Reviewerly/exHarmony huggingface dataset:
Countering Inconsistent Labelling by Google's Vision API for Rotated Images

Google's Vision API analyses images and provides a variety of output predictions, one such type is context-based labelling. In this paper, it is shown that adversarial examples that cause incorrect label prediction and spoofing can be generated by rotating the images. Due to the black-boxed nature of the API, a modular context-based pre-processing pipeline is proposed consisting of a Res-Net50 model, that predicts the angle by which the image must be rotated to correct its orientation. The pipeline successfully performs the correction whilst maintaining the image's resolution and feeds it to the API which generates labels similar to the original correctly oriented image and using a Percentage Error metric, the performance of the corrected images as compared to its rotated counter-parts is found to be significantly higher. These observations imply that the API can benefit from such a pre-processing pipeline to increase robustness to rotational perturbances.
Taking the Wave of Digitalization: Reflection on the Psychological Readiness of Teachers in Using Information and Communication Technologies

Considering the brainwave of the psychological approach for the use of Information and Communication Technology (ICT), collaboration, expectation, and motivation have been identified as the aspects of readiness for integrating technology into the teaching strategies. Studies have investigated the integration of ICTs in the teaching/learning process; still there is a lack of research probing the significance of the psychological readiness of teachers on their perceptions about utilizing ICTs in the Algerian EFL context. The research, between hands, aspires to bridge the gaps, reveal the significant obstacles; and barriers encountered while exploring ICTs, and investigate the following query: What are the significant psychological obstacles that might prevent the effective implementation of ICTs? A mixed-method design was used in this research, employing a survey questionnaire and semi-structured interview to collect data from thirty- five EFL teachers at the University of Mohammed Ben Ahmed in Oran; Algeria. Participant teachers reveal a satisfactory willingness for using education technologies. Lack of training, insufficient ICT skills, technology anxiety, resistance to change, and no perception of benefits were the major psychological obstacles reported by the informants. To perk up the digital proficiencies of teachers, to train them to utilize ICTs, and to explore the potential of ICTs were highly recommended to rouse in teachers, the emotional readiness to implement Information Technologies.
Integration of technological pedagogical content knowledge (TPACK) in classrooms through a teacher's lens

Technological literacy has become an unavoidable requirement in today's classroom. The importance of incorporating technology into traditional learning settings aims to create meaningful learning experiences while also cultivating positive perspectives and relationships with technology. While technological knowledge has become indispensable in most classrooms and has been incorporated into various curriculum and frameworks, the incorporation of technology in traditional teaching is accompanied by barriers and limitations based on preconceived notions of technology, school culture, and access to technology. This paper attempts to assist teachers/educators in understanding how Mishra and Koehler's Technology Pedagogical Content Knowledge (TPACK) model can resolve issues and help them overcome the monotony of the online teaching-learning process. The goal of this paper is to provide a brief overview of the TPACK model, examples of how the framework can be integrated with science content, and recommendations for solutions to common barriers and limitations across other subject areas.
Perbedaan hasil belajar dan motivasi belajar siswa dengan menggunakan model PBL dan pembelajaran konvensional berbantuan media ispring pada materi laju reaksi

This study aims to determine whether there is a significant difference in student learning outcomes and student learning motivation who are taught using the PBL model assisted by the Ispring media with the conventional learning model assisted by the iSpring media on the rate material at PAB 8 Saentis Private High School. The research population was all students of class XI SMA Private PAB 8 Saentis. The sample consisted of class XI students of SMA Swasta PAB 8 Saentis, which consisted of 2 classes, namely the experimental class (XI IPA 2) with the PBL model and the control class (XI IPA 3) with the conventional model. The instruments used are test and non-test instruments. The student learning outcomes data were first tested for normality and homogeneity, where the results obtained by the two samples were normally distributed and homogeneous. Hypothesis testing was carried out using the independent sample t-test, the results of this study showed that in hypothesis 1, a sig value of 0.001 was produced at a significance level of 5% (α= 0.05). if the value of sig &lt; then Ho is rejected. Thus , it shows that there are differences in student learning outcomes who are taught using the PBL model assisted by the Ispring media with the conventional learning model assisted by the iSpring media . while hypothesis 2 obtained a sig value of 0.04 indicating differences in student learning motivation and hypothesis 3 obtained a sig value of 0.035 indicating a correlation between motivation and student learning outcomes who were taught using the PBL model assisted by the Ispring media with the conventional learning model assisted by the iSpring media.
Manajemen Arsip Dinamis Dalam Meningkatkan Akuntabilitas Sekolah

