Reviewerly/exHarmony huggingface dataset:
Towards More Objective Evaluation of Class Incremental Learning: Representation Learning Perspective

Class incremental learning (CIL) is the process of continually learning new object classes from incremental data while not forgetting past learned classes. While the common method for evaluating CIL algorithms is based on average test accuracy for all learned classes, we argue that maximizing accuracy alone does not necessarily lead to effective CIL algorithms. In this paper, we experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning and propose a new analysis method. Our experiments show that most state-of-the-art algorithms prioritize high stability and do not significantly change the learned representation, and sometimes even learn a representation of lower quality than a naive baseline. However, we observe that these algorithms can still achieve high test accuracy because they learn a classifier that is closer to the optimal classifier. We also found that the base model learned in the first task varies in representation quality across different algorithms, and changes in the final performance were observed when each algorithm was trained under similar representation quality of the base model. Thus, we suggest that representation-level evaluation is an additional recipe for more objective evaluation and effective development of CIL algorithms.
Double Sampling Randomized Smoothing

Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and thus there is a line of work aiming to provide robustness certification for NNs, such as randomized smoothing, which samples smoothing noises from a certain distribution to certify the robustness for a smoothed classifier. However, as shown by previous work, the certified robust radius in randomized smoothing suffers from scaling to large datasets ("curse of dimensionality"). To overcome this hurdle, we propose a Double Sampling Randomized Smoothing (DSRS) framework, which exploits the sampled probability from an additional smoothing distribution to tighten the robustness certification of the previous smoothed classifier. Theoretically, under mild assumptions, we prove that DSRS can certify $\Theta(\sqrt d)$ robust radius under $\ell_2$ norm where $d$ is the input dimension, implying that DSRS may be able to break the curse of dimensionality of randomized smoothing. We instantiate DSRS for a generalized family of Gaussian smoothing and propose an efficient and sound computing method based on customized dual optimization considering sampling error. Extensive experiments on MNIST, CIFAR-10, and ImageNet verify our theory and show that DSRS certifies larger robust radii than existing baselines consistently under different settings. Code is available at https://github.com/llylly/DSRS.
Whatsapp Digital Language among Undergraduate Students in Mount Kenya University

The rapid spread of mobile messaging applications like WhatsApp has its impact on generating special linguistic features archetypal for this type of Computer Mediated Discourse. This study investigates Whatsapp digital language from a discourse analysis approach. The study aims at analyzing the linguistic features and the functions of WhatsApp language among undergraduate students in Mount Kenya University. The study employed a descriptive research design that utilized qualitative research methods. The study employed Computer Mediated Discourse analysis framework. The findings of the study revealed that The findings of the study revealed that linguistic features of WhatsApp digital language are characterized by the use of casual speech and a netlingo among undergraduate students. Linguistic features were reflected in terms of contractions, phonetic spellings, abbreviations, regiolectal contractions, emoticons/Emojis and Punctuation Marks. The data examined revealed that language plays a key role in social media as it is the form of communication. The data revealed the following functions of language: phatic function of language for social connection, Interactional function of language for friendship maintenance, Informational function of language, directive function of language and multlingual function of language. The study recommends that that educators should reconsider how they respond to this new form of writing since the study shows how students are using WhatsApp digital language for academic purposes. The study adds knowledge to linguistics, Computer Mediated Discourse and language and communication.
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization

Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancing optimization objectives that regularize models to capture the underlying invariance; however, there often are compromises in the optimization process of these OOD objectives: i) Many OOD objectives have to be relaxed as penalty terms of Empirical Risk Minimization (ERM) for the ease of optimization, while the relaxed forms can weaken the robustness of the original objective; ii) The penalty terms also require careful tuning of the penalty weights due to the intrinsic conflicts between ERM and OOD objectives. Consequently, these compromises could easily lead to suboptimal performance of either the ERM or OOD objective. To address these issues, we introduce a multi-objective optimization (MOO) perspective to understand the OOD optimization process, and propose a new optimization scheme called PAreto Invariant Risk Minimization (PAIR). PAIR improves the robustness of OOD objectives by cooperatively optimizing with other OOD objectives, thereby bridging the gaps caused by the relaxations. Then PAIR approaches a Pareto optimal solution that trades off the ERM and OOD objectives properly. Extensive experiments on challenging benchmarks, WILDS, show that PAIR alleviates the compromises and yields top OOD performances.
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos

