Reviewerly/exHarmony huggingface dataset:
Enhancing Recursive Brute Force Algorithm with Static Memory Allocation: Solving Motif Finding Problem as a Case Study

Parallel Recursive Brute Force (PRBF) is one of the algorithms that need high memory. Memory allocation techniques play an important role and have a great effect on the performance of the PRBF. This paper proposes a modified implementation of the PRBF algorithm that uses the static memory allocation technique instead of dynamic memory allocation techniques. This is to avoid the memory management overhead and heap contention problems associated with dynamic memory allocation technique. This paper uses the Motif Finding Problem (MFP), one of the well-known computationally intensive problems in the field of bioinformatics, as a case study. The exponential memory requirements based on the problem size of the MFP make it very challenging to use static memory allocation. Experimental results show that the use of static memory allocation has achieved a significant speedup factor when using 16 MPI rank in comparison with the same implementation using dynamic memory allocation. The proposed R-BF modification scalability is also tested by using different number of MPI nodes and distributing the search space among them, and the results proved a significant reduction in the execution time by increasing the number of working MPI nodes.
THE PHENOMENON OF «MASS CONSCIOUSNESS» IN E. FROMM'S PHILOSOPHY

&#x0D; &#x0D; &#x0D; In this article, the author considered the concept of «Mass Consciousness» in the philosophy of Erich Fromm. The phenomenon of «Mass Consciousness» was studied from the standpoint of E. Fromm’s «Humanistic Radicalism», and was analyzed in comparison with classical Freudian psychoanalysis and other prominent thinkers of the twentieth century. It was found that according to Erich Fromm's social psychoanalysis, aggression is the result of social exclusion, mass consciousness, and conformism. The prevention of these phenomena can only be realized by a healthy humanistic society, in which faith, hope, love, understanding, sanity, mutual respect, empathy that cannot be explained by human instincts.&#x0D; &#x0D; &#x0D;
Strategies to Overcome the Difficulty of Learning Indonesian Language Affixes for Chinese Students

Abstract This study discusses the mastery of Indonesian affixes among Chinese learners. For Chinese learners, Indonesian affixes are difficult to understand because the addition of affixes to basic words can change the meaning of words and types of words. In addition, affixes in Indonesian also experience what is called sound fusion in affix letters and basic words. From the survey, respondents are not familiar with the prefixes of me-, ber-, di-, se-, ke-, per-, the suffix of -i, and the confix ke-an, per-an, se-nya. Therefore, at the time of prefix teaching, it should be stated that the sound fusion in prefix of me- will occur if the initial letter of the basic word has the sound of /a/,/e/,/g/,/h/,/i/,/u/,/o/,/k/. When explaining the prefix di-, the learners need to be shown a comparison form and meaning with the preposition di, so that the learner understands the different uses. As for learning prefixes and other confixes, the learners need to be provided with various examples and sentence exercises so that they are accustomed to the sound fusión in prefixes and types of words changes that are formed with the addition of the affix.
Deceiving Machines: Sabotaging Machine Learning

The growing abundance of high-quality data sets, combined with substantial technical developments, has advanced machine learning into a major tool that is employed in a broad array of applications,...
Incorporating Morphological Compostions with Transformer to Improve BERT

Abstract BERT model achieves huge performance gains by modeling words and their subwords as input units. However, it still neglects the semantic information of morpheme which has been verified in many previous works. In this paper, we propose Transformer Morpheme Model (TMM), which is based on BERT and explores the effect of morpheme. Since the process of previous works about morpheme are context-independent.TMM model adopts Transformer to process morpheme information on the input layer to overcome this problem. Experiments on MRPC task are conducted to validate the feasibility of our model. TMM model has achieved about 1% gains over BERT model on MRPC task. The results demonstrate the superiority of our method and the effectiveness of morpheme information in the BERT model.
Facial Landmark Detection Experiment

Abstract Facial landmark detection or face alignment is an important problem in computer vision. Different types of methods have been proposed to target this problem, with their advantages and disadvan- tages. This paper classified the past methods into three categories, briefly compared them, then attempted to reimplementing part of a recent work. Experiments were done with a common, current dataset to compare the method with other methods.
On the Replicability of Combining Word Embeddings and Retrieval Models

We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval. Specifically, the hypothesis was that the use of a mixture model of von Mises-Fisher (VMF) distributions instead of Gaussian distributions would be beneficial because of the focus on cosine distances of both VMF and the vector space model traditionally used in information retrieval. Previous experiments had validated this hypothesis. Our replication was not able to validate it, despite a large parameter scan space.
Deterministic vs. Random Initializations for K-Means Color Image Quantization

