Reviewerly/exHarmony huggingface dataset:
Trust Assessment Model Based on a Hierarchical Decision-Making Process in IoT Sensor Networks

The Internet of Things (IoT) and its security issues have been gaining increasing attention in recent years due to the increasing number of threats and attacks against IoT devices. Specifically, false data injection attacks aim at tampering with the sensed data that reflect the normal system operation state. Therefore, a time-efficient and accurate approach to assessing sensor trustworthiness is necessary. To this end, we propose T-LHDM (a trust model based on low and high decision-making processes), a sensor state trust model based on a Markov chain to represent sensor state transitions in normal operating conditions. To demonstrate the model, we first observed sensed data in a hierarchical decision-making process by intentionally exploiting the sensor data’s temporal and spatial correlations. Then, the consistency between the precomputed set of sensor state transitions and the observed ones was used to detect corruptions and assess the sensor trustworthiness. Our experiments using two real-world datasets demonstrated that the proposed model accurately assessed the sensor trustworthiness with an average accuracy of 95%. We also stress the significance of considering the sensor’s spatiotemporal correlations to assess the trust of the sensed data, which has been neglected in previous studies. To this end, we conducted a comparative evaluation against a contemporary temporal correlation-based method and three well-known correlation tests. The results showed that T-LHDM outperformed all the competing methods.
Active Gaze Control for Foveal Scene Exploration

Active perception and foveal vision are the foundations of the human visual system. While foveal vision reduces the amount of information to process during a gaze fixation, active perception will change the gaze direction to the most promising parts of the visual field. We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene, identifying the objects present in their surroundings within least number of gaze shifts. Our approach is based on three key methods. First, we take an off-the-shelf deep object detector, pre-trained on a large dataset of regular images, and calibrate the classification outputs to the case of foveated images. Second, a body-centered semantic map, encoding the objects classifications and corresponding uncertainties, is sequentially updated with the calibrated detections, considering several data fusion techniques. Third, the next best gaze fixation point is determined based on information-theoretic metrics that aim at minimizing the overall expected uncertainty of the semantic map. When compared to the random selection of next gaze shifts, the proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts and reduces to one third the number of required gaze shifts to attain similar performance.
Knowledge creation cycle impact project success: work experience and position as moderator variables

One of the main problems of organisations has always been the lack of adequate consideration about employees' knowledge. In this regard, the importance of this factor in project-oriented firms can be a debatable subject. This article aimed to respond on how project success can be influenced by the knowledge creation process. This quantitative study was conducted based on the questionnaire, and confirmed through face validity and constructs validity. Two hundred thirty-four managers and employees from the National Iranian Copper Industries Company as a project-oriented organisation participated in this study. Analysing the data depicted that there was a positive relationship between the knowledge creation processes (0.697) and project success, and, work experience has a moderator effect on the hypothesis. By implementing the knowledge creation cycle, in the way of paying attention to the current knowledge and information, project-based organisations can enhance the success of projects.
Implementation of GKMC Algorithm for Data Anonymization on Big Data Platform Spark

Data mining on data set might reveal some private information. Because of this, privacy preserving data mining can be used, to protect data privacy, yet still preserve the data utility. When the data utility is high, some meaningful insights can still be obtained. The data utility can be measured by computing the information loss. The higher the information loss, the lower the data utility of the data set. One of the privacy preserving data mining techniques is k-anonymity. The k-anonymity technique is not yet implemented on big data platform. Now, the data size grows very fast, hence the used of techniques to protect big data privacy is needed. However, some modification in the implementation should be done, so that the technique can still work properly and fast enough on the platform. In this research, two k-anonymity techniques are used, namely, Greedy k-Member Clustering (GKMC) algorithm and generalization. The two techniques are implemented on big data platform Spark; parts of the steps are already made parallel, thus can work very fast. Experiments are conducted to find out the execution time of each technique and the total information loss. From the experiments, it can be concluded that, as the value of k grows, the execution time and the total information loss decrease.
Learning Enriched Features for Image Super Resolution

In recent years, significant progress has been made in image super-resolution (SR) methods based on convolutional neural networks. However, most of them do not fully utilize multi-scale feature correspondence in the image SR process, resulting in blurred and artifact detail restoration, especially for SR tasks with larger scaling factors (i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 8$ </tex-math></inline-formula> ). A multi-scale feature-enhanced SR network (MFENet) is proposed to solve the problems mentioned above. Specifically, the super-resolution method is enhanced by utilizing multi-scale feature correspondence in two ways. On the one hand, we propose an effective multi-scale non-local attention module and it can fully exploit a large number of low-level features at different scales of a single low-resolution (LR) feature map by exhaustively evaluating the correlations in local feature, in-scale non-local feature, and cross-scale non-local feature. On the other hand, the mapping from high-resolution (HR) to LR images is explored by a feedback branch we introduced, that provides an additional constraint for images reconstruction. It can penalize incorrect SR predictions to achieve better SR performance. Experimental results on 5 benchmark datasets show that our method outperforms state-of-the-art methods in terms of both subjective visual quality and objective quantitative metrics.
“Chevalier’s Epee”, “Victor” and “Actor”: Unity and Continuity of the Three Plays by M. E. Levberg

