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Processing of grapes and other fruits in the wine and fruit juice industry generates huge amounts of solid residues (pomace) which, if not properly disposed of, can represent an environmental concern due to their high organic load. To avoid landfill and incineration, different alternative uses are available and commonly applied. Among these, the most common are for feeding, composting and biogas production which are, anyway, not proper valorisation strategies. On the other hand, fruit pomaces are by-products still rich in bioactive components, such as dietary fibre and phenolic/antioxidant compounds. Considering the positive health potentials of such components, together with their potential technological role (as texturing and antioxidant ingredients), fruit pomaces may then be simply dried and milled to get functional food ingredients. It is known however, that incorporation of high levels of raw fibres into food products often causes unpleasant textures and colours. Furthermore, phenolic compounds only partially occur in fruit pomace as free compounds, while they are bound to cell wall (fibre) fractions. Enzymatic hydrolysis processes could be applied as a pre-drying treatment of fruit pomace to improve the functional properties of the final powders in terms of fibre composition and antioxidants release. In this study different fruit skins separated from different pomaces (grape, apple and blackcurrant) were submitted to an enzymatic treatment before drying. Two different commercial pectinase preparations were used: One already used in the apple juice processing for the treatment of apple and blackcurrant skins, the other currently used in the winemaking process for the treatment of grape skins. Untreated and treated dried skins were analysed for structural carbohydrates, soluble and insoluble dietary fibre, free glucose and xylose, water holding capacity, water solubility and total phenolics and antioxidants release. The results were highly variable depending on the fruit type, probably due to a different cell wall composition which requires targeted enzyme selection. In general, the enzyme treatment led to an increase in water solubility, water and oil holding capacity and free monosaccharides. Release of antioxidant compounds was observed only for apple peels.

Containerisation demonstrates its efficiency in application deployment in Cloud Computing. Containers can encapsulate complex programs with their dependencies in isolated environments making applications more portable, hence are being adopted in High Performance Computing (HPC) clusters. Singularity, initially designed for HPC systems, has become their de facto standard container runtime. Nevertheless, conventional HPC workload managers lack micro-service support and deeply-integrated container management, as opposed to container orchestrators. We introduce a Torque-Operator which serves as a bridge between HPC workload manager (TORQUE) and container orchestrator (Kubernetes). We propose a hybrid architecture that integrates HPC and Cloud clusters seamlessly with little interference to HPC systems where container orchestration is performed on two levels., European Union’s Horizon 2020 research and innovation programme, Projekt DEAL

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.

Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs&rsquo, ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

Front Matter and Back Matter

We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.

Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.

The IEEE 1588 precision time protocol (PTP) is very important for many industrial sectors and applications that require time synchronization accuracy between computers down to microsecond and even nanosecond levels. Nevertheless, PTP and its underlying network infrastructure are vulnerable to cyber-attacks, which can stealthily reduce the time synchronization accuracy to unacceptable and even damage-causing levels for individual clocks or an entire network, leading to financial loss or even physical destruction. Existing security protocol extensions only partially address this problem. This paper provides a comprehensive analysis of strategies for advanced persistent threats to PTP infrastructure, possible attacker locations, and the impact on clock and network synchronization in the presence of security protocol extensions, infrastructure redundancy, and protocol redundancy. It distinguishes between attack strategies and attacker types as described in RFC7384, but further distinguishes between the spoofing and time source attack, the simple internal attack, and the advanced internal attack. Some experiments were conducted to demonstrate the impact of PTP attacks. Our analysis shows that a sophisticated attacker has a range of methodologies to compromise a PTP network. Moreover, all PTP infrastructure components can host an attacker, making the comprehensive protection of a PTP network against a malware infiltration, as for example exercised by Stuxnet, a very tedious task.

The objective of this paper is to identify credit card fraud and this topic can be solved with the help of advanced machine learning and deep learning techniques. Due to the fact that credit card fraud is a serious worldwide problem, we have chosen to create a model for detecting im-poster scams by using deep neural networks. The purpose is to understand, determine and learn the normal behavior of the user and more precisely the detection of identity fraud. Each person has a trading pattern, uses certain operating systems, has a specific time to complete the transaction and spends large amounts of money usually within a certain time range. Transac-tions made by a certain user have a certain pattern, which can be identified with the help of neural networks. Machine learning involves teaching computers to recognize patterns in data in the same way as our brains do. Deep learning is just a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain. Deep learning at the core is the ability to form higher and higher level of abstractions of representations in data and raw patterns. The data used to train the model is real, and it will be processed using the one-hot encoding method, so that categorical data/variables can be used by the machine learning algorithm.

