Reviewerly/exHarmony huggingface dataset:
Label-Specific Alignment with Adversarial Multi-View Representation

In Multi-View Multi-Label (MVML) learning, each instance is represented with several feature vectors and associated with multiple labels simultaneously. Existing models are mainly built on a shared latent space among multiple views and tend to select features that are distinguishable for all labels. However, they ignore the distinctive characteristic of each view and fail to extract the discriminative information related to corresponding labels. To address the above challenges, we proposed a novel approach named Label-specific Alignment with Adversarial Multi-view Representation (LAAMR). For one thing, shared and private information is extracted with adversarial training and orthogonal constraint, which ensures complementarity and consistency among multiple views. For another, label semantic information is well captured with multi-head attention, and then utilized as the guidance to exploit label-view dependence with alignment attention, resulting in label-specific representations. Experiments on various MVML datasets validate the effectiveness of LAAMR.
Map-Bot: Mapping Model of Indoor Work Environments in Mobile Robotics

This work presents a mapping model of indoor work environments in mobile robotics, called Map-Bot. The model integrates hardware and software modules for navigation, data acquisition & transfer and mapping. Additionally, the model incorporates a computer that runs the software responsible for the construction of two-dimensional representations of the environment (Vespucci module), a mobile robot that collects sensory information from the workplace and a wireless communications module for data transfer between the computer and the robot. The results obtained allow the implementation of the reactive behavior “follow walls” located on its right side on paths of 560 cm. The model allowed to reach a safe and stable navigation for indoor work environments, using this distributed approach.
A distributed and unified API service for machine learning models

Machine learning models are usually based on a single dataset and a single architecture approach. But this approach falls behind in many practical scenarios. Many techniques such as boosting and bagging have been developed in the past that enables better predictions by ensembling multiple models. In this paper, we propose an extension to the existing ensembling techniques by developing a community-driven system of distributed API that unifies several models to produce an ensembling effect along with several other benefits such as wider availability, greater usability, lesser resource constraints and an infrastructure for future developments in the field.
Optical simulation of a parabolic solar concentrator

This work presents a 3D ray tracing approach for the optical simulation of a parabolic solar concentrator for a thermo-magnetic (Curie motor) experiment. An approach based on Snell's law of reflection (geometrical optics) in vector form is used in this study. We consider the solid angle of the sun through a rotation of the concentrator around an axis. This paper aims to set up a digital technical tool as flexible as possible for applications in the field of renewable energy.Mathematical modelling as well as numerical simulation in Python environment are presented. The aim of the proposed algorithm is to be used for the concentration of light rays on any optical system, symmetrical or not. Better yet, offers a solar tracking model.
Interval value fuzzy hyper AT-algebras

Abstract The aim of this work is to a connection between two concepts which are an interval value fuzzy set and a hyper AT-algebra. Also, some properties of these concepts are found. The notions of IVF hyper AT-subalgebras, IVF hyper ideals and IVF hyper AT-ideals are defined. Then IVF (weak, strong) hyper ideals and IVF (weak, strong) hyper AT-ideals are discussed. After that, some relations among these ideals are presented and some interesting theorems are proved.
Algorithm for Dynamic Scale Adaptation of Reference Functions Based on Image Implementations

An algorithm for large-scale adaptation of prototype functions representing image classes is proposed. The algorithm identifies the parameters of nonlinear scale distortions contained in the functions representing the observed image realizations, and then transforms the original prototype function using the previously proposed model. The algorithm works on a class of images of regular phase processes that have the property of quasi-similarity of shape. Two models of large-scale nonlinear transformations are considered: symmetric and asymmetric. The algorithm was experimentally tested on the images of prototypes of fragments of speech signals and electrocardiosignals. Examples and experimental data confirming the effectiveness of the algorithm are given.
Expressiveness and Nash Equilibrium in Iterated Boolean Games

