Reviewerly/exHarmony huggingface dataset:
Pengaruh Penerapan Teknik Data Mining Dalam Menemukan Kriteria Kelayakan Penerima Bonus Tambahan Pegawai Menggunakan Algoritma C4.5

Employees are components where the company's future depends on how the performance and contributions are given. Employee performance can also be determined by how the company treats employees, both in terms of awards to each employee, work location determination and salary. Every employee who works in a company basically has one reason, namely getting a decent salary in accordance with his field. Here the company takes the initiative to provide additional bonuses to each decent employee. Therefore companies need to know the criteria that greatly influence the feasibility of giving bonuses so that companies can more easily draw conclusions. This research will help companies use data mining techniques with the c4.5 algorithm. Data mining is a series of processes to explore values in information that might not be known manually. C4.5 algorithm is used to make a decision tree that will display the results of the problem under study.
The Effect of partner technique on the students' achievement in writing descriptive text at SMP Negeri 1 Sibolangit.

This research was aimed to find out the effect partner technique on the students’ achievement in writing descriptive text. The subject of this research were the eighth grade of SMP Negeri 1 Sibolangit which consisted of 24 students. This research applied experimental research. The data of this research were quantitative data. The quantitative data were taken from tests. The tests were given to the students in form of pre-test, treatment and the post test. In the finding , researcher analyzing by t-test . After analyzing the data, the researcher got; (1) the students learning by partner technique on the Students’ writing Skill got the mean 71,45 and standard deviation was 8,55; (2) The students’ learning by conventional method on writing skill got the mean 66,04 and standard deviation was 7,79. The value of tobserved was 6,848 and that of ttable was 2,013. So the value of tobserved was higher than that of ttable. It means that there was significant effect of partner technique on the students’ achievement in writing descriptive.
Discrete logarithm and Diffie-Hellman problems in identity black-box groups

We investigate the computational complexity of the discrete logarithm, the computational Diffie-Hellman and the decisional Diffie-Hellman problems in some identity black-box groups G_{p,t}, where p is a prime number and t is a positive integer. These are defined as quotient groups of vector space Z_p^{t+1} by a hyperplane H given through an identity oracle. While in general black-box groups with unique encoding these computational problems are classically all hard and quantumly all easy, we find that in the groups G_{p,t} the situation is more contrasted. We prove that while there is a polynomial time probabilistic algorithm to solve the decisional Diffie-Hellman problem in $G_{p,1}$, the probabilistic query complexity of all the other problems is Omega(p), and their quantum query complexity is Omega(sqrt(p)). Our results therefore provide a new example of a group where the computational and the decisional Diffie-Hellman problems have widely different complexity.
체계기능언어학의 한국어교육학적 적용 방안

The purpose of this study is to show applicable methods in Korean educational linguistics of Systemic Functional Linguistics which Halliday has made and developed in England, China, Australia, etc. Halliday, known as an applied linguist, has researched linguistics, especially Chinese linguistics in England and China, and has been an active researcher in practical language education and policy. ‘System’ in SFL means that language expressions in use had paradigmatic relation, and ‘function’ means that language in use refers to meaning function in context. Various meaning functions are divided into ideational, interpersonal, textual metafunctions. These 3 metafunctions are realized through texts which the speaker and listener express and understand. In the context of higher level Chinese speakers studying with an academic purpose, they may have difficulty making a choice for some expressions in order to deliver many meaning functions, In this case, the Korean classroom can make use of SFL and teaching appropriate Korean expressions. (Korea University)
Web Based Car Rental Information System at PT. Frasindo Lima Mandiri

