Reviewerly/exHarmony huggingface dataset:
The Website-Based Food Booking System Equipped with Real-Time Booking Status to Address Queuing Issues at Restaurants

In the era of increasingly rapid technological development, the human need for primary goods is increasing. The higher demand triggers the accumulation of processes in distribution to consumers or what is called a queue. The queuing phenomenon above arises because of limited services and the need for these services is very high. This problem can trigger boredom from consumers because of long queue times, including food service such as in restaurants. As a primary need, the process of obtaining food commodities in the form of food requires quick and easy time in today's fast-paced era. Moreover, during the current COVID-19 pandemic, where human mobility is very limited. So that primary needs need to be facilitated with a system that is easy to use but can reduce physical contact. To solve this problem, the author makes a website-based food ordering system. This system is very easily accessible by customers on their respective smartphones because it is based on a website. In the client-side, customers can see various food menus provided by the restaurant. To order food, customers are required to log in first according to the code provided by the restaurant. Customers are asked to enter identification such as name, telephone number, and address before confirming the order. Because the client-side is already connected to the server-side, the restaurant admin can verify the ordering process according to the data entered by the customer. This system provides accurate data and is always updated if there is a change (real-time) such as the status of the remaining queue, remaining meal times and the status of the ordering process. The method used in this research is the waterfall method which starts from needs analysis, design, development, testing using black-box and research respondents, and improvement. Based on research respondents conducted by the author, 97 percent of respondents agreed with the features in this system and its benefits that make it easier for food booking system than conventional booking system.
iChat: Interactive Eyes for Specially Challenged People Using OpenCV Python

Dream of a world with no complaints or demands, just peace and happiness of every individual doesn't seem so far now. Interaction of eyes with computers is a rising and much awaited innovation which alludes to countless calculations, algorithms, strategies that help in maintaining and improving the process. Recalling the era of Stephen hawking and seeking reference from the work of Dennis Klatt, Eye Gaze development in that context is the most encouraging innovation known up until now and in this paper utilizing the assistance of Kivy in python, we have the ideal philosophy to make the world one stride nearer to a smart digital world. We utilize the 68 facial points dataset which must be present in every face specifically eye, nose, jaw, etc. Precision is what we seek for in this work and in order to achieve that we keep our face still which makes eye detection from camera easier and accuracy certainly increases. Further, the division of the Virtual keyboard made using python dictionaries into left and right makes it more convenient to choose by an eye blink as one can first select the section of the keyboard which consists of the key/alphabet required to be printed. We use Pyglet module for including sound in our project. Further, Integration of our project with a platform like Kivy makes it a miracle app for differently abled. Kivy, an open-source platform, includes detection of eye blinking, right from the webcam/mobile camera using OpenCV library in python along with tracking (left or right) of the eyes to left or right. Our main aim is a typing and chatting environment without the use of hands, fingers which is a blessing for those born specially challenged or have lost the physical ability or have paralysis. An app that has never happened before will change the lives of millions seeking help. The methodology includes descriptive flowcharts in each phase followed by the expected and implemented results.
Anomaly Detection in Crowded Places: Review

In today's world, security is based on the manually monitored surveillance cameras used at various places. Since, human based monitoring is very tiring and difficult task, this raises the demand for automatic system to detect anomaly at crowded places. It involves understanding crowd behavior, their interaction/collision with each other, individual speed, crowd density, different classification methods to identify anomaly etc. This paper studies the different techniques used to study crowd behavior, individual and crowd characteristics. It also compares the results of various methods used in survey papers for common datasets.
Weakly Supervised Temporal Action Detection With Temporal Dependency Learning