Record is one of the most fundamental components of school accountability. This study aims to investigate and explain how dynamic record management can improve school performance accountability. Qualitative descriptive with an interactive model was a kind of research that we used. Collecting data through techniques of observation, interviews, and documentation. Data analysis includes reduction, display, and conclusion. The number of informants is 3 archivists, 11 users, and one of the record experts. Finding of this study proves that the application of dynamic record management is able to improve school accountability. Some constraints in dynamic record management are the lack of human resources in the record field. Moreover, the facilities and infrastructure are still not adequate, and the system of record storage has not been consistent. Furthermore, the maintenance record can provide the authentic document in arranging the report of performance and accountability, because the maintenance record is able to save the physical record.
Pengembangan Bahan Ajar Berbasis E-Modul Mata Kuliah Komunikasi Perkantoran Pada Mahasiswa S1 Pendidikan Administrasi Perkantoran Di Unesa

This development research aims to determine the development process, feasibility, and student responses to module-based teaching materials in the office communication course in the UNESA Office Administration Education undergraduate study program. The data analysis technique used is quantitative descriptive analysis. The results of the study stated that the development of e-module-based teaching materials was carried out according to the 4D development model which only reached the development stage. The results of expert validation and the results of a limited trial of e-module-based teaching materials for students are categorized as very feasible. This shows that the e-module-based teaching materials that have been developed by researchers are declared very suitable for use in learning.
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don't. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language. Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast "what happens next" given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.
Generating Adversarial Disturbances for Controller Verification

We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it. We propose an online learning approach to this problem that \emph{adaptively} generates disturbances based on control inputs chosen by the controller. The goal of the disturbance generator is to minimize \emph{regret} versus a benchmark disturbance-generating policy class, i.e., to maximize the cost incurred by the controller as well as possible compared to the best possible disturbance generator \emph{in hindsight} (chosen from a benchmark policy class). In the setting where the dynamics are linear and the costs are quadratic, we formulate our problem as an online trust region (OTR) problem with memory and present a new online learning algorithm (\emph{MOTR}) for this problem. We prove that this method competes with the best disturbance generator in hindsight (chosen from a rich class of benchmark policies that includes linear-dynamical disturbance generating policies). We demonstrate our approach on two simulated examples: (i) synthetically generated linear systems, and (ii) generating wind disturbances for the popular PX4 controller in the AirSim simulator. On these examples, we demonstrate that our approach outperforms several baseline approaches, including $H_{\infty}$ disturbance generation and gradient-based methods.
Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node