Multimodal sentiment analysis in videos is a key task in many real-world applications, which usually requires integrating multimodal streams including visual, verbal and acoustic behaviors. To improve the robustness of multimodal fusion, some of the existing methods let different modalities communicate with each other and modal the crossmodal interaction via transformers. However, these methods only use the single-scale representations during the interaction but forget to exploit multi-scale representations that contain different levels of semantic information. As a result, the representations learned by transformers could be biased especially for unaligned multimodal data. In this paper, we propose a multi-scale cooperative multimodal transformer (MCMulT) architecture for multimodal sentiment analysis. On the whole, the "multi-scale" mechanism is capable of exploiting the different levels of semantic information of each modality which are used for fine-grained crossmodal interactions. Meanwhile, each modality learns its feature hierarchies via integrating the crossmodal interactions from multiple level features of its source modality. In this way, each pair of modalities progressively builds feature hierarchies respectively in a cooperative manner. The empirical results illustrate that our MCMulT model not only outperforms existing approaches on unaligned multimodal sequences but also has strong performance on aligned multimodal sequences.
Histopathological analyses of breast cancer using deep learning

Deep Learning hosts a plethora of variants and models in Convolution Neural Networks (CNN), where the prudence of these methods is algorithmically proven when implemented with sturdy datasets. Much number of haphazard structures and textures are found in the histopathological images of breast cancer, where dealing with such multicolor and multi-structure components in the images is a challenging task. Working with such data in wet labs proves clinically consistent results, but added with the computational models will improvise them empirically. In this paper, we proposed a model to diagnose breast cancer using raw images of breast cancer with different resolutions, irrespective of the structures and textures. The floating image is mapped with the healthy reference image and examined using different statistics such as cross correlations and phase correlations. Experiments are carried out with the aim of establishing the optimal performance on histopathological images. The model attained satisfactory results and are proved good for decision making in cancer diagnosis.
DIALOG-22 RuATD Generated Text Detection

Text Generation Models (TGMs) succeed in creating text that matches human language style reasonably well. Detectors that can distinguish between TGM-generated text and human-written ones play an important role in preventing abuse of TGM. In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks: detecting generated text (binary task) and classification of which model was used to generate text (multiclass task). We achieved 1st place on the binary classification task with an accuracy score of 0.82995 on the private test set and 4th place on the multiclass classification task with an accuracy score of 0.62856 on the private test set. We proposed an ensemble method of different pre-trained models based on the attention mechanism.
Diffusion Incentives in Cooperative Games

We study a cooperative game setting where we want to gather more players through their social connections. Social connections can be modeled as a graph, and initially, only a subset of the players are in the game. We want to introduce diffusion incentives in such a cooperative game, i.e., incentivize the players to use their connections to invite more players to join the game. Our goal cannot be achieved by existing classical solutions, such as the Shapley value. Hence, to combat this problem, we have already proposed a solution called weighted permission Shapley value. Under this solution, for each player, inviting all of her neighbors is a dominant strategy in all monotone games. As one special application of the diffusion cooperative game, we also considered the diffusion incentives in query networks and the weighted permission Shapley value successfully characterizes the solution to the query network. Furthermore, we also characterize a Sybil-proof solution to the query network called the double geometric mechanism.
Cryptocurrency: The Present and the Future Scenario

Abstract: Cryptocurrency is a digital means of exchange of non-physical or digital currencies. It is a universal currency system which resolves the problem of currency exchange between two different nations with different currencies. Thus, cryptocurrencies have risen as imperative financial software systems in various countries. It is dependent on a secure distributed ledger data structure for the transactions. The exchange rates on the cryptocurrencies are not stable and keep changing with time, thus it is exploited the most by the traders. Mining is a method through which new units of cryptocurrencies are generated and earned. It also adds past transactions to the distributed and open ledger among the users known as the blockchain. In this paper, we shall discuss the mining techniques, growth of cryptocurrencies and the associated risks and returns. Keywords: Types of cryptocurrencies, Mining, Factors of its growth, Risks and returns, Challenges and problems
Chatbot for Interview