Weakly supervised temporal action detection aims at localizing temporal positions of action instances in untrimmed videos with only action class labels. In general, previous methods individually classify each frame based on the appearance information and the short-term motion information, and then integrate consecutive high-response action frames into entities which serve as detected action instances. However, the long-range temporal dependencies between action frames are not fully utilized, and the detection results are more likely to be trapped in the most discriminative action segments. To alleviate this issue, we propose a novel two-branch (i.e., the coarse detection branch and the refining detection branch) detection framework with learning the long-range temporal dependencies for obtaining more accurate detection results, where only action class labels are required. The coarse detection branch is used to localize the most discriminative segments of action instances based on a typical multi-instance learning paradigm under the supervision of action class labels, whereas the refining detection branch is expected to localize the less discriminative segments of action instances via learning the long-range temporal dependencies between frames based on the proposed Transformer-style architecture and learning strategies. This collaboration mechanism takes full advantage of complementary information from the provided action class labels and the natural temporal dependencies between action frames, forming a more comprehensive solution. Consequently, our method obtains more precise detection results. Expectedly, the proposed method outperforms recent weakly supervised temporal action detection methods on dataset THUMOS14 and ActivityNet measured by mAP@tIoU and AR@AN.
Research on Neural Style Transfer

Abstract The process of using CNN (Convolutional Neural Network) to blend the contents of a picture with different styles is called neural style transfer (NST). The purpose of this paper is to introduce current progress of NST, and introduce in detail the classification of the main NST algorithms based on deep learning, and make qualitative and quantitative comparisons of different algorithms, and then analyze the application prospects of image style migration in related fields, and finally summarize the existing problems and future research directions of NST.
Robust Federated Opportunistic Learning in the Presence of Label Quality Disparity

In this article, the problem of federated learning (FL) in the presence of label quality disparity is considered. To address this problem, the federated opportunistic computing for ubiquitous system (FOCUS) has been proposed very recently. In FOCUS, the central server utilizes its accurately labeled benchmark samples to quantify the credibility of different clients by computing the cross-entropy (CE) loss of the locally updated models on the benchmark data set and the CE loss of the global model on the local data sets. However, FOCUS assumes the availability of the accurate labels of the benchmark data set, which is difficult to guarantee under many practical scenarios. To overcome this limitation of FOCUS, we propose a new algorithm named robust federated opportunistic learning (RFOL), which does not require the benchmark samples at the central server to be labeled. In RFOL, the client credibility is evaluated by computing the Kullback–Leibler (KL) divergence among the soft predictions on the benchmark samples of different locally updated models and the CE loss of the global model on the local data sets. The experimental results on several popular data sets reveal that: 1) with an unlabeled benchmark data set at the server, the proposed RFOL algorithm attains almost the same learning performance as FOCUS, which requires an accurately labeled benchmark data set at the server; 2) with an inaccurately labeled benchmark data set, RFOL outperforms FOCUS, which shows that the former is more robust to the inaccurate labels of the benchmark samples; and 3) RFOL outperforms FedAvg, which assigns equal credibility to all the clients.
CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance

Realtime and intelligent video surveillance via camera networks involve computation-intensive vision detection tasks with massive video data, which is crucial for safety in the edge-enabled industrial Internet of Things (IIoT). Multiple video streams compete for limited communication resources on the link between edge devices and camera networks, resulting in considerable communication congestion. It postpones the completion time and degrades the accuracy of vision detection tasks. Thus, achieving high accuracy of vision detection tasks under the communication constraints and vision task deadline constraints is challenging. Previous works focus on single camera configuration to balance the tradeoff between accuracy and processing time of detection tasks by setting video quality parameters. In this paper, an adaptive camera network self-configuration method (CANS) of video surveillance is proposed to cope with multiple video streams of heterogeneous quality of service (QoS) demands for edge-enabled IIoT. Moreover, it adapts to video content and network dynamics. Specifically, the tradeoff between two key performance metrics, i.e., accuracy and latency, is formulated as an NP-hard optimization problem with latency constraints. A low-complexity algorithm is proposed to solve the optimization problem based on greedy searching. Simulation on real-world surveillance datasets demonstrates that the proposed CANS method achieves low end-to-end latency (13 ms on average) with high accuracy (92%) with network dynamics, which validates its effectiveness.
ABE for DFA from LWE Against Bounded Collusions, Revisited

