Reviewerly/exHarmony huggingface dataset:
SALES FORECASTING INFORMATION SYSTEM USING THE LEAST SQUARE METHOD IN WINDI MEBEL

Windi Mebel is a business engaged in sales that sell goods and services. This home-based business was established a long time ago, but sales do not get maximum results because consumer interest has also begun to diminish due to competitors selling the same product. Therefore, with the increasingly sophisticated technology at this time it can be utilized to use a system that can forecast sales in the next few years so that the calculations generated when sales forecasting are more accurate, effective and efficient. Sales prediction system or sales forecasting can be used to estimate how much demand or demand for consumers and the market for the products produced. The more requests, the increase in sales results is also greater and as expected. To calculate the prediction of sales, a Least Square Method is applied using sales data a few years ago as a benchmark in predicting sales in the next few years. Based on these problems, the authors carry out a problem solving strategy by creating a system that uses the Least Square method to predict how much demand for furniture products the market wants in the future.
Effectiveness of the application of Quantum Learning Model in terms of students' written mathematical communication skills

AbstractThis research aimed (1) describe the implementation of learning in the classroom with the Quantum Learning model, (2) describe the students' written mathematical communication skills with Quantum Learning, and (3) describe the effectiveness of applying the Quantum Learning model to the written mathematics communication skills of junior high school students class VIII. The research was conducted in the even semester of the 2019/2020 school year in class VIII of SMP Muhammadiyah 2 Malang. This research is a type of descriptive research with quantitative methods. The initial stage of the research is the planning stage starting from determining the location and time of the study up to the validation of the research instruments used. The next stage is the implementation stage which is held three times by giving a description test to measure the ability of written mathematical communication at the third or final meeting. The last step is analyzing data obtained from the field. The data analysis technique used descriptive statistics. The results showed the average overall implementation of learning was in the good criteria of 83.92%. The ability of written mathematics communication in grade VIII A students of SMP Muhammadiyah 2 Malang is a very good criterion of 3.04. Therefore, it can be concluded that the application of the Quantum Learning model is effective to the written mathematics communication of junior high school students, especially in class VIII A.Keywords: Effectiveness, Quantum Learning, The implementation of learning, written mathematics communication.
Sentiment Analysis of Arabic Reviews for Saudi Hotels Using Unsupervised Machine Learning

Virtual worlds such as social networking sites, blogs and content communities are extremely becoming one of the most powerful sources for news, markets, industries etc.These virtual worlds can be used for many aspects, because they are rich platforms full of feedback, emotions, thoughts and reviews.The main objective of this paper is to cluster Arabic reviews of Saudi hotels for sentiment analysis into positive and negative clusters.We used web scraping to collect Arabic reviews associated only with Saudi hotels, from the tourism website TripAdvisor and obtained in total 4604 Arabic reviews.Then the TF-IDF was applied to extract relevant features.An unsupervised learning approach was applied, in particular Kmeans and Hierarchical algorithms with two distance metrics: Cosine and Euclidean.Our manual labelled test data shows that the K-means algorithm with cosine distance performed well when applying all of our prepossessing steps.We concluded that the suggested prepossessing steps play a critical role in Arabic language processing and sentiment analysis.
Anti-Oppression Mindsets for Collaborative Design

There is a growing synergy between design research and social justice.As design moves from commercial contexts into the community, we become advocates for creating more inclusive and equitable systems.But the traditionally nonpolitical posture of design practice leaves us ill-equipped for approaching complex challenges.Learning new approaches to be open to other perspectives improves our insights and fosters deeper collaboration.Without an awareness of historical privilege and oppression, we can unintentionally harm the people with whom we collaborate.Design Research practices are not yet considerate of people at the margins-people we may be working side by side with to unravel wicked problems.Drawing from multi-cultural psychology and design for social justice, I propose ways that collaborative design projects can be more aware of power and equity throughout the process.
Artifical Intelligence Librarian as Promotion of IAIN Lhokseumawe Library in the Revolutionary Era 4.0

Era 4.0. is a revolution in the industrial world, in the era referred to as the phenomenon distruptive innovation. In the industrial era 4.0, the emphasis lies on the digital economy pattern, artificial intelligence (artificial intelligence) big data, robotics, and automation. The impact of the industrial era 4.0 influential in various fields of work are no exception librarians. Librarian is someone who has the ability and expertise librarianship the librarians in charge to prepare themselves to face the era that is the way to equip themselves with information technology and analytical capabilities of the library so that airport users effective. Then by applying a librarian AI (artificial intelligence) to guide users in using the integrated library information. The presence of librarians AI (artificial intelligence) at IAIN Lhokseumawe made of evidence that has entered the era of disruption 4.0 will be the college library promotion strategy.
Superconducting ‘twin’ qubit

