Reviewerly/exHarmony huggingface dataset:
A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games

We consider a pursuit-evasion game [11] played between two agents, 'Blue' (the pursuer) and 'Red' (the evader), over $T$ time steps. Red aims to attack Blue's territory. Blue's objective is to intercept Red by time $T$ and thereby limit the success of Red's attack. Blue must plan its pursuit trajectory by choosing parameters that determine its course of movement (speed and angle in our setup) such that it intercepts Red by time $T$. We show that Blue's path-planning problem in pursuing Red, can be posed as a sequential decision making problem under uncertainty. Blue's unawareness of Red's action policy renders the analytic dynamic programming approach intractable for finding the optimal action policy for Blue. In this work, we are interested in exploring data-driven approaches to the policy optimization problem that Blue faces. We apply generative machine learning (ML) approaches to learn optimal action policies for Blue. This highlights the ability of generative ML model to learn the relevant implicit representations for the dynamics of simulated pursuit-evasion games. We demonstrate the effectiveness of our modeling approach via extensive statistical assessments. This work can be viewed as a preliminary step towards further adoption of generative modeling approaches for addressing policy optimization problems that arise in the context of multi-agent learning and planning [1].
Algorithms and Improved bounds for online learning under finite hypothesis class

Online learning is the process of answering a sequence of questions based on the correct answers to the previous questions. It is studied in many research areas such as game theory, information theory and machine learning. There are two main components of online learning framework. First, the learning algorithm also known as the learner and second, the hypothesis class which is essentially a set of functions which learner uses to predict answers to the questions. Sometimes, this class contains some functions which have the capability to provide correct answers to the entire sequence of questions. This case is called realizable case. And when hypothesis class does not contain such functions is called unrealizable case. The goal of the learner, in both the cases, is to make as few mistakes as that could have been made by most powerful functions in hypothesis class over the entire sequence of questions. Performance of the learners is analysed by theoretical bounds on the number of mistakes made by them. This paper proposes three algorithms to improve the mistakes bound in the unrealizable case. Proposed algorithms perform highly better than the existing ones in the long run when most of the input sequences presented to the learner are likely to be realizable.
Gradient Kernel Regression

In this article a surprising result is demonstrated using the neural tangent kernel. This kernel is defined as the inner product of the vector of the gradient of an underlying model evaluated at training points. This kernel is used to perform kernel regression. The surprising thing is that the accuracy of that regression is independent of the accuracy of the underlying network.
Language learnability in the limit for general metrics: a Gold-Angluin result

In his pioneering work in the field of Inductive Inference, Gold (1967) proved that a set containing all finite languages and at least one infinite language over the same fixed alphabet is not learnable in the exact sense. Within the same framework, Angluin (1980) provided a complete characterization for the learnability of language families. Mathematically, the concept of exact learning in that classical setting can be seen as the use of a particular type of metric for learning in the limit. In this short research note we use Niyogi's extended version of a theorem by Blum and Blum (1975) on the existence of locking data sets to prove a necessary condition for learnability in the limit of any family of languages in any given metric. This recovers Gold's theorem as a special case. Moreover, when the language family is further assumed to contain all finite languages, the same condition also becomes sufficient for learnability in the limit.
Subpopulation Data Poisoning Attacks

Machine learning systems are deployed in critical settings, but they might fail in unexpected ways, impacting the accuracy of their predictions. Poisoning attacks against machine learning induce adversarial modification of data used by a machine learning algorithm to selectively change its output when it is deployed. In this work, we introduce a novel data poisoning attack called a \emph{subpopulation attack}, which is particularly relevant when datasets are large and diverse. We design a modular framework for subpopulation attacks, instantiate it with different building blocks, and show that the attacks are effective for a variety of datasets and machine learning models. We further optimize the attacks in continuous domains using influence functions and gradient optimization methods. Compared to existing backdoor poisoning attacks, subpopulation attacks have the advantage of inducing misclassification in naturally distributed data points at inference time, making the attacks extremely stealthy. We also show that our attack strategy can be used to improve upon existing targeted attacks. We prove that, under some assumptions, subpopulation attacks are impossible to defend against, and empirically demonstrate the limitations of existing defenses against our attacks, highlighting the difficulty of protecting machine learning against this threat.
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy

Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often crowd-sourced and contains sensitive information about its contributors, which leaks into models that are trained on it. To achieve rigorous privacy guarantees, differentially private training mechanisms are used. However, it has recently been shown that differential privacy can exacerbate existing biases in the data and have disparate impacts on the accuracy of different subgroups of data. In this paper, we aim to study these effects within differentially private deep learning. Specifically, we aim to study how different levels of imbalance in the data affect the accuracy and the fairness of the decisions made by the model, given different levels of privacy. We demonstrate that even small imbalances and loose privacy guarantees can cause disparate impacts.
Early Work on Efficient Patching for Coordinating Edge Applications

Multiple applications running on Edge computers can be orchestrated to achieve the desired goal. Orchestration of applications is prominent when working with Internet of Things based applications, Autonomous driving and Autonomous Aerial vehicles. As the applications receive modified classifiers/code, there will be multiple applications that need to be updated. If all the classifiers are synchronously updated there would be increased throughput and bandwidth degradation. On the other hand, delaying updates of applications which need immediate update hinders performance and delays progress towards end goal. The updates of applications should be prioritized and updates should happen according to this priority. This paper explores the setup and benchmarks to understand the impact of updates when multiple applications working to achieve same objective are orchestrated with prioritized updates. We discuss methods to build a distributed, reliable and scalable system called "DSOC"(Docker Swarm Orchestration Component).
An empirical study of computing with words approaches for multi-person and single-person systems

Computing with words (CWW) has emerged as a powerful tool for processing the linguistic information, especially the one generated by human beings. Various CWW approaches have emerged since the inception of CWW, such as perceptual computing, extension principle based CWW approach, symbolic method based CWW approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel approach (HMA), for data processing. There have been numerous works in which HMA was shown to be better at word modelling than EIA, and EIA better than IA. But, a deeper study of these works reveals that HMA captures lesser fuzziness than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in multi-person systems and HMA for single-person systems (as EIA is an improvement over IA). Furthermore, another set of works, compared the performances perceptual computing to the other above said CWW approaches. In all these works, perceptual computing was shown to be better than other CWW approaches. However, none of the works tried to investigate the reason behind this observed better performance of perceptual computing. Also, no comparison has been performed for scenarios where the inputs are differentially weighted. Thus, the aim of this work is to empirically establish that EIA is suitable for multi-person systems and HMA for single-person systems. Another dimension of this work is also to empirically prove that perceptual computing gives better performance than other CWW approaches based on extension principle, symbolic method and 2-tuple especially in scenarios where inputs are differentially weighted.
On the Tour Towards DPLL(MAPF) and Beyond

We discuss milestones on the tour towards DPLL(MAPF), a multi-agent path finding (MAPF) solver fully integrated with the Davis-Putnam-Logemann-Loveland (DPLL) propositional satisfiability testing algorithm through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph in a non-colliding way so that each agent eventually reaches its unique goal vertex. At most one agent can reside in a vertex at a time. Agents can move instantaneously by traversing edges provided the movement does not result in a collision. Recently attempts to solve MAPF optimally w.r.t. the sum-of-costs or the makespan based on the reduction of MAPF to propositional satisfiability (SAT) have appeared. The most successful methods rely on building the propositional encoding for the given MAPF instance lazily by a process inspired in the SMT paradigm. The integration of satisfiability testing by the SAT solver and the high-level construction of the encoding is however relatively loose in existing methods. Therefore the ultimate goal of research in this direction is to build the DPLL(MAPF) algorithm, a MAPF solver where the construction of the encoding is fully integrated with the underlying SAT solver. We discuss the current state-of-the-art in MAPF solving and what steps need to be done to get DPLL(MAPF). The advantages of DPLL(MAPF) in terms of its potential to be alternatively parametrized with MAPF$^R$, a theory of continuous MAPF with geometric agents, are also discussed.
Graph Embeddings at Scale

