Reviewerly/exHarmony huggingface dataset:
Losing Less: A Loss for Differentially Private Deep Learning

Differentially Private Stochastic Gradient Descent, DP-SGD, is the canonical approach to training deep neural networks with guarantees of Differential Privacy (DP). However, the modifications DP-SGD introduces to vanilla gradient descent negatively impact the accuracy of deep neural networks. In this paper, we are the first to observe that some of this performance can be recovered when training with a loss tailored to DP-SGD; we challenge cross-entropy as the de facto loss for deep learning with DP. Specifically, we introduce a loss combining three terms: the summed squared error, the focal loss, and a regularization penalty. The first term encourages learning with faster convergence. The second term emphasizes hard-to-learn examples in the later stages of training. Both are beneficial because the privacy cost of learning increases with every step of DP-SGD. The third term helps control the sensitivity of learning, decreasing the bias introduced by gradient clipping in DP-SGD. Using our loss function, we achieve new state-of-the-art tradeoffs between privacy and accuracy on MNIST, FashionMNIST, and CIFAR10. Most importantly, we improve the accuracy of DP-SGD on CIFAR10 by 4% for a DP guarantee of 𝜀 = 3.
CERTainty: Detecting DNS Manipulation at Scale using TLS Certificates

DNS manipulation is an increasingly common technique used by censors and other network adversaries to prevent users from accessing restricted Internet resources and hijack their connections. Prior work in detecting DNS manipulation relies largely on comparing DNS resolutions with trusted control results to identify inconsistencies. However, the emergence of CDNs and other cloud providers practicing content localization and load balancing leads to these heuristics being inaccurate, paving the need for more verifiable signals of DNS manipulation. In this paper, we develop a new technique, CERTainty, that utilizes the widely established TLS certificate ecosystem to accurately detect DNS manipulation, and obtain more information about the adversaries performing such manipulation. We find that untrusted certificates, mismatching hostnames, and blockpages are powerful proxies for detecting DNS manipulation. Our results show that previous work using consistency-based heuristics is inaccurate, allowing for 72.45% false positives in the cases detected as DNS manipulation. Further, we identify 17 commercial DNS filtering products in 52 countries, including products such as SafeDNS, SkyDNS, and Fortinet, and identify the presence of 55 ASes in 26 countries that perform ISP-level DNS manipulation. We also identify 226 new blockpage clusters that are not covered by previous research. We are integrating techniques used by CERTainty into active measurement platforms to continuously and accurately monitor DNS manipulation.
Differential Privacy for Black-Box Statistical Analyses

We formalize a notion of a privacy wrapper, defined as an algorithm that can take an arbitrary and untrusted script and produce an output with differential privacy guarantees. Our novel privacy wrapper, named TAHOE, incorporates two design ideas: a type of stability under subsetting, and randomization over subset size. We show that TAHOE imposes differential privacy for every possible script. When the data alphabet is finite and small enough, TAHOE can be practically run on a single computer. Performance simulations show that TAHOE has greater accuracy than a benchmark algorithm based on a subsample-and-aggregate approach for certain scenarios and parameter values.
Time-Deniable Signatures

In this work we propose time-deniable signatures (TDS), a new primitive that facilitates deniable authentication in protocols such as DKIM-signed email. As with traditional signatures, TDS provide strong authenticity for message content, at least {\em for a sender-chosen period of time}. Once this time period has elapsed, however, time-deniable signatures can be forged by any party who obtains a signature. This forgery property ensures that signatures serve a useful authentication purpose for a bounded time period, while also allowing signers to plausibly disavow the creation of older signed content. Most critically, and unlike many past proposals for deniable authentication, TDS do not require interaction with the receiver or the deployment of any persistent cryptographic infrastructure or services beyond the signing process ( e.g., APIs to publish secrets or author timestamp certificates.) We first investigate the security definitions for time-deniability, demonstrating that past definition attempts are insufficient (and indeed, allow for broken signature schemes.) We then propose an efficient construction of TDS based on well-studied assumptions.
Convolutions in Overdrive: Maliciously Secure Convolutions for MPC

