Parallel Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

MuLSi-Co: Multilayer Sinks and Cooperation-Based Data Routing Techniques for Underwater Acoustic Wireless Sensor Networks (UA-WSNs)

Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA-WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi-Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi-Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

Analysis of Bluetooth Versions (4.0, 4.2, 5, 5.1, and 5.2) for IoT Applications

IoT includes many sensors that have to collect the data and send it to the superior nodes; for such interaction between the IoT devices, various wireless technologies are available, like infrared, Li-Fi, WI-Fi, Zigbee, Bluetooth, etc. Among all the available, Bluetooth proved the most promising short-range wireless communication technology due to various factors. To fulfil the increasing demand for wireless connectivity, the Bluetooth SIG must continuously perform up-gradation. Here, analysis of Bluetooth versions are discussed based on the characteristics such as speed, bandwidth, range, power, message capacity, beacon provision, compatibility, reliability, errors detection, correction capability, advertisement packets, duty cycle, slot availability masks, and many more. This analysis concluded that all the versions have their own set of merits and limitations. For the basic IoT applications (limited functionalities), Bluetooth 4.0/4.2 is a good choice, while for the complex IoT applications (advance functionalities), Bluetooth 5/ 5.1/ 5.2 is better.

Levenberg-Marquardt method for absolute value equation associated with second-order cone

<p style='text-indent:20px;'>In this paper, we suggest the Levenberg-Marquardt method with Armijo line search for solving absolute value equations associated with the second-order cone (SOCAVE for short), which is a generalization of the standard absolute value equation frequently discussed in the literature during the past decade. We analyze the convergence of the proposed algorithm. For numerical reports, we not only show the efficiency of the proposed method, but also present numerical comparison with smoothing Newton method. It indicates that the proposed algorithm could also be a good choice for solving the SOCAVE.</p>

Model Checking M2M and Centralised IOT authentication Protocols.

It is very difficult to develop a perfect security protocol for communication over the IoT network and developing a reliable authentication protocol requires a detailed understanding of cryptography. To ensure the reliability of security protocols of IoT, the validation method is not a good choice because of its several disadvantages and limitations. To prove the high reliability of Cryptographic Security Protocols(CSP) for IoT networks, the functional correctness of security protocols must be proved secure mathematically. Using the Formal Verification technique we can prove the functional correctness of IoT security protocols by providing the proofs mathematically. In this work, The CoAP Machine to Machine authentication protocol and centralied IoT network Authentication Protocol RADIUS is formally verified using the well-known verification technique known as model checking technique and we have used the Scyther model checker for the verification of security properties of the respective protocols. The abstract protocol models of the IoT authentication protocols were specified in the security protocol description language and the security requirements of the authentication protocols were specified as claim events.

The Influence of Rootstock on Fruit Ethylene, Respiration, Index of Absorbance Difference, Fruit Quality, and Production of ‘Aztec Fuji’ Apple under a Full-crop Condition

Selection of dwarfing rootstocks that facilitate optimum production of high-quality fruit is crucial in modern high-density apple orchards. In addition to tree growth and yield, rootstocks can influence fruit maturity of scion cultivars in apples. In this study, the impact of 17 rootstocks on fruit maturity, yield, and quality attributes of ‘Aztec Fuji’ apples (Malus domestica Borkh.) at harvest were evaluated in a season when all trees were in a “full-crop” condition. Keeping sealed fruit at room temperature, a typical climacteric pattern was observed in ethylene evolution, respiration, and oxygen consumption, peaking after 5–7 days in fruit from trees on all rootstocks. During the ripening period, ethylene evolution and respiration rates in fruit from trees on Supp.3, G.3001, and G.202 were often in the high-range category, whereas those on CG.4004, CG.4214, G.41N, and B.9 were in the midrange category and those on M.9Pajam2, M.26EMLA, and G.11 were in the low-range category. Evolved ethylene and respiration in fruit from trees on M9.T337 steadily and slowly increased from 7 days after harvest (7DAH) to 13 days after which harvest (13DAH) ethylene sharply increased, signaling occurrence of climacteric peak, while respiration declined after the peak of 13DAH. In fruit from trees on most rootstocks, the rates of oxygen consumption had inverse relationships with the rates of respiration, so that fruit from trees on M9.T337 had higher and those on G.41N and Supp.3 had lower rates of oxygen consumption. Trees on G.41N, CG.4004, and M.26EMLA had higher and those on CG.4003 had lower yield per tree than trees on other rootstocks. Trees on B.9 and M.9T337 were most yield efficient among trees on all rootstocks. Trees on CG.4004 had larger fruits than those on other rootstocks. Considering all fruit maturity, quality, and yield attributes, CG.4004 seems to be a good choice of rootstock for ‘Aztec Fuji’ under the conditions of this study.