We present six methods for initialising the K-means clustering algorithm used for color image quantization. We test these initialization methods on a few quantization levels and on 24 color images contained in the Kodak image dataset. In the vast majority of the examined cases the best results were obtained for the initialization of KM++. The evaluation of the results was carried out using the MSE and several new perceptual quality indices.
The Propagation Strategy Model of Taint Analysis

Abstract The taint propagation strategy is the core of the taint analysis technology. When the taint analysis toolsanalyses the target program, it needs to mark the target data according to the formulatedtaint propagation strategy. Formulating a reasonable strategy for taint propagation can effectively improve the accuracy of taint analysis. There are two difficulties in developing the taint propagation strategy, namely, alias analysis in static taint analysis and indirect jump in dynamic taint analysis. In our paper, we propose a taint propagation strategy at the intermediate representation language level, which can effectively improve the accuracy of taint propagation.
Matteo Farina. <i>Facebook and conversation analysis: The structure and organization of comment threads</i>

Abstract This article reviews Facebook and Conversation Analysis: The Structure and Organization of Comment Threads 978-1-3500-3828-8
Signs of Vertically Extending Mineralization in the Nizhnetaezhny Polymetallic Ore District (Northern Primorye)

Abstract In this work, to study the vertical extent of mineralization, determine the level of erosion, and identify signs of longitudinal placement of silver mineralization of the Nizhnetaezhny ore district (NOD) For this, the main indicator indicators Ag, Au, Cu, Zn, Pb were identified based on the maximum correlation coefficient, Sn, Sb, As, Mo. Then, the ratios of the linear productivities of the indicator elements for the main ore zones of the Scientific and Technological Logging were made: Kumirnoe - Sb * Ag * As / Cu * Sn, Kabaniy - Sb * As * Ag / Cu * Sn, SredniySukhoy - Sn * Pb * Zn / Au * As, Levoberezhnoe -Sn * Pb * Zn / W * Mo. The obtained geochemical data of changes in the multiplicative coefficients allowed us to identify areas according to their erosion: weakly eroded (Kumirny, SredniySukhoy) and significantly eroded (Kabany, Levoberezhnoe), and then divide them into upper ore, ore and under ore. At all sites (except for the SredniySukhoy), at the level of the conditionally subsided horizon, a tendency of gradual increase in productivity was revealed, which indicates the long-range placement of productive ore zones and makes it possible to suggest the presence of powerful and productive silver zones hidden at depth. The data obtained provide the prerequisites for the revaluation of the prospects of the deep horizons of the NOD silver mineralization.
Strategic Planning of Education Information Systems on Bonavita Foundation as a Decision Support in Facing Industrial Revolution 4.0

A very rapid technological development has become a serious concern for the world of education, especially relating to the administration of registration, the learning process and administration of graduation to support industry 4.0. This is due to the inability of human resources in schools to offset the utilization of technology that grows very fast. The problem also impacted the new student administration system, the learning process and graduation as the main activity became late and influential in the lack of enthusiasts to enter as a student. The strategic planning of information systems with Enterprise Architecture Planning (EAP) is a solution to parse and define the main activities run by the Bonavita Education Foundation. Strategic planning can be used as a basis for creating data architectures that are application architecture entities. The technology architecture is designed to illustrate the technology needed to provide a data application environment. The writing of this paper aims to create an educational administration strategy to increase the absorption of prospective students. The interview and observation methods are conducted to obtain accurate data on school leader, administrative staff, alumni and parents of prospective students. Analysis and data processing using value chain. With the planning of information system strategy and information technology it is expected that vocational high schools can face the 4.0 Industrial Revolution and increase the interest of prospective students to enter the school on the Bonavita Foundation without rule out to the vision and mission of the school.
A new algorithm to find monic irreducible polynomials over extended Galois field <scp>GF</scp>(p<sup>q</sup>) using positional arithmetic

Abstract Search for monic irreducible polynomials (IPs) over extended Galois field GF(p q ) for a large value of the prime moduli p and a large extension to the Galois Field q is a well‐needed solution in the field of cryptography. In this article, a new algorithm to obtain monic IPs over extended Galois field GF(p q ) for the large values of p and q is introduced. Here in this paper the positional arithmetic is used to multiply all possible two monic elemental polynomials (EPs) with their Galois field number (GFN) to generate all the monic reducible polynomials (RPs). All the monic RPs are canceled out from the list of monic basic polynomials (BPs) leaving behind all the monic IPs. Time complexity analysis of the said algorithm is also executed that ensures the algorithm to be less time consuming.
A Comparative Study Among Recursive Metaheuristics for Gene Selection