In this study, we discussed how can be the managementhierarchy and management process in a university campus. Erciyes University isin the middle of Anatolia Region that has about 60.000 population. Theuniversity have faculty /institutional administrations (29) and hospitalrelated buildings (9) and research centers (39) It has an area about 20 m2for each person in the campus. The university is responsible in terms oflegislative directives that are in operation. Waste directive is published bythe university in 2017 that share the responsibilities and distribute the tasksfor each unit in the university campus. In Turkey, zero waste project and zerowaste legislation is also in operation by 2019. The importance of waste management strategy is important for SustainableDevelopment Goals (SDG) that is published by United Nations and put inconsideration of Times Higher Education (THE) which is one of the mostconfidential ranking systems for universities. There is also, environmentalassessment system for university campuses, the Green-Metric. One of the gradingsubjects of Council of Higher Education (YÖK) in Turkey is the zero wastestrategy in campus. Therefore, the operation of zero waste strategy gainsimportance.

The quick improvement of nanotechnology permits a wide range of utilization of engineered nanoparticles, such as personal care products, medicals, optics, electronics, and automobiles. The nanoparticles manufactured from Ag, Au carbon-nanotube, ZnO, SiO2, TiO2, Cu, Ni, and magnetic ferrites are among the generally utilized in products. The nanoparticles are produced and utilized in large quantities and release into marine and freshwater ecosystems during production, use, discharge, treatment, and deposition. Those particles with a mean size of 1 nm - 100 nm are of potential environmental risks because of their particular qualifications and high reactivity although their great economical values. Based on the studies, the size, shape, and surface physical and chemical characteristics of the nanoparticles show the level of aggregation, solubility, structural and chemical composition, the importance of the use of nanoparticles, and their toxicity with biological systems. Nanoparticles can potentially cause adverse impacts on tissue, cellular, genetic materials, and protein- enzyme levels due to their unique physical and chemical qualifications. In this study, the effects of nanoparticles on aquatic organisms and aquatic ecosystems were evaluated.

When analyzing a block cipher, the first step is to search for some valid distinguishers, for example, the differential trails in the differential cryptanalysis and the linear trails in the linear cryptanalysis. A distinguisher is advantageous if it can be utilized to attack more rounds and the amount of the involved key bits during the key-recovery process is small, as this leads to a long attack with a low complexity. In this article, we propose a two-step strategy to search for such advantageous distinguishers. This strategy is inspired by the intuition that if a differential is advantageous only when some properties are satisfied, then we can predefine some constraints describing these properties and search for the differentials in the small set.As applications, our strategy is used to analyze GIFT-128, which was proposed in CHES 2017. Based on some 20-round differentials, we give the first 27-round differential attack on GIFT-128, which covers one more round than the best previous result. Also, based on two 17-round linear trails, we give the first linear hull attack on GIFT-128, which covers 22 rounds. In addition, we also give some results on two GIFT-128 based AEADs GIFT-COFB and SUNDAE-GIFT.

We present Orthros, a 128-bit block pseudorandom function. It is designed with primary focus on latency of fully unrolled circuits. For this purpose, we adopt a parallel structure comprising two keyed permutations. The round function of each permutation is similar to Midori, a low-energy block cipher, however we thoroughly revise it to reduce latency, and introduce different rounds to significantly improve cryptographic strength in a small number of rounds. We provide a comprehensive, dedicated security analysis. For hardware implementation, Orthros achieves the lowest latency among the state-of-the-art low-latency primitives. For example, using the STM 90nm library, Orthros achieves a minimum latency of around 2.4 ns, while other constructions like PRINCE, Midori-128 and QARMA9-128- σ0 achieve 2.56 ns, 4.10 ns, 4.38 ns respectively.

Many problems have been seen in cities because of increasing vehicle density. One of these problems is vehicle density in parking lots. People look for empty parking areas and they spend too much time. While people look for empty parking areas, CO2 (carbon dioxide) emission and energy consumption increase due to density in parking lots. We worked to solve these problems by doing Magnetic Car Park Sensor. Magnetic Car Park Sensor is the system which detects cars in car parks. After cars detected with the system, the system sends information to center server and we can see information data in the system interface. The system helps people to find empty parking lots. As people find empty car park areas fastly, energy consumption and CO2 emission are decreased significantly.

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

Idioms are multi-word expressions whose meaning cannot always be deduced from the literal meaning of constituent words. A key feature of idioms that is central to this paper is their peculiar mixture of fixedness and variability, which poses challenges for their retrieval from large corpora using traditional search approaches. These challenges hinder insights into idiom usage affecting users who are conducting linguistic research as well as those involved in language ed-ucation. To facilitate access to idioms examples taken from real-world contexts, we introduce an information retrieval system designed specifically for idioms. Given a search query that represents an idiom, typically in its canonical form, the system expands it automatically to account for the most common types of idiom variation including inflection, open slots, adjectival or adverbial modification, and passivisation. As a by-product of query expansion, other types of idiom varia-tion captured include derivation, compounding, negation, distribution across multiple clauses as well as other unforeseen types of variation. The system was implemented on top of Elasticsearch, an open-source, distributed, scalable, real-time search engine. Flexible retrieval of idioms is supported by a combination of linguistic pre-processing of the search queries, their translation into a set of query clauses written in a query language called Query DSL, and analysis, an indexing process that involves tokenisation and normalisation. Our system outperformed the phrase search in terms of recall and outperformed the keyword search in terms of precision. Out of the three, our approach was found to provide the best balance between precision and recall. By providing a fast and easy way of finding idioms in large corpora, our approach can facilitate further developments in fields such as linguistics, language education and natural language processing. Keywords: information retrieval; natural language processing; corpus linguistics; multi-word expressions; idioms

