Reviewerly/exHarmony huggingface dataset:
Inherent Inconsistencies of Feature Importance

The rapid advancement and widespread adoption of machine learning-driven technologies have underscored the practical and ethical need for creating interpretable artificial intelligence systems. Feature importance, a method that assigns scores to the contribution of individual features on prediction outcomes, seeks to bridge this gap as a tool for enhancing human comprehension of these systems. Feature importance serves as an explanation of predictions in diverse contexts, whether by providing a global interpretation of a phenomenon across the entire dataset or by offering a localized explanation for the outcome of a specific data point. Furthermore, feature importance is being used both for explaining models and for identifying plausible causal relations in the data, independently from the model. However, it is worth noting that these various contexts have traditionally been explored in isolation, with limited theoretical foundations. This paper presents an axiomatic framework designed to establish coherent relationships among the different contexts of feature importance scores. Notably, our work unveils a surprising conclusion: when we combine the proposed properties with those previously outlined in the literature, we demonstrate the existence of an inconsistency. This inconsistency highlights that certain essential properties of feature importance scores cannot coexist harmoniously within a single framework.
On the Concept of Frequency in Signals Processing: A discussion

&lt;p&gt;Frequency is a central concept in Mathematics, Physics, and Signal Processing. It is the main tool for describing the oscillatory behavior of signals, which is usually argued to be the manifestation of some of their key features, depending on their nature. For instance, this is the case of Electroencephalographic signals. Hence, frequency is substantially present in the most common methodologies for analyzing signals, as the Fourier Analysis or the Time-Frequency Analysis. &lt;/p&gt; &lt;p&gt;However, in spite of its importance as a keystone in Signal Processing, and its seemingly simple meaning, its mathematical foundation is not as straightforward as it may seem at first glance. A naive interpretation of the different mathematical concepts modelling frequency can be misleading, as their actual meanings essentially differ from the intuitive notion which are supposed to represent. &lt;/p&gt; &lt;p&gt;In our opinion, this circumstance should be taken into account to develop and apply appropriate signal analysis and processing tools in some applications. In the current text we discuss this topic, with the main goal to draw the attention of the mathematical and engineering community to this point, often overlooked. &lt;/p&gt;
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data

We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaboratively train a model on their respective features utilizing several local iterations and sharing compressed intermediate results periodically. Our work provides the first theoretical analysis of the effect message compression has on distributed training over vertically partitioned data. We prove convergence of non-convex objectives at a rate of $O(\frac{1}{\sqrt{T}})$ when the compression error is bounded over the course of training. We provide specific requirements for convergence with common compression techniques, such as quantization and top-$k$ sparsification. Finally, we experimentally show compression can reduce communication by over $90\%$ without a significant decrease in accuracy over VFL without compression.
Development of Online Movie Recommendation System based on Neighborhood-based Collaborative Filtering

The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie selection. In the process of building the website, apart from building diagram of movie recommendation system’s functions, class diagram of movie recommendation function, sequence diagram of movie recommendation function, we also build a user-recommended movie model based on the Movies Lens[9] dataset for a fairly high accuracy, which is 99%.
Special Issue on Application of Artificial Neural Networks for Seismic Design and Assessment

The application of methods and techniques of Machine Learning (ML) in many scientific fields has been increasing rapidly over recent decades [...]
Disparate Impact in Differential Privacy from Gradient Misalignment

As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly regulated industries. Unfortunately, the application of privacy enhancing technologies can worsen unfair tendencies in models. In particular, one of the most widely used techniques for private model training, differentially private stochastic gradient descent (DPSGD), frequently intensifies disparate impact on groups within data. In this work we study the fine-grained causes of unfairness in DPSGD and identify gradient misalignment due to inequitable gradient clipping as the most significant source. This observation leads us to a new method for reducing unfairness by preventing gradient misalignment in DPSGD.
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?

Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success of WGANs, it is still unclear how well the underlying OT dual solvers approximate the OT cost (Wasserstein-1 distance, $\mathbb{W}_{1}$) and the OT gradient needed to update the generator. In this paper, we address these questions. We construct 1-Lipschitz functions and use them to build ray monotone transport plans. This strategy yields pairs of continuous benchmark distributions with the analytically known OT plan, OT cost and OT gradient in high-dimensional spaces such as spaces of images. We thoroughly evaluate popular WGAN dual form solvers (gradient penalty, spectral normalization, entropic regularization, etc.) using these benchmark pairs. Even though these solvers perform well in WGANs, none of them faithfully compute $\mathbb{W}_{1}$ in high dimensions. Nevertheless, many provide a meaningful approximation of the OT gradient. These observations suggest that these solvers should not be treated as good estimators of $\mathbb{W}_{1}$, but to some extent they indeed can be used in variational problems requiring the minimization of $\mathbb{W}_{1}$.
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation

The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work begins this examination by evaluating the how radio frequency (RF) domain changes encourage or prevent the transfer of features learned by convolutional neural network (CNN)-based automatic modulation classifiers. Additionally, we examine existing transferability metrics, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), as a means to both select source models for RF domain adaptation and predict post-transfer accuracy without further training.
Explainable Models via Compression of Tree Ensembles

Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of compressing a large set of learned trees into a single explainable model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that produces a single small decision list as a compressed representation. CoTE first converts the trees to decision lists and then performs the combination and compression with the aid of the original training set. An experimental evaluation demonstrates the effectiveness of CoTE in several benchmark relational data sets.
Lifelong Wandering: A realistic few-shot online continual learning setting

Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classification when learning from data-streams composed of a single indoor environment, we propose to extend this setting to consider object classification on a series of several indoor environments, which is likely to occur in applications such as robotics. Importantly, our setting, which we refer to as online few-shot continual learning, injects the well-studied issue of catastrophic forgetting into the few-shot online learning paradigm. In this work, we benchmark several existing methods and adapted baselines within our setting, and show there exists a trade-off between catastrophic forgetting and online performance. Our findings motivate the need for future work in this setting, which can achieve better online performance without catastrophic forgetting.
Quality Inspection of Dengue kits using YOLOv4 architecture

Abstract: With the increasing advancements in Artificial Intelligence and its varied applications across multiple domains, the manufacturing industry is not left behind. Manufacturing and Production require a lot of labour force to ensure good quality end results. While this may be a necessity in the rudimentary stages of development, there is a way to cut down on this while checking the quality of the end product. This project aims at using the power of Artificial Intelligence, specifically Computer vision to create a quality inspecting tool that entails localizing and predicting the required objects in the image of the Dengue kit. This project highlights the entire process including simulation, design of conveyor belt and displays the final process of how both combined can help catalyse quality inspection by subtracting the manual crunch. Keywords: Artificial Intelligence, Inspection, Computer vision, Industry 4.0 Revolution, Object Detection, Yolov4
Continual Learning with Guarantees via Weight Interval Constraints

We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting. Contemporary Continual Learning (CL) methods focus on training neural networks efficiently from a stream of data, while reducing the negative impact of catastrophic forgetting, yet they do not provide any firm guarantees that network performance will not deteriorate uncontrollably over time. In this work, we show how to put bounds on forgetting by reformulating continual learning of a model as a continual contraction of its parameter space. To that end, we propose Hyperrectangle Training, a new training methodology where each task is represented by a hyperrectangle in the parameter space, fully contained in the hyperrectangles of the previous tasks. This formulation reduces the NP-hard CL problem back to polynomial time while providing full resilience against forgetting. We validate our claim by developing InterContiNet (Interval Continual Learning) algorithm which leverages interval arithmetic to effectively model parameter regions as hyperrectangles. Through experimental results, we show that our approach performs well in a continual learning setup without storing data from previous tasks.
THE ROLE OF ELECTRONIC TEXTBOOKS IN HIGHER EDUCATION IN IMPROVING THE QUALITY OF EDUCATION

The article discusses the process of learning how to develop a site in PHP through an electronic textbook. The ostentatious possibilities of an electronic textbook to improve the quality of education. The effectiveness of training using an electronic textbook has been substantiated.
YouTube Data Analysis and Prediction of Views and Categories

