Reviewerly/exHarmony huggingface dataset:
A Bicameralism Voting Framework for Combining Knowledge from Clients into Better Prediction

In this paper, we propose a bicameralism voting to improve the accuracy of a deep learning network. After we train a deep learning network with existing data, we may want to improve it with some newly collected data. However, it would be time consuming if we retrain the model with all the available data. Instead, we propose a collective framework that train models on mobile devices with new data (also collected from the mobile devices) via transfer learning. Then we collect the predictions from these new models from the mobile devices, and achieve more accurate predictions by combining their predictions via voting. The proposed bicameralism voting is different from federated learning, since we do not average the weights of models from mobile devices, but let them vote by bicameralism. The proposed bicameralism voting mechanism has three advantages. First, this collective mechanism improves the accuracy of the deep learning model. The accuracy of bicameralism voting (VGG-19 on the data set Food-101 dataset) is 77.838%, higher than that of a single model (75.517%) with the same amount of training data. Second, the bicameralism voting saves computation resource, because it only updates an existing model, and can be done in parallel by multiple devices. For example, in our experiments to update an existing model via transfer learning takes about 10 minutes on a server, but to train a model from scratch with both the original and the new data will take more than a week. Finally, the bicameralism voting is flexible. Unlike federated learning, bicameralism voting can use any architecture of model, any preprocessing of input data, and any format of model when the models are trained on different mobile devices.
Ontological Approach to Analyze Traveler’s Interest Towards Adventures Club

Now tourisms has become an industry and many adventure clubs offer different adventure trips for the travelers. So there is a need for a system for travelers which prefer their choices and give them all needed information in one place. We proposed a new travel recommended system for adventurers, which provides a defined solution to user demands. It is a “Knowledge Base system” a smart recommender system with domain-specific ontology. Queries can be constructed in natural language and a related query management strategy is developed. The solution space is searched from two perspectives: user demand and offers relevance. Many systems working on this perspective but in Pakistan not such systems present which preference user choices. Pakistan is a country which is rich in adventure tourism and a good choice for international adventurers. So there is need such a system for based on user interest and their desired information. In the following system, we will deal with the main problems that adventurer presents in terms of information search and decision-making processes according to the domain ontology.
The Implementation of K-Means Algorithm to Analyse the Level of Employee Job Satisfaction Using Laravel Framework

Every company needs to know the level of job satisfaction of their employees that can be used as suggestion and critics to help consider the way to manage their human resources. It aims to encourage their employees to work optimally. One of the ways to find out the level of employee job satisfaction is by conducting one on one (face-to-face meeting between boss and employee) to get information about job satisfaction in terms of workload, work environment, team relationship, and other aspects. The information given to HRD and top manager is then used as consideration for the company to increase the human resource management. For time efficiency, it is made an application using Laravel framework that implements K-Means algorithm. In this case, the implementation of K-Means algorithm becomes a solution by analyzing data of employee job satisfaction in the company. The result of calculation process of K-Means algorithm forms a cluster used to obtain the description of the level of employee job satisfaction toward policies in the company.
IoT Based Sea Wave Analyzing and Weather Forecasting Systems

This paper presents a solar powered IoT based wave detecting system which helps to easily trace out the frequency of the waves and to receive an early warning about dangerous waves like tsunami. This wave detecting system has multiple applications and placing this system in the sea helps us to check out pollution, temperature variation, pH variation of sea water, turbidity etc. This entire system works on the basis of waves and its frequency variations. With its IoT enabled feature, we can easily receive up-to-date information and findings from the ocean and also the attached GPS, makes it easier to locate the system in the wide water surface.
Single image super-resolution using deep hierarchical attention network

In this paper, we present a compact and accurate super-resolution algorithm using the attention-augmented convolutional neural network, which can exploit and weight hierarchical features at multiple scales and levels to improve learning capability. The proposed network employs cascading U-net structure to allow the flow of low-frequency information to focus on learning high- and mid-level features. In addition, deep hierarchical channel attention is developed to help in learning from high-level complex features. Moreover, we propose a hierarchical pyramid attention to learn the inter and intra-level dependencies between the feature maps. Furthermore, the comprehensive quantitative and qualitative experiments on low-resolution and real image benchmark datasets illustrate that our algorithm performs favorably against the state-of-the-art methods.
Salient Edge(s) and Region(s) Extraction for RGB-D Image