Context While the digital economy requires a new generation of technology for scientists and practitioners, the software engineering (SE) field faces a gender crisis. SE research is a global enterprise that requires the participation of both genders for the advancement of science and evidence-based practice. However, women across the world tend to be significantly underrepresented in such research, receiving less funding and less participation, frequently, than men as authors in research publications. Data about this phenomenon is still sparse and incomplete; particularly in evidence-based software engineering (EBSE), there are no studies that analyze the participation of women in this research area. Objective The objective of this work is to present the results of a systematic mapping study (SM) conducted to collect and evaluate evidence on female researchers who have contributed to the area of EBSE. Method Our SM was performed by manually searching studies in the major conferences and journals of EBSE. We identified 981 studies and 183 were authored/co-authored by women and, therefore, included. Results Contributions from women in secondary studies have globally increased over the years, but it is still concentrated in European countries. Additionally, collaboration among research groups is still fragile, based on a few women as a bridge. Latin American researchers contribute a great deal to the field, despite they do not collaborate as much within their region. Conclusions The findings from this study are expected to be aggregated to the existing knowledge with respect to women’s contribution to the EBSE area. We expect that our results bring up a reflection on the gender issue and motivate actions and policies to attract female researchers to this area.

Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

As a result of Turkey's economic growth, industrial development has accelerated across the country and this has ultimately led to the environmental sector and waste management gaining importance. In Turkey, where there is a depletion of natural resources, the expansion of energy demand, and the orientation in environmental technologies, waste is no longer a problematic issue that needs to be eliminated; but it has become a source of raw materials whose processing and recycling can be achieved with today's technology. In the scope of sustainable development, the waste hierarchy includes the three priority targets. These are prevention of waste production, reuse, recycling, or recovery respectively. The method for non-recoverable wastes is landfilling with energy recovery if possible. In this context, this study aims to investigate the electricity generation potential of the solid wastes disposed at the Edirne Solid Waste Landfill Facility. When the amount of waste to be sent to the facility, which was assumed to have a 20-year economic life, increases by 5% in parallel with the population profile, it is seen that electricity production will go up rapidly until 2040. However, the results of the study present that the potential of methane production will fall as the stored waste age increases so that a significant decrease in electricity production should be expected from 2045 onwards. Once it is considered that the facility continues to produce electricity for a hundred years beyond 2040, the electricity generated from the facility will contribute to the electricity network and provide the avoidance of approximately 25 thousand tons of CO2 on a national basis.

The paper presents an upgraded configuration of compact T-shaped microstrip patch antenna design, simulation, fabrication, and test on 1.6mm FR-4 substrate. The microstrip patch antenna is simulated using Sonnet Software. The proposed design is developed to be used as a transmitting antenna operating at 2.4 GHz radio signals with bandwidth ranging from 2.2 GHz to 2.6 GHz, and with input, match S11 less than -27 dB on 2.4 GHz frequency. The gain of the proposed antenna is 7.28 dB. The antenna was fabricated and measured results match with simulated in terms of frequency but measured S11 is lower due to lossy dielectric FR-4.

The global computing scale of the Internet has made it possible for users globally to Ubiquitously Interact Socially (UIS). This level of interaction and its gains have influenced the cases of privacy disclosures and data breaches, which are rampant due to the vulnerability of users’ personal information. The centralized architecture that drives UIS also contributed to this influence. There are also documented evidences of efforts to deal with this and enforce Sensitive Data Protection (SDP). Of particular interest in this paper is the use of the Theoretic of Leveraging the Technology of Blockchain (ToLToB) due to the synergistic techniques it offers to realise SDP. However, the right requirements and methods of elicitation must be applied to use the ToLToB in order to provide deployable software artifacts for SDP among untrusted peers. Interestingly, there is yet no known systematic approach to the best of the authors’ knowledge to elicit suitable requirements to use the ToLToB within the context of softwarization for SDP. To fill this gap, this work presents a user requirement engineering-based framework that is reproducible going by the outcomes from its use. These outcomes and paper’s contribution include a taxonomy, scenarios, functional and non-functional requirements, and examples profiling of attackers. In the future, personas will be extensively considered along with analysis using theoretic models to further expand and deepen the knowledge and understanding of specific protection mechanisms for online sensitive data.