Current developments and advances in technology and information in Indonesia have begun to develop and can be utilized to develop business. PT. Frasindo Lima Mandiri is a company engaged in car rental services. In the rental process, the customer must come to the company to find out the type and availability of cars rented by the company. The payment process is still recorded in the payment journal, so that company employees will have difficulty in checking the status of customer payments. Seeing the problems that occur in the rental process, the purpose of this research is to create a web-based car rental information system. With this rental car information systems can simplify data management and provide complete information related to car rental. The method used in this study is the prototype system development method, analysis and design tools used are UML (Unified Modeling Language). The results of this study are that customers can find information related to rental cars, make car rental transactions online, can facilitate employees in the process of finding customer payment data and companies can provide services more quickly and accurately.
Cyber risk management in business enterprises

Risk is an intrinsic part of business activity. By making business decisions, organisations ought to be aware of the risk related to a given decision. Admittedly, most businesses are aware of this fact. Yet, one keeps wondering to what extent they take adequate steps to minimise such risk, and, hence, to make sure any decision made brings an expected outcome. One of the possible solutions could be to launch a comprehensive process of risk management, i.e. to introduce a systemic and holistic solution based on a systemic approach to facilitate the identification, categorisation, evaluation and proactive optimisation of all the risk groups that a business is exposed to in the process of building its goodwill. This paper strays to some extent from the general concept of “risk management”. By doing so, it aims to pay more attention to the increasingly more popular – and extremely dangerous – notion present in the theory of disruption of a proper functioning of business enterprises, i.e. cyberattacks. The consequent destabilisation of the desired functioning of a business enterprise, which rests on the idea of the most effective financial incomes, may result in a quick, or even complete, fall of a given organisation in the market. According to a number of acknowledged definitions, risk can be defined as a combination of the probability of an event and its consequences or effects that may occur and have a positive or negative impact on the attainment of business objectives, as well as the execution of a company’s business strategy (remaining sources). The knowledge of any estimates related to the probability and effects of an event gives one the possibility to take appropriate action geared at minimizing of risk. This paper looks at the theoretical approaches to cyber threats, whose instrumental use and application by third persons has a major influence on the loss of stability of business enterprises.
An Approach for Sending a Confidential Message to the Restricted Users in Defence Based Organization

After the creation of the internet, the file sharing process has been changed. Several third-party applications have come to live for sharing and chatting purposes. A spammer can profit by these applications in different ways like, can achieve countless data, can acquire the user's personal information, and furthermore. Later that untrusted cloud storages are used for uploading a file even it is maintained by the third party If they use an untrusted cloud, there is a security problem. We need to give more security for file transfer in the defense-based organization. So, we developed a secure application for group member communication in a secure medium. The user belongs to a specific department from a specific group can access the data from the storage node and decrypt it. Every user in the group needs to register in the node to send or receive the data. Group Manager can restrict the access of the users in a Defense Network and he generates a user list, users in that list can only login to the node and share or download the files. We created a secure platform to upload files and share the data with multiple users by using Dynamic broadcasting Encryption. Users in the list can only download and decrypt the files from the storage node.
Learning To Characterize Adversarial Subspaces

Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of \textit{adversarial subspaces} where adversarial examples lie. However, we find these methods are not working in practical attack detection scenarios. Because the artificially defined features are lack of robustness and show limitation in discriminative power to detect strong attacks. To solve this problem, we propose a novel adversarial detection method which identifies adversaries by adaptively learning reasonable metrics to characterize adversarial subspaces. As auxiliary context information, \textit{k} nearest neighbors are used to represent the surrounded subspace of the detected sample. We propose an innovative model called Neighbor Context Encoder (NCE) to learn from \textit{k} neighbors context and infer if the detected sample is normal or adversarial. We conduct thorough experiment on CIFAR-10, CIFAR-100 and ImageNet dataset. The results demonstrate that our approach surpasses all existing methods under three settings: \textit{attack-aware black-box detection}, \textit{attack-unaware black-box detection} and \textit{white-box detection}.
Case Analysis of Applications for Deep Learning Technology in the Mining Industry