We define and investigate a novel notion of expressiveness for temporal logics that is based on game theoretic equilibria of multi-agent systems. We use iterated Boolean games as our abstract model of multi-agent systems [Gutierrez et al. 2013, 2015a]. In such a game, each agent <?TeX $i$?> has a goal <?TeX $\gamma _i$?> , represented using (a fragment of) Linear Temporal Logic ( <?TeX $\mathrm{LTL}$?> ) . The goal <?TeX $\gamma _i$?> captures agent <?TeX $i$?> ’s preferences, in the sense that the models of <?TeX $\gamma _i$?> represent system behaviours that would satisfy <?TeX $i$?> . Each player controls a subset of Boolean variables <?TeX $\Phi _i$?> , and at each round in the game, player <?TeX $i$?> is at liberty to choose values for variables <?TeX $\Phi _i$?> in any way that she sees fit. Play continues for an infinite sequence of rounds, and so as players act they collectively trace out a model for <?TeX $\mathrm{LTL}$?> , which for every player will either satisfy or fail to satisfy their goal. Players are assumed to act strategically, taking into account the goals of other players, in an attempt to bring about computations satisfying their goal. In this setting, we apply the standard game-theoretic concept of (pure) Nash equilibria. The (possibly empty) set of Nash equilibria of an iterated Boolean game can be understood as inducing a set of computations, each computation representing one way the system could evolve if players chose strategies that together constitute a Nash equilibrium. Such a set of equilibrium computations expresses a temporal property—which may or may not be expressible within a particular <?TeX $\mathrm{LTL}$?> fragment. The new notion of expressiveness that we formally define and investigate is then as follows: What temporal properties are characterised by the Nash equilibria of games in which agent goals are expressed in specific fragments of <?TeX $\mathrm{LTL}$?> ? We formally define and investigate this notion of expressiveness for a range of <?TeX $\mathrm{LTL}$?> fragments. For example, a very natural question is the following: Suppose we have an iterated Boolean game in which every goal is represented using a particular fragment <?TeX $L$?> of <?TeX $\mathrm{LTL}$?> : is it then always the case that the equilibria of the game can be characterised within <?TeX $L$?> ? We show that this is not true in general.
Selective perception and foreign languages: a proposal for a different language learning

German Abstract: Wir betrachten Sprache einmal rein technisch: als Senden und Empfangen komplexer Signale. Unter einem Signal kann man hierbei einen Laut, aber z.B. auch einen Begriff verstehen. In verschiedenen Sprachen sind die verwendeten Signale teilweise verschieden oder verschieden wichtig. Leider ist das kein Allgemeinwissen. Unkenntnis der wesentlichen Signale einer Fremdsprache, die man lernt, führt nämlich zu Fehlern, derer man sich nicht bewusst ist, und damit zu ziemlich ineffizientem Lernen. Abhilfe lässt sich schaffen, indem man sich gezielt mit den relevanten Signalen der eigenen und der Fremdsprache befasst und sich die Unterschiede bewusst macht. English Abstract: We see language in a purely technical way: as sending and receiving complex signals. A signal can be understood to mean a sound, but also, for example, a term. In different languages, the signals used are partly different or different in importance. Unfortunately, this is not general knowledge. Ignorance of the essential signals of a foreign language that one is learning leads to mistakes of which one is not aware and thus to rather inefficient learning. This can be remedied by consciously dealing with the relevant signals of your own and the foreign language and making yourself aware of the differences.
Bitcoin’s Value Proposition: Shorting Expansionary Monetary Policies

In this paper I discuss Bitcoin’s defining features that make it a unique asset. I argue that Bitcoin should not be considered as a single purpose asset only, but rather as a new digital financial asset serving several function. In addition to functioning as a means of payment (at least partially) and a diversification tool, I argue that part of Bitcoin’s value proposition stems from its worth as a short position on modern expansionary monetary policies. Indeed, Bitcoin’s value should rise if expansionary monetary policies are maintained, amounting to a tool to short these policies, which should be considered in future attempts to value Bitcoin. I discuss the role of Bitcoin in the traditional financial system, contrast Bitcoin to gold, consider the implications of the continuance of expansionary policies on Bitcoin, and discuss the impact of the emergence of cryptocurrencies as a new asset class on public policies.
Web-Based Document Tracking System Using Barcode Technology with SMS Notification

Web-Based Document Tracking System Using Barcode Technology with SMS Notification (WBDTSUBTWSN) is a software application that monitors the progress movement of the document. WBDTSUBTWSN was a system which was person to person tracking. Person to person tracking to identify easily where, when and who to contact regarding documents location. This system would develop to ease the user in tracking the location status of the document. From the series of testing of Web-based Document Tracking System using Barcode technology with SMS Notification, the proponent concludes that in creating the system it must be tested first to know if it was working well or not and to know that if you had drawback on it. In that way, when in final implementation there will be fewer problems on it. For now, the system was working well and help the benefactor ease some work. Base on the findings of result the following the system was working and may help the benefactor to ease some work (f) functionality has the consolidated mean of 4.46 which mark as Moderately Agree and this indicates with the interpretation in Linkert scale of that the certain functionality are stable and according to Chen et al., 2013 the program is also capable of managing documents regardless of document type. As for (g) reliability has the consolidated mean of 4.07 which mark as Moderately Agree and this indicates once only (encountered errors) and according to Omoregbee et al., 2018 files such as reports can be processed and tracked by the system in real time. Basing from the result of the study the researcher recommends the following: Improve Graphical User Interface of the system. Add Office Code in Every document. Uploading whole document and can review the details. Add more document type such as DTRs, Moa and any other. Improve Security of the system. However, for more accurate recommendation, it is suggested that it should have similar studies to be conducted with more number of documents. Moreover, it is also recommending to conduct similar transaction to other system to further verify the consistency of the result of the study.
Design a Hybrid Algorithm Using ACO and Best-Fit Optimization Algorithm for Energy Consumption in Cloud Data Center