The advent of virtual assistants has made communicating with computers a reality. Chatbots are virtual assistant tools designed to simplify the communication between humans and computers. A chatbot will answer your queries and execute a certain computation if required. Chatbots can be developed using Natural Language Processing (NLP) and Deep Learning. Natural Language Process technique like Naïve bayes can be used. Chatbot can be implemented for a fun purpose like chit-chat; these are called Conversational chatbots. Chatbots designed to answer any questions is known as horizontal chatbots and the specific task-oriented chatbots are known as vertical chatbots (also known as Closed Domain Chatbots). In this paper, we will be discussing a task-oriented chatbot to help recruitment team in the technical round of interview process.
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios.
Faster Decomposition of Weighted Graphs into Cliques using Fisher's Inequality

Mining groups of genes that consistently co-express is an important problem in biomedical research, where it is critical for applications such as drug-repositioning and designing new disease treatments. Recently, Cooley et al. modeled this problem as Exact Weighted Clique Decomposition (EWCD) in which, given an edge-weighted graph $G$ and a positive integer $k$, the goal is to decompose $G$ into at most $k$ (overlapping) weighted cliques so that an edge's weight is exactly equal to the sum of weights for cliques it participates in. They show EWCD is fixed-parameter-tractable, giving a $4^k$-kernel alongside a backtracking algorithm (together called cricca) to iteratively build a decomposition. Unfortunately, because of inherent exponential growth in the space of potential solutions, cricca is typically able to decompose graphs only when $k \leq 11$. In this work, we establish reduction rules that exponentially decrease the size of the kernel (from $4^k$ to $k2^k$) for EWCD. In addition, we use insights about the structure of potential solutions to give new search rules that speed up the decomposition algorithm. At the core of our techniques is a result from combinatorial design theory called Fisher's inequality characterizing set systems with restricted intersections. We deploy our kernelization and decomposition algorithms (together called DeCAF) on a corpus of biologically-inspired data and obtain over two orders of magnitude speed-up over cricca. As a result, DeCAF scales to instances with $k \geq 17$.
ON-GRID PERFORMANCE AND ECONOMIC ANALYSIS OF A SOLAR ENERGY PLANT FOR KING ABULAZIZ AIRPORT LOADS

According to Saudi Arabia vision of 2030, the Kingdom of Saudi Arabia is undergoing through various stages and working on projects to make their airports environment friendly and pollution free. For this, they are working to install solar panels and make electricity to meet up their requirements. In the Kingdom of Saudi Arabia, all the airports will be transformed in environmental friendly airports. We are working on the project to install a solar PV system in Jeddah International airport. It is taken the values for electricity consumption of the airport and designed two different systems: On-Grid and OFF-Grid and implement them with the desired requirements. Using PVsyst software, the On-grid design has been simulated as well as the economic analysis has been calculated. The results indicated that cost analysis shows that energy produced will cost 0.073USD/kWh. Detailed economic analysis of ON-Grid system is introduced and get On-grid design system of PV more beneficial. This system provides our investment back within 4.3 years and its environment friendly obvious.
ANGLEr: A Next-Generation Natural Language Exploratory Framework

Natural language processing is used for solving a wide variety of problems. Some scholars and interest groups working with language resources are not well versed in programming, so there is a need for a good graphical framework that allows users to quickly design and test natural language processing pipelines without the need for programming. The existing frameworks do not satisfy all the requirements for such a tool. We, therefore, propose a new framework that provides a simple way for its users to build language processing pipelines. It also allows a simple programming language agnostic way for adding new modules, which will help the adoption by natural language processing developers and researchers. The main parts of the proposed framework consist of (a) a pluggable Docker-based architecture, (b) a general data model, and (c) APIs description along with the graphical user interface. The proposed design is being used for implementation of a new natural language processing framework, called ANGLEr.
Penerapan Teknologi Tepat Guna/Sasaran Berbasis Energi Terbarukan di desa Lamreh Kabupaten Aceh Besar

