Reviewerly/exHarmony huggingface dataset:
Economic Growth Implications of Inflation Targeting and Inflation Volatility: An Emerging Economy’s Perspective

Purpose: The purpose of this study was to comparatively examine the extent to which inflation targeting and inflation volatility impact on economic growth from an emerging economy’s perspective.&#x0D; Methodology: Annual data from the World Development Indicator (WDI) and the Bank of Ghana (BoG) from 1985 to 2014 were used for the study. The data was interpolated into quarterly series using E-views. The stationarity characteristics of the variables were tested using the Augmented Dickey-Fuller and the Phillip-Perron unit root tests. The GARCH (1 1) and ARDL models were employed for the analysis.&#x0D; Findings: The study found that inflation targeting had a positive significant effect on economic growth in Ghana. Also, despite the volatile nature of inflation in Ghana, inflation volatility had no significant effect on economic growth.&#x0D; Implications: This scholarly work would aid policy makers in policy formulation pertaining to inflation targeting and inflation volatility on economic growth. It will also open up the circles of knowledge regarding inflation targeting monetary policy’s regime in empirical literature, institutions of higher learning and the understanding of the general public. The study recommends that the Bank of Ghana maintains its current monetary policy framework - Inflation Targeting, but should strengthen the preconditions and subsequent requirements of the monetary policy in order to maximize the gains to economic growth.
Skew differential Goppa codes and their application to Mceliece cryptosystem

Abstract A class of linear codes that extends classical Goppa codes to a non-commutative context is defined. An efficient decoding algorithm, based on the solution of a non-commutative key equation, is designed. We show how the parameters of these codes, when the alphabet is a finite field, may be adjusted to propose a McEliece-type cryptosystem.
Multi‐output regression for imbalanced data stream

Abstract In this article, we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (Multiple Output Regression for Streaming Time Series), an online ensemble regressors devoted to non‐stationary and imbalanced data streams. MORSTS relies on several multiple output regressor submodels, adopts a cost sensitive weighting technique for dealing with imbalanced datasets, and handles overfitting by means of the K‐fold cross validation. For assessment purposes, experiments have been conducted on known real datasets and compared with known base regression techniques.
Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations —instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations and to assess any differences between purely predictive and causal models of the same data, this article discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses and then use the causal information to guide the construction of a statistical model that captures genuine causal relations—such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant’s performance in the contest. While the overall effect associated with programming languages is weak compared to other variables—regardless of whether we consider correlational or causal links—we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques—where genuine causal effects may be confounded.
Mining parallel sentences from internet with multi-view knowledge distillation for low-resource language pairs

The neural machine translation (NMT), which relies on a large training data (bilingual parallel sentences, for NMT) to obtain the state-of-the-art performance, is similar with deep learning. In order to construct NMT systems, the number of parallel sentences is very important. However, these bilingual resources are scarce for many low-resource language pairs. Although several works attempt to obtain bilingual parallel data from Internet, the quality and quantity of mined bilingual corpus are limited for low-resource language pairs. To address this problem, we propose the multi-view knowledge distillation model (MvKD) that use the knowledge of high-resource language pairs transfer into low-resource languages by leveraging internal language invariant in different languages. In particular, we treat the mining bilingual parallel sentence pair task as classifying task and use the multi-view classifier to detect bilingual parallel sentence pair. For multi-view classifier, we use two views to recognize the semantic difference of two sentences: (i) word-level representations and (ii) sentence-level representations. We encode sentence-level representations to capture semantically similar of two sentences. Moreover, we encode word-level representations to capture word translations in a pair of parallel sentences to avoid the problem that semantically similar but non-parallel sentences. Experimental results demonstrate that our proposed method can significantly mine amount of bilingual corpus and improve the quality of parallel sentences. In particular, we carry out the experiments on several real-world low-resource situations and achieve excellent results.
Scraping Relevant Images from Web Pages without Download