This chapter compares 4 variants of metaheuristics (RFA, EMVO, RPSO, and RBAT). The purpose is to test the impact of refinement on different types of metaheuristics (FA, MVO, PSO, and BAT). The refinement helps to enhance exploitation and to speed up the search process in multidimensional spaces. Moreover, it presents a powerful tool to solve different issues such as slow convergence. The different methods have been used for gene selection on 11 microarrays datasets to solve their various issues related to the presence of irrelevant genes. The obtained results reveal the positive impact of refinement on FA, MVO, and PSO, where all performances have been improved. On the other hand, this process harmed the BAT algorithm. The comparative study between the 4 variants highlights the efficiency of EMVO and FA in terms of precision and dimensionality reduction, respectively. Overall, this study suggests drawing attention to the choice of embedded metaheuristics in the refinement procedure, where powerful methods in exploration are recommended. Moreover, metaheuristics that risk form fast convergence are not advised.
Application of GAN in Semi-Supervised Learning

The third wave of artificial intelligence are inseparable with deep learning. The success of deep learning is mainly attributed to three major factors - big data, big models and big calculations, but they also brought some constraints to the further development and popularization of deep learning. The first challenge is labeling data is expensive, we should design a new learning method that can learn from unlabeled data. Ian Goodfellow who is inventor of GAN, mentioned that the method by which GAN can be used for semi supervised learning is called SSGAN. The experimental results show that the discriminant model trained by this processing method has better effects than other methods in rationally using unlabeled data.
A Job-Seeking Advisor Bot Based in Data Mining

Promentor is a solution that advises job seekers how to effectively improve their chances of getting a job in a certain area of interest by focusing on what, at least historically, seems to work best. To this end, Promentor first analyzes previous selection processes, trying to quantitatively evaluate the effective value of the characteristics that the candidates put into play in the selection. With this evaluation, Promentor can estimate the value of a profile of the job seeker who requests advice based on the characteristics that make it up. Promentor then makes a simulation by applying each of the suggestions on the job seeker’s profile exhaustively and evaluating the modified profile. In this way, Promentor identifies which suggestions offer the greatest increase in qualification, and are therefore more recommendable. Promentor is a module of the employment web portal GetaJob.es , which has been developed in parallel and equipped with specific capabilities for collecting the data required by Promentor.
Information Systems Curriculum Research

While information system (IS) curriculum research has enjoyed recent attention from IS scholars, not many reviews exist. Those reviews are either outdated or focused on a particular strand of IS curriculum research which may not be comprehensive. In this chapter, the author presents a systematic review of information systems curriculum research published in information systems journals and selected conference proceedings in the past decade. The results point to many studies on identifying information systems competencies and their implications for curriculum design and delivery. Having observed that many of these issues are discussed at the undergraduate level and predominantly in the USA and UK, this chapter suggests, among others, future research at the graduate level and from other regions.
Pedestrian Image Generation with Target Pose Based on the Improved Cyclegan

Person image generation mainly contains two parts: The first part is to learn people's appearance information, and the second part is to learn people's posture information. this paper is to generate pedestrian images by using the improved Cycle-Consistent Adversarial Networks (CycleGAN[2]). The network uses a loop structure consisting of two generators G1, G2 and two discriminators D1, D2. In order to preserve the appearance and target pose information of the original pedestrian, we used paired images of the same pedestrian during training and a jump connection is used in the generators G1, G2. The experimental results show that the network structure can generate pedestrian images with original appearance feature information and target pose information.
Application Performance Prediction in the Datacenter to Accelerate Cloud Servers

Abstract Nowadays, cloud services become more and more important while datacenters are growing in scale. But the low effectiveness on cloud servers becomes an increasing issue as time goes. In this research, we focus on predicting application performance on cloud servers for acceleration. Linear regression and random forest are used in this study. Random foreset proves to be a precise, stable and effective tool to do performance counter prediction.
MCMF: Max-Criticality Max-Feasibility Method for Cloud Task Scheduling

Task scheduling is one of the key challenges in cloud computing, and deadline is one of the most important criteria for prioritizing tasks when scheduling on resources, which provides useful information for the scheduler about the priority of tasks. This paper presents a method called MCMF for scheduling tasks based on priority in the cloud environment. In addition to scheduling deadline-sensitive tasks, the MCMF also attempts to balance the load of virtual machines at the time of allocation. The proposed method is simulated in CloudSim toolkit, and the simulation results show that the reject rate and the system throughput were improved. Also, makespan and resource utilization have been improved with regard to the prioritization technique of virtual machines in the proposed method compared to recent studies.
Modified Bat Algorithm with Hybridization of Gaussian Probability and Doppler Effect

The term “swarm” uses to reference when expressing a large or dense group of collection of similar species, while it is being remarked every now and then very special intelligent behavior in nature and creatures. This paper, the talk is about a hybridization of the Original Bat Algorithm with extends of some modifications that disclosed comparatively high possibilities of optimized result where the echolocation system used by micro-bats are used for exploration & exploitation. In the modification, it is being stated Rechenberg's one fifth mutation rule and Gaussian probabilities function. The concern that make the new dimension is Doppler Effect in this existing algorithm and fabricated a Modified Bat Algorithm with Doppler Effect. This algorithm provides much enhanced results against the Original Bat Algorithm.
Robustness Comparison of Scheduling Algorithms in MapReduce Framework