AI-powered edge devices currently lack the ability to adapt their embedded inference models to the ever-changing environment. To tackle this issue, Continual Learning (CL) strategies aim at incrementally improving the decision capabilities based on newly acquired data. In this work, after quantifying memory and computational requirements of CL algorithms, we define a novel HW/SW extreme-edge platform featuring a low power RISC-V octa-core cluster tailored for on-demand incremental learning over locally sensed data. The presented multi-core HW/SW architecture achieves a peak performance of 2.21 and 1.70 MAC/cycle, respectively, when running forward and backward steps of the gradient descent. We report the trade-off between memory footprint, latency, and accuracy for learning a new class with Latent Replay CL when targeting an image classification task on the CORe50 dataset. For a CL setting that retrains all the layers, taking 5h to learn a new class and achieving up to 77.3% of precision, a more efficient solution retrains only part of the network, reaching an accuracy of 72.5% with a memory requirement of 300 MB and a computation latency of 1.5 hours. On the other side, retraining only the last layer results in the fastest (867 ms) and less memory hungry (20 MB) solution but scoring 58% on the CORe50 dataset. Thanks to the parallelism of the low-power cluster engine, our HW/SW platform results 25x faster than typical MCU device, on which CL is still impractical, and demonstrates an 11x gain in terms of energy consumption with respect to mobile-class solutions.
TheFragebogen: A Web Browser-based Questionnaire Framework for Scientific Research

Quality of Experience (QoE) typically involves conducting experiments in which stimuli are presented to participants and their judgments as well as behavioral data are collected. Nowadays, many experiments require software for the presentation of stimuli and the data collection from participants. While different software solutions exist, these are not tailored to conduct experiments on QoE. Moreover, replicating experiments or repeating the same experiment in different settings (e. g., laboratory vs. crowdsourcing) can further increase the software complexity. TheFragebogen is an open-source, versatile, extendable software framework for the implementation of questionnaires - especially for research on QoE. Implemented questionnaires can be presented with a state-of-the-art web browser to support a broad range of devices while the use of a web server being optional. Out-of-the-box, TheFragebogen provides graphical exact scales as well as free-hand input, the ability to collect behavioral data, and playback multimedia content.
Unlikely intersections over finite fields: polynomial orbits in small subgroups

We estimate the frequency of polynomial iterations which falls in a given multiplicative subgroup of a finite field of $p$ elements. We also give a lower bound on the size of the subgroup which is multiplicatively generated by the first $N$ elements in an orbit. We derive these from more general results about sequences of compositions or a fixed set of polynomials.
Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks.
Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.
AdMap: a framework for advertising using MapReduce pipeline

There is a vast collection of data for consumers due to tremendous development in digital marketing. For their ads or for consumers to validate nearby services which already are upgraded to the dataset systems, consumers are more concerned with the amount of data. Hence there is a void formed between the producer and the client. To fill that void, there is the need for a framework which can facilitate all the needs for query updating of the data. The present systems have some shortcomings by a vast number of information that each time lead to decision tree-based approach. A systematic solution to the automated incorporation of data into a Hadoop distributed file system (HDFS) warehouse (Hadoop file system) includes a data hub server, a generic data charging mechanism and a metadata model. In our model framework, the database would be able to govern the data processing schema. In the future, as a variety of data is archived, the datalake will play a critical role in managing that data. To order to carry out a planned loading function, the setup files immense catalogue move the datahub server together to attach the miscellaneous details dynamically to its schemas.
Ghost: Graph Higher-Order Similarity Transformation for Classification

Exploring and identifying a good feature representation to describe high- dimensional datasets is a challenge of prime importance. However, plenty of feature selection techniques and distance metrics exist, which entails an intricacy for identifying the one best suited to the task. This paper provides an algorithm to design high-order distance metrics over a sparse selection of features dedicated to classification. Our approach is based on Conditional Random Field (CRF) energy minimization and Dual Decomposition, which allow efficiency and great flexibility in the considered features. The optimization technique ensures the tractability of high-dimensionality problems using hundreds of features and samples. Our approach is evaluated on synthetic data as well as on Covid-19 patient stratification. Comparisons with state-of-the-art baselines and our proposed method on different classification results prove the learned metric’s relevance.
Automating Control of Overestimation Bias for Reinforcement Learning

Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms. However, most of these techniques rely on pre-defined bias correction policies that are either not flexible enough or require environment-specific tuning of hyperparameters. In this work, we present a general data-driven approach for the automatic selection of bias control hyperparameters. We demonstrate its effectiveness on three algorithms: Truncated Quantile Critics, Weighted Delayed DDPG, and Maxmin Q-learning. The proposed technique eliminates the need for an extensive hyperparameter search. We show that it leads to a significant reduction of the actual number of interactions while preserving the performance.
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack

Both generic and domain-specific BERT models are widely used for natural language processing (NLP) tasks. In this paper we investigate the vulnerability of BERT models to variation in input data for Named Entity Recognition (NER) through adversarial attack. Experimental results show that BERT models are vulnerable to variation in the entity context with 20.2 to 45.0% of entities predicted completely wrong and another 29.3 to 53.3% of entities predicted wrong partially. BERT models seem most vulnerable to changes in the local context of entities and often a single change is sufficient to fool the model. The domain-specific BERT model trained from scratch (SciBERT) is more vulnerable than the original BERT model or the domain-specific model that retains the BERT vocabulary (BioBERT). We also find that BERT models are particularly vulnerable to emergent entities. Our results chart the vulnerabilities of BERT models for NER and emphasize the importance of further research into uncovering and reducing these weaknesses.
Fractional total variation denoising model with $L^1$ fidelity

We study a nonlocal version of the total variation-based model with $L^1-$fidelity for image denoising, where the regularizing term is replaced with the fractional $s$-total variation. We discuss regularity of the level sets and uniqueness of solutions, both for high and low values of the fidelity parameter. We analyse in detail the case of binary data given by the characteristic functions of convex sets.
A Review on C3I Systems' Security: Vulnerabilities, Attacks, and Countermeasures

Command, Control, Communication, and Intelligence (C3I) systems are increasingly used in critical civil and military domains for achieving information superiority, operational efficacy, and greater situational awareness. Unlike traditional systems facing widespread cyber-attacks, the sensitive nature of C3I tactical operations make their cybersecurity a critical concern. For instance, tampering or intercepting confidential information in military battlefields not only damages C3I operations, but also causes irreversible consequences such as loss of human lives and mission failures. Therefore, C3I systems have become a focal point for cyber adversaries. Moreover, technological advancements and modernization of C3I systems have significantly increased the potential risk of cyber-attacks on C3I systems. Consequently, cyber adversaries leverage highly sophisticated attack vectors to exploit security vulnerabilities in C3I systems. Despite the burgeoning significance of cybersecurity for C3I systems, the existing literature lacks a comprehensive review to systematize the body of knowledge on C3I systems' security. Therefore, in this paper, we have gathered, analyzed, and synthesized the state-of-the-art on the cybersecurity of C3I systems. In particular, this paper has identified security vulnerabilities, attack vectors, and countermeasures/defenses for C3I systems. Furthermore, our survey has enabled us to: (i) propose a taxonomy for security vulnerabilities, attack vectors and countermeasures; (ii) interrelate attack vectors with security vulnerabilities and countermeasures; and (iii) propose future research directions for advancing the state-of-the-art on the cybersecurity of C3I systems.
Extracting total Amb programs from proofs

We present a logical system CFP (Concurrent Fixed Point Logic) from whose proofs one can extract nondeterministic and concurrent programs that are provably total and correct with respect to the proven formula. CFP is an intuitionistic first-order logic with inductive and coinductive definitions extended by two propositional operators, A || B (restriction, a strengthening of the implication B -> A) and $\ddownarrow(A)$ (total concurrency). The target of the extraction is a lambda calculus with constructors and recursion extended by a constructor Amb (for McCarthy's amb) which is interpreted operationally as globally angelic choice. The correctness of extracted programs is proven via an intermediate domain-theoretic denotational semantics. We demonstrate the usefulness of our system by extracting a concurrent program that translates infinite Gray code into the signed digit representation. A noteworthy feature of our system is that the proof rules for restriction and concurrency involve variants of the classical law of excluded middle that would not be interpretable computationally without Amb.