In human visual systems, detection of the salient region plays an important role, as it ensures effective allocation of resources and fast processing. Though depth is an essential cue for visual saliency, it has not been well explored. This introduces a salient edge-based, region extraction model for RGB-D images. Most of the computational saliency models, reported in, produce smooth saliency maps; however, the edge information is not preserved. This paper presents a simple framework for the detection of salient edges by the preservation of the edges with high saliency scores. The final saliency map is obtained by fusion of the generated salient edge map and the RGB saliency map. Experiments are conducted over the publicly available RGB-D-2 dataset. The stability of the proposed RGB-D saliency model was assessed against standard evaluation metrics such as precision, recall, F-measure, MAE, NSS and AUC scores.
Volumetric Density of Triangulated Range Images for Face Recognition

In this paper, a volumetric space representation of 3D range face image has been established for developing a robust 3D face recognition system. A volumetric space has been created on some distinct triangular regions of the 3D range face image. Further, we have constructed 3D voxels corresponding to those regions for developing voxelization-based 3D face classification system. The proposed 3D face recognition system has mainly three parts. At first, seven significant landmarks are detected on the face. Secondly, any three individual landmarks are used to create a triangular region; in this way, six distinct triangular areas have been generated, where the nose tip is a common landmark to all the triangles. Next, assume a plane at the nose tip level for representing the volumetric space. The total density volume and some statistical features are considered for the experiment. From the volumetric space, construct 3D voxel representation. Further, geometrical features from 3D voxel are used for the experiment. Three popular 3D face databases: Frav3D, Bosphorus, and Gavabdb are used as the input of the system. On these databases, the system acquires 94.28%, 95.3%, and 90.83% recognition rates using kNN and 95.59%, 96.37%, and 92.51% recognition rates using SVM classifier. Using geometric features with SVM classifier, the system acquires 92.09%, 93.67%, and 89.7% recognition rates.
PROFESSIONAL PREPAREDNESS AS AN INCREDIBLE COMPONENT OF SUCCESSFUL PROFESSIONAL ACTIVITY OF THE PERSON

The historical aspect and the essence of the term of readiness for work as a special mental state; a necessary and extremely important component of a successful professional life of a person is studied in the article. Readiness for a certain phenomenon helps person to adapt more quickly and to fulfill his duties more successfully. It contributes to the development of personality. The author points out that an analysis of the problem of readiness shows that the genesis of this concept is related to the development and understanding of the concept of "psychological readiness for work". It can be defined as permanent (as a result of labor education) or temporary (as a result of psychological training or psychological mobilization during this period) mental condition, which is caused due to the need for work. The question of the readiness of a specialist for professional activity is studied within such sciences as: psychology, pedagogy, philosophy. The author's determination of readiness is formulated during the conduction of the research. The author proposes to understand the readiness as a complex integrative formation, characterizing a particular mental state. It is a necessary and essential component of a successful professional life of the individual. It helps a person to adapt more quickly, to fulfill his professional duties more successfully; it promotes professional development of a specialist. Readiness includes not only professional characteristics and skills, but either personal trait. The conclusion is made that since readiness is a dynamic value, it means that its course is determined by age, by learning experience, individual abilities of the person and so on. The author summarizes the importance of the development of psychological readiness for work during the formation of the personality of a modern specialist. It is emphasized that professional readiness cannot be the same as a subjective professional readiness. According to the modern scientific researches the author distinguishes particular levels of professional readiness, such as: high (autonomy in setting and solving new tasks, adequacy of assessment and self-assessment of professionally important qualities, ability to effectively solve problems during time deficit, etc.); average (average level of expression of the mentioned qualities); and low (inability to set and solve complex problems independently, inadequate assessment and self-assessment of professionally important features, etc.
Low Illumination Image Enhancement Based on U-Net

Low illumination image has the characteristics of low overall brightness, contrast and signal-to-noise ratio. The classical image enhancement algorithms have limited enhancement effect and need to adjust parameters manually. In this paper, a deep full convolutional coding-decoder based on U-type network is proposed to solve the problem of low illumination image degradation. The experimental results show that, compared with the existing mainstream image enhancement algorithms, the proposed algorithm can improve the brightness and contrast adaptively, avoid artifacts on image edges, and further improve the objective evaluation index and subjective evaluation.
Graph classification with the hypernetwork, a molecule interaction based evolutionary architecture