Federated Learning for 5G Radio Spectrum Sensing

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Prior latent distribution comparison for the RNN variational autoencoder in low-resource language modeling

Probabilistic Bayesian methods are widely used in the machine learning domain. Variational Autoencoder (VAE) is a common architecture for solving the Language Modeling task in a self-supervised way. VAE consists of a concept of latent variables inside the model. Latent variables are described as a random variable that is fit by the data. Up to now, in the majority of cases, latent variables are considered normally distributed. The normal distribution is a well-known distribution that can be easily included in any pipeline. Moreover, the normal distribution is a good choice when the Central Limit Theorem (CLT) holds. It makes it effective when one is working with i.i.d. (independent and identically distributed) random variables. However, the conditions of CLT in Natural Language Processing are not easy to check. So, the choice of distribution family is unclear in the domain. This paper studies the priors selection impact of continuous distributions in the Low-Resource Language Modeling task with VAE. The experiment shows that there is a statistical difference between the different priors in the encoder-decoder architecture. We showed that family distribution hyperparameter is important in the Low-Resource Language Modeling task and should be considered for the model training.

Speech Scrambling based on Particle Swarm Optimisation

Securing any communication system where important data may be transmitted through the channel is a very crucial issue. One of the good solutions in providing security for the speech is to use speech scrambling techniques. The chaotic system used in security has properties that make it a good choice for scrambling speech signal and the optimisation algorithm can provide a perfect performance when used to enhance the hybrid of more than one method. In this paper, we suggest a system that uses an optimisation method, namely, particle swarm optimisation. The evaluation measures prove that the output of the optimisation method has better performance among the methods used in the comparison, including chaotic maps and hybrid chaotic maps.

THEORETICAL AND EXPERIMENTAL STUDY OF MACROMOLECULAR NANOSTRUCTURES BASED ON HEPARIN AND LANTHANOIDS

Исследование синтетических и природных материалов пригодных для создания наноносителей и их модификация обеспечит прорыв в лечении многих заболеваний. Хорошим выбором для создания наноносителей являются гликозаминогликаны (гепарин и его производные), благодаря их уникальным биологическим и физико-химическим особенностям. Формирование композиций было исследовано методом pH -метрического титрования при 37 °С на фоне 0,15 М NaCl. С использованием программы NewDALSFEK определены значимые формы и химические равновесия. В диапазоне pH от 2,7 до 5 образуется комплекс вида {[LnHep]}, где Hep - мономерное звено макромолекулы гепарина. Получены данные об устойчивости нанокомпозиций: lgβ[PrHep] = 4,27 ± 0,04, lgβ[SmHep] = 4,28 ± 0,03 , lgβ[EuHep] = 4,28 ± 0,03. Методом M06-HF в сочетании с базисным набором CSDZ+* выполнено квантово-химическое моделирование комплексов. Study of synthetic and natural materials suitable for the creation of nanocarriers and their modification will provide a breakthrough in the treatment of many diseases. Glycosaminoglycans (heparin and its derivatives) are a good choice for creating nanocarriers due to their unique biological and physicochemical properties. The complexation of Pr (III), Sm (III), Eu (III) with heparin anions was studied by potentiometric titration at 37 °C and an ionic strength of 0,15 M NaCl. Significant forms and chemical equilibria were determined using the NewDALSFEK program. In the pH range from 2,7 to 5 , a complex of the type {[LnHep]} is formed, where Hep is a monomeric unit of the heparin macromolecule. Data on the stability of nanocompositions were obtained: lgβ[PrHep] = 4,27 ±0,04, lgβ[SmHep] = 4,28±0,03, lgβ[EuHep] = 4,28±0,03. The M06-HF method in combination with the CSDZ+* basic set was used to perform quantum chemical modeling of the complexes.