Reviewerly/exHarmony huggingface dataset:
Research on Performance evaluation of Software industry

Software industry in China started late, but it still faces many difficulties, to find out the limitations and advantages, strengths and weaknesses is a necessary task of the software industry currently. In order to clarify the present situation of software industry in China, , this dissertation uses quantitative analysis method and takes an example of software industry in Chengdu to analyze the performance evaluation of software industry and its influence on national economy. After analysis, the financial aspect, the software industry benefit gradually increase, the business income continues the high growth rate every year and the employee average wage level ranks first in each industry. As for technical aspects, the construction of software districts and investment in intellectual property protection devote to enhance the software technology. At the same time, the software industry is also facing the problem of development constraints, most of the domestic software market led by foreign enterprises and too few international leading software enterprises that China has, resulting in China's longterm dependence on foreign software technology.
Keyword Extraction From Malayalam News Articles Using Conditional Random Fields

Keywords are those words of a document that can describe the meaning of the document precisely and effectively. This paper is putting forward a novel automatic keyword extraction method for Malayalam news articles using a CRF model. An automatic keyword extraction system for Malayalam documents has not been implemented previously, and manually assigning high quality keywords uses fixed taxonomy, is expensive, and error prone. Conditional Random Fields (CRF) model is a highly advanced, probabilistic sequence labeling model, which can effectively use the features of a document for the task of keyword extraction.
Progress in Neural Network Based Statistical Language Modeling

Statistical Language Modeling (LM) is one of the central steps in many Natural Language Processing (NLP) tasks including Automatic Speech recognition (ASR), Statistical Machine Translation (SMT) , Sentence completion, Automatic Text Generation to name a few. Good Quality Language Model has been one of the key success factors for many commercial NLP applications. Since past three decades diverse research communities like psychology, neuroscience, data compression, machine translation, speech recognition, linguistics etc, have advanced research in the field of Language Modeling. First we understand the mathematical background of LM problem. Further we review various Neural Network based LM techniques in the order they were developed. We also review recent developments in Recurrent Neural Network (RNN) Based Language Models. Early LM research in ASR gave rise to commercially successful class of LMs called as N-gram LMs. These class of models were purely statistical based and lacked in utilising the linguistic information present in the text itself. With the advancement in the computing power, availability of humongous and rich sources of textual data Neural Network based LM paved their way into the arena. These techniques proved significant, since they mapped word tokens into a continuous space than treating them as discrete. As NNLM performance was proved to be comparable to existing state of the art N-gram LMs researchers also successfully used Deep Neural Network to LM. Researchers soon realised that the inherent sequential nature of textual input make LM problem a good Candidate for use of Recurrent Neural Network (RNN) architecture. Today RNN is the choice of Neural Architecture to solve LM by most practitioners. This chapter sheds light on variants of Neural Network Based LMs.
Destroying the silo: how breaking down barriers can lead to proactive and co-operative researcher support

In this article we evidence some of the opportunities which the Research Excellence Framework (REF) open access agenda has created for closer collaboration between the Library Research Support Team and the Research Office at Liverpool John Moores University (LJMU). Drawing on personal reflections and shared experiences, we suggest that this new co-operative spirit has yielded three important outcomes.First, it has allowed for the dismantling of research support silos and forms of duplication. Second, it has enhanced the visibility and profile of research within academic departments and across the University as a whole. Thirdly, it has enabled relationships and engagement to be developed beyond our institution, allowing us to learn from others. This leads us to further suggest that in the context of research support within an institution, the library should take an active role to engage with collaborative forms of research support.
Pixel2Field: Single Image Transformation to Physical Field of Sports Videos

Locating players on a 2D field plane for sports match is the first step towards developing many types of sports analytics applications. Most existing mechanisms of locating players require them to wear sensors during sports play. Sports games can be easily recorded by cameras with low cost. Current human detection and tracking techniques can be used to locate players in the video, which is typically distorted for panorama view. We propose an end-to-end system named Pixel2Field, which can transform every pixel location into their scaled 2d field image. This is done by first undistorting the image by estimating the distortion coefficients, followed by a homography recovery. Experiments using detected soccer players from a distorted video show the proposed transformation method works well. To the best of knowledge, this is the first end-to-end system that can transform frame pixel location to field location without any human intervention. This unlock a lot of opportunities for developing sports analytics applications.
Pupil Center Localization Using SOMA and CNN