A novel architecture for information processing, called the hypernetwork architecture is described here. This model is based on the hierarchical organization and principles of biological information processing. The hypernetwork model has a representation of the molecular, cellular, and organismic levels of biological organization. Molecules are enzyme-like structures, and interactions are typical activation and inhibition processes. The representation of molecules and their interactions is comprised of binary strings and string matching respectively. Molecules are placed in cells, modeled by cellular automata. An organized group of cells forms an organism. Cell to cell interactions are produced by the effector-receptor molecules of the cells. The hypernetwork receives environmental influences at its input cells, creates cascades of molecular interactions inside the cells, passing through internal cells, and delivers an output from its output cells. Hypernetwork organisms learn classification tasks, including graph classification, by an adaptive algorithm based on molecular evolution. An organism is reproduced with random molecular mutation and the selection chooses the organism with the best structure for the problem to be solved. With its molecule based variation-selection learning algorithm, the hypernetwork is able to learn fairly complex classification tasks. Besides learning, the hypernetwork exhibits mutation buffering capabilities, intracellular feedback regulation, and can be used as a tool for understanding how hierarchies work, for studying evolutionary strategies, and as a model for building molecular computers.
Towards the Internet of Things Patterns Dictionary

The Internet of Things patterns dictionary represents an aspect of a lexicon - it provides a set of definitions for the IoT patterns. This dictionary fulfills a function in language standardization for the IoT paradigm by providing patterns definitions that are based on a number of parameters. Definitions convey the meaning of a concept to the audience. In the field of linguistics, various types of definitions exist - each with a different purpose and focus. However, since a definition expresses the meaning of a word using other words, forming clear descriptive definitions can be a challenging venture. In the literature, definitions for patterns are often formed based on the author's knowledge and intuition without following any formal blueprint. In this paper, a systematic method for constructing lexical descriptive definitions for the IoT patterns is proposed. The aim is to evolve the theoretical framework presented here into a formal IoT patterns dictionary. Since it is common knowledge that each IoT pattern consists of a set of attributes, one can, in practice, derive a lexical definition for each pattern based on these attributes. In linguistics, the meaning of a lexical term is often derived from a combination of different components describing that particular object. Similarly, a definition of each IoT pattern is composed of at least one or more general and/or specific attributes that essentially, when juxstaposed, make up a unique definition of each pattern. Of course, our approach to definition formation is linguistics-based, and not rooted in software engineering. Thus, most of the content in this paper is dedicated to the discussion of several mechanisms for defining languages and showing that the attributes of a given pattern can be concatenated using an algebraic linguistics modus operandi to construct formal definitions.
Generalized Net Model of the Network for Automatic Turning and Setting the Lighting in the Room with Intuitionistic Fuzzy Estimations

Turning the lights on automatically helps people who enter a current room to turn on the lighting depending on the settings made by a person. In this paper we will take a look how by scanning the iris the system recognizes the person who is entering the room depending on if the the outside light exceeds the set in advance threshold value, the lighting turns on with the settings for lighting of a specific person and it also lets corrections to be made in the set in advance parameters.
Similarity-Aware EQUALS and IN Operator in Cassandra and Its Application in Agriculture

With the exponential growth of the Internet, the need of handling the sort of unstructured, messy and unpredictable data became so urgent. This makes NoSQL databases to be introduced to compensate for the inefficiency of traditional relational databases in dealing with these kind of data. Among those NoSQL databases, Cassandra gained popularity than the rest and today it is at use in over 1500 companies like Reddit, Netflix, Instagram, GitHub, eBay. The EQUALS and IN operator are the two most basic tools used in Cassandra Query Language (CQL) to compare two or more data items. However, the two operators are not capable of dealing with non-traditional data types like images as instead of looking for similarity between two images, they use identity (=) which is not always the correct method. Thus, this paper introduces similarity-aware EQUALS and IN operator which is very much capable of dealing with modern complex data types like images. These operators based on similarity can be used in various fields like Agriculture for finding regions capable of growing a particular crop. In this paper, we demonstrate the application of the two operators in Agriculture.
Query Relaxation using Spreading-Activation and SKOS-Ontologies

Digital libraries and archives adopting the Linked Open Data (LOD) approach add descriptive metadata to the objects stored in their inventory in order to facilitate searching and sorting. In many cases the available metadata terms are organized as a controlled vocabulary. Technically, the controlled vocabularies are often provided in the form of SKOS ontologies thus allowing to apply semantic web technologies to establish inter-vocabulary relations and ask queries.
Using Neural Network to Identify Forgery in Offline Signatures