Automatically scraping relevant images from web pages is an error-prone and time-consuming task, leading experts to prefer manually preparing extraction patterns for a website. Existing web scraping tools are built on these patterns. However, this manual approach is laborious and requires specialized knowledge. Automatic extraction approaches, while a potential solution, require large training datasets and numerous features, including width, height, pixels, and file size, that can be difficult and time-consuming to obtain. To address these challenges, we propose a semi-automatic approach that does not require an expert, utilizes small training datasets, and has a low error rate while saving time and storage. Our approach involves clustering web pages from a website and suggesting several pages for a non-expert to annotate relevant images. The approach then uses these annotations to construct a learning model based on textual data from the HTML elements. In the experiments, we used a dataset of 635,015 images from 200 news websites, each containing 100 pages, with 22,632 relevant images. When comparing several machine learning methods for both automatic approaches and our proposed approach, the AdaBoost method yields the best performance results. When using automatic extraction approaches, the best f-Measure that can be achieved is 0.805 with a learning model constructed from a large training dataset consisting of 120 websites (12,000 web pages). In contrast, our approach achieved an average f-Measure of 0.958 for 200 websites with only six web pages annotated per website. This means that a non-expert only needs to examine 1,200 web pages to determine the relevant images for 200 websites. Our approach also saves time and storage space by not requiring the download of images and can be easily integrated into currently available web scraping tools, because it is based on textual data.
On the relation between quantum walks and absolute zeta functions

The quantum walk is a quantum counterpart of the classical random walk. On the other hand, the absolute zeta function can be considered as a zeta function over $$\mathbb {F}_1$$ . This paper presents a connection between the quantum walk and the absolute zeta function. First, we deal with a zeta function determined by a time evolution matrix of the Grover walk on a graph. The Grover walk is a typical model of the quantum walk. Then, we prove that the zeta function given by the quantum walk is an absolute automorphic form of weight depending on the number of edges of the graph. Furthermore we consider an absolute zeta function for the zeta function based on a quantum walk. As an example, we compute an absolute zeta function for the cycle graph and show that it is expressed as the multiple gamma function of order 2.
Dynamic evaluation on slope ecological restoration effect based on cosine similarity and markov chain

It is important to evaluate the slope ecological restoration effect for diagnosing the slope restoration state in time. Several soft computing methods require experts to determine the index weight, which will affect the rationality of the evaluation results. Moreover, they are all static evaluation methods and cannot reflect the time effect of restoration. Therefore, a dynamic evaluation method has been proposed without determining the index weight based on Cosine Similarity and Markov Chain. Several cases were applied to prove the effectiveness of the proposed method. The results presented that the results of this method are more consistent with the actual situations and can reflect the variability of the restoration effect. Finally, the sensitivity of indexes under different ecological restoration methods was analyzed. The results show that the core link of the restoration method was consistent with the sensitivity result. The proposed method provides a basis for optimizing the restoration methods.
SISTEM INFORMASI PENDAFTARAN PESERTA DIDIK BARU (PPDB) MTS BAITURRAHMAN

This study analyzes the development and implementation of the New Student Registration Information System (PPDB) at MTS Baiturrahman with a qualitative approach. The research focus is on designing the registration interface and the resulting output. The research location is in Cipanggulaan Village, Kompa Village, Parungkuda District, Sukabumi Regency, with a research period from 1 to 31 May 2022. The results show that the design of the registration interface in the PPDB Information System features registration forms, uploading documents, choosing study programs, and exam schedule. This interface makes it easier for prospective students and parents to fill in registration information, select courses, and select exam schedules. The resulting output includes registration number, registration confirmation, test ticket, and acceptance status. Using this interface helps increase efficiency and transparency in the registration process. This study recommends user training, periodic system maintenance, communication improvement, advanced data analysis, responsiveness to feedback, and functionality expansion as steps to improve the implementation of the PPDB Information System. By adopting these suggestions, it is hoped that MTS Baiturrahman can continue to optimize the benefits and effectiveness of the system, strengthen collaboration with prospective students and parents, and support better decisions in managing new student registrations
A New Text Encryption Scheme Suitable for Combating Sniffing Attacks in IoT Applications via Non-supersingular Elliptic Curves over Binary Extension Fields