Finding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present study aims at designing a mathematical model of co-author recommender system in bioinformatics using graph mining techniques and big data applications. The present study employed an applied-developmental research method and a mixed-methods research design. The research population consisted of all scientific products in bioinformatics in the PubMed database. To achieve the research objectives, the most appropriate effective features in choosing a co-author were selected, prioritized, and weighted by experts. Then, they were weighted using graph mining techniques and big data applications. Finally, the mathematical co-author recommender system model in bioinformatics was presented. Data analysis instruments included Expert Choice, Excel, Spark, Scala and Python programming languages in a big data server. The research was conducted in four steps: (1) identifying and prioritizing the criteria effective in choosing a co-author using AHP; (2) determining the correlation degree of articles based on the criteria obtained from step 1 using algorithms and big data applications; (3) developing a mathematical co-author recommender system model; and (4) evaluating the developed mathematical model. Findings showed that the journal titles and citations criteria have the highest weight while the abstract has the lowest weight in the mathematical co-author recommender system model. The accuracy of the proposed model was 72.26. It was concluded that using content-based features and expert opinions have high potentials in recommending the most appropriate co-authors. It is expected that the proposed co-author recommender system model can provide appropriate recommendations for choosing co-authors on various fields in different contexts of scientific information. The most important innovation of this model is the use of a combination of expert opinions and systemic weights, which can accelerate the finding of co-authors and consequently saving time and achieving a greater quality of scientific products.

El uso apropiado de los recursos informáticos se comprobará que es la herramienta que facilita al maestro lograr que los estudiantes desarrollen sus destrezas y criterios que les dará la posibilidad de un óptimo desenvolvimiento en el campo laboral y en sus actividades diarias. Encaminar en forma correcta la utilización de estas tecnologías modernas constituye una responsabilidad muy grande, razón por la cual, el docente que se encuentre encargado de capacitar a los educandos, deberá también poseer la capacidad y dominio con el fin de que ponga en práctica lo que enseña y para mejorar el proceso de enseñanza aprendizaje ya que, con la globalización, la información viaja con mayor rapidez y el conocimiento de cosas nuevas se encuentra al alcance de quien lo necesita. Se requiere, además, una apropiada preparación de los docentes para guiar a los jóvenes en mejorar y renovar los conocimientos de forma continua, al ritmo de los cambios tecnológicos que se producen constantemente. La propuesta sugiere dar el uso apropiado a los recursos informáticos existentes en las instituciones educativas, con el fin de optimizar la calidad de la educación tecnológica que se brinda a los estudiantes y de igual forma estimular a los docentes.

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks.

Modern applications demand resources at an unprecedented level. In this sense, data-centers are required to scale efficiently to cope with such demand. Resource disaggregation has the potential to improve resource-efficiency by allowing the deployment of workloads in more flexible ways. Therefore, the industry is shifting towards disaggregated architectures, which enables new ways to structure hardware resources in data centers. However, determining the best performing resource provisioning is a complicated task. The optimality of resource allocation in a disaggregated data center depends on its topology and the workload collocation. This paper presents DRMaestro, a framework to orchestrate disaggregated resources transparently from the applications. DRMaestro uses a novel flow-network model to determine the optimal placement in multiple phases while employing best-efforts on preventing workload performance interference. We first evaluate the impact of disaggregation regarding the additional network requirements under higher network load. The results show that for some applications the impact is minimal, but other ones can suffer up to 80% slowdown in the data transfer part. After that, we evaluate DRMaestro via a real prototype on Kubernetes and a trace-driven simulation. The results show that DRMaestro can reduce the total job makespan with a speedup of up to ≈1.20x and decrease the QoS violation up to ≈2.64x comparing with another orchestrator that does not support resource disaggregation. This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).

A partir del escenario marcado por el Covid-19, el mundo ha tenido que asumir trasformaciones en la forma de realizar sus diferentes actividades. Ecuador ha realizado ajustes estratégicos en el área de salud, ciencia, tecnología y educación; como los pilares básicos para la toma de las decisiones fundamentadas del país. Con la declaración de emergencia sanitaria en todo el territorio nacional, garantizar la eficiencia en los diferentes procesos representa una tarea de vital importancia. Las Instituciones de la Educación Superior, fueron pioneras en la adopción de las medidas preventivas asociadas a la Covid-19, y en la implementación del teletrabajo. En este escenario, cuantificar el comportamiento del talento humano ayudaría a garantizar los objetivos planteados y fomentar su crecimiento. La presente investigación tiene como objetivo implementar un modelo para la evaluación del desempeño. El modelo fue aplicado en trabajadores de la Universidad Estatal del Sur de Manabí. Como resultado se aplicó la técnica de Iadov para evaluar el nivel de satisfacción de los usuarios finales. La propuesta fue valorada de 0.94 representando alta satisfacción.

Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks. Many encryption techniques have been introduced to address the inability of such devices, lacking in robust security provision at low cost. This article presents an encryption technique, implemented on a resource constrained IoT device (AVR ATmega2560) through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function. The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.

