model and a velocity mapping model of the robot is deduced to map the linear and angular velocities to the rotation speeds of the wheels. Finally, the experiments are carried out to verify the feasibility of the proposed method, especially in the forming stage of the formation control. The comparison of the experimental results between the conventional DWA and the dynamics based DWA show that the latter is faster and more stable in the forming stage of the formation control.
Business Model Design and the Performance of Startups: Evidence from the Digital Economy

In the last two decades, the concept of business model emerges and has been recognized as a key determinant of superior firm performance. Recently, the burst of new digital technologies is fundamentally reshaping the business landscape by generating abundant new entrepreneurial opportunities and novel business models. By integrating the resource-based view and the demand-side perspective, this study examines how business model design impacts the performance of startups in the context of new digital economy. Using a unique dataset of 322 startups, our empirical results clearly show that both technology-driven and demand-driven business model are beneficial to the performance of startups. Furthermore, user interactivity strengthens the positive relationship between technology-driven business model and the performance of startups, but it weakens the positive relationship between demand-driven business model and the performance of startups. In addition, openness to technology strengthens the positive impact of demand-driven business model on the performance of startups. This study advances the scholarly conversation of business model and firm performance by offering a new theoretical lens that integrates the resource-side and the demand-side perspective, and by contextualizing it into the increasingly digital business environment.