Applications for deep learning technology in the mining industry were investigated to utilize deep learning technology in the domestic mining field. Deep learning is specialized in abstracting high-dimensional nonlinear problems, such as geological problems. The author investigated the applications of deep learning technology for mining cycle that is divided into investigation, exploration, development, mine operation, and disaster analysis. In the survey and exploration phase, a number of studies have been proposed to carry out mineral potential mapping by integrating geochemical and geophysical explorations, geological maps, fault lines, and topographic information. In the mine development and management phase, studies on autonomous situation detection, automatic calculation, and equipment control were carried out using mine status data measured by sensors or cameras. The hazard analysis phase was mainly used for status awareness and hazard prediction. In order for deep learning technology to be effectively utilized in the domestic mining field, relevant domestic research is required.
On the supersingular GPST attack

We explain why the first Galbraith-Petit-Shani-Ti attack on the Supersingular Isogeny Diffie-Hellman and the Supersingular Isogeny Key Encapsulation fails in some cases.
GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis

Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years, spectral sparsification techniques have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is also introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications. For instance, a sparse SDD matrix with 40 million unknowns and 180 million nonzeros can be solved (1E-3 accuracy level) within two minutes using a single CPU core and about 6GB memory.
Time-Dependent Hybrid-State A* and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environment

The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varying and sometimes very narrow road layout pose special challenges. The ability to make fast decisions about exact maneuvers and to execute them by applying sophisticated control commands is one of the key requirements for autonomous vehicles in such situations. In this context we present an algorithmic concept of three correlated methods. Its basis is a novel technique for the automated generation of a free-space polygon, providing a generic representation of the currently drivable area. We then develop a time-dependent hybrid-state A* algorithm as a model-based planner for the efficient and precise computation of possible driving maneuvers in arbitrary dynamic environments. While on the one hand its results can be used as a basis for making short-term decisions, we also show their applicability as an initial guess for a subsequent trajectory optimization in order to compute applicable control signals. Finally, we provide numerical results for a variety of simulated situations demonstrating the efficiency and robustness of the proposed methods.
VASCO: Developing AI-Crawlers for ML-Blink

The Vanishing and Appearing Sources during a Century of Observations (VASCO) initiative aims at finding inexplicable effects among all-sky surveys. The VASCO project is a collaboration between as ...
4TH IR – IMPLICATIONS FOR HIGHER EDUCATION

During the 4th industrial revolution (4th IR) the world will see the emergence of a new form of institutions of higher learning.These institutions will have no classrooms, no library and no onsite lecturers.The institutions will be inter-disciplinary, will have virtual classrooms and laboratories, the library will be online and the lecturers will either be virtual or can sit anywhere in the world.Higher Education in the 4Th IR is creating an exciting but complex opportunity which has the opportunity to transform the society for the better.Artificial intelligence is driving the 4th IR and the needs of the workplace will be transformed from a task-based characteristics to human-centred characteristics.Due to the merging of "man" and machine, the distance between social sciences and humanities as well as between technology and science will be reduced.This will necessarily require more interdisciplinary teaching, research and innovation.All the components of the "new" higher education system will be inter-dependable of each other but will also be independent of each other.This paper looks at the impact of the 4th IR on the mission of higher education institutions.
A comparative study between MLP and CNN for noise reduction on images : The impact of different input dataset sizes and the impact of different types of noise on performance

Images damaged by noise present a problem that can be addressed by performing noise-reduction using neural networks. This thesis analyses the performance of two different neural networks, a Mulilay ...
Signal Processing: Data analysis, machine learning, and imaging sources

Whether it is locating an enemy submarine, finding that annoying sound generated inside an airplane, optimizing the focusing of sound to destroy kidney stones, or learning how bats track their food, advanced signal processing is key to the success of these research topics. The Technical Committee on Signal Processing in Acoustics was formed to foster interdisciplinary interaction among the Technical Committees. Techniques used to track a submarine in the ocean, should also work to track a swarm of bees flying through the air or to track the delivery of medicine to a targeted place in the body. For example, at the previous ASA meeting a signal processing session on machine learning brought together researchers using machine learning to identify earthquakes from the global smartphone seismic network, to determine whether noise is further endangering a certain species of birds, and to map out the ocean floor. This presentation will highlight some of the exciting techniques being used in signal processing and in the process give an appreciation for the breadth of the applications of the techniques being developed.
Selkirk College Library: Business: Databases