Abstract: There's enormous growth and fashionability of YouTube. It has all implicit to move billion of lives encyclopedically as the number of observers is growing day by day. Nearly billions of vids are watched on YouTube every single day, generating a huge quantum of data daily. YouTube data is actually in unshaped form, so there's great demand to store the data, process the data and assaying the data. This analysis will help in discovering how people are performing on YouTube, one can fluently identify what contentworks best on YouTube. The primary purpose ofthis design is to find how real time data can be anatomized to get the rearmost analysis and trends in YouTube. The analysis is done using stoner features similar as views, commentary, markers, likes, and dislikes. Analysis can be performed using algorithms like direct retrogression, bracket and other machine literacy models and python libraries like pandas, matplotlib library to classify the YouTube data and gain useful information. Keywords: YouTube, titles, Prediction ofcategories, Data analysis.
Analisis Penggunaan Google Meet dalam Pembelajaran terhadap Kecerdasan Emosional Siswa

In the current era of online learning since the Corona pandemic, many platforms are used to simplify the learning process. Like the Google Meet platform, one of the applications used. Students' emotional intelligence will be influenced by what tools are used in learning. The usefulness of this application can be accessed easily anytime and anywhere, and the advantages of this application include making it easier to communicate so that interaction can occur even though they are in different places and one of the drawbacks is that if it is used for a large number of participants there is a provision to pay and can’ot change the background at will. The method is carried out by sampling a questionnaire that was carried out at SMA Muhammadiyah 1 Banjarnegara in class 12 and received 66 responses. By using this method, the results of the data in the form of emotional intelligence can be influenced by the platform because in the learning there is still interaction between teachers and students as well as students with students even though they are separated by different distances and spaces so that students can receive and understand the material presented by the teacher when learning takes place using Google Meet because this platform can display presentations or powerpoints so that it can facilitate the learning process.
Improving Diversity with Adversarially Learned Transformations for Domain Generalization

To be successful in single source domain generalization, maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Many of the recent successes have come from methods that pre-specify the types of diversity that a model is exposed to during training, so that it can ultimately generalize well to new domains. However, na\"ive diversity based augmentations do not work effectively for domain generalization either because they cannot model large domain shift, or because the span of transforms that are pre-specified do not cover the types of shift commonly occurring in domain generalization. To address this issue, we present a novel framework that uses adversarially learned transformations (ALT) using a neural network to model plausible, yet hard image transformations that fool the classifier. This network is randomly initialized for each batch and trained for a fixed number of steps to maximize classification error. Further, we enforce consistency between the classifier's predictions on the clean and transformed images. With extensive empirical analysis, we find that this new form of adversarial transformations achieve both objectives of diversity and hardness simultaneously, outperforming all existing techniques on competitive benchmarks for single source domain generalization. We also show that ALT can naturally work with existing diversity modules to produce highly distinct, and large transformations of the source domain leading to state-of-the-art performance.
Developing Rural Communities in Nigeria Through Information and Communication Technologies and the Potential Barriers

The roles of Information and Communication Technologies (ICTs) in all spheres of human activities have been applauded by researchers and/or professionals in the field. Rural community development is a critical driving force in a nation’s advancement. The effort is to address and successfully support the specific needs and opportunities of the communities and their inhabitants. As a result, any nation that has weak or undeveloped rural communities finds it extremely difficult to reach the height of its developmental stride. In the same manner, the role of new technologies in modern times cannot be quickly exhausted in a hurry. As a result, this paper attempts to identify and conceptualize “ICTs” and “Community development”. It also attempts to understand the technological tools, and how these various tools are used to address community developments, particularly in Nigeria and the wider world at large. The paper further examined the challenges or obstacles hindering the effective use of Information and Communication Technology tools in rural community development in contemporary times. The authors finally made suggestions for combating the effective implementation of ICT tools in rural community developments.
Bayesian MCMC Approach to Learning About the SIR Model