This paper proposesAgrawal, Piyush an efficientBhalsodia, Rahul method forGarg, Yash the identification of forged signatures from offline handmade signatures. Signatures are majorly used as personal verification that can be abused by any unauthorized third party who would feign the identification of an individual which signifies the need for an automatic forgery identification system. Over a past few decades, there are significant attempts for the identification of forgery of signatures. Identification can be done either online- or offline-based mode. Offline mode works on the input image of a sign. We aim to purpose a technique for offline identification by using a simple shape based on geometrical features including area, center of gravity, eccentricity, kurtosis. Database of signatures is trained for two classes, genuine and forged. Two different classifiers, one based on SVM and other on artificial neural network (ANN), were used to identify and classify the signatures as genuine and forged.
L’impact du processus de la numérisation sur ľhistoire de la traduction. Regard sur les possibilités et les défis induits par la numérisation dans ce domaine en Roumanie

Abstract In the context of global digital evolution, the implementation of dynamic (online) platforms seems to be essential in order to facilitate the access to rare and/or valuable documents. Insufficiently exploited as seen in the small number of requests, the digitization of these resources is a concept of important value to research activities dealing with the history of translation and not only. Consequently, digitization is likely to bring together a larger number of readers but also to reveal multiple directions of future research.
Content Management using Google Website and the Formation of Digital Libraries: A Case Study on NIFM Library Website

In the era of digitization, it is becoming a need of Library and Information Managers (LIMs) to prepare an environment of the digital library. This environment provides library users with easy access to the library collection in online search, full text documents search, information sharing, online public access catalogue (OPAC) etc. A website can provide all the information related to the library at one place. Library and Information Manager (LIM) is not a web designer but has to organize information from time to time for the users on the web with the help of Google website. This process can be easily implemented in the library to keep the users updated. Google website allows designing a website in an easy way which is discussed in this paper as an example of a website of National Institute of Financial Management's Library, Faridabad.
Assessing the Statistical Quality of RNGs

There has and still is an ongoing discourse on how to measure the quality of random numbers generated by Random Number Generators (RNGs).
Image Watermarking Based on Modified Harmonic Mean Filter

In this paper, a spatial domain image watermarking method based on the modified harmonic mean filter is proposed. In the embedding process, the blue component of a color image in the RGB color space is modified to embed a binary watermark image with the same size. In the extraction process, the embedded watermark is blindly extracted without the need of original image. The harmonic mean filter is considered and modified by using the concept of outliers in the filtering area. The modified harmonic mean filter is then applied to the watermarked image to obtain a better prediction of original image. The extracted watermark can finally be achieved by subtracting the predicted image from the watermarked one. Set of experiments are carried out to verify its efficiency. The performance of the proposed method in terms of weighted Peak Signal to Noise Ratio and Normal Correlation is evaluated and compared with the previous watermarking methods. Its robustness against attacks is also evaluated using the Stirmark benchmark. According to the results obtained, the proposed method provides a higher robustness on average at equivalent quality of watermarked image, compared to the existing methods.
AWDF: An Adaptive Weighted Deep Fusion Architecture for Multi-modality Learning

Fusion has been widely used in machine learning community, especially for problems dealing with multiple input sources and classifiers. The general strategy for information fusion in deep neural network is to directly concatenate the embedding features on the latent space of input sources. However, it is very hard to capture the relative importance of fused sources. It is also impossible to learn the correlation among fused multimodalities inputs, e.g., intra-class and inter-class similarities. Besides, most existing deep learning fusion approaches use universal fusion weights strategy, which cannot fully exploit the relative importance of different inputs. In order to address these problems, in this work we propose an Adaptive Weighted Deep Fusion scheme (AWDF) to capture potential relationships among various input sources. It integrates the feature level and decision level fusion in one framework. Furthermore, in order to address the limitations of existing fusing models with fixed weights, we propose a new scheme named Cross Decision Weights Method (CDWM). It can dynamically learn the weight for each input branch during the fusion process instead of utilizing pre-defined weights. To evaluate the performance of AWDF, we conduct experiments on three different real-world datasets: Wild Business Terms (WBT) Dataset, Iceberg Detection Dataset and CareerCon Dataset. Our experimental results demonstrate the superiority of AWDF over other fusion approaches.