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are specially optimized for privacy-preserving machine learning applications. We concentrate on the optimization of (tensor) convolutions which belong to the most commonly used components in ML architectures, especially in convolutional neural networks but also in recurrent neural networks or transformers, and therefore have a major impact on the overall performance. Our approach is based on a generalized form of structured randomness that speeds up convolutions in a fast online phase. The structured randomness is generated with homomorphic encryption using adapted and newly constructed packing methods for convolutions, which might be of independent interest. Overall our protocols extend the state-of-the-art Overdrive family of protocols (Keller et al., EUROCRYPT 2018). We implemented our protocols on-top of MP-SPDZ (Keller, CCS 2020) resulting in a full-featured implementation with support for faster convolutions. Our evaluation shows that our protocols outperform state-of-the-art actively secure MPC protocols on ML tasks like evaluating ResNet50 by a factor of 3 or more. Benchmarks for depthwise convolutions show order-of-magnitude speed-ups compared to existing approaches.
Authentication System for Enterprise Security

Abstract: Authentication plays a central role in boosting an organization’s security posture. It helps enable an organization to keep its systems secure by permitting only authenticated users (or processes) to access the protected resources, such as computer systems, networks, databases, websites, and other network-based applications or services. Passwords are the more traditional form of authentication, but they constitute a weak form of protection; users are prone to bad password practices, such as reusing passwords, using predictable passwords, or even sharing passwords with others. To combat this issue, more companies are leveraging artificial intelligence (AI) and machine learning (ML) technologies, such as deep learning-based techniques, to develop better and more secure authentication approaches. AI/ML algorithms have been shown to bolster cybersecurity by protecting devices against cyber-attacks and preventing fraudulent activities. In this blog post, we introduce an AI solution based on deep learning and computer vision to perform face recognition-based biometrical authentication. So, what exactly does face recognition do? Face recognition is a broad problem of identifying or verifying a person in digital images or video frames through facial biometric patterns and data. The technology collects a set of unique biometric data of each person associated with their face and facial expression to authenticate a person. Face recognition technology is mostly used for two types of tasks:
Forecasting with Erratic Ingredient Transformation

Abstract: In this paper, we acquire the objectives of the clients and ascertain the prospects for appropriate strategies for them based on the way they approach proposals. Here, we have proposed a feasible proposal for approaching the demands of the customers and delivering to them. In our proposal, in the field of Machine Learning, we have been able to achieve an accurate results by using Logistic Regression. This enables us to provide a better solution while manipulating the request and satisfying the customer's requirements. Logistic regression is a Machine Learning classification algorithm that is used to predict the probability of certain classes based on some dependent variables. Supervised learning is the types of machine learning in which machines are trained using well labelled training data, and on basis of that data, machines predict the output. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression in our model of approach which used to predicts the output of a categorical dependent variable. As part of our proposed model for this project, we propose that we develop a natural dye and pigment that is biodegradable, non-toxic, and allergy-free that contains fewer inorganic substances, and then we will make research-based inorganic pigments. It is possible that with our proposal we will be able to satisfy all customer requirements, We use a logistic regression algorithm to determine whether or not. They have a far lower environmental effect than synthetic dyes, provided that they are treated in a way that eliminates the use of hazardous chemicals throughout the dyeing and finishing process.
Gesture Controlled Virtual Mouse using AI

Abstract: This project offers a cursor controlsystem that quickly navigates system controls whileusing a voice assistant and a camera to record usermotions. Using the aid of MediaPipe, the user can control the computer cursor with hand gestures. It will perform actions like left clicking and draggingusing a variety of hand motions. Additionally, you have a choice to adjust the brightness, loudness, and a number of other things. The system is constructed using advanced Python packages like MediaPipe, OpenCV, etc. All i/o activities are physically controlled by a hand motion and a voiceassistance. The research uses advanced technologies like machine learning and computer vision techniques, which operates well without the use of any additional computer resources, to recognize hand movements and spoken instructions.
Categorizing cyber effects