A subject guide to help locate print and electronic resources for Business Databases that are relvant to the subject.
Computer Assisted Annotation of Tension Development in TED Talks through Crowdsourcing

We propose a method of machine-assisted annotation for the identification of tension development, annotating whether the tension is increasing, decreasing, or staying unchanged.We use a neural network based prediction model, whose predicted results are given to the annotators as initial values for the options that they are asked to choose.By presenting such initial values to the annotators, the annotation task becomes an evaluation task where the annotators inspect whether or not the predicted results are correct.To demonstrate the effectiveness of our method, we performed the annotation task in both in-house and crowdsourced environments.For the crowdsourced environment, we compared the annotation results with and without our method of machineassisted annotation.We find that the results with our method showed a higher agreement to the gold standard than those without, though our method had little effect at reducing the time for annotation.Our codes for the experiment are made publicly available 1 .
Homophobia in Serbian Online Discourse: The Case of the 2016 Belgrade Pride Parade

The Belgrade Pride Parade has been associated with numerous acts of violence and vandalism since the first gay march in 2001. Although it seems that in the last couple of years the attitude towards the parade and human rights in Serbia has improved, the public discourse remains deeply imbued in national myths and stereotypes that instigate homophobia and contribute to the perception of homosexuality as deviance. This study examined public reactions to the 2016 Belgrade Pride Parade in order to outline how the Pride Parade, and by extension homosexuals and homosexuality, are viewed and represented by the commenters on some of the most visited online multimedia portals in Serbia. The online comments (approximately 500 comments) were considered as a web-based corpus and observed and analysed from a perspective of Critical Discourse Analysis (CDA). The use of online comments as corpus in this study is encouraged by multidisciplinary approaches and studies. Suzanne R. McMillen, for instance, shows in her research that online comments represent important spaces for debate and discussion and that they are an important form of public expression. They are also, according to the author (McMillen, 2013), reliable and trustworthy sources because the anonymous commenters are less inhibited and tend to be more honest while communicating online. Van Dijk’s framework adopted from Politics, ideology and discourse (van Dijk, 2006) was used to detect discursive structures within the online comments in an attempt to discover the underlying ideologies. The macro strategies of ''positive self-representation'' and ''negative other- representation'' plus the other 9 specific strategies were used for the evaluation of attitudes and opinions on the Pride Parade, homosexuals and homosexuality. The study ultimately showed how homophobic speech in the public discourse was distorted and presented as ''non-ideological common sense'' (Fairclough, 1995).
Spectrum Sensing for Modulated Radio Signals Using Deep Temporal Convolutional Networks

Detecting the presence of radio signals in noise has been a decades-long problem in the signal processing domain. More recently, there has been a resurgence of interest in detecting signals with relatively low signal-to-noise ratio in the context of cognitive radios and spectrum sharing. A majority of existing algorithms rely on detection criteria which are carefully-crafted by domain experts to exploit specific features of the target signal. Motivated by the ability of deep neural networks to learn useful representations directly from raw data, in this work we use a deep temporal convolutional network for detection of signals, under multi-path fading and noise, directly from raw complex baseband samples with no other pre-processing or feature engineering.Our results indicate that the proposed deep learning approach outperforms a popular eigenvalue-based method without requiring any expert feature engineering. We further show that the performance is robust to new signal types not used during the training of the neural network.
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification

Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network parameters. The proposed approach leverages a data-aware strategy that sets up a taxonomy of classification problems automatically derived by the model behavior. To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models. The proposed strategy has been tested on four benchmarks coming from two different domains, i.e., Speech Recognition and Remote Sensing. Results underline the significance of our approach and point out that data-aware strategies positively support the initialization of Recurrent Neural Network based classification models.