La educación superior y sus procesos están en permanente cambio innovador, lo que la convierte en un campo propicio para la aplicación de las tecnologías disruptivas que favorecen al perfeccionamiento y desarrollo del conocimiento. El objetivo de esta investigación es conocer la importancia e identificar los beneficios del uso de estas herramientas, pues, se insertan con rapidez en la formación educativa con gran potencial, algunas de ellas son: la realidad virtual, la gamificación, la inteligencia artificial, realidad aumentada, las pizarras táctiles interactivas, plataformas colaborativas, las bibliotecas digitales en la nube, la era de big data y el análisis de datos, unas buscan hacer del aprendizaje una experiencia divertida y memorable, mientras que otras se pueden ubicar en el ámbito de los recursos para garantizar el acceso a la información y mejorar las estrategias educativas. Se utilizaron varios métodos y técnicas de investigación científica para el desarrollo del estudio, tales como, del nivel teórico, histórico – lógico y análisis – síntesis. Del nivel empírico: la observación y la revisión bibliográfica.Como resultados se tiene que para lograr estas innovaciones es necesario promover plataformas virtuales que amplíen los conocimientos y permitan alcanzar las competencias. Se concluyó que el futuro depende del uso de las tecnologías, estas están revolucionando la educación con éxito y marcan un antes y un después, los nuevos avances son herramientas poderosas que contribuyen a mejorar la enseñanza-aprendizaje superior.

La nueva sociedad del conocimiento propone el modelo propuesto se basa en la inteligencia de negocios, para la recolección, análisis e integración de datos que permita de una manera eficaz optimizar y controlar los procesos de calidad; la otra estrategia propuesta es computación en la nube. Está conformado por cuatro fases, que está relacionadas con procesos de Diagnosticar de cómo está el Aseguramiento de la Calidad, el proceso de planificar la Calidad, el proceso de Asegurar la Calidad y aspectos referidos al Control de Calidad. Los formatos propuestos para el Modelo de Estrategias, se pudieron validad y medir median una encuesta de Nivel de Aceptación (88) aplicada a colaboradores de la Empresa 1 y a expertos, para obtener el Nivel de Aprobación según su Juicio Experto (88.53)

El desarrollo de habilidades en la expresión oral representa un elemento esencial para garantizar la correcta comunicación entre las personas. Lograr el desarrollo de habilidades comunicativas permite a los estudiantes intercambiar ideas, pensamientos y la comprensión de conceptos. Sin embargo, se ha evidenciado notables deficiencias comunicativas en los estudiantes de la Universidad Estatal del Sur de Manabí, lo que dificulta el aprendizaje. La presente investigación propone una solución a la problemática planteada a partir de la elaboración de una estrategia comunicativa para el desarrollo de habilidades orales. La estrategia está compuesta por una secuencia de actividades que, en su conjunto, conforman la propuesta. Para validar la investigación se utilizó el método de experto a partir del cual se obtuvo una valoración de Muy Adecuada, considerándose un buen resultado.

Decompilation aims to analyze and transform low-level program language (PL) codes such as binary code or assembly code to obtain an equivalent high-level PL. Decompilation plays a vital role in the cyberspace security fields such as software vulnerability discovery and analysis, malicious code detection and analysis, and software engineering fields such as source code analysis, optimization, and cross-language cross-operating system migration. Unfortunately, the existing decompilers mainly rely on experts to write rules, which leads to bottlenecks such as low scalability, development difficulties, and long cycles. The generated high-level PL codes often violate the code writing specifications. Further, their readability is still relatively low. The problems mentioned above hinder the efficiency of advanced applications (e.g., vulnerability discovery) based on decompiled high-level PL codes.In this paper, we propose a decompilation approach based on the attention-based neural machine translation (NMT) mechanism, which converts low-level PL into high-level PL while acquiring legibility and keeping functionally similar. To compensate for the information asymmetry between the low-level and high-level PL, a translation method based on basic operations of low-level PL is designed. This method improves the generalization of the NMT model and captures the translation rules between PLs more accurately and efficiently. Besides, we implement a neural decompilation framework called Neutron. The evaluation of two practical applications shows that Neutron’s average program accuracy is 96.96%, which is better than the traditional NMT model.

Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

El software estadístico se ha convertido en el principal soporte para gestionar los grandes volúmenes de información que se generan diariamente en las organizaciones, las cuales hacen uso de la tecnología instalando poderosas herramientas de análisis de datos para obtener con mayor eficiencia información de los datos almacenados. Debido al gran cúmulo de información que genera el proceso de perfeccionamiento del sistema educacional cubano se deicide desarrollar un Mercado de Datos que apoye el proceso de toma de decisiones en el Instituto Central de Ciencias Pedagógicas sobre dicho proceso. Para guiar el proceso de desarrollo se utilizó la metodología de Almacenes de Datos del Centro de Representación y Análisis de Datos. Para el diseño e implemantación de la solución se utilizaron varias herramientas de software libre. Como herramienta de modelado Visual paradimg 8.0, para la extracción, transformación y carga de los datos el Pentaho Data Integration 7.1 y para la visualización de la información y los dashboard el Pentaho BI Server 6.0. Para comprobar el correcto funcionamiento del sistema se realizaron pruebas hacienda uso de las herramientas casos de prueba y manual de usuario. Las vistas de análisis y los cuadros de mando que conforman la capa de visualización del Mercado de Datos, permiten que los especialistas del Insttituto Central de Ciencias Pedagógicas realicen un análisis certero de la información referente a las actividades que son evaluadas en el proceso de perfeccionamiento en el sistema educacional cubano

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Las sociedades modernas confieren a la innovación una especial importancia, debido a la repercusión que en la actualidad representa sobre la subsistencia de la raza humana y el desarrollo social de los territorios. En la Asociación Nacional de Innovadores y Racionalizadores (ANIR) en Ciego de Ávila existen dificultades relacionadas con el registro, búsqueda y procesamiento de las innovaciones y racionalizaciones debido a que la información del modelo de libro de inscripción, los listados de plan temático y modelos de expediente único son confeccionados de forma manual, ocasionando demoras excesivas. Por estas razones se hace necesario desarrollar un sistema informático para la gestión de información de las innovaciones y racionalizaciones, que posibilite agilizar los registros de las investigaciones y realizar los pagos de los resultados en el tiempo establecido. El nuevo sistema se implementa con el Framework Symfony2 que utiliza los lenguajes de programación PHP y JavaScript con el gestor de base de datos MySQL que garantiza la seguridad requerida. La nueva propuesta permite contar con una herramienta que mejore el procesamiento de la información y el almacenamiento de un gran volumen de datos de forma centralizada. Además, un mejor tratamiento y visualización de la información, disminución del tiempo de ejecución de búsqueda de información y posibilita la organización y un estricto control de la información que se procese a través de una interfaz amigable y sencilla.

La generación, procesamiento y visualización de modelos tridimensionales es un estándar en cuanto a diseño o generación de gráficos por computadoras para numerosas ramas de la industria. En el presente trabajo se aborda el desarrollo de un visualizador Web avanzado de modelos tridimensionales para la Línea de Desarrollo de Videojuegos, del “Centro de Tecnologías Interactivas”, de la Universidad de las Ciencias Informáticas (UCI), en, debido a que las opciones de visualización del visor existente, no cumplen con los estándares del centro y la distribución de las funcionalidades es deficiente. Para el desarrollo del visor, se utilizó la metodología XP, la tecnología WebGL y las bibliotecas Three.js, OrbitControl y Stats. El visor propuesto, permite cargar ficheros en los formatos OBJ, STL, FBX y glTF, desde los principales navegadores Web, comportándose entre los 50 y 60 FPS. Adicionalmente, ofrece funcionalidades para manipular el color de fondo de la escena, las luces en la escena, el movimiento de la cámara y las animaciones del modelo. Además de posibilitar la rotación automática del modelo y la activación del filtro Wireframe.

El Aprendizaje Basado en Problemas (ABP) es una de las metodologías educativas que han tenido buena aceptación en instituciones educativas universitarias. Es un proceso activo de aprendizaje que funciona mediante la solución de problemas relacionados con la interacción de los estudiantes y su entorno profesional. La esencia del Aprendizaje Basado en Problemas consiste en identificar, describir, analizar y resolver tales problemas, lo cual se logra con ayuda del docente, desempeñando así otro papel tanto el proceso de enseñanza-aprendizaje como los estudiantes. Una forma en la que se ha incorporado recientemente esta metodología en el salón de clases de las carreras de ingeniería ha sido mediante trabajos teóricos que los estudiantes discuten. Por ejemplo, discuten los resultados de un artículo científico reciente en particular, del cual deben analizar los planteamientos presentados y que están relacionados con los problemas analizados y resueltos en las clases de matemática. Los objetivos principales son implementar el Aprendizaje Basado en Problemas en las clases del área de matemáticas como parte de las metodologías que los docentes tienen a su alcance e involucrar a los estudiantes en investigaciones de ciencia e ingenierías vinculadas a la Agropecuaria; la meta principal es que los estudiantes presenten sus resultados tanto en publicaciones de artículos, foros estudiantiles , en congresos nacionales y proyectos integradores de saber, como producto de la aplicación del Aprendizaje Basado en Problemas en el proceso de enseñaza-aprendizaje.

Al Proyecto Técnico Ejecutivo de los edificios multifamiliares del MININT en Villa Nueva, Holguín, Cuba, se le introducen modificaciones referentes a las cargas actuantes, el refuerzo estructural, las uniones y la secuencia constructiva de la cubierta y el entrepiso. Todas estas transformaciones, generadas por causas económicas, derivan en la variante integral de LPECP. Su ejecución resulta ventajosa, desde el punto de vista económico y de tiempo, para la Empresa Constructora del MININT, pero se desea corroborar si esta variante de LPECP ofrece seguridad a la estructura, mediante el cumplimiento de las Normas Cubanas de la Construcción y las Normas Ecuatorianas de la Construcción.