Immature classification methods for cyber events prevent technical staff, organizational leaders, and policy makers from engaging in meaningful and nuanced conversations about the threats they face. This chapter differentiates cyber effects across the technical impacts on the IT network themselves (primary), the impacts on organizations (secondary), and larger impacts to society (second order). A taxonomy of primary effects is applied and used to analyze over 7,000 publicized cyber events from 2014–2020. Industry sectors vary in the scale of events they are subjected to, the distribution between exploitive and disruptive event types, and the method by which data is stolen or organizational operations are disrupted. These results highlight significant differences by sector and demonstrate that strategies may vary based on deeper understandings of the threat environment.
A novel intelligent global harmony search algorithm based on improved search stability strategy

Abstract Harmony search (HS) is a new swarm intelligent algorithm inspired by the process of music improvisation. Over the past decade, HS algorithm has been applied to many practical engineering problems. However, for some complex practical problems, there are some remaining issues such as premature convergence, low optimization accuracy and slow convergence speed. To address these issues, this paper proposes a novel intelligent global harmony search algorithm based on improved search stability strategy (NIGHS). In the search process, NIGHS uses the adaptive mean of harmony memory library to build a stable trust region around the global best harmony, and proposes a new coupling operation based on linear proportional relation, so that the algorithm can adaptively adjust the ability of exploration and exploitation in the search process and avoid premature convergence. In addition, the dynamic Gauss fine-tuning is adopted in the stable trust region to accelerate the convergence speed and improve the optimization accuracy. The common CEC2017 test functions are employed to test the proposed algorithm, the results show that NIGHS algorithm has a faster convergence speed and better optimization accuracy compared to the HS algorithm and its improved versions.
Performance Investigation of IP Security Virtual Private Network in Ipv6 Environment using Quality of Service

Different types of data are transmitted through the internet, these data such as Email, HTTP, FTP and VoIP conversation might contain important and sensitive information which can be read or seen by other users. Many attacks such as man in the middle, packet sniffing and phishing are available and used by many attackers, as a way to access the protected information. Based on this fact, security and privacy become a necessity in the field of private network (intranet) and public network (extranet). IP sec VPN is considered to be the strongest technique for protecting packets from any kind of attack. In this paper, an analytical study is accomplished using opnet simulation software to design and analyze the proposed network. The proposed network represents four sites in different cities are communicating using the technique of IPsec VPN, different scenarios are created and studied to investigate the performance of network using IPsec VPN in IPV6 environment with QoS. The investigated metrics were packet end to end delay, jitter, packet delay variation, download and upload response time.
Solving a seismic mystery with a diver’s camera: A case of shallow water T -waves in the Persian Gulf

Sparse offshore coverage of seismic networks has hindered detailed studies of submarine earthquakes and their associated seismic hazard. We present results of our analysis of a diver’s recording of acoustic signals from an ML=5.6 earthquake in the Persian Gulf. We model the signals as a set of several shallow water T phases the frequency and group velocity of which are determined by bathymetry. We show that the audio track from this recording can provide reliable estimates of earthquake location and seismic moment. We also show that the reported shaking in the southern Persian Gulf, &gt;170 km from the source of this small earthquake could result from T waves traveling through the entire width of the basin. Our results point to rudimentary and affordable underwater microphones similar to those used in the divers' cameras as tools to build efficient, low-cost networks for the study of offshore events and early warning.
TINJAUAN DATA SOSIAL UNTUK ENTRY DATA IDENTITAS PASIEN DI RSI JAKARTA SUKAPURA