The article introduces the unpublished plays by Maria Evgenievna Levberg (1892–1934) “Victor” and “Actor” (1926). A hypothesis is put forward about the dating of the “Victor” (c. 1914–1915), based on comparison of this “dramatic study” with the published “dramatic presto” by Levberg “Stones of Death” (1915). The plot and motif complex and the system of images, which are common for “Victor” and “Actor”, as well as for the published drama by Levberg “Chevalier’s Epee” (1916), are revealed. In all three cases, the plot may be reduced to the formula of the role and mask becoming the “true” face of their wearer; in the later play “Actor” everything that is “heavy”, “ambivalent” and “tragic” associated with such a plot turns out to be “removed” on the metalevel. In all three plays, the central character, represented by the type of a young man “of the people”, a witty adventurer, greedy for life and success, but not devoid of noble impulses, is charged with the motifs of excess strength, love of life, attraction at the same time to the great and simple, high and “low”, as well as the motifs of luck, of being “the chosen one”. The motif of love for the “extraordinary man”, which the female characters of three plays feel towards these heroes, is also constant. Due to the self-reflexive layer of the play “Actor” and the panegyric of the theater which it contains (including the ancient topos of “consolation by art”), the development of the play’s conflict ends not ambiguously, as in “Chevalier’s Epee”, and not tragically, as in “Victor”, but in a comical way that resolves life’s contradictions.
Development of the skills of foreign language writing in high school students on the basis of a personality-oriented approach

The necessity of developing the skills of foreign language writing in high school students based on a personality-oriented approach and the principles of self-actualization, individualization, subjectivity, choice, creativity and success, trust and support is substantiated. The specificity of written speech is considered in comparison with oral speech based on the analysis of the literature of well-known psychologists, linguists, methodologists in the field of teaching foreign languages. Groups of skills (cognitive-pragmatic, logical and rhetorical) are defined. The developed system of exercises is implemented in three stages. At the preparatory stage, there is an activation of the students’ knowledge on the topic, familiarization with new material and the beginning of the development of cognitive-pragmatic, logical and rhetorical skills. At the training stage, the development of logical and cognitive-pragmatic skills continues, as well as exercises for the development of rhetorical skills, with a gradual increase in students’ independence in completing tasks. The main goal of these stages is to write an essay in English.
Predictive Analysis on Sensor Data Using Distributed Machine Learning

The science of getting computers to act without being explicitly programmed is known as machine learning. Machine learning is comparable to data mining in terms of how it works. Both systems sift through data in order to find patterns. Machine learning, on the other hand, instead of extracting data for human comprehension as in data mining applications, uses that data to increase the program's own understanding. Data patterns are detected by machine learning programmes, which then alter Programme behaviours accordingly. This research is based on real-world data obtained from sensors in an oil and Gas Corporation that monitor drilling procedures and equipment. The sensor data is streamed in at a one-second period, resulting in 86400 rows of data per day. Ox data's H2O has chosen for this problem after researching state-of-the-art Big Data analytics tools such as Mahout, RHadoop, and Spark because of its rapid in-memory processing, robust machine learning engine, and ease of use. Missed values can be estimated using accurate predictive analytics of massive sensor data, or wrong readings can be replaced owing to malfunctioning sensors or a broken communication line. It can also be used to predict circumstances to aid in various decision-making processes, such as maintenance planning and operation. In this project, sensor data has been evaluated and anticipate output using the H2O tool. The machine learning techniques has been employed in distributed systems, such as connecting five nodes to accomplish parallel processing.
Mixed-Variable Tuning with Particle Swarm Optimization

This work builds an implementation of particle swarm optimization that can be used to tune hyperparameters, such as those required to conFigure detection and classification algorithms for automatic target recognition. The proposed optimization method must include the capability to optimize over sets of parameters with diverse types. This is done by incorporating several previous developments from particle swarm optimization research into a single polymorphic and extensible framework. The method is tested by application to a set of standard test cases, and proof-of-concept for ATR algorithm parameter tuning is demonstrated for a detection algorithm on a set of dual-band synthetic aperture sonar data.
Effects of Volunteer Club Activity Using Nudge Strategies on Aggression, Self-Esteem, and School Life Satisfaction in Middle School Students

