Reviewerly/exHarmony huggingface dataset:
Smoothing with Fake Label

Label Smoothing is a widely used technique in many areas. It can prevent the network from being over-confident. However, it hypotheses that the prior distribution of all classes is uniform. Here, we decide to abandon this hypothesis and propose a new smoothing method, called Smoothing with Fake Label. It shares a part of the prediction probability to a new fake class. Our experiment results show that the method can increase the performance of the models on most tasks and outperform the Label Smoothing on text classification and cross-lingual transfer tasks.
Spatial Steganalysis Based on Gradient-Based Neural Architecture Search

Most existing steganalytic networks are designed empirically, which probably limits their performances. Neural architecture search (NAS) is a technology that can automatically find the optimal network architecture in the search space without excessive manual intervention. In this paper, we introduce a gradient-based NAS method called PC-DARTS in steganalysis. We firstly define the overall network architecture, and the search spaces of the corresponding cells in the network. We then use softmax over all candidate operations to construct an over-parameterized network. By updating the parameters of such a network based on gradient descent, the optimal operations, i.e., the high-pass filters in pre-processing module and operations in feature extraction module, can be obtained. Experimental results show that the resulting steganalytic network via NAS can achieve competitive performance with some advanced well-designed steganalytic networks, while the searching time is relatively short.
A Study on Varieties of Computer Intelligent Models for Early Detection of Prostate Cancer

Cancer is a disease that attacks several organs in the human body and rapidly spreads to other parts of the body. For the last few decades, cancer has become the most vulnerable illness on the planet, with the highest mortality rate due to late diagnosis. Since the 1960s, researchers have been studying computational methods for learning from cancer data, with number of advanced techniques directly applied on medical images. Depending on the organ, cancer can manifest in a number of ways. Different treatment processes can be used to remove the contaminated organ, prevent its spread, and save the person's life when these symptoms are considered. As a result, early detection of cancer has emerged as one of the most pressing issues in medical science and technology. This paper presents a review of recent work on the methods of computational intelligence for prostate cancer prediction modelling, as well as the issues that must be addressed. The paper focuses on a broad concept of computational intelligence that includes Artificial Neural Networks and Deep Learning, as well as a survey of various deep learning application areas for prostate cancer diagnosis and detection. Initially we will start with an overview of architectural principles and the use of various common deep learning technologies such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and auto-encoders (AE). With that, we'll move on to the study of these various algorithms for early cancer detection using technology.
RNS Elements of Fast Multiplication for Finite Galois Field Arithmetic

The concept of design fast multiplication modules for parallel and stream computations using the data coding system in residual classes is considered. Variants of functional and algorithmic implementation of the main elements of coding are presented, and the main limitations for their hardware implementation are determined.
The Stories We Tell: Experience, Research, or Patterns?

Abstract Several “story” formats are used by software researchers and practitioners to document research results and share best practices. Research papers are the staple of software conferences and journals: papers report the results of research projects and a wide range of empirical experiments. Experience reports and software patterns are two alternative formats to share results and propagate knowledge and best practices. Experience reports relate experience and cautionary tales while software patterns distill experience into a compact form. An XP2021 panel session orchestrated by Steven Fraser – featuring panelists Ademar Aguiar, Casper Lassenius, Mary Lynn Manns, Ken Power, and Rebecca Wirfs-Brock – discussed how “story” formats each have a role to advance the practice of software engineering.
Efficient Representation of Interaction Patterns with Hyperbolic Hierarchical Clustering for Classification of Users on Twitter

