Reviewerly/exHarmony huggingface dataset:
On the Unbearable Lightness of FIPS 140–2 Randomness Tests

Random number generation is critical to many applications.Gaming, gambling, and particularly cryptography all require random numbers that are uniform and unpredictable.For testing whether supposedly random sources feature particular characteristics commonly found in random sequences, batteries of statistical tests are used.These are fundamental tools in the evaluation of random number generators and form part of the pathway to certification of secure systems implementing them.Although there have been previous studies into this subject (Becker, 2013), RNG manufacturers and vendors continue to use statistical tests known to be of dubious reliability, in their RNG verification processes.Our research shows that FIPS-140-2 cannot identify adversarial biases effectively, even very primitive ones.Concretely, this work illustrates the inability of the FIPS 140 family of tests to detect bias in three obviously flawed PRNGs.Deprecated by official standards, these tests are nevertheless still widely used, for example in hardware-level selftest schemes incorporated into the design of many True RNGs (TRNGs).They are also popular with engineers and cryptographers for quickly assessing the randomness characteristics of security primitives and protocols, and even with manufacturers aiming to market the randomness features of their products to potential customers.In the following, we present three biasedby-design RNGs to show in explicit detail how simple, glaringly obvious biases are not detected by any of the FIPS 140-2 tests.One of these RNGs is backdoored, leaking key material, while others suffer from significantly reduced unpredictability in their output sequences.To make our point even more straightforward, we show how files containing images can also fool the FIPS 140 family of tests.We end with a discussion on the security issues affecting an interesting and active project to create a randomness
Sign Language Translation

Sign language is the way of communication for hearing impaired people. There is a challenge for common people to communicate with deaf people which makes this system helpful in assisting them. This project aims at implementing computer vision which can take the sign from the users and convert them into text in real time. The proposed system contains four modules such as: image capturing, preprocessing classification and prediction. By using image processing the segmentation can be done. Sign gestures are captured and processed using OpenCV python library. The captured gesture is resized, converted to grey scale image and the noise is filtered to achieve prediction with high accuracy. The classification and predication are done using convolution neural network.
Machine learning as a detection method of Strombolian eruptions in infrared images from Mount Erebus, Antarctica

Mount Erebus, Antarctica, has a persistent lava lake with Strombolian eruptions. Volcanic eruptions can be automatically detected with multiple methods such as cross-correlation of seismic recordings and identifying anomalies in gas emissions. We demonstrate a new method of detecting Strombolian eruptions by training a convolutional neural network to automatically categorize eruptions in infrared images obtained from the rim of the crater above the Ray lava lake atop Mount Erebus. Over 9 million images were obtained from previous research (Peters et al., 2014a). The infrared images were shot during a span of over 2 years; one image every 2 s when weather conditions did not hinder the electrical supply. Training was performed with infrared images of the eruptions detected through seismic cross-correlation with a stacked waveform. Eruptions detected using machine learning on infrared images from December 2013 through December 2014 correctly categorized 84% of the detections as eruptions. The remaining 16% were caused by effects from the plume confounding the neural network. Nearly all of the eruptions detected utilizing seismic cross-correlation were also categorized correctly by the neural network during periods for which image data was available. We concluded machine learning is an effective method for classifying the characteristics of Strombolian eruptions which further improves the ability to study their origins while assessing the hazards posed by volcanic eruptions.
Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates

This article develops a fully decentralized multiagent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the first scenario, a collection of agents have distinct datasets gathered by following different behavior policies (none of which is required to explore the full state space) in different instances of the same environment and they all collaborate to evaluate a common target policy. The network approach allows for efficient exploration of the state space and allows all agents to converge to the optimal solution even in situations where neither agent can converge on its own without cooperation. The second scenario is that of multiagent games, in which the state is global and rewards are local. In this scenario, agents collaborate to estimate the value function of a target team policy. The proposed algorithm combines off-policy learning, eligibility traces, and linear function approximation. The proposed algorithm is of the variance-reduced kind and achieves linear convergence with O(1) memory requirements. The linear convergence of the algorithm is established analytically and simulations are used to illustrate the effectiveness of the method.
A dense connection based network for real-time object tracking

With the development of deep learning, the performance of many computer vision tasks has been greatly improved. For visual tracking, deep learning methods mainly focus on extracting better features or designing end-to-end trackers. However, during tracking specific targets most of the existing trackers based on deep learning are less discriminative and time-consuming. In this paper, a cascade based tracking algorithm is proposed to promote the robustness of the tracker and reduce time consumption. First, we propose a novel deep network for feature extraction. Since some pruning strategies are applied, the speed of the feature extraction stage can be more than 50 frames per second. Then, a cascade tracker named DCCT is presented to improve the performance and enhance the robustness by utilizing both texture and semantic features. Similar to the cascade classifier, the proposed DCCT tracker consists of several weaker trackers. Each weak tracker rejects some false candidates of the tracked object, and the final tracking results are obtained by synthesizing these weak trackers. Intensive experiments are conducted in some public datasets and the results have demonstrated the effectiveness of the proposed framework.
Analyzing Differentiable Fuzzy Implications