We study a flux qubit consisting of a symmetrical pair of superconducting loops, with two Josephson junctions in each, joined by a common Josephson junction---a `twin' flux qubit. The qubit is capacitively coupled to a transmission line, which allows us to characterize the spectrum of the device by measuring the scattering of propagated electromagnetic waves. We perform a detailed analytical analysis of the double-loop system, revealing its properties, and compare experimental results with numerical simulations. At half-flux quantum bias of both loops, the qubit is protected against global and local magnetic field fluctuations with much less sensitivity to the global field in the second order. The system selection rules allow even-odd transitions and prohibit transitions between even-even or odd-odd levels due to the symmetry of the device.
Evaluating EAP Textbooks for Indonesian University Students Using Impressionistic and In-depth Assessment

This study was qualitative content analysis that evaluated the quality of English for University Teaching (EUT), English for Specific Purposes (ESP), and English for Academic Purposes (EAP) textbooks through impressionistic and in-depth analysis. The participants were 9 EAP teachers and 15 EAP students from three universities in Surakarta, Indonesia. Instruments to collect data were checklist and interview guide. The checklist consisted of impressionistic analysis to see (1) cover, (2) content scope, (3) learning activities, (4) book organization, and (5) layout; and the in-depth analysis, namely (1) aim and approach, (2) organization design, language and content, (3) language skills, and (4) practical consideration. Data were collected using checklist and Focus Group Discussion. Data were analyzed through summative content analysis with seven stages: transforming data into narrative text, determining unit of analysis, developing categories, coding, coding the entire text, checking consistency of coding, and drawing conclusion. Results show EUT and ESP have low quality based on the impressionistic evaluation, and fair quality of their in-depth evaluation. Both books are register and grammar oriented. EAP textbook is good in general, and very good in depth. EAP presents its contents in terms of genre reading, academic speaking, academic reading, academic writing and grammar.
Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification

In the field of speaker verification, probabilistic linear discriminant analysis (PLDA) is the dominant method for back-end scoring. To estimate the PLDA model, the between-class covariance and within-class precision matrices must be estimated from samples. However, the empirical covariance/precision estimated from samples has estimation errors due to the limited number of samples available. In this paper, we propose a method to improve the conventional PLDA by estimating the PLDA model using the regularized within-class precision matrix. We use graphical least absolute shrinking and selection operator (GLASSO) for the regularization. The GLASSO regularization decreases the estimation errors in the empirical precision matrix by making the precision matrix sparse, which corresponds to the reflection of the conditional independence structure. The experimental results on text-dependent speaker verification reveal that the proposed method reduce the relative equal error rate by up to 23% compared with the conventional PLDA.
Formal Method of Structural-Logical Identification of Functional Model of Subject Area Polycubic Data Matrix

This work proposes a technique of presenting a system model of a subject area in the form of a category of processes.The rule of synthesis of self-organizing structure of process models is proposed.It is shown that the sequence of process decomposition compositions has the property of fractal transformations (self-like).The core of these transformations are commutative triangles that define the structure of interacting processes in composition-decomposition. Such structures have been shown to retain the properties of self-organization in accordance with the laws of the required diversity of the EShBI.The possibility of building a number of identical formal algorithms of control of integrity of the structure of initial processes, in case of violation of which "external" control is required, is shown.The proposed transformation is demonstrated by the example of synthesis of the self-organizing process of the agreement, the architecture of whichб is defined in ISO/IEC 15288 standard "System Engineering."Polycubic data organization for a software system is proposed herein.
The Understanding of Local Cultural Treasures on Foreign Students in Indonesian Language Learning

The role of broad language may be implied in the increased role of the nation.The implications also can be seen in the role of the Indonesian language in the international area.This phenomenon is evidenced by the approval of that Indonesian language becomes subjects at 200 universities in ASEAN.However, there are some problems faced by foreign students studying in Indonesia.The problems commonly are issues related to cultural differences.These issues become an obstacle for foreign students studying in Indonesia.This research was conducted using a case study and qualitative approaches.The informants were foreign students who were studying the Indonesian language in Surakarta.The research results found that lack of the understanding of foreign students to the culture obstructs to the Indonesian language skills.The differences in the cultural background between the countries of origin of foreign students to the culture that exists in Surakarta make foreign students adjust the process of cultural adaptation.The adaptation was performed using the approach of familiarity and proximity.
A Deeper Study on Features for Named Entity Recognition