Social media platforms play an important role in democratic processes. During the 2019 General Elections of India, political parties and politicians widely used Twitter to share their ideals, advocate their agenda and gain popularity. Twitter served as a ground for journalists, politicians and voters to interact. The organic nature of these interactions can be upended by malicious accounts on Twitter, which end up being suspended or deleted from the platform. Such accounts aim to modify the reach of content by inorganically interacting with particular handles. These interactions are a threat to the integrity of the platform, as such activity has the potential to affect entire results of democratic processes. In this work, we design a feature extraction framework which compactly captures potentially insidious interaction patterns. Our proposed features are designed to bring out communities amongst the users that work to boost the content of particular accounts. We use Hyperbolic Hierarchical Clustering (HypHC) which represents the features in the hyperbolic manifold to further separate such communities. HypHC gives the added benefit of representing these features in a lower dimensional space – thus serving as a dimensionality reduction technique. We use these features to distinguish between different classes of users that emerged in the aftermath of the 2019 General Elections of India. Amongst the users active on Twitter during the elections, 2.8% of the users participating were suspended and 1% of the users were deleted from the platform. We demonstrate the effectiveness of our proposed features in differentiating between regular users (users who were neither suspended nor deleted), suspended users and deleted users. By leveraging HypHC in our pipeline, we obtain F1 scores of upto 93%.
Valorisation of Auriferous Indices: Case of the Siribaya Deposit, Western Mali

The purpose of this paper is to understand the valorization of gold showings in the Seribaya deposit, western Mali.Samples taken from Air Core (AC) boreholes were subjected respectively to the magnetic susceptibility test and the X-ray fluorescence analysis.These results lead to the development of a simple methodology, based on signatures/indicators geochemical to identify lithology or to locate mineralization.We note that, compared to other chemical elements, Arsenic remains the most correlated with gold in the study area.Since this correlation remains weak (r = 0.16), it shows that Arsenic does not always accompany gold.There would be a generation of Arsenic related to mineralization, and other generations that would not be.The observation of the strip logs led to the identification of the lithologies (focusing on the immobile elements, Ti, V, Cr, Y, Zr) and the establishment of the alteration profile.Highly altered levels (Saprolite ± Saprock) are characterized by the disappearance of most mobile elements.A medium to strong correlation between gold mineralization and magnetic susceptibility measurements has been observed, in some places, and at depths.These mineralized intervals typically consist of quartz veins, suggesting that they have structural control over the mineralization.Gold mineralization is believed to be related to quartz veins.In addition, gold would be in paragenesis with a generation of Arsenic hosted in coarse sediments.
Resilience as “Political Decentralization”: An alternate history of the Cypherpunks origins of decentralised technology

The origins of decentralised technologies are often attributed to the Cypherpunks who pursued the development of privacy preserving tools against government surveillance. The Cypherpunk story is often portrayed as a “David versus Goliath” battle between counterculture rebels who wanted to subvert state surveillance with cryptographic tools, and the government who tried and failed to regulate public access to strong cryptography (Levy, 2002). That narrative - of the Cypherpunks versus The State in developing decentralized technology - is somewhat inaccurate. Historical documentation reveals that the idea of decentralization as a means to resilience evolved through non-linear developments by government sponsored, and independent, researchers in relation to policy questions of the day. The real contribution of Cypherpunk ideology is the idea that resilience can be achieved via technology that enables political decentralization, and that this leads to the distribution of control or influence over the systems that govern society for any given community to enable self-governance.
Metric Sentiment Learning for Label Representation

Label representation aims to generate a so-called verbalizer to an input text, which has a broad application in the field of text classification, event detection, question answering, etc. Previous works on label representation, especially in a few-shot setting, mainly define the verbalizers manually, which is accurate but time-consuming. Other models fail to correctly produce antonymous verbalizers for two semantically opposite classes. Thus, in this paper, we propose a metric sentiment learning framework (MSeLF) to generate the verbalizers automatically, which can capture the sentiment differences between the verbalizers accurately. In detail, MSeLF consists of two major components, i.e., the contrastive mapping learning (CML) module and the equal-gradient verbalizer acquisition (EVA) module. CML learns a transformation matrix to project the initial word embeddings to the antonym-aware embeddings by enlarging the distance between the antonyms. After that, in the antonym-aware embedding space, EVA first takes a pair of antonymous words as verbalizers for two opposite classes and then applies a sentiment transition vector to generate verbalizers for intermediate classes. We use the generated verbalizers for the downstream text classification task in a few-shot setting on two publicly available fine-grained datasets. The results indicate that our proposal outperforms the state-of-the-art baselines in terms of accuracy. In addition, we find CML can be used as a flexible plug-in component in other verbalizer acquisition approaches.
FORMATION OF PROFESSIONAL COMPETENCE IN FUTURE TEACHERS