Background Adding buffers to networks is part of the fundamental advance in data communication. Since edge cloud computing is based on the heterogeneous collaboration network model in a federated environment, it is natural to consider buffer-aided data communication for edge cloud applications. However, the existing studies generally pursue the beneficial features of buffering at a cost of time, not to mention that many investigations are focused on lower-layer data packets rather than application-level communication transactions. Aims Driven by our argument against the claim that buffers “can introduce additional delay to the communication between the source and destination”, this research aims to investigate whether or not (and if yes, to what extent) the application-level buffering mechanism can improve the time efficiency in edge-cloud data transmissions. Method To collect empirical evidence for the theoretical discussion, we built up a testbed to simulate a remote health monitoring system, and conducted both experimental and modeling investigations into the first-in-first-served (FIFS) and buffer-aided data transmissions at a relay node in the system. Results An empirical inequality system is established for revealing the time efficiency of buffer-aided edge cloud communication. For example, given the reference of transmitting the 11th data entity in the FIFS manner, the inequality system suggests buffering up to 50 data entities into one transmission transaction on our testbed. Conclusions Despite the trade-off benefits (e.g., energy efficiency and fault tolerance) of buffering data, our investigation argues that the buffering mechanism can also speed up data transmission under certain circumstances, and thus it would be worth taking data buffering into account when designing and developing edge cloud applications even in the time-critical context.

Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

Los docentes universitarios están sometidos a condiciones laborales en constante cambio y evolución, una de ellas, es la calidad de vida laboral, sinónimo de bienestar personal como uno de los ámbitos importantes para el profesorado. Este bienestar es derivado de la satisfacción o insatisfacción que generan sus actividades laborales virtuales. El objetivo de la presente investigación es conocer el nivel de satisfacción de los docentes de la Universidad Estatal del Sur de Manabí, y establecer recomendaciones para la mejora de este. Se realizó un estudio con un enfoque cuantitativo de tipo descriptivo, transversal no experimental que permitió medir la calidad de vida laboral del personal académico de la Carrera de Enfermería. De un total de 48 docentes a los que se les aplicó la encuesta, se encontró bajos niveles de satisfacción en el trabajo en todos los factores estudiados, siendo los más preocupantes: el desarrollo personal logrado en el trabajo y la administración de tiempo libre y la insatisfacción que esto genera. La satisfacción laboral del docente es indispensable para el desarrollo eficiente y eficaz de las actividades académicas y personales. Resulta importante que las instituciones puedan generar estrategias para visibilizar y mejorar las condiciones laborales existentes que proporcionalmente mejorarán la satisfacción de los docentes y el desarrollo del proceso enseñanza aprendizaje.

El Proceso de Enseñanza-Aprendizaje se encuentra regulado por el nivel de asimilación de los conocimientos de los estudiantes. En un enfoque constructivista el estudiante auto regula su aprendizaje en la creación del nuevo conocimiento. Estimar el nivel alcanzado para la introducción de nuevos contenidos representa una problemática que debe ser estimada por el profesor para lograr el mejor escenario de aprendizaje. La presente investigación describe una solución al escenario antes planteado a partir de la implementación de algoritmos computaciones de Inteligencia Artificial en la predicción de nuevos conocimientos mediante una enseñanza constructivista. El núcleo de inferencia se gestiona mediante el algoritmo de árboles de decisión y el algoritmo de los k vecinos más cercanos. Como resultado se obtiene la propuesta de predicción de los nuevos conocimientos a enseñar.

Con el avance de las nuevas tecnologías de información y comunicaciones, diversos procesos que se llevan a cabo en las organizaciones se han favorecido sustancialmente, al punto de llegar a convertirse estas nuevas alternativas en un factor determinante en la eficiencia y organización. En la Facultad de Ciencias y Tecnologías Computacionales de la Universidad de las Ciencias Informáticas, se desarrolla el proceso de planificación y control de la Guardia Obrero Estudiantil. En el desarrollo del proceso se han manifestado varias deficiencias como continuas interrupciones por problemas en la planificación, insuficiente control de asistencia y duplicidad de información, que en su conjunto influyen negativamente en el funcionamiento y la calidad de este vital proceso que garantiza el cuidado y salvaguarda de los recursos. La presente investigación tiene como objetivo realizar el análisis, diseño, implementación y evaluación de un sistema informático, que contribuya a la planificación de la guardia, para favorecer la eficiencia, calidad y control de este proceso. Para llevar a cabo la propuesta de solución se utilizó como principal tecnología de desarrollo el framework Symfony 2.3, haciendo uso del lenguaje de programación PHP en su versión 5.5.