Several research works propose the use of Elliptic Curve Cryptography (ECC) to provide security for the Internet of Things (IoT) and cloud computing due to its shorter key requirement of approximately 160-571 bits vs. 1,024-15,360 bits of the others whilst achieving the same level of security. As a result, several ECC based text encryption schemes have been proposed in recent times. However, due to the mathematical foundations behind some of these schemes, there is the need for improvement to make them efficiently suitable for applications targeting IoT platforms. In addition, many of the existing schemes are either limited to some languages and/or use lookup tables which increase their computational overheads in terms of storage and processing. Against this background that this paper proposes a new ECC based text encryption scheme using efficient elliptic curve arithmetic to reduce the computational overheads. The scheme resists the major forms of sniffing attack in software implementation of ECC-based schemes. A test implementation proves that a very high key sensitivity is also achieved.
Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.
Non‐contact vacuum pumps

Summary An overview of the main methods for mathematical modeling of the working process of non‐contact vacuum pumps is given. The specifics and the main stages of mathematical model development for screw, scroll, claw and Roots vacuum pumps are considered. The results of mathematical modeling of pump characteristics are presented and compared with experimental data. The mathematical modeling technique presented here can be recommended for analyzing the influence of pump geometry and operating conditions on the pump characteristics of non‐contact oil‐free vacuum pumps.
A method for extracting 3D‐element based on local cost metrics resistant to radiometric distortion for realistic media application

Abstract The intensity values of pairs of stereoscopic images could be affected by radiometric elements such as camera exposure, illumination direction, etc. Therefore, it is a challenging problem to get consistent and exact disparity values under a variety of real‐world environment. Here, the proposed approach extracts disparity values based on a local cost metric and is resistant to global and local radiometric changes. It is designed in a way that disparity values are extracted using local uniformity of pixel values between pairs of stereoscopic images. The proposed metric‐based method is defined as the resistant local cost metric (RLCM) model. As a result of the experiment, the proposed model performed better than the comparative methods under experimental conditions in which the radiometric distortion between pairs of stereoscopic images was significantly different. The average peak signal‐to‐noise ratio (PSNR) value of the RLCM method is 20.0 dB under the global radiometric change condition and 19.3 dB under the local condition, showing the highest value compared to the comparison group. Consequentially, it is demonstrated that the proposed method is less sensitive to various radiometric changes.
Dense Hebbian neural networks: A replica symmetric picture of unsupervised learning

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via statistical-mechanics tools, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters (e.g. quality and quantity of the training dataset, network storage, noise) that is valid in the limit of large network size and structureless datasets. Moreover, we establish a bridge between macroscopic observables standardly used in statistical mechanics and loss functions typically used in the machine learning. As technical remarks, from the analytical side, we extend Guerra's interpolation to tackle the non-Gaussian distributions involved in the post-synaptic potentials while, from the computational counterpart, we insert Plefka's approximation in the Monte Carlo scheme, to speed up the evaluation of the synaptic tensor, overall obtaining a novel and broad approach to investigate unsupervised learning in neural networks, beyond the shallow limit.
High-security automatic path planning of radiofrequency ablation for liver tumors

Radiofrequency ablation (RFA) is an effective method for the treatment of liver tumors. Preoperative path planning, which plays a crucial role in RFA treatment, requires doctors to have significant experience and ability. Specifically, correct and highly active preoperative path planning should ensure the safety of the whole puncturing process, complete ablation of tumors and minimal damage to healthy tissues.In this paper, a high-security automatic multiple puncture path planning method for liver tumors is proposed, in which the optimization of the ablation number, puncture number, target positions and puncture point positions subject to comprehensive clinical constraints are studied. In particular, both the safety of the puncture path and the distribution of ablation ellipsoids are taken into consideration. The influence of each constraint on the safety of the whole puncturing process is discussed in detail. On this basis, the efficiency of the planning method is obviously improved by simplifying the computational data and optimized variables. In addition, the performance and adaptability of the proposed method to large and small tumors are compared and summarized.The proposed method is evaluated on 10 liver tumors of various geometric characteristics from 7 cases. The test results show that the average path planning time and average ablation efficiency are 41.4 s and 60.19%, respectively. For tumors of different sizes, the planning results obtained from the proposed method have similar healthy tissue coverage. Through the clinical evaluation of doctors, the planning results meet the needs of RFA for liver tumors.The proposed method can provide reasonable puncture paths in RFA planning, which is beneficial to ensure the safety and efficiency of liver tumor ablation.
g-normal fuzzy relational models are universal approximators