This study is a non-equivalent control group pretest-posttest design similar experimental study to verify the effects of volunteer club incorporating nudge strategies on aggression, self-esteem, and school life satisfaction of middle school students. From March 31, 2021 to July 30, 2021, it was conducted as a club activity during the creative experience time, which was held on average 2.5 times or more every month for 2 hours on Friday afternoons, a total of 12 times. The data of 35 in the experimental group and 35 in the control group were collected. As a result of the study, first, the experimental group who participated in the volunteer club using the nudge strategy had a decrease in aggression than the control group, and the self-esteem and school life satisfaction increased. This study is meaningful in proving that it is a very useful educational tool for forming good habits if it induces behavior to choose the right values and identity through a free and gentle nudge, rather than the rigid method of the past in middle school. The purpose is to help establish policy measures and to emphasize the justification of education.
Spike Encoding of Mel Frequency Cepstrum as an Input Stage for Speech Recognition

Spiking Neural Network (SNN) uses spike which is a discrete event that occurs in time, as opposed to continuous time signals. This allows low cost resources implementation in electronic devices because the spikes occurs in particular time events. In this research, we implement and test various spike encoding techniques of mel frequency cepstrum designed for automatic speech recognition system with SNN method. The implementation is done in the Xilinx Pynq-Z2 Field Programmable Gate Array (FPGA). This Pynq-Z2 is equipped with an ARM Processor that will be used as the whole computational process. The preprocessing audio signal uses Mel Spectogram method which is the output spectrogram and translated into spike trains the will serve as input to a SNN. Results show that the latency spike encoding method results in the least amount of spikes. Substantial memory and power savings from spike encoding of audio signals has been shown and can potentially be used as the input stage for a low-cost hardware implementation of a speech recognition system.
TEACHING ENGLISH THROUGH ZOOM VIDEO CONFERENCING: SOME EFL TEACHERS’ EXPERIENCES

This article attempts to explore the EFL teachers’ experiences in using zoom video conferencing platform as the technological massive changes during remote learning in the pandemic situation. The research question is how EFL teachers’ experience in the perception toward zoom video conferencing platform. The aims are to know EFL teachers’ experience toward zoom video conferencing platform in the perception of its usefulness, ease of use and satisfaction during the zoom video session. The design of this study is a mixed method analysis. The data instrument was collected using close ended questionnaire and interview. The data was gathered from 27 respondents. The findings are most of teachers have positive experience on its usefulness, attractiveness and user friendly. Only few teachers have negative experiences due to the limited internet quota and unstable connection.
FEATURE UTILIZATION BY MACHINE LEARNING MODELS FOR COLON CANCER CLASSIFICATION

Many machine learning methods are now available for classification and prediction tasks in the healthcare domain. Some traditional statistical methods, such as logistic regression, are much more readily interpretable than the newer machine learning
Research on the reform system of interactive teaching curriculum based on computer big data

Taking the problems existing in the teaching of college English majors as the breakthrough point, the author puts forward the application of innovative interactive teaching mode in college English majors under the background of big data. On the basis of summarizing the characteristics and forms of interactive teaching mode, the author discusses the application path of innovative interactive teaching curriculum reform in college English majors under the background of big data. The author hopes to provide new directions for college English majors curriculum teaching reform.
Tight Bounds on the Randomness Complexity of Secure Multiparty Computation

We revisit the question of minimizing the randomness complexity of protocols for secure multiparty computation (MPC) in the setting of perfect information-theoretic security. Kushilevitz and Mansour (SIAM J. Discret. Math., 1997) studied the case of n-party semi-honest MPC for the XOR function with security threshold $$t<n$$ , showing that $$O(t^2\log (n/t))$$ random bits are sufficient and $$\varOmega (t)$$ random bits are necessary. Their positive result was obtained via a non-explicit protocol, whose existence was proved using the probabilistic method. We essentially close the question by proving an $$\varOmega (t^2)$$ lower bound on the randomness complexity of XOR, matching the previous upper bound up to a logarithmic factor (or constant factor when $$t=\varOmega (n)$$ ). We also obtain an explicit protocol that uses $$O(t^2\cdot \log ^2n)$$ random bits, matching our lower bound up to a polylogarithmic factor. We extend these results from XOR to general symmetric Boolean functions and to addition over a finite Abelian group, showing how to amortize the randomness complexity over multiple additions. Finally, combining our techniques with recent randomness-efficient constructions of private circuits, we obtain an explicit protocol for evaluating a general circuit C using only $$O(t^2\cdot \log |C|)$$ random bits, by employing additional “helper parties” who do not contribute any inputs. This upper bound too matches our lower bound up to a logarithmic factor.
Examining the role of environmental sustainability measures cleanliness, health, safety, and its impact on visitors satisfaction and perception of service quality at expo 2020, Dubai