The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes are commonly built for intra-organization purposes and tailored towards specific use cases with the assumption of integrated model repositories and feature pools. Transferring such services and models beyond organization boundaries is a challenging task that requires human intervention to find the matching models and evaluate them. This paper investigates the potential of communication and transfer learning between smart environments in order to empower a decentralized and peer-to-peer ecosystem for seamless and automatic transfer of services and machine learning models. To this end, we explore different knowledge types in the context of smart built environments and propose a collaboration framework based on knowledge graph principles for describing the machine learning models and their corresponding dependencies.

La presente investigación brindó un aporte significativo, dando a conocer el impacto que han tenido las ciudades inteligentes en el Ecuador, el objetivo fue analizar el desarrollo en los últimos años de los servicios e infraestructura de telecomunicaciones y de las Ciudades Inteligentes.En el Ecuador se han desarrollado cambios en el área de las telecomunicaciones, existen avances significativos como utilización de las TIC, desarrollo de conceptos de ciudades inteligentes y sostenibles, Internet de las Cosas, entre otros. Las Ciudades Inteligentes innovan y aprovechan las TIC para mejorar la calidad de vida de los ciudadanos; así como, la eficiencia de los servicios urbanos y la competitividad.Los métodos científicos utilizados en el estudio fueron del nivel teórico y empírico.Los principales resultados en el país en telecomunicaciones son: 60 mil km de fibra óptica; reducción de 17 puntos porcentuales en la brecha digital; cobertura de red 4G en el 49% del territorio nacional.Los resultados evidenciaron los avances por años y la situación actual de las TIC’S en el Ecuador. Se concluye que el acceso a las Tic’s constituyen un derecho de todos los ciudadanos, pues promueve el desarrollo de la sociedad ecuatoriana.

Relay attacks pose a serious security threat to wireless systems, such as, contactless payment systems, keyless entry systems, or smart access control systems. Distance bounding protocols, which allow an entity to not only authenticate another entity but also determine whether it is physically close by, effectively mitigate relay attacks. However, secure implementation of distance bounding protocols, especially of the time critical challenge-response phase, has been a challenging task. In this paper, we design and implement a secure and accurate distance bounding protocol based on Narrow-Band signals, such as Bluetooth Low Energy (BLE), to particularly mitigate relay attacks. Narrow-Band ranging, specifically, phase-based ranging, enables accurate distance measurement, but it is vulnerable to phase rollover attacks. In our solution, we mitigate phase rollover attacks by also measuring time-of-flight (ToF) to detect the delay introduced by such attacks. Therefore, our protocol effectively combines the best of both worlds: phase-based ranging for accuracy and time-of-flight (ToF) measurement for security. To demonstrate the feasibility and practicality of our solution, we prototype it on NXP KW36 BLE chips and evaluate its performance and relay attack resistance. The obtained precision and accuracy of the presented ranging solution are 2.5 cm and 30 cm, respectively, in wireless measurements. ispartof: IACR Transactions on Cryptographic Hardware and Embedded Systems vol:2021 issue:2 pages:106-135 status: published

Parallel versions of collision search algorithms require a significant amount of memory to store a proportion of the points computed by the pseudo-random walks. Implementations available in the literature use a hash table to store these points and allow fast memory access. We provide theoretical evidence that memory is an important factor in determining the runtime of this method. We propose to replace the traditional hash table by a simple structure, inspired by radix trees, which saves space and provides fast look-up and insertion. In the case of many-collision search algorithms, our variant has a constant-factor improved runtime. We give benchmarks that show the linear parallel performance of the attack on elliptic curves discrete logarithms and improved running times for meet-in-the-middle applications.

White-box cryptography attempts to protect cryptographic secrets in pure software implementations. Due to their high utility, white-box cryptosystems (WBC) are deployed by the industry even though the security of these constructions is not well defined. A major breakthrough in generic cryptanalysis of WBC was Differential Computation Analysis (DCA), which requires minimal knowledge of the underlying white-box protection and also thwarts many obfuscation methods. To avert DCA, classic masking countermeasures originally intended to protect against highly related side-channel attacks have been proposed for use in WBC. However, due to the controlled environment of WBCs, new algebraic attacks against classic masking schemes have quickly been found. These algebraic DCA attacks break all classic masking countermeasures efficiently, as they are independent of the masking order.In this work, we propose a novel generic masking scheme that can resist both DCA and algebraic DCA attacks. The proposed scheme extends the seminal work by Ishai et al. which is probing secure and thus resists DCA, to also resist algebraic attacks. To prove the security of our scheme, we demonstrate the connection between two main security notions in white-box cryptography: probing security and prediction security. Resistance of our masking scheme to DCA is proven for an arbitrary order of protection, using the well-known strong non-interference notion by Barthe et al. Our masking scheme also resists algebraic attacks, which we show concretely for first and second-order algebraic protection. Moreover, we present an extensive performance analysis and quantify the overhead of our scheme, for a proof-of-concept protection of an AES implementation.