The relevance of the stated research topic is determined by the urgent importance of the formation of professional competencies of future teachers in the process of their education in higher pedagogical education, as a factor that determines the success of their further professional activities. The aim of the article is to study the peculiarities of the formation of professional competencies of future teachers and to determine the main criteria that influence this process. At the same time, the authors took into account both the stage of students’ education in universities and their pedagogical practice in schools. The leading methodological approach in this research work is a combination of methods of systematic analysis of the process of formation of competence of the future teacher at the stage of his education in higher education, with analytical study of prospects for professional competence of future teachers in the process of direct entry into the profession and gradual mastery. pedagogical skills. The results of this research clearly demonstrate the importance of forming professional competence in future teachers at the stage of their education in higher education, in terms of understanding the basics of the teaching profession and creating future teachers the right level of motivation to perform their professional duties in the future. The results and conclusions of this research are of great practical importance for future teachers who learn the basics of professional skills at the stage of higher education, as well as for employees of the education system interested in improving their own professional competencies.
A Formal Analysis of Recommendation Quality of Adversarially-trained Recommenders

Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has been found vulnerable against adversarial perturbations of its model parameters. Adversarial Personalized Ranking (APR) mitigates this issue by robustifying BPR via an adversarial training procedure. The empirical improvements of APR's accuracy performance on BPR have led to its wide use in several recommender models. However, a key overlooked aspect has been the beyond-accuracy performance of APR, i.e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty. In this work, we model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items. Using matrix factorization (MF), we empirically validate the theoretical results by performing preliminary experiments on two public datasets to compare BPR-MF and APR-MF performance on accuracy and beyond-accuracy metrics. The experimental results consistently show the degradation of novelty and coverage measures and a worrying amplification of bias.
Victimization

Not all groups are equally likely to be subject to acts of aggression; specific subgroups are more likely to be victimized. For example, youth who identify as a sexual minority are more likely to be victims of traditional forms of bullying than their heterosexual friends. There has been less research, however, on population subgroups and the likelihood of becoming a victim of cyber aggression. In exploring this topic, this chapter examines several questions including, “How important is the amount of time spent online as an intermediate variable in predicting whether an individual will become a victim of cyber aggression?” and “Does sexual orientation impact the likelihood of being a victim of cyberaggression above and beyond the amount of time spent online?” Multivariate statistical methods and survey data from the Pew Research Center for the year 2014 was used in this analysis.
Abstractive Representation Modeling for Image Classification

In recent years, artificial intelligence has achieved remarkable progress by showing its applicable values in various industries. In the perspective of handling visual data, Convolutional Neural Network (CNN) and its derivative approaches are well known for their robustness and accuracy. However, as a neural network approach, CNN also has limitations. In exchange for good performance, CNN requires large amount of training data as well as heavy training process. The complex neural network layer design also needs to be reconstructed and tuned by experienced researches for different problems. Last but not least, the “curse of Blackbox”, a well-known disadvantage of neural network prevents CNN from providing reasonable explanation for the prediction results. All above limitations remind us that the most cutting-edge approach is still in the state of weak AI. In this paper, an approach called Abstractive Representation Model (ARM) is proposed which is different from the traditional neural network approaches. This goal of experimenting with such model is trying to address the CNN’s weaknesses and possibly developing a new way of handling image data.
Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Abstract Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.
Research on the Transmission Optimization Schemes of User Side Power Consumption Information

With the comprehensive completion of the ‘Full Coverage, Full Collection’ construction of the electricity information collection system and the development of various business applications, there are higher requirements for user-side power consumption information data exchange efficiency, access capability and real-time performance. Therefore, there is an urgent need to study information transmission optimization methods on the user side, so as to improve the energy efficiency of the system and save resources. This article first sorts out the characteristics of user-side measurement data in the Energy Internet. According to the object-oriented characteristics, it gives the user-side electricity information communication protocol layered structure. Finally, this paper proposes an optimization method for user-side measurement information transmission based on compressed sensing, compares different compression schemes, and the validity of the algorithms applicated in the transmission of electricity information data is verified.
Recalibration of Structured-Light RGB-D Cameras with Parametric Depth Error Correction