We present a new method for eye pupil detection in images. The algorithm runs in two steps. Firstly, a reasonable number of good candidates for pupil position are determined quickly by making use of the self-organizing migrating algorithm. Subsequently, the final position of pupil, among the preselected candidates, is determined precisely by making use of a convolutional neural network. The motivation for this two-step architecture is to create the algorithm that is both precise and fast. The favorable computational speed follows from the fact that only the meaningful positions and sizes are checked in the potentially most time-consuming second step. Moreover, the demands on training and the training set for the network are lower than if the network is used exclusively in one step architecture. The algorithm is capable to run on less powerful computers, e.g. on embedded computers in cars. In our tests, the algorithm achieved good results.
The Hough Transform

The Hough Transform is widely used to detect parametrically described curves in an image that can contain noise or partial occlusion. It is shown how a two dimensional linear Hough Transform can be used to identify track candidates within the CMS tracker. Three implementations of this algorithm in FPGA firmware are presented: a systolic array, a pipelined array, and an optimised pipelined solution called the daisy-chained array. For each, the performance in terms of track finding efficiency and fake rate is presented, alongside the corresponding FPGA resource utilisation and latency. The method used to pre-process and distribute the tracker hits is also described. Potential algorithmic and technical improvements are discussed, in addition to the scaling of the implementation to a variety of FPGA devices.
Artificial neural network based particle swarm optimization in predictions mortality rate of broiler chicken

Abstract Success in broiler farms can be assessed from the mortality rate of chickens and also a solution to reduce the mortality rate of chickens. But in the process there are obstacles that is the death of hickens that tend to fluctuate so that of course resulted in financial losses for farmers. To prevent it required a method that can predict and control the mortality rate of broiler chickens. In this research, data mining method used is Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO), to produce accurate prediction with excellent iteration and also small error rate. The results of analysis in predicting mortality rate of broiler chickens by artificial neural network method in combination with Particle swarm optimization get better RMSE result (0.135) than Artificial Neural Network have not been optimized (0.381) with january mortality data and result of application quisioner made value 83,125 which can be categorized well and enough help the farmer in controlling prediction of chicken mortality rate broiler.
Constructing Strong Designated Verifier Signatures from Key Encapsulation Mechanisms

A designated verifier signature (DVS) allows a signer to convince a verifier that a message has been endorsed in a way that the conviction cannot be transferred to any third party. This is achieved by the property that the signature can be generated by one of them. Since DVS is publicly verifiable, a valid DVS implies that the signature must be created by either the signer or the verifier. To enhance privacy of signers' identity, a strong DVS (SDVS) disallows public verification. In this paper, we investigate various aspects of SDVS with making two contributions. Firstly, we consider SDVS in the multi-user setting and propose two strengthened models, namely, multi-user and multi-user+. To illustrate the significance of our models, we show that it is possible to forge an SDVS when the attacker is given signatures from an honest signer to multiple dishonest verifiers. Secondly, we give a generic construction of SDVS from Key Encapsulation Mechanism (KEM) and Pseudorandom Function (PRF) in the standard model. Our generic construction is secure in the multi-user setting if the underlying KEM and PRF are secure. We also give instantiations based on DDH and LWE assumptions respectively.
A New Lightweight Encryption Approach for Correlated Content Exchange in Internet of Things

So far, many cryptography systems adapted to Internet of Things (IoT) paradigm have been introduced. However, in these approaches, the constraint of images exchange has not been taken into consideration. In fact, image content is characterized by the correlation between its elements e.g. pixels. Correlation impacts negatively the security. Actually, using correlation, one can disclose data content once he/she has recognized a part of it. In this paper, we introduce a new cryptography system that takes into consideration correlation issue. Furthermore, our system allows reducing significantly memory use and time consumption, which are critical factor in IoT dedicated cryptosystems.
Study on the Matching Algorithm of Turf Grass Introduction Features Based on Big Data Analysis