Parallel computing is the fundamental base for MapReduce framework in Hadoop. A big data is split into small data chunks, where Map task is referred to processing a data chunk. Each data chunk is replicated over three servers in Hadoop for increasing the availability of data and decreasing the probability of data loss. As a result, the three servers that have the Map task stored on their disk are the fastest servers to process them, which are called local servers. All the servers in the same rack as local servers are called rack-local servers that are slower than local servers in processing Map tasks since the data chunk associated with the Map task should be fetched through the top of the rack switch. All the other servers are called remote servers that are the slowest servers for processing a Map task since they need to fetch data from a local server in another rack, so data should be transmitted through at least two top of the rack switches and a core switch. Note that the number of switches in the path of data transfer depends on the internal network structure of data centers. The First-In-First-Out (FIFO) and Hadoop Fair Scheduler (HFS) algorithms do not take the rack structure of data centers into account, so they are known to not be heavy-traffic delay optimal or even throughput optimal. The recent advances on scheduling for data centers considering the rack structure of them and the heterogeneity of servers resulted in the state-of-the-art Balanced-PANDAS algorithm that outperforms the classic MaxWeight algorithm and its derivation, JSQ-MaxWeight algorithm. In both Balanced-PANDAS and MaxWeight algorithms, the processing rate of local, rack-local, and remote servers are assumed to be known. However, with the change of traffic over time in addition to estimation errors of processing rates, it is not realistic to consider the processing rates to be known. In this work, we study the robustness of Balanced-PANDAS and MaxWeight algorithms in terms of inaccurate estimations of processing rates. We observe that Balanced-PANDAS is not as sensitive as MaxWeight on the accuracy of processing rates, making it more appealing to use in data centers.
Study on the Influence of Learning Intervention on Learning Behavior of Learners in MOOC

As a new form of education, MOOC has attracted a large number of learners to participate in the study, but the problems of high rate of dropout, poor sustainability, and low qualification rate are more prominent. Based on the RTI three-level intervention model, this study constructs the MOOC learning intervention research framework, combines the learning process data of the two types of learners under the constraints and non-constrained conditions, and uses the comparative analysis method to compare the learning behaviors of the two types of learners. A horizontal comparison is made to analyze the effect of learning intervention on the learning behavior of MOOC learners. Studies have shown that appropriate learning interventions have a positive effect on learning retention, learning engagement, depth of interaction, and learning outcomes in the MOOC learning process.
CRITICAL ANALYSES OF NATURAL LANGUAGE PROCESSING (NLP) IN UNDERSTANDING SEMANTICS

&#x0D; &#x0D; &#x0D; Today, language understanding systems do quite many useful things with processing natural language, even they are able to process the data much faster than humans are. Nevertheless, they do not have the same logical understanding of natural language yet as humans have and the interpretation capabilities of a language understanding system depending on the semantic theory is not sufficient in all aspects. The research is centered on some of the important issues that arise using it in natural language processing.&#x0D; &#x0D; &#x0D;
Research on Cognitive Diagnostic Model Based on BP Neural Network

This paper proposed a cognitive diagnostic model based on BP neural network and maximum likelihood function, with an ultimate goal of realizing the cognitive diagnostic computerized adaptive test. The model was verified through the actual data from 1029 British middle school students who participated in the English reading test of the Program for International Student Assessment (PISA). The experimental results show that both the model’s mode accuracy rate and the average attribute accuracy rate are relatively ideal. Moreover, the model, which is of flexibility, can be effectively applied to cognitive diagnosis in small-scale classrooms.
An Error Correction Method for Manipulator Flexibility Based on Elliptic Target Positioning

In the teleoperation of lunar surface sampling, a four axis robotic arm is adopted to collect lunar soils and transfer them to the sealed pot. Due to the flexibility of slender sampling arm and interval error of the joint, it is difficult to control the end-effector aligned with the mouth of the sealed pot without any feedback. In order to handle this problem, this paper proposes a method to calculate the position and normal orientation of sealed pot with respect to end-effector by using the elliptic features obtained from the monocular vision camera, based on which the angle of each arm joint is to be solved. Due to the rotational symmetry of round sealed pot, it is difficult to determine the rotations of the normal of the sealed pot. Nevertheless, considering special configuration constraints of the robotic arm, we can ignore the influence of undetermined rotation and solve the four joint angles uniquely. Thus, error of joint angles could be corrected and an effective control is exerted to the arm to guide the end effector gradually approaching the sealed pot. Finally, the effectiveness of the method is verified by experiments.