Graph embedding is a popular algorithmic approach for creating vector representations for individual vertices in networks. Training these algorithms at scale is important for creating embeddings that can be used for classification, ranking, recommendation and other common applications in industry. While industrial systems exist for training graph embeddings on large datasets, many of these distributed architectures are forced to partition copious amounts of data and model logic across many worker nodes. In this paper, we propose a distributed infrastructure that completely avoids graph partitioning, dynamically creates size constrained computational graphs across worker nodes, and uses highly efficient indexing operations for updating embeddings that allow the system to function at scale. We show that our system can scale an existing embeddings algorithm - skip-gram - to train on the open-source Friendster network (68 million vertices) and on an internal heterogeneous graph (50 million vertices). We measure the performance of our system on two key quantitative metrics: link-prediction accuracy and rate of convergence. We conclude this work by analyzing how a greater number of worker nodes actually improves our system's performance on the aforementioned metrics and discuss our next steps for rigorously evaluating the embedding vectors produced by our system.
The Effect of Finger Print Absence and Work Motivation on Employee Discipline in Stimi Banjarmasin

Advances in science and technology, especially in the field of computer technology, have provided much faster, more accurate information, one of which is the use of the fingerprint biometric attendance system (finger print), which previously used manual presence. With the development of science, especially information technology, and supported by human resources who must have innovation and initiative and creative power to advance themselves. Having employees who have high work performance is the hope or dream of every company. The attendance system using finger print has been implemented in STIMI Banjarmasin since several years ago, to monitor the level of employee discipline at work. The formulation of the problem in this study is whether the fingerprint attendance (finger print) and work motivation simultaneously affect the increase in employee discipline. And this study also aims to determine: the extent to which fingerprint attendance (finger print) and work motivation partially adhere to the increase in employee discipline. The T test is used to test partially and the F test is used to test simultaneously.
Automatic Non-Linear Video Editing Transfer

We propose an automatic approach that extracts editing styles in a source video and applies the edits to matched footage for video creation. Our Computer Vision based techniques considers framing, content type, playback speed, and lighting of each input video segment. By applying a combination of these features, we demonstrate an effective method that automatically transfers the visual and temporal styles from professionally edited videos to unseen raw footage. We evaluated our approach with real-world videos that contained a total of 3872 video shots of a variety of editing styles, including different subjects, camera motions, and lighting. We reported feedback from survey participants who reviewed a set of our results.
USABILITY LEARNING MANAGEMENT SYSTEM PADA PELATIHAN TENAGA KESEHATAN DI MASA PANDEMI COVID-19

This study is intended to assess the usability of LMS use (Learning Management System) in the training of health workers during the Covid-19 pandemic in Bapelkes Cikarang. In previous research LMS was used for the processing of master data needed to support training and learning activities among teachers, formal education and companies. But this study focused on the utilization of LMS in remote training, due to the Covid-19 pandemic in health training halls. The study was conducted in August 2021, using Neilsen's usability theory model, with the subjects of special assignment trainee alumni in the thirteenth class of 38 people. Research instruments in the form of questionnaires, interview guidelines and review of training implementation reports. Research analysis is conducted with descriptive qualitative methods. This study measured the usability of the LMS SI Tangkas Bapelkes Cikarang application, to get an idea of how much benefit, understanding and satisfaction levels of participants. Measurements are taken on the learning outcomes and satisfaction levels of participants and the use of LMS SI Tangkas through five criteria, namely learnability, efficiency, memorability, errors and satisfaction. Results from this study showed that learning outcomes met graduation scores and average participants' satisfaction levels were satisfactory. While the usability level of the SI Tangkas application, is easy to learn and users expressed satisfaction with this application. But in terms of efficiency, usage procedures and error rates in operationalizing applications are obtained results that do not meet usability. The results of this study can be used as input in the development of application interfaces for subsequent training and also for training in other health fields.. The recommendation for this study is for LMS to be developed into an LMS that is easier to remember, more efficient, and fewer errors caused.&#x0D;
Improving Neural Architecture Search Image Classifiers via Ensemble Learning