In recent years, a large-scale cultivation and introduction of turf grass has been carried out in China with a wide application in various urban greening, gardening and real estate business. Considering the wide variety of grass species. it is difficult to decide the introduction of certain grass species for the site. Given the great coverage of land of China, a wide variety of grass species is applicable to be introduced. Taken the following factors into consideration, different regions, climates, lights, moisture and fertilizer demands, their influence by introduced grass species to the grass species survival rate and the growth cycle is supposed to be paid more attention. Based on the big data mining of the experiment to cultivate grass species, this paper extracts key factors and makes them into a time-varying neural network model, which could be exerted to solve the grass species matching problem when introducing. The experimental data verified that the algorithm proposed in this paper could optimize the matching function between the selected grass species and site conditions.
How Perceived Benefits and Barriers Affect Millennial Professionals’ Online Security Behaviors

Over the past decade, there has been an increase in cybersecurity breaches through identity theft, hacking, phishing attacks, and the use of malware such as viruses, worms, or trojans.The breaches have triggered an increase in investment in information security in organizations.As technology continues to improve, the risks of having cybersecurity incidents also increase.Cybersecurity firms reported that in 2016, there were 1209 total breaches with 1.1 billion identities exposed.Most experts agree that human vulnerability is a significant factor in cybersecurity.Most issues related to advanced threats come from human nature and ignorance.For the study, the researcher examined the relationship between Millennial professionals' perceptions of cybersecurity risks and users' online security behaviors.The study focused on two elements of perception which are perceived benefits and perceived barriers.The researcher administered a survey to 109 participants randomly selected among Survey Monkey audience members.The Spearman's correlation test performed supported the analysis of the strength of the relationship and the level of significance between each of the independent variables and the dependent variable.The results from the statistical test provided enough evidence to reject each of the null hypothesis tested in this study.There were significant correlations between each of the independent variables, Perceived Benefits (PBE) and Perceived Barriers (PBA) and the dependent variable Online Security Behaviors (OSB).
Improved OpenLR decoding using a stepwise increased deviation range

Exchanging information regarding traffic network related data, parties traditionally use a set of static references to the actual (road) network. Examples of such static references appear in TMC and DATEX II. These references use lists of road identifiers. The problem with this static road references approach is that the road network is continuously being changed which requires periodic administrative activity for all parties. Dynamic location referencing methods seek to reference locations without requiring a common definition of the road network. Examples of these methods are Agora-C, OpenLR, and Hidden Markov map matching. The challenge that these methods face is that each party can have a network definition that deviates in respect of the geographic location and of other descriptive information. Although the current OpenLR method takes possible deviations into account, its decoding methods can still result in false positives. This paper shows how the OpenLR decoding method can be improved by using a stepwise increased deviation range.
Scalable Activity Recognition Framework using Ensemble Models

Recognizing Activities from raw video streams has been one of the most challenging problems in the domain of computer vision since past three decades. In this paper we propose a method of combining unsupervised and supervised methods to develop a scalable solution which can have real world applications in surveillance, video indexing, security and could help us have an efficient way of tackling large scale surveillance with minimal human intervention. With our unsupervised reconstruction CNN we can cluster different videos based on their dense frame representations and then train a separate classifier for each of the clusters. This methods allows us to easily add new activities to the system without training the entire system from scratch. For activity recognition among the clusters we use LSTM based Recurrent neural networks with different hyperparameter for each clusters' classifier.
Comparative analysis on seed point placement and magnitude-based visual enhancement for streamlines generation

This paper presents an improved seed point placement to locate initial seed points based on magnitude value. The vector field is separated in to grid to ensure the uniformity in generating streamlines and broader streamlines coverage, thus reducing poor visualisation information especially near to visualisation boundary. An evaluation using mean square error is conducted to evaluate the improvement in term of streamlines placement uniformity. Result shows that proposed method able to come out with lower mean square error value compared to evenly-spaced in across different separation distance, resulting better streamlines control to produce uniform visualisation result. Finally, the proposed method can be improved in many aspects to come out with a better visualisation result in two and possibly three dimension flow visualisation.
Measure your API Complexity and Reliability

APIs are the new communication interfaces that enable two software applications to talk to each other. These interfaces play a vital role in the functioning of new generation technologies. With the widespread usage of these interfaces, their prominence in technology and business has been dramatically increasing. Failure of these programming interfaces can cause huge economic losses to the business firms as the majority of their services rely on them; it is vital that we understand the complexities of our APIs and exhaustively validate them before deploying. Conformity to the specification and functionality are two major factors that contribute to a successful implementation. Given the wide range of functions performed by these interfaces, a manual validation will be cumbersome and an exhaustive automation process is essential. In this paper, we propose such a framework for conformity and functionality validation. Simple and less complex APIs are easy to adopt and integrate, the ability to quantify those complexities will help us design our interfaces better. Reliability is yet another factor that drives the utilization of these interfaces, lack of proper quantifying methods has made it difficult for both the providers and consumers to perceive it. In this paper, along with the automation framework we present a methodology for measuring the complexity and reliability of APIs. We also present the test metrics of Cisco Spark, Microsoft Teams and Slack, the prominent public APIs in the domain of collaboration, using our proposed validation framework and methodologies.
Multidimensional Hierarchical Model of Behavioral Testing of Distributed Information Systems