Making a fuzzy relational model (FRM) g-normal by a specific configuration of the FRM and putting some constraints on both its fuzzifier and its fuzzy relational matrix, results in some favorite features at the expense of loosing some degrees of freedom. In this paper, we show that the g-normal fuzzy relational modeling structure has the universal approximation property despite all the constraints that makes the model g-normal. Indeed, we prove that any real function in Lp (including continuous functions) can be modeled by a g-normal model with any arbitrary accuracy when the universe of discourse is a compact set.
Quantum dynamics of two photons in the coherent feedback system composing of two separated resonators coupling to a waveguide

Quantum coherent feedback based on waveguide quantum electrodynamics plays important roles in quantum devices design and quantum information processing. In this paper, we study the coherent dynamics of the quantum network composed of two separated resonators coupling to a waveguide. Using the dynamical feedback equation in Markovian regime, we obtain the analytical solutions to the output states for the two photons incident from the same or different directions of the waveguide. The two- photon scattering properties are analyzed based on the analytical expressions. It is found that the output states of the two photons can be controlled well by the phase induced by the single photon propagating between the two separated resonators. The results given in this paper are helpful in designing quantum devices at few-photon level.
Measuring dense false positive regions from segmentation result for whole slide tissue histology image

Evaluating a segmentation model is critical for constructing computer-aided diagnosis (CAD) systems, in which the segmented regions will be used for downstream analysis. However, existing segmentation evaluation metrics may not reflect every aspect of a trained segmentation model performance in the context of the whole slide tissue histology images (WSIs). Specifically, existing segmentation metrics generally ignore the impact of densely packed false positive pixels (DFP) in the WSI segmentation result. This study presents a new and efficient metric, named MAFaR, accounting for DFP regions in digital WSI segmentation. The proposed metric consists of two modules: 1) Estimation of DFP regions; 2) Calculation of MAFaR score. In module 1, a Gaussian Kernel Density Estimation method was used to estimate the density of the false positive (FP) regions in segmentation result (SR). Then a two-step Mean-shift clustering algorithm was applied to the high-density FP regions to estimate the DFP regions. In module 2, the ratio of DFP regions area to the positive regions area were used for MAFaR calculation. We conducted two experiments to evaluate the effectiveness of the MAFaR score. In the first experiment, we compared MAFaR with existing metrics related to FP regions, the proposed MAFaR score can reflect the impact of DFP regions. In the second experiment, MAFaR scores were compared with the manual evaluation scores given by three experienced engineers and found high correlation (Spearman’s rank correlation coefficients greater than 0.7) and high agreement (Kendall coefficient = 0.839). Therefore, the MAFaR can be used with other segmentation metrics for evaluating WSI segmentation.
Three-way classification for sequences of observations

This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame rates. The coarse-grained granule is described as an aggregation (mean pooling) of deep embeddings of an object from a few frames extracted with a low frame rate. A descriptor for a fine-grained granule is computed using the embeddings of most frames. The classifiers are learned for every granularity level. At the classification phase, the coarse-grained descriptor of the input sequence is fed into the first classifier, and the classes with high confidence scores fill a positive set from three-way decisions. The decision-making procedure is terminated at a granularity level for which the only one category is included in its positive set or the last fine-grained granule is reached. It is experimentally shown for the video-based facial expression recognition problem that our technique is up to 30 times faster than traditional processing of all frames without significant accuracy degradation.