Abstract: This project aims to study the parameters of the Deterministic SIR(Susceptible → Infected → Recovered) model of COVID-19 in a Bayesian MCMC framework. Several deterministic mathematical models are being developed everyday to forecast the spread of COVID-19 correctly. Here, I have tried to model and study the parameters of the SIR Infectious disease model using the Bayesian Framework and Markov-Chain Monte-Carlo (MCMC) techniques. I have used Bayesian Inference to predict the Basic Reproductive Rate ࢚ࡾ in real time using and following this, demonstrated the process of how the parameters of the SIR Model can be estimated using Bayesian Statistics and Markov-Chain Monte-Carlo Methods. Keywords: COVID-19, Bayesian Inference, Dynamical Systems, SIR Model, Basic Reproductive Rate, Markov-Chain MonteCarlo(MCMC)
Environmental Impact Assessment of Mining Activities Around Rimin-Zayam, Toro LGA, Bauchi State

Mining environment is known for a high level of heavy metals due to the activities which cause adverse alteration of the ecological system. This research study the partitioning of heavy metals in artisanal mining sites in RiminZayan, Toro Local Government Area Bauchi state, Nigeria. The concentration of heavy metals like lead, Cadmium, Iron, Zinc, Cobalt (Co), Copper , Chromium, Manganes and Nickel, were determined using the sequential extraction method recommended by Community Bureau of Reference (BCR) and physic-chemical properties such as the pH, cation exchange capacity, total organic carbon, total organic matter, electrical conductivity and soil particle size. The result show that the soil in the area has a clayey texture with the percentage of clay, slit and sand ranging from 9.29–27.10, 6.31–13.41 and 59.32–84.39 %, respectively. The pH levels of the soil ranged from 6.04 to 6.45 indicating that the soil in thee study is slightly acidic in nature. The observed electrical conductivity (EC) obtained in this study for the samples of soil sediments taken from around the mining site were 26.76, 31,30 and 42.66 µs/cm respectively. The percentages of organic matter of the soil in the same location are 0.423, 0.061 and 0.452 %. Cation exchange capacity (CEC) of the soil in the same vanity were respectively found to be 4.003, 7.385 and 3.994 cmol/kg. The metals were fractionated into six fractions and determined using Atomic Absorption Spectrophotometric techniques (AAS). The results of the soil analysed indicated that there was high abundance of heavy metals such as cadmium, chromium, copper, iron, lead, nickel, and zinc in the residual fraction, pointing to their lithogenic origins, and therefore primarily inherited from the parent material. Cobalt and manganese are bound to other fractions (exchangeable, carbonate, manganese oxide, iron-manganes oxide, organic matter-sulphide, and residual metals which shows their anthropogenic origin. The ratio of bioavailability factor (BF) ranged from 27.47 to 89.66, Contamination factor showed values ranging from 0.001 to 16.299 which suggests that some soils were not contaminated while some were moderately contaminated metals investigated. The degree of contamination (DC) showed that the highest recorded were from non -mining area (19.517) while active mining sites and abandoned mining sites values are: 5.985 and 3.843, respectively. This trend could be owing to the bioavailability and mobility of the metals from the mining sites to non-mining locations. The data generated were subjected to one-way ANOVA. The least significant difference test (p ≤ 0.05).
TransDrift: Modeling Word-Embedding Drift using Transformer

In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the flexibility of transformer, our model accurately learns the dynamics of the embedding drift and predicts the future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods.
A Dataset Containing Tweets and Their Meta Data for Understanding Social Media Conversations around Movies during Their Release

In this paper we intend to present a dataset that contain a collection of tweets generated as reactions of the release of 50 different movies. The dataset can be used for gaining useful insights regarding the conversation that is generated around a particular movie. It is particularly suitable for conducting sentiment analysis and other NLP techniques. The dataset contains approximately 2.5 million tweets with their related meta data and cover 50 movies. For each movie, its IMDb rating is included. The movies are the 25 releases with the highest number of votes during 2020 and 2021. The collected tweets represent the reactions of the twitter community during the first week of the release date in US of that particular movie. The tweets per movie ranged from 1.000 to approximately 200.000 tweets with an average of 50.000 per release. We used The Internet Archive Wayback Machine in order to retrieve the IMDb movie rating after one week of the US release date. The tweets and related metadata have been collected using the Tweet Downloader tool.