Reviewerly/exHarmony huggingface dataset:
Stratigraphic sequence and Deformation Style of the Part of a Geologically Complex Terrain, NW Himalayas, Pakistan

Abstract The project area forms the outer rim of Northwestern Himalayan Syntaxis in Sub-Himalayan and Lesser Himalayan rock sequences of Hazara area, Pakistan. The detail scale studies may produce a well presentation of deformational style of structurally unexplored project area. The study deals with the stratigraphic sequence and structural style of geologically complex Himalayan Terrain. The Stratigraphic Formations mapped on ArcGIS 10.4 includes Precambrian to Cambrian unconformable rock sequences of Hazara Formation, Tanawal Formation and Abbottabad Formation intruded by a Paleozoic Manshera granite, Eocene Kuldana Formation and the Quaternary alluvium along drainage. The structural deformations includes folds such as Ghari Habibullah syncline, Ghari Habibullah overturned anticline, Besian overturned anticline and Balakot overturned anticline. The folds are either northwest-southeast trending or northeast-southwest trending, northeast or southwest vergent and close to tight in nature. The Main Boundary Thrust (MBT), Panjal Thrust (PT) and Balakot Bagh Fault (B-B fault) are regional faults, reverse and thrust as well as active in nature.
Net-Force for Email Tracing

Phone: +233244087207 ABSTRACT Email is a widely used communication channel between businesses and individuals. Its importance has increased a lot nowadays, and the majority of the businesses prefer email as their primary communication medium to contact other businesses. Billions of emails are exchanged between businesses on a daily basis, and it is essential to note that most of them also contain spam or viruses. It is estimated that in Africa over 1 billion people use mail for their various communications. Malicious actors have also observed the lucrative advantages associated with mail communications on unsuspecting victims. They are able to reach masses and this has played a major role in the recent trend of romance scammers syndicate. The egregious impact of these cyber offenses offered the need to introduce tools to help investigate the mail communications and reveal actions of malicious actors in cyber space, this will also assist Africa in decreasing its cybercrime rate especially among home users with no or limited cyber safety knowledge. The legal implications of using NET-Force for email tracing to reveal actions of malicious actors will be assessed in this document. Keywords: Cyber Offenses, Cybeyspace, Net-Force Suite, E-mails, legal implications, Africa BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Nana Kwame Kwakye (2022): Net-Force for Email Tracing Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 273-278 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P44
Understanding Advanced Persistent Threats

Advanced Persistent Threats (APTs) are a new type of threat that has piqued the interest of experts, particularly in the industrial security industry. APTs are cyber-attacks carried out by skilled and well-resourced adversaries who target specific information in high-profile organizations and governments, usually as part of a multi-step operation. The academic community has largely ignored the specifics of these threats, and as a result, an objective solution to the APT problem is absent. In terms of cybercrime activity, Africa has been one of the fastest rising regions. The continent is also a major source of cyberattacks on the rest of the world. A number of initiatives, however, have been implemented to mitigate cyber-threats and strengthen cybersecurity across the continent. The results of a complete study on APT are presented in this paper, which characterizes its differentiating traits and attack model while also assessing strategies often used in APT attacks. We also list various non-traditional countermeasures that can aid in the mitigation of APTs, highlighting future research prospects. Keywords : Advanced Persistent Threat, APT(s), Sophisticated Attacks, Cyber Security, Africa BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Akuffo-Badoo Erastus B. (2022). Understanding Advanced Persistent Threats . SMART-IEEE-Creative Research Publications Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 15-22. www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P3
Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

The existence of an ATM location requires a placement evaluation that aims to support business and provide convenience and comfort to customers when using or transacting. This study aims to evaluate the placement of ATM locations using the K-Means method, and research using data obtained from data mining to obtain decisions that lead to not strategic, strategic, and very strategic ATM locations. This study uses data sourced from the BNI ATM database and performance data in one semester or six months, namely January to June 2021, as many as 121 ATM locations spread across the island of Bali. The application of the K-Means Algorithm in this study uses 6 clustering criteria, namely ATMs Usage, ATMs Fee-Based Income/ FBI, ATMs Service Level Agreement/ SLA, ATMs distance, competitor ATMs, and Business Distance. In addition to presenting calculations using spreadsheets, this research also produces implementations in web-based software. The results of alternative classifications based on K-Means on very strategic centroids of 27 locations or covering 22.31%, strategic several 77 locations or covering 63.64%, and non-strategic 17 locations or covering 14.05%. Although the location of ATMs that are classified as non-strategic criteria is quite small, this can be optimized by several strategic steps that can be taken by stakeholders within the company, such as evaluating the bank's business plan in 2022 and improving the supervision of machines at ATM locations.
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true since the object detector can hardly distinguish between class-agnostic knowledge and class-specific knowledge automatically without explicit modeling. In this work we propose to learn three types of class-agnostic commonalities between base and novel classes explicitly: recognition-related semantic commonalities, localization-related semantic commonalities and distribution commonalities. We design a unified distillation framework based on a memory bank, which is able to perform distillation of all three types of commonalities jointly and efficiently. Extensive experiments demonstrate that our method can be readily integrated into most of existing fine-tuning based methods and consistently improve the performance by a large margin.
On-chip generation of hybrid polarization-frequency entangled biphoton states