Aim: This research paper examined the role of environmental sustainability measures, including cleanliness, healthy, and safety, and their impact on visitors’ satisfaction and perception of service quality at Expo 2020 Dubai. To limit the extent of the review, the analyst focused primally on the tourist sector of the United Arab Emirates. This constrained the outcome of the research and established research boundaries.Methodology: The analyst examined 3 to 5 tourists and 3 students or locals from various places to get the result. The qualitative technique was conveyed using an interview format.Findings: The major findings point to positive and encouraging reactions from locals and tourists, as well as opportunities to raise residents’ level of engagement to make the event a success. The key discoveries from Expo 2020 Dubai will assist policymakers and decision-makers in the Dubai economy.Implications/Novel Contribution: The major findings point to positive and encouraging reactions from locals and tourists, as well as opportunities to raise residents’ level of engagement to make the event a success. The key discoveries from Expo 2020 Dubai will assist policymakers and decision-makers in the Dubai economy.
Substring Complexities on Run-Length Compressed Strings

Let $$ S _{T}(k)$$ denote the set of distinct substrings of length k in a string T, then its cardinality $$| S _{T}(k)|$$ is called the k-th substring complexity of T. Recently, $$\delta = \max \{ | S _{T}(k)| / k : k \ge 1 \}$$ has been shown to be a good compressibility measure of highly-repetitive strings. In this paper, given T of length n in the run-length compressed form of size $$\rho $$ , we show that $$\delta $$ can be computed in $$ C _{\textsf{sort}}(\rho , n)$$ time and $$O(\rho )$$ space, where $$ C _{\textsf{sort}}(\rho , n) = O(\min (\rho \lg \lg \rho , \rho \lg _{\rho } n))$$ is the time complexity for sorting $$\rho $$ integers with $$O(\lg n)$$ bits each in $$O(\rho )$$ space in the Word-RAM model with word size $$\varOmega (\lg n)$$ .
Gpu Acceleration of Levenshtein Distance Computation between Long Strings

Computing edit distance for very long strings has been hampered by quadratic time complexity with respect to string length. The WFA algorithm reduces the time complexity to a quadratic time complexity with respect to the distance between the strings. A GPU implementation of the WFA algorithm and a new optimization are presented that can halve the elements to be computed, providing additional performance gains. The level of performance achieved is above TCUPS for strings with similarities greater than 70%.
THE LAKE MEAD FAULT SYSTEM AND OLIGO-MIOCENE BASINS IN THE VIRGIN MOUNTAINS: COLLABORATION BETWEEN DR. PAUL UMHOEFER, HIS STUDENTS, AND COLLEAGUES

Basin and Range extension in the Lake Mead region, Nevada, is unique in that strike-slip faulting exerts major control on extensional processes. The south Virgin Mountains (SVM) in eastern Lake Mead are cut by multiple NE-striking faults that form the Lake Mead left-lateral fault system (LMFS). Here, details of early basin formation and the onset of left-normal oblique faulting of the LMFS are well preserved because they are not overprinted by younger deformation as is the case to the west. In the 1980s, Bob Bohannon defined the Oligo-Miocene Horse Spring Formation for the Lake Mead region, and in the 1990s Beard focused within the SVM on the interaction between lower basin strata and onset of faulting. Starting in the 2000s, an NSF-funded university research group, led by the highly enthusiastic team of Paul Umhoefer, Lisa Lamb, Tom Hickson, Bill McIntosh, Nelia Dunbar, along with Sue Beard (USGS), began a new effort in the Lake Mead area. Within the SVM, Lamb et al. (2018) elevated the Rainbow Gardens Member to formation status, and MS students of Umhoefer’s (Matt Schmidt, 2014; Lane Tucker, 2017; Daniel Conrad, 2018) as well as undergraduate students led by Lamb and Hickson, focused on detailed stratigraphic and structural studies of Oligo-Miocene geology. These NSF-funded studies are being used to update USGS 100k and 24k geologic maps from the 1980s to the 2000s. In particular, the eastern termination and stepover structures of the individual LMFS strands are key structural features of the SVM. The interaction of sedimentation and faulting at the NE end of the Lime Ridge fault (LRF) strand was mapped originally by Beard and improved upon by Schmidt, using higher precision Ar-dating and detailed facies mapping. Schmidt determined that extension initiated ~17–16.3 Ma along east-side down normal faults within a stepover zone between the LRF and the Piedmont fault, a LMFS strand to the NNE. As displacement progressed, deformation shifted to left normal oblique faulting along the LRF ~15.4 Ma. By ~14 Ma, most deformation had shifted westward and the SVM were uplifted, such that basins were inverted and cannibalized into younger basins to both the east and west (Beard, 1996). In honor of Paul Umhoefer, who encouraged us for years to do this, we present the melding of mapping and ideas along the eastern LMFS as they evolved through the decades.