Power consumption has become an essential area of research as the cloud computing technique use increased due to easy usage of the Internet, storage, and processing on the cloud. Even small firms are nowadays initiated to use cloud computing techniques for their users. This led to the development of cloud computing techniques and cloud data centers for the processing which outcomes in a huge number of barriers in surroundings like the emission of carbon. Due to the huge need for cloud services, data centers are working all the time. This enhances the consumption of energy which leads to expensive business. A cloud data center consumes a huge volume of energy resulting in a greater carbon footprint level. The costs of these data centers have developed with the rise in the capacity of computing with a huge percent of operational expenses due to the consumption of energy in cloud data centers. The energy consumption of the data center is a critical problem that could be resolved by consolidating the workload of the server and turning off physical servers. In cloud data centers, various mapping associations between physical machines and virtual machines resulted in varied utilization of resource therefore placing virtual machines (VM) on physical machines to develop utilization of resource and reduce consumption of energy which is one of the main issues for providers of the cloud. The research attempts to propose a hybrid algorithm using ant colony optimization (ACO) and the best-fit optimization algorithm for energy consumption in the cloud data center. The best-fit optimization algorithm generally places virtual machines and is particularly known to provide a close optimal solution in a reasonable interval of time by sorting the virtual machines in increasing and decreasing order. It was found from the current research that the ACO and best-fit optimization algorithms perform better than existing algorithms in terms of power consumption and usage of resources.
A Literature Review of Factors Related to Teaching that Influence the Quality of Online Education

This review discussed the influential factors of online education from the perspective of teaching. Based on the “effective teaching”, the most closely related to the quality of online education, the instructional design and the engagement are analyzed. These two aspects were closely related to the teaching process of the instructor in online education. Specifically, these two aspects can be subdivided into four factors including teaching content, technology, teaching skills and methods for engaged online teaching and the three types of interaction. It is found that these factors have a particular impact on the quality of online education according to the students’ academic performance, satisfaction, learning experience and so on. The quality/level of these factors positively correlates with the quality of online education. It means the more attentive the instructor is or the more energy and time they exert, the higher the quality of online education is. Finally, the suggestions on how to improve the quality of online education were also provided.
Location-Aware Privacy Preserving in Edge Computing

Edge computing migrates computing to the end user. It directly processes and makes decisions on the data locally. To a certain extent, similar to cloud computing, it avoids the long-distance transmission of data in the network and reduces the risk of privacy leakage. However, due to the users’ real-time data obtained by edge nodes, a large number of sensitive privacy data can be obtained by adversaries. The methodologies ensures the usage of the service without disclosing their sensitive location information have proposed higher requirements for privacy protection algorithms in edge computing.
Improve Code Search via Reformulating Queries With Evolving Contexts

To use code search, numerous question extensions and query expansion uses the program application interface or group information for growing an inquiry. Notwithstanding, these methodologies may once in a while contrarily sway the recovery execution. This is on the grounds viewed that, it cannot recognize the pertinent items from the unimportant items present in a huge arrangement of applicant development terms that extend an inquiry with superfluous terms. In this perspective, this research article proposes an inquiry reformulation approach called QREC with the developing settings that allude to new or erased terms and ward terms, while undergoing the code advancement.
Truncated Cauchy for Robust fast supervised discrete hashing

We propose a novel data-dependent hashing algorithm named Truncated Cauchy for Robust fast supervised discrete hashing (RFSDH) for robust subspace learning. In this paper, a Truncated Cauchy loss is proposed to measure the factorization error, which can handle outliers by truncating large errors. The proposed method can inhibit the unreliable binary codes, which generate the optimal binary codes. Our method can be expressed as a mixed-integer optimization problem, which can be to solve by iterative discrete cyclic coordinate descent. RFSDH method accomplishes outperform other hashing learning methods in image retrieval effect on two large scale datasets.
Literature review synthesis on predictors of Green IoT irrigation adoption in Morocco: Theoretical construct essay