This paper deals with the various features used for the identification of named entities. The performance of the machine learning system heavily depends on the feature selection criteria. The intention to trace the essential features required for the development of named entity system across languages motivated us to conduct this study. The linguistic analysis was done to find out the part of speech patterns surrounding the context of named entities and from the observation linguistic oriented features are identified for both Indian and European languages. The Indian languages belongs to Dravidian language family such as Tamil, Telugu, Malayalam, Indo-Aryan language family such as Hindi, Punjabi, Bengali and Marathi, European languages such as English, Spanish, Dutch, German and Hungarian are used in this work. The machine learning technique CRFs was used for the system development. The experiments were conducted using the linguistic features and the results obtained for each languages are comparable with state-of-art systems.
Query as Context for Item-to-Item Recommendation

Recommender Systems is one of the main machine learning applications for an e-commerce platforms such as Etsy, a two sided marketplace. A frequent usage of such system is for item-to-item recommendations that show similar items (also referred as listings) based on the listing a user is currently viewing. Item-to-item recommendations typically take into account information that are only associated with the target listing and other listings in the inventory. However, other contextual information such as user intents, queries and seasonality, are often not taken into account. In this talk, we will present two approaches we developed to utilize additional contextual information in the form of queries in generating item-to-item recommendations. Moreover, we will present our journey in migrating Etsy's rankers from linear to non-linear models. Additionally, we propose new metrics to evaluate candidate sets that accesses diversity and price spread, while not compromising relevance. The proposed metrics can also be used beyond the current application. Our proposed candidate set generation approach outperforms the model in production as well as yielding significant lift in conversion rate and other engagement metrics as indicated by several A/B tests.
An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer Strategy

Excessive time complexity has severely restricted the application of support vector machine (SVM) in large-scale multi-label classification. Thus, this paper proposes an efficient multi-label SVM classification algorithm by combining approximate extreme points method and divide-and-conquer strategy (AEDC-MLSVM). The AEDC-MLSVM classification algorithm firstly uses the approximate extreme points method to obtain the representative set from the multi-label training data set. While persisting almost all the useful information of multi-label training set, representative set can effectively reduce the scale of multi-label training set. Secondly, to acquire an efficient multi-label SVM classification model, the SVM based on the improved divide-and-conquer strategy is trained on the representative set, which will further improve the training speed and classification performance. The improvement is reflected in two aspects. (1) The improved divide-and-conquer strategy is applied to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances. This will avoid singular problems and overcome computation load imbalance problem. (2) The different error cost (DEC) method is applied to overcome the label imbalance problem. Effective experiments have proved that the training and testing speed of AEDC-MLSVM classification algorithm can be accelerated substantially while ensuring the classification performance.
A Small Subgroup Attack on Bitcoin Address Generation

We show how a small subgroup confinement-like attack may be mounted on the Bitcoin addresses generation protocol, by inspecting a special subgroup of the group associated to point multiplication. This approach does not undermine the system security but highlights the importance of using fair random sources during the private key selection.
Supporting people with dementia- Understanding their interactions with Mixed Reality Technologies

Emerging technologies such as Mixed Reality Technologies (MRTs) could offer possibilities to support People with Dementia (PwD).This study examined interactions of PwD with two MRTs -HoloLens and Osmo.Ten participants (MoCA = 18 to 23, Age= 63 to 88 years) played a game of Tangram on Osmo.Six of these participants played Young Conker on HoloLens.The study found that PwD used gestural actions in the physical world more correctly than speech.Audio prompts in human voice were more correctly perceived than visual prompts.Physical affordances, embodied actions and familiarity to verbal instructions contribute to this success.Visual prompts such as text present promising opportunities to complement with audio prompts.Interaction with MRTs require prompts to direct PwD towards physical or virtual worlds.The research outcomes are significant as the focus on interactions of PwD could open up avenues for further research on actions and perceptions with emerging technologies.
REAF: Reducing Approximation of Channels by Reducing Feature Reuse Within Convolution