The focus of community service activities in Lamreh Village is to provide solutions to the problem of limited electrical energy experienced by the people in Aceh Besar. The purpose of community service is to downstream the results of the author's research on renewable energy technology for the island community. This community service method is (1) the application of appropriate technology based on renewable energy in the form of solar street lights and wind power to increase access to electricity; (2) Training and technical guidance by the implementing team to partners regarding the operation and maintenance of the appropriate technology system that is applied. Through technology transfer in this Community Service activity, it is hoped that the implementation of technology will be maintained continuously; The results of community service are the installation of the application of appropriate technology in the form of solar and wind power and increasing public knowledge about appropriate technology which is carried out through training to community youth partners in Lamreh Village, Aceh Besar District.
Development of a robust real-time synchronized data transmission technique from a Magnetic Observatory to an INTERMAGNET GIN

This paper presents the step-by-step development and implementation of real-time data transmission from a distant Observatory. Using Python programming language, we have developed an algorithm for automated data transmission of high resolution real-time magnetic data from Choutuppal (CPL) and Hyderabad (HYB), India to INTERMAGNET Edinburgh GIN, with minimal internet services. We have designed a system to transfer the data in a secured and encrypted pattern with SSH keys and save the same dataset in the local server at CSIR-NGRI. Both the Observatories data are transmitted to GIN in real-time with a time frame of less than 300 s. After successfully transmitting real-time 1 min geomagnetic data from CPL and HYB Observatories to Edinburgh GIN, CPL's 1 s real-time data is also initiated and is one of the first Indian observatories to transmit real-time 1 s data to GIN. The details of algorithm development, function libraries and the challenges faced for 1 s and 1 min data transmission at both the Observatories are discussed.
Investigating the Relationship between Home Literacy Environment (HLE) Activities and Emergent Literacy Skills Development

Early intervention is thought to be necessary to stimulate children's word knowledge. Children will be unable to encode and decode inputs on their own without adult assistance. As a result, interventions play a critical role in literacy development at all levels, especially during the emerging literacy phase, and parents are often the first to cast such interventions. The goal of this study is to identify the types of HLE Activities that parents regularly use, as well as the characteristics that contribute to the success of such activities in supporting the development of emerging literacy abilities in preschoolers. The current study surveyed 107 parents with preschoolers at home using a descriptive approach. The HLE activities in this study are limited to parent-child contact, phonological awareness, and decoding skills. The activities that parents most commonly carry out to promote the growth of emergent literacy skills are Phonic Awareness, which involves pointing out letters in isolation and words, and Decoding Skills, which involve introducing letters/words and their sound or pronunciation in songs or other fun ways. Then, in order to ensure the success of the implementation, parental education becomes a crucial aspect. Other issues, such as who raises preschoolers at home and infrastructure availability, become non-negotiable factors in determining the degree of HLE implementation. Overall, the study proposes that further research be done to help parents with less education and non-kinship caregivers practice HLE to help preschoolers develop their emergent reading abilities.
THE USE OF PEDAGOGICAL SOFTWARE, THAT IS, VIRTUAL LABORATORIES, IN THE TEACHING OF PHYSICS

The article considers the features of virtual laboratory work on physics in higher educational institutions. Their advantages and disadvantages compared to traditional laboratory practice. The importance of filling real experience with virtual is noted. Also, in the teaching of physics, opinions and opinions are made about the use of pedagogical software, that is, virtual laboratories.
Comparison of Different Cloud Providers

Abstract: It is a better alternative for enterprises and ordinary people to use cloud service providers' services (IaaS, PaaS, SaaS, DaaS, and so on) and pay as you go. There are already a huge number of service providers on the market, and finding a good supplier for long-term needs is tough owing to the range of features and services. Amazon, Microsoft, and Google are the top three providers in terms of market share. The goal of this article is to introduce cloud consumers to the most prominent Cloud Service Providers (CSPs). We also compare and contrast these CSPs based on a variety of parameters linked to the services they supply. Before moving their business to the cloud, the analysis described in this article can assist people and companies in making key judgments on the benefits and costs of cloud technology. Keywords: Amazon, AWS, Google Cloud Platform, Microsoft Azure, GCP, CSP’s, Cloud Service Providers.