The agricultural sector is unpredictable and complicated for farmers to manage it, especially with all the immoderate challenges that condemn the development of agricultural production. Regarding Morocco, water scarcity remains the major challenge that face the sector and put at risk the irrigation based fields-that represents approximately the half of the agricultural GDP-, the nutritional needs of the Moroccan population as well as the global sustainable growth. A way to handle this issue is adopting green IT that enable farmers to establish one of the latest trends in the global smart irrigation systems which are Green Internet Of Things or G-IoT driven smart irrigation. These technologies are currently highly requested and used in the Moroccan agricultural context contributing to the economic and environmental performance. Consequently, the aim of this study is to inspect the factors that affect the farmer's intention to adopt Green IoT based irrigation systems in the Moroccan case. The study is conducted on a literature review based on studies concerning the same subject matter in an international level, and contribute to the research development by proposing a theoretical model related to farmers’ Green IoT adoption.
EPIC Collab: Supporting Asynchronous Collaboration in Big Data Analysis Systems

The rise of big data has led to the creation of large datasets that require teams to collaborate to analyze data effectively. Unfortunately, the software systems that collect and analyze large datasets are not often designed to support this kind of collaboration. Accordingly, our work investigates issues related to supporting collaboration in big data analysis systems. We use the domain of crisis informatics and the software infrastructure of Project EPIC as a case study to gain insight into the features that analysts need to effectively perform analysis at scale. This paper focuses on supporting asynchronous collaboration among analysts who work in small distributed teams on big data software systems. It describes the challenges faced by researchers who work collaboratively to analyze large crisis datasets (consisting, typically, of Twitter data). It then describes the work performed to redesign an existing big data analysis environment to substantially improve its support for collaboration. The impact of this research lies in its ability to improve the work of similar teams performing large-scale data analysis. While our work is based on insights gleaned from crisis informatics, we believe that our design, results, and lessons learned are broadly applicable to other application domains.
Content-Neutral Immersive Environments for Cultivating Scalable Camaraderie

Drawbacks of online education, especially at scale, include the isolation and loss of community that come with taking classes from one's home rather than in a typical coordinated classroom. In this series of studies, we explore the potential of an immersive virtual environment to recreate shared educational contexts in an online program, emphasizing those that focus on universal dynamics like discussions, Q&A, and lecture-watching. Using Mozilla Hubs-which offers virtual reality as well as a browser-based immersive environment for participants without VR headsets-we implement three common, student-driven on-campus environments: a lecture hall, a student lounge, and a poster session. We test these environments with students in an online graduate program through surveys and a controlled experiment. We find significant interest in the potential of these environments, but hurdles to their adoption as well.
Mining Time-Stamped Electronic Health Records with Referenced Sequences

Electronic Health Records (EHRs) are typically stored as time-stamped encounter records. Judicious interpretation of temporal relationship between medical records is an integral part of assessing clinical information. Analogously, data analyzed by statistical or data mining methods need to contain time-interdependent analysis variables (TIAVs), whose values represent the clinical embodiment to be investigated. Unlike directly measured data, TIAV formulation is an iterative collaboration between programmer and investigator. This is because clinical TIAVs are context specific and often not absolute, and a custom program is needed to create and assess TIAVs. With rapidly growing interest in mining EHRs, there is a need for scalable solutions to optimize TIAV generation. We describe a framework of using sequences of time-referenced entities as building blocks. Scripts of simple functions are used with these entities to create TIAVs that incorporates multiple interdependencies, hence reducing the need for custom programs. We provide three examples to illustrate the principles of this method using the Veterans Health Administration’s EHR data.
Review on Robotic Machine to Solve the Matrix with Natural Language Processing and Image Processing

As the world is moving faster, humans are not taking a step back to make the world more better place, may it be by enhanced technology or extreme creativity. We know that a human life now is full of technology and every little thing is just a click away that is the favour of automation for everything. If we explore Automation more, the major concepts which will still the spotlight are Artificial Intelligent (AI), Machine Learning (ML) and the list will continue. The other concept which steals the limelight and make every automation possible is the programming language we use to make the features we are now using possible. The language which is exponentially gaining fame is Python. If all the above mentioned technology is combined together, we get most of the automation possible today. At every corner of the world, this technology is being used to make things lively and possible. Not only companies but every industry (food, entertainment, education, manufacturers and many more) are relying on this technology. The calculation which plays a significant role in almost every bit is also being automated to make the things very simpler and quicker. Even though, we have calculator but it still cannot solve the problems which are needed to be solve in some platforms. In this paper, we shall be discussing about a similar concept.
Hybrid Approach to Define Semantic Relationships

With the proliferation of textual data on the web, efficient access to pertinent information to meet user’s needs becomes an important problem in information retrieval field. Semantic relationships between terms plays an important role in information retrieval field in order to disambiguate document content.This paper presents a hybrid approach to define semantic relationships between terms. The evaluation of the proposed method has led to optimal results. Experimental results show that the use of these relationships in query expansion process improves the retrieval results.