Combining symbolic and neural approaches has gained considerable attention in the AI community, as it is argued that their strengths and weaknesses are complementary. One trend in the literature are weakly supervised learning techniques that employ operators from fuzzy logics. They use prior background knowledge described in such logics to help training neural networks from unlabeled and noisy data. By interpreting logical symbols using neural networks (or grounding them), this background knowledge can be added to regular loss functions, hence making reasoning a part of learning. We investigate how implications from the fuzzy logic literature behave in a differentiable setting. In this setting, we analyze the differences between the formal properties of these fuzzy implications. It turns out that various fuzzy implications, including some of the most well-known, are highly unsuitable for use in a differentiable learning setting. A further finding shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication. Furthermore, we introduce a new family of fuzzy implications (called sigmoidal implications) to tackle this phenomenon. Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and show that sigmoidal implications outperform other choices of fuzzy implications.
Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans' trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users' retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore.
NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature

As part of the NLP Scholar project, we created a single unified dataset of NLP papers and their meta-information (including citation numbers), by extracting and aligning information from the ACL Anthology and Google Scholar.In this paper, we describe several interconnected interactive visualizations (dashboards) that present various aspects of the data.Clicking on an item within a visualization or entering query terms in the search boxes filters the data in all visualizations in the dashboard.This allows users to search for papers in the area of their interest, published within specific time periods, published by specified authors, etc.The interactive visualizations presented here, and the associated dataset of papers mapped to citations, have additional uses as well including understanding how the field is growing (both overall and across sub-areas), as well as quantifying the impact of different types of papers on subsequent publications.
A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Handwritten signatures are of eminent importance in many business and legal activities around the world. That is, signatures have been used as authentication and verification measure for several centuries. However, the high relevance of signatures is accompanied with a certain risk of misuse. To mitigate this risk, automatic signature verification was proposed. Given a questioned signature, signature verification systems aim to distinguish between genuine and forged signatures. In the last decades, a large number of different signature verification frameworks have been proposed. Basically, these frameworks can be divided into online and offline approaches. In the case of online signature verification, temporal information about the writing process is available, while offline signature verification is limited to spatial information only. Hence, offline signature verification is generally regarded as the more challenging task. The present chapter reviews the field of offline signature verification and presents a comprehensive overview of methods typically employed in the general process of offline signature verification.
Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

The need for reducing our energy consumption footprint and the increasing number of electric devices in today’s homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of a significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household’s occupants for their safety. By breaking down aggregate power consumption into its components, Non-Intrusive Load Monitoring provides information on individual appliances and their current state of operation. Since no additional metering equipment is required, residents are not confronted with intrusion into their familiar environment. Our work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows to visualize and process real-time data about the total energy consumed. As a case study, we describe a series of measurements from common household devices and show how abnormal behavior can be detected.
Real time object detection for aerial search and rescue missions for missing persons

This paper introduces a solution to stand-alone system based, real-time object-detection, can efficiently facilitate the search for missing persons with an unmanned aerial vehicle. The challenge is the real-time implementation of the systems and training the given deep neural network for the desired task. The paper describes the methods and procedures currently in use, as well as the possible tools. Subsequently, the autonomous aircraft system, which carries a real-time detection system, is introduced. In the section about real-time detection, we will introduce the TensorFlow lite-based application, building on SSD topology, in detail, which was implemented on mobile phones. We will also introduce the dataset used for training, testing and the results achieved. In summary, the recall achieved is 65.4% and precision is 96.4%, besides the fact that the android-based application, using the phone’s camera, performs image analysis at a rate of 11 to 17 FPS in real-time, while continuously providing
Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning

Source Code Authorship Attribution (SCAA) is a direct challenge to the privacy and anonymity of developers. However, it is important to recognize the malicious authors and the origin of the attack. In this paper, we proposed Source Code Authorship Attribution using Abstract Syntax Tree (SCAA-AST) for efficient classification of programmers. First, the AST hierarchal features are generated from different programming codes. Second, preprocessing techniques are used to obtain useful features without sound data. Third, the Term Frequency Inverse Document Frequency (TFIDF) weighting technique is used to zoom in on the significance of each feature. Fourth, the Adaptive Synthetic (ADASYN) oversampling method is used to solve the imbalanced class problem. Finally, a deep learning algorithm is designed with the TensorFlow framework, and the Keras API is used to classify programming authors. A deep learning algorithm is further configured with a dropout layer, learning error rate, loss and activation function, and dense layers to enhance the classification results. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy.
Software Requirement in Iterative SDLC Model