Perspective distributed information systems (DIS), along with the often present property of criticality of use, increasingly possess the agent properties of autonomy, mobility, intelligence, cooperativeness. Automated systems of technical diagnostics (ASTD) of such DIS in accordance with the first property should ensure their complete real-time testing, and for both static hardware-software and information environment of DIS placement, and for a dynamic set of information-control flows corresponding to the main functions and scenarios of her work, that is, her behavior. DIS agent properties only increase the need and deepen the content of testing this behavioral DIS model by ASTD. This paper discusses a complex model of behavioral check for DIS components, in particular, for cooperativeness of agents, based on a multidimensional, multi-level, heterogeneous and multipurpose structure of their behavioral interactions. In accordance with this, both within each level, and between the levels of the near circle (adjacent) and the far circle (connected by one or more intermediate levels), behavior check and recognition are performed for input models of the automaton class. The check model has features, firstly, determining the structure of identifiers, atomic and compositional checks, corresponding to the multidimensional and multilevel structure of behavioral interactions; secondly, the signatures of special operations and relations of cooperation for these checks; thirdly, the basic laws of reception/transfer, transformation, inheritance, encapsulation, preservation/ /accumulation. The behavioral check model allows defining the basic conditions for constructing multi-level DIS verification methods, reducing the time and resource costs due to decomposition of the check analysis, in particular, multi-agent hierarchical, which is important for real-time verification.
Comparative Review of Cross-Domain Generative Adversarial Networks

Abstract This paper provides the comparative analysis between two recent image-to-image translation models that based on Generative Adversarial Networks. The first one is UNIT which consists of coupled GANs and variational autoencoders (VAEs) with shared-latent space, and the second one is Star-GAN which contains a single GAN model. Given training data from two different domains from dataset CelebA, these two models learn translation task in two directions. The term domain denotes as a set of images sharing the same attribute value. So, the attributes that are prepared: eye glasses, blond hair, beard, smiling and age. Five UNIT models are trained separately, while only one Star-GAN model is trained. For evaluation, we conduct some experiments and provide a quantitative comparison using direct metric GAM (Generative Adversarial Metric) to quantify the ability of generalization and the ability of generating photorealistic photos. The experimental results show the superiority of cross-model UNIT over multi-model StarGAN on generating age and eye glasses attributes, and the equivalent performance to synthesize other attributes.
Implementation of hybrid sampling technique for predicting active compound and protein interaction in unbalanced dataset

Abstract Indonesia Jamu Herbs (Ijah) web server aims to predict Jamu efficacy based on interaction between active compound and disease’s protein. However, the interaction between compound and protein data is unbalance since there are many unknown interactions between active compounds and protein target. Thus, the prediction result is still not optimal. In this research, the hybrid sampling technique, combining complementary fuzzy support vector machine (CMTFSVM) and synthetic minority oversampling technique (SMOTE) was used to handle imbalanced data interaction between active compound and protein for Ijah, web server to predict candidate Jamu formula for certain disease. Performance was measured using geometric mean (Gmean), area under curve (AUC), and accuracy. The evaluation results showed that the hybrid sampling technique could increase the instance of minority class three times. Moreover, the prediction model could obtain the value of 0.8346, 0.6812, and 0.5319 for accuracy, Gmean, and AUC, respectively.
The Discrete Structure of the Zeros and Poles Location in the z-Plane of the Arbitrary Order IIR Digital Filters with a Finite Word Length

It is known that the resolved positions of the zeros and poles of the second order IIR digital filters with finite wordlength form a discrete structure called in this paper the topography of a discretized z-plane. In the publications of the authors, the z-plane topography was described in detail. This paper is devoted to the definition of equations describing plane algebraic curves on which the allowed positions for the zeros and poles of filters of arbitrary order are located.