Finding the best neural network architecture requires significant time, resources, and human expertise. These challenges are partially addressed by neural architecture search (NAS) which is able to find the best convolutional layer or cell that is then used as a building block for the network. However, once a good building block is found, manual design is still required to assemble the final architecture as a combination of multiple blocks under a predefined parameter budget constraint. A common solution is to stack these blocks into a single tower and adjust the width and depth to fill the parameter budget. However, these single tower architectures may not be optimal. Instead, in this paper we present the AdaNAS algorithm, that uses ensemble techniques to compose a neural network as an ensemble of smaller networks automatically. Additionally, we introduce a novel technique based on knowledge distillation to iteratively train the smaller networks using the previous ensemble as a teacher. Our experiments demonstrate that ensembles of networks improve accuracy upon a single neural network while keeping the same number of parameters. Our models achieve comparable results with the state-of-the-art on CIFAR-10 and sets a new state-of-the-art on CIFAR-100.
Extremely low-resource machine translation for closely related languages

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for V\~oro, North and South Saami and present first results of neural machine translation for these languages.
Robots State Estimation and Observability Analysis Based on Statistical Motion Models

This paper presents a generic motion model to capture mobile robots' dynamic behaviors (translation and rotation). The model is based on statistical models driven by white random processes and is formulated into a full state estimation algorithm based on the error-state extended Kalman filtering framework (ESEKF). Major benefits of this method are its versatility, being applicable to different robotic systems without accurately modeling the robots' specific dynamics, and ability to estimate the robot's (angular) acceleration, jerk, or higher-order dynamic states with low delay. Mathematical analysis with numerical simulations are presented to show the properties of the statistical model-based estimation framework and to reveal its connection to existing low-pass filters. Furthermore, a new paradigm is developed for robots observability analysis by developing Lie derivatives and associated partial differentiation directly on manifolds. It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations. It is also scalable to high dimensional systems. A novel \textbf{\textit{thin}} set concept is introduced to characterize the unobservable subset of the system states, providing the theoretical foundation to observability analysis of robotic systems operating on manifolds and in high dimension. Finally, extensive experiments including full state estimation and extrinsic calibration (both POS-IMU and IMU-IMU) on a quadrotor UAV, a handheld platform and a ground vehicle are conducted. Comparisons with existing methods show that the proposed method can effectively estimate all extrinsic parameters, the robot's translation/angular acceleration and other state variables (e.g., position, velocity, attitude) of high accuracy and low delay.
Extraction of Dataset for Indian Sign Language Recognition from News Video

Sign language recognition can provide excellent platform for the hearing-impaired people in society. In day-to-day life physically challenged people face problem to communicate with normal people. There are many research works done for sign language recognition, but there is, lack of availability of dataset for the research work which leads to less development of tools for training and testing for the recognition of signs. This paper proposes an algorithm that takes offline NEWS video stream to create dataset of sign language video with the corresponding text. The signs are recognized from the gesture of the person in the news-video with the help of lip movement, facial expression. Therefore, those signs will be mapped to the audio text in the video up to sentence level. The proposed algorithm takes input of offline video and apply various techniques such as extraction, segmentation, template matching, speech conversion to generate dataset which consist of sentence level segmented videos which will be pushed to pre-trained model to get the output for the sign language which will be compared with the audio text and comparison score will be calculated. Therefore, this technique will help to enhance the dataset for the future research for Sign Language recognition.
Advance the Energy Usage in Cloud Centers Utilizing Hybrid Approach

Cloud Computing is one of the new arising advances that offers types of assistance to buyers in compensation as you go model. Distributed computing offers ITC based administrations over the web and the utilization of virtualization permits it to give figuring assets. Server farms are the center of cloud processing, which comprises of: arranged workers, links, power sources, and so forth which have the running applications and store Business data. Elite has consistently been the most basic worry in cloud server farms, which comes at the cost of energy utilization. The imperative test is adjusting between framework execution and force utilization by lessening energy utilization without biased effect on the execution and nature of administrations conveyed. There are numerous procedures and calculations proposed to accomplish effective energy usage in distributed computing, these methods include: VM Relocation, Consolidation and Resources coordination in cloud figuring. This paper gives an overview of approaches and procedures for energy effectiveness in Cloud computing.