Software development life cycle (SDLC) is critical and remains the most significant part of software development in requirement engineering. There exists several SDLC each designed for specific needs to accommodate available resources and skills; they are compatible with certain programming languages and toolkits, and are of immense benefits to the software engineers. Despite its popularity and strengths over major weaknesses of other models, Iterative model is less acceptable by programmers and not widely in use in industry. This study examines the concepts of Iterative models and presents its real time examples to make the SDLC more understood to software developers. The practical application of the model is exemplified, motivation for its use are highlighted, and the requirement as well as requirement activities in each phase are tabulated. This will raise users’ awareness on the Agile Software Development Model and attract software developers to appreciate the Iterative Model and make a better use.
An Enhanced Firefly Algorithm Using Pattern Search for Solving Optimization Problems

Firefly Algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches used for solving optimization problems. The conventional FA use randomization factor during generation of solution search space and fireflies position changing, which results in imbalanced relationship between exploration and exploitation. This imbalanced relationship causes in incapability of FA to find the most optimum values at termination stage. In the proposed model, this issue has been resolved by incorporating PS at the termination stage of standard FA. The optimized values obtained from the FA are set as the initial starting points for the PS algorithm and the values are further optimized by PS to get the most optimal values or at least better values than the values obtained by conventional FA during its maximum number of iterations. The performance of the newly developed FA-PS model has been tested on eight minimization functions and six maximization functions by considering various performance evaluation parameters. The results obtained have been compared with other optimization algorithms namely genetic algorithm (GA), standard FA, artificial bee colony (ABC), ant colony optimization (ACO), differential equations (DE), bat algorithm (BA), grey wolf optimization (GWO), Self-Adaptive Step Firefly Algorithm (SASFA), and FA-Cross algorithm in terms of convergence rate and various numerical performance evaluation parameters. A significant improvement has been observed in the solution quality by embedding PS in the standard FA at the termination stage. The result behind this improvement is the better exploration and exploitation of the solution search space at this stage.
A Novel Tracking Method Based on Improved FAST Corner Detection and Pyramid LK Optical Flow

Object tracking is a challenging problem with accuracy and large motion in computer vision. Though optical flow has achieved great performance, it would possibly fail to track fast moving object, and accuracy and big movement both suffer from the size of integration window. Pyramid LK (Lucas-Kanade) method can solve large motion in a small integration window. However, the basic pyramid LK is affected by accuracy. So, a combination of improved FAST corner detection and pyramid LK optical flow is applied. In this method, sub-pixel computation can increase the accuracy. The large movement can be satisfied by scaling the source image. The strongest gray changing corner point can be rapidly extracted by the improved FAST algorithm. This approach can better estimate the optical flow with strong corner to track moving object. In the experiment, a video of actual movement of ball is used to test algorithm validity. Average errors in the x direction and y direction are 0.9386 pixel and 0.6792 pixel separately. The detection results demonstrate that this algorithm can track moving target of large motion accurately.
Adaptive distributed methods under communication constraints

We study estimation methods under communication constraints in a distributed version of the nonparametric random design regression model.We derive minimax lower bounds and exhibit methods that attain those bounds.Moreover, we show that adaptive estimation is possible in this setting.
Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.
Individual Acceptance of Using Health Data for Private and Public Benefit: Changes During the COVID-19 Pandemic

While the COVID-19 pandemic has been devastating, data collected in this context has unprecedented opportunities for data scientists.The stunning breadth of data obtained through new gathering systems put in place to manage the pandemic offers a richly textured view of a transformed world.Looking forward, privacy researchers worry that these new data-gathering systems risk running afoul of societal norms regarding the flow of information.Looking back at pre-pandemic public preferences with respect to data sharing may provide us some idea of what to expect in the future.In July of 2019, we happened to conduct a vignette study in Germany to examine the public's willingness to share data for fighting an outbreak of an infectious disease.In April of 2020, during the first peak of the pandemic, we repeated the study to examine crisis-driven changes in respondents' willingness to share data for public health purposes with three different samples.Public acceptance of the use of individual health data to combat an infectious disease outbreak increased notably between the two measurements, while acceptance of data use in several other scenarios barely changed over time.This shift aligns with the predictive framework of contextual integrity theory, and the data presented here may serve as a good reminder for policymakers to carefully consider the intended purpose of and appropriate limitations on data use.
Automatic Discovery of Privacy–Utility Pareto Fronts

Abstract Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can be provided in a particular application while maintaining acceptable utility. Analytical utility guarantees offer a rigorous tool to reason about this tradeoff, but are generally only available for relatively simple problems. For more complex tasks, such as training neural networks under differential privacy, the utility achieved by a given algorithm can only be measured empirically. This paper presents a Bayesian optimization methodology for efficiently characterizing the privacy– utility trade-off of any differentially private algorithm using only empirical measurements of its utility. The versatility of our method is illustrated on a number of machine learning tasks involving multiple models, optimizers, and datasets.