High-level feature maps of Convolutional Neural Networks are computed by reusing their corresponding low-level feature maps, which brings into full play feature reuse to improve the computational efficiency. This form of feature reuse is referred to as feature reuse between convolutional layers. The second type of feature reuse is referred to as feature reuse within the convolution, where the channels of the output feature maps of the convolution are computed by reusing the same channels of the input feature maps, which results in an approximation of the channels of the output feature maps. To compute them accurately, we need specialized input feature maps for every channel of the output feature maps. In this paper, we first discuss the approximation problem introduced by full feature reuse within the convolution and then propose a new feature reuse scheme called Reducing Approximation of channels by Reducing Feature reuse (REAF). The paper also shows that group convolution is a special case of our REAF scheme and we analyze the advantage of REAF compared to such group convolution. Moreover, we develop the REAF+ scheme and integrate it with group convolution-based models. Compared with baselines, experiments on image classification demonstrate the effectiveness of our REAF and REAF+ schemes. Under the given computational complexity budget, the Top-1 accuracy of REAF-ResNet50 and REAF+-MobileNetV2 on ImageNet will increase by 0.37% and 0.69% respectively. The code and pre-trained models will be publicly available.
Empirical Performance Analysis of Collective Communication for Distributed Deep Learning in a Many-Core CPU Environment

To accommodate lots of training data and complex training models, “distributed” deep learning training has become employed more and more frequently. However, communication bottlenecks between distributed systems lead to poor performance of distributed deep learning training. In this study, we proposed a new collective communication method in a Python environment by utilizing Multi-Channel Dynamic Random Access Memory (MCDRAM) in Intel Xeon Phi Knights Landing processors. Major deep learning software platforms, such as TensorFlow and PyTorch, offer Python as a main development language, so we developed an efficient communication library by adapting Memkind library, which is a C-based library to utilize high-performance memory MCDRAM. For performance evaluation, we tested the popular collective communication methods in distributed deep learning, such as Broadcast, Gather, and AllReduce. We conducted experiments to analyze the effect of high-performance memory and processor location on communication performance. In addition, we analyze performance in a Docker environment for further relevance given the recent major trend of Cloud computing. By extensive experiments in our testbed, we confirmed that the communication in our proposed method showed performance improvement by up to 487%.
Integration of Cluster Centers and Gaussian Distributions in Fuzzy C-Means for the Construction of Trapezoidal Membership Function

Fuzzy C-Means (FCM) is one of the mostly used techniques for fuzzy clustering and proven to be robust and more efficient based on various applications.Image segmentation, stock market and web analytics are examples of popular applications which use FCM.One limitation of FCM is that it only produces Gaussian membership function (MF).The literature shows that different types of membership functions may perform better than other types based on the data used.This means that, by only having Gaussian membership function as an option, it limits the capability of fuzzy systems to produce accurate outcomes.Hence, this paper presents a method to generate another popular shape of MF, the trapezoidal shape (trapMF) from FCM to allow more flexibility to FCM in producing outputs.The construction of trapMF is using mathematical theory of Gaussian distributions, confidence interval and inflection points.The cluster centers or mean (μ) and standard deviation (σ) from the Gaussian output are fully used to determine four trapezoidal parameters; lower limit a, upper limit d, lower support limit b, and upper support limit c with the assistance of function trapmf() in Matlab fuzzy toolbox.The result shows that the mathematical theory of Gaussian distributions can be applied to generate trapMF from FCM.
A QoE-Aware Mode Selection Framework for Video Streaming in D2D Networks

Device-to-Device (D2D) communication is a novel approach that has been proposed to enhance the performance of traditional cellular networks. This work focuses on video streaming using D2D communications underlaying a Long Term Evolution (LTE) network where resources are allocated to each D2D pair in three modes of operation; cellular, dedicated and reuse. The objective is to maximize the sum-rate of the D2D pairs while maintaining continuous video playback with an acceptable quality level. Unlike previous works, which assign one channel to each user or divide the available resources equally between them or assume constant bit rate streaming (CBR), we estimate the number of Resource Blocks (RBs) needed for all users according to their rate and Signal-to-Noise Ratio (SNR) requirements and consider Scalable Video Coding (SVC). A complete Quality of Experience (QoE)-aware framework for D2D-based video streaming that includes resource allocation, power control, mode selection and SVC layer selection is then proposed while taking into consideration buffer occupancies and the video peak signal-to-noise ratio (PSNR). The efficacy of the proposed scheme is evaluated through extensive simulations and compared to the scenario where only one mode of operation is assigned to all D2D pairs. Furthermore, the effect of the number of D2D users as well as their mobility is also investigated. Simulation results show that the proposed framework improves the throughput of the D2D pairs when compared to a single mode of operation while at the same time providing better video quality and continuity.