We demonstrate a chip-integrated semiconductor source that combines polarization and frequency entanglement, allowing the generation of entangled biphoton states in a hybrid degree of freedom without postmanipulation. Our AlGaAs device is based on type-II spontaneous parametric down-conversion (SPDC) in a counterpropagating phase-matching scheme, in which the modal birefringence lifts the degeneracy between the two possible nonlinear interactions. This allows the direct generation of polarization-frequency entangled photons, at room temperature and telecom wavelength, and in two distinct spatial modes, offering enhanced flexibility for quantum information protocols. The state entanglement is quantified by a combined measurement of the joint spectrum and Hong-ou-Mandel interference of the biphotons, allowing to reconstruct a restricted density matrix in the hybrid polarization-frequency space.
Geometry of Music Perception

Prevalent neuroscientific theories are combined with acoustic observations from various studies to create a consistent geometric model for music perception in order to rationalize, explain and predict psycho-acoustic phenomena. The space of all chords is shown to be a Whitney stratified space. Each stratum is a Riemannian manifold which naturally yields a geodesic distance across strata. The resulting metric is compatible with voice-leading satisfying the triangle inequality. The geometric model allows for rigorous studies of psychoacoustic quantities like roughness and harmonicity as height functions. In order to show how to use the geometric framework in psychoacoustic studies, concepts for the perception of chord resolutions are introduced and analyzed.
GesSure- A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application

Using physical interactive devices like mouse and keyboards hinders naturalistic human-machine interaction and increases the probability of surface contact during a pandemic. Existing gesture-recognition systems do not possess user authentication, making them unreliable. Static gestures in current gesture-recognition technology introduce long adaptation periods and reduce user compatibility. Our technology places a strong emphasis on user recognition and safety. We use meaningful and relevant gestures for task operation, resulting in a better user experience. This paper aims to design a robust, face-verification-enabled gesture recognition system that utilizes a graphical user interface and primarily focuses on security through user recognition and authorization. The face model uses MTCNN and FaceNet to verify the user, and our LSTM-CNN architecture for gesture recognition, achieving an accuracy of 95% with five classes of gestures. The prototype developed through our research has successfully executed context-dependent tasks like save, print, control video-player operations and exit, and context-free operating system tasks like sleep, shut-down, and unlock intuitively. Our application and dataset are available as open source.
Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP

We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unknown and context-dependent. For the latter, our algorithm obtains regret bound of $\widetilde{O}( (H+{1}/{p_{min}})H|S|^{3/2}\sqrt{|A|T\log(\max\{|\mathcal{G}|,|\mathcal{P}|\}/\delta)})$ with probability $1-\delta$, where $\mathcal{P}$ and $\mathcal{G}$ are finite and realizable function classes used to approximate the dynamics and rewards respectively, $p_{min}$ is the minimum reachability parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon, and $T$ the number of episodes. To our knowledge, our approach is the first optimistic approach applied to contextual MDPs with general function approximation (i.e., without additional knowledge regarding the function class, such as it being linear and etc.). We present a lower bound of $\Omega(\sqrt{T H |S| |A| \ln(|\mathcal{G}|)/\ln(|A|)})$, on the expected regret which holds even in the case of known dynamics. Lastly, we discuss an extension of our results to CMDPs without minimum reachability, that obtains $\widetilde{O}(T^{3/4})$ regret.
Few-shot Object Counting and Detection

We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter leverages the pseudo ground-truth provided by the former but takes the necessary steps to account for the imperfection of pseudo ground-truth. To validate the performance of our method on the new task, we introduce two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scenes, multiple object classes per image, and a huge variation in object shapes, sizes, and appearance. Our proposed approach outperforms very strong baselines adapted from few-shot object counting and few-shot object detection with a large margin in both counting and detection metrics. The code and models are available at https://github.com/VinAIResearch/Counting-DETR.
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the "outer" meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.
ASR Error Detection via Audio-Transcript entailment

Despite improved performances of the latest Automatic Speech Recognition (ASR) systems, transcription errors are still unavoidable. These errors can have a considerable impact in critical domains such as healthcare, when used to help with clinical documentation. Therefore, detecting ASR errors is a critical first step in preventing further error propagation to downstream applications. To this end, we propose a novel end-to-end approach for ASR error detection using audio-transcript entailment. To the best of our knowledge, we are the first to frame this problem as an end-to-end entailment task between the audio segment and its corresponding transcript segment. Our intuition is that there should be a bidirectional entailment between audio and transcript when there is no recognition error and vice versa. The proposed model utilizes an acoustic encoder and a linguistic encoder to model the speech and transcript respectively. The encoded representations of both modalities are fused to predict the entailment. Since doctor-patient conversations are used in our experiments, a particular emphasis is placed on medical terms. Our proposed model achieves classification error rates (CER) of 26.2% on all transcription errors and 23% on medical errors specifically, leading to improvements upon a strong baseline by 12% and 15.4%, respectively.
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments

We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining the temporal similarity score between the videos. Moreover, we introduce a few-shot video classification framework that leverages the above appearance and temporal similarity scores across multiple steps, namely prototype-based training and testing as well as inductive and transductive prototype refinement. To the best of our knowledge, our work is the first to explore transductive few-shot video classification. Extensive experiments on both Kinetics and Something-Something V2 datasets show that both appearance and temporal alignments are crucial for datasets with temporal order sensitivity such as Something-Something V2. Our approach achieves similar or better results than previous methods on both datasets. Our code is available at https://github.com/VinAIResearch/fsvc-ata.
BigIssue: A Realistic Bug Localization Benchmark

As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused on automatic program repair (APR), and while the results on synthetic or highly filtered datasets are promising, such models are hard to apply in real-world scenarios because of inadequate bug localization. We propose BigIssue: a benchmark for realistic bug localization. The goal of the benchmark is two-fold. We provide (1) a general benchmark with a diversity of real and synthetic Java bugs and (2) a motivation to improve bug localization capabilities of models through attention to the full repository context. With the introduction of BigIssue, we hope to advance the state of the art in bug localization, in turn improving APR performance and increasing its applicability to the modern development cycle.
Gender-responsive remote data collection, analysis, and use during crisis: UN Women’s Rapid Gender Assessments (RGAs)

At a time when timely and relevant data was needed to understand the gendered impacts of COVID-19, the standard face-to-face data collection was limited due to lockdowns and other restrictions. Data producers, both from national and international organizations, had to explore remote methodologies such as phone- or web-based surveys. While differences in women’s and men’s situations must always be considered in all stages of any survey design, added attention should be given to remote data collection wherein there may be immediate gender bias in sampling selection, interviewer-respondent interaction, and handling respondent burden. This paper presents UN Women’s experience in intentionally applying a gender-responsive approach in remotely conducting 78 rapid gender assessment surveys (RGAs) across regions. Based on the learnings, recommendations will be provided to further improve remote data collection such as highlighting the importance of identification and monitoring of use cases, gender mainstreaming in statistical operations, and underscoring the role of national statistics offices in responding to gender data gaps during crisis.
Selection of Feature Driven Development (FDD) Model in Agile Method for Developing Information System of Mosque Management

This paper discusses about software development for building a project using the Feature Driven Development (FDD) model contained in the agile method. As in other agile methods, the Feature Driven Development model has additional properties to implement functions and needs in a short iteration. In terms of the characteristics contained in several other agile models almost all of them have similarities so that makes the stakeholder confusion in determining which model will be chosen as a method of software development. This study focuses on the search for a number of suitable project specifications in the selection of Feature Driven Development models for software development. From the research that has been done on several papers there are several aspects of design and construction in software development by emphasizing the quality and high level of features in developing a software using the Feature Driven Development model. In facilitating the results of the Feature Driven Development model, we will provide a case study of Mosque Management Information System which has several features, such as content management, information of prayer time, online reading Qur’an, and Petty Cash Mosque management. This case is implemented by Feature Driven Development model with short iteration, because this project had done in several months
FewGAN: Generating from the Joint Distribution of a Few Images

We introduce FewGAN, a generative model for generating novel, high-quality and diverse images whose patch distribution lies in the joint patch distribution of a small number of N>1 training samples. The method is, in essence, a hierarchical patch-GAN that applies quantization at the first coarse scale, in a similar fashion to VQ-GAN, followed by a pyramid of residual fully convolutional GANs at finer scales. Our key idea is to first use quantization to learn a fixed set of patch embeddings for training images. We then use a separate set of side images to model the structure of generated images using an autoregressive model trained on the learned patch embeddings of training images. Using quantization at the coarsest scale allows the model to generate both conditional and unconditional novel images. Subsequently, a patch-GAN renders the fine details, resulting in high-quality images. In an extensive set of experiments, it is shown that FewGAN outperforms baselines both quantitatively and qualitatively.
Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads

The application of machine learning has been used in various sectors, one of which is digital marketing. This research compares the performance of six machine learning algorithms to predict customer transaction decisions. The six algorithms used for comparison are Perceptron, Linear Regression, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. The dataset is obtained from Facebook ads transaction data in 2020. The goal is to get a model that has the best performance so that it can be deployed to the web. The method that is used to compare the results is a confusion matrix and also uses visualization of the model to get the prediction error that occurred. Based on the test results, the random forest algorithm has the highest accuracy, recall, and f1-score values, with scores of 96.35%, 95.45%, and 93.32%. The highest precision value was generated by the logistic regression algorithm, which was 94.44%. Based on the data visualization presented by the random forest algorithm, it has the least prediction errors, there are four data. Therefore, it can be concluded that the random forest algorithm has the best performance because it has the highest value in the three confusion matrix measurements and the smallest data prediction error. The model of the random forest algorithm is deployed to the web platform and can be accessed at the link iklan-sosmed.herokuapp.com.