Social data is used to complete the RME when officers identify new patients into the medical record database. social data helps fill in electronic medical record data. The purpose of this study is to review filling in social data to complete the identity of patients at the Islamic Hospital of Jakarta Sukapura. The research method used descriptive method with quantitative analysis of 81 samples of new outpatient identities. The results of the study There are already standard operating procedures for outpatient registration of new patients, the average completeness of social data for new patients is 62 samples (76%) and 19 samples are incomplete (24%), complete average RME data is complete 65 samples (80%) and incomplete 16 samples (20%). the filling of social data and RME is not in accordance with the minimum service standards set by PERMENKES, which is 100% filled. social data does not include all patient identity data in the RME. Identity data in RME is more complete than social data, in social data there are 21 items while in RME there are 25 items. Conclusion: social data has not been able to fully assist officers in completing electronic medical record patient identity data.
ANALISIS KOMPONEN UTAMA PADA DINAS KETENAGAKERJAAN BAGIAN PENEMPATAN DAN PERLUASAN KERJA MENCARI PEKERJAAN MENURUT GOLONGAN PEKERJAAN

Job seekers are a process that matches jobs to match. If all workers and all types of workers are the same, then all workers are suitable for all types of work, then job search will not be a problem. In this research, an analysis is needed to determine the job seeker who chooses according to the desired job group. By using the analysis of the Pricipal Component Analysis using SPSS, the output is the value of KMO and Barlett's Test, MSA, communality, Total Variance, Scree plot and Matrix Components. By using the Main Component Analysis methodology to reduce/reduce the factors that affect job seekers according to groups from 9 existing variables, the results of the reduction are obtained.
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales

Among the remarkable emergent capabilities of large language models (LMs) is free-text rationalization; beyond a certain scale, large LMs are capable of generating seemingly useful rationalizations, which in turn, can dramatically enhance their performances on leaderboards. This phenomenon raises a question: can machine generated rationales also be useful for humans, especially when lay humans try to answer questions based on those machine rationales? We observe that human utility of existing rationales is far from satisfactory, and expensive to estimate with human studies. Existing metrics like task performance of the LM generating the rationales, or similarity between generated and gold rationales are not good indicators of their human utility. While we observe that certain properties of rationales like conciseness and novelty are correlated with their human utility, estimating them without human involvement is challenging. We show that, by estimating a rationale's helpfulness in answering similar unseen instances, we can measure its human utility to a better extent. We also translate this finding into an automated score, GEN-U, that we propose, which can help improve LMs' ability to generate rationales with better human utility, while maintaining most of its task performance. Lastly, we release all code and collected data with this project.
Combining OCR Models for Reading Early Modern Printed Books

In this paper, we investigate the usage of fine-grained font recognition on OCR for books printed from the 15th to the 18th century. We used a newly created dataset for OCR of early printed books for which fonts are labeled with bounding boxes. We know not only the font group used for each character, but the locations of font changes as well. In books of this period, we frequently find font group changes mid-line or even mid-word that indicate changes in language. We consider 8 different font groups present in our corpus and investigate 13 different subsets: the whole dataset and text lines with a single font, multiple fonts, Roman fonts, Gothic fonts, and each of the considered fonts, respectively. We show that OCR performance is strongly impacted by font style and that selecting fine-tuned models with font group recognition has a very positive impact on the results. Moreover, we developed a system using local font group recognition in order to combine the output of multiple font recognition models, and show that while slower, this approach performs better not only on text lines composed of multiple fonts but on the ones containing a single font only as well.
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images

This paper presents an autoencoder-based neural network architecture to compress histopathological images while retaining the denser and more meaningful representation of the original images. Current research into improving compression algorithms is focused on methods allowing lower compression rates for Regions of Interest (ROI-based approaches). Neural networks are great at extracting meaningful semantic representations from images, therefore are able to select the regions to be considered of interest for the compression process. In this work, we focus on the compression of whole slide histopathology images. The objective is to build an ensemble of neural networks that enables a compressive autoencoder in a supervised fashion to retain a denser and more meaningful representation of the input histology images. Our proposed system is a simple and novel method to supervise compressive neural networks. We test the compressed images using transfer learning-based classifiers and show that they provide promising accuracy and classification performance.
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition

In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code will be available at https://github.com/facebookresearch/MMG_Ego4D.