Structured-light RGB-D cameras have been widely used in various applications. However, due to the deformation of internal camera parts, their depth estimation accuracy degrades with time. While it is easy to calibrate the camera parameters, updating the calibrated parameters to the camera firmware is difficult. Therefore, existing methods compensate for the depth measurements with different error correction functions. At present, as there are no simple and accurate parametric error correction methods, non-parametric calibration methods must be used when accurate depth measurements are required. The main drawback of such nonparametric approaches is that they require a large number of calibration images to calibrate a large error correction lookup tables. In this paper, we propose a simple parametric depth error correction model based on Taylor-series approximation of depth measurement equations. Experimental results show that the proposed method outperforms other parametric approaches and achieves results comparable to the state-of-the-art nonparametric method although the proposed method uses only nine parameters.
Addressing environmental and parasitic contamination problem of the water and sediments of the euphrates river by granular activated date (GAD) seeds and studying some physical and chemical properties in Al-Najaf governorate

In order to know the efficiency of sterilization in eliminating parasites and its suitability for environment measurements, samples of the water and sediments was taken from six stations the Euphrates River. The samples were transported in clean and sterile bottles to the laboratory. A sedimentation method was used for parasites. Ten species of parasites isolated from water of the six stations while isolated seven were exists in the sediments of these stations. The water and sediments of the station first were contaminate with parasites rate 25.8% and 13.7%, the water and sediments of the second station are contaminated with parasites rate 12.8% and 20.2%, the water and sediments of the third station are contaminated with parasites rate 4.2% and 18.2%, the water and sediments of the fourth station are contaminated with parasites rate 22.8% and 18%, the water and sediments of the fifth station are contaminated with parasites rate 12.0% and 5.4% and the water and sediments of the sixth station are contaminated with parasites rate 12.9% and 1.7%. Giardia lamblia rate 25.4% it is most found in water while Trichomonas hominis rate 18.4% It is most found in sediments. A result of the study shows significant difference between of the temperature of air and water in all study stations, the air temperature ranged (40.10–15.2°C) and the water temperature between (26.3–9.4°C) while PH was (9.89–7.5), recorded electrical conductivity (5745–2515 μs/cm) and salinity was (4.80–1.56 g/cm) the river's water was of low salinity also dissolved oxygen was (12.9–6.2 mg/L). Date seed used as granular by activated carbon used for purification and remove parasite from water filter and waste water treatment.
OPTIMIZING IRRIGATION BENEFITS IN THE WATER SYSTEM USING DYNAMIC PROGRAMMING

Irrigation is defined as the system through which water is utilized in agriculture. There are various ways in which an irrigation system, as implemented, can ensure maximum use of water. One such technique is the optimization technique. Optimization techniques are commonly used to solve several water resource development problems in an area. A variety of aspects need to be explored on this front. These include putting in place an irrigation system. The present study focuses on the Gembleng Kanan irrigation area in Banyuwangi, which is spread over 407 ha. There is water shortage and decrease in the growth intensity during the dry season. The systems analysis and the crop production improvement uses dynamic program optimization. The results of this study show that the average irrigation water requirement in the growing season depending on the selected crop layout pattern is 0.236 m3.s-1 during the rainy season, 0.275 m3.s-1 during the dry season-1, and 0.14 m3.s-1 during dry season-2. Based on the water system availability, irrigation water can be continuously supplied during the rainy season and dry season-1, whereas the rotation system is carried out during dry season-2. Through the use of dynamic programming, it has been estimated that the benefits of the water irrigation system during dry season-2 in the Gembleng Kanan irrigation area amount to Rp 1,835,570,000.00. In other words, there is an 11.5 % increase in profits.