We present a new public-key ABE for DFA based on the LWE assumption, achieving security against collusions of a-priori bounded size. Our scheme achieves ciphertext size \(\tilde{O}(\ell + B)\) for attributes of length \(\ell \) and collusion size B. Prior LWE-based schemes has either larger ciphertext size \(\tilde{O}(\ell \cdot B)\), or are limited to the secret-key setting. Along the way, we introduce a new technique for lattice trapdoor sampling, which we believe would be of independent interest. Finally, we present a simple candidate public-key ABE for DFA for the unbounded collusion setting.
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics

Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data are one of the most commonly used modes of data in diverse applications such as healthcare and finance. Much of the existing interpretability methods used for tabular data only report feature-importance scores—either locally (per example) or globally (per model)—but they do not provide interpretation or visualization of how the features interact. We address this limitation by introducing Feature Vectors, a new global interpretability method designed for tabular datasets. In addition to providing feature-importance, Feature Vectors discovers the inherent semantic relationship among features via an intuitive feature visualization technique. Our systematic experiments demonstrate the empirical utility of this new method by applying it to several real-world datasets. We further provide an easy-to-use Python package for Feature Vectors.
Sdft: A PDG-based Summarization for Efficient Dynamic Data Flow Tracking

Dynamic taint analysis (DTA) has been widely used in various security-relevant scenarios that need to track the runtime information flow of programs. Dynamic binary instrumentation (DBI) is a prevalent technique in achieving effective dynamic taint tracking on commodity hardware and systems. However, the significant performance overhead incurred by dynamic taint analysis restricts its usage in production systems. Previous efforts on mitigating the performance penalty fall into two categories, parallelizing taint tracking from program execution and abstracting the tainting logic to a higher granularity. Both approaches have only met with limited success. In this work, we propose Sdft, an efficient approach that combines the precision of DBI-based instruction-level taint tracking and the efficiency of function-level abstract taint propagation. First, we build the library function summaries automatically with reachability analysis on the program dependency graph (PDG) to specify the control- and data dependencies between the input parameters, output parameters, and global variables of the target library. Then we derive the taint rules for the target library functions and develop taint tracking for library function that is tightly integrated into the state-of-the-art DTA framework Libdft. By applying our approach to the core C library functions of glibc, we report an average of 1.58x speed up of the tracking performance compared with Libdft64. We also validate the effectiveness of the hybrid taint tracking and the ability on detecting real-world vulnerabilities.
Quantum metrology with superposition spin coherent states: Insights from Fisher information

We present a closed-form analytical description for the metrological performance of a generic superposition of spin coherent states (SSCS) used as probes for quantum phase estimation. Working in this framework, we derive transparent analytical expressions for the pertinent quantum Fisher information and identify a general class of spin coherent states enabling quantum metrology with Heisenberg-limit precision. Bloch-sphere analysis of antipodal SSCS shows that the phase-estimation precision attainable with such states increases with the distance from the equator of the Bloch sphere, growing from the shot-noise limit level for the equatorial states all the way up to the Heisenberg limit for SSCS that can be represented as superpositions of the poles of the Bloch sphere.
Cryptocurrencies with Security Policies and Two-Factor Authentication

Blockchain-based cryptocurrencies offer an appealing alternative to Fiat currencies, due to their decentralized and borderless nature. However the decentralized settings make the authentication process more challenging: Standard cryptographic methods often rely on the ability of users to reliably store a (large) secret information. What happens if one user's key is lost or stolen? Blockchain systems lack of fallback mechanisms that allow one to recover from such an event, whereas the traditional banking system has developed and deploys quite effective solutions. In this work, we develop new cryptographic techniques to integrate security policies (developed in the traditional banking domain) in the blockchain settings. We propose a system where a smart contract is given the custody of the user's funds and has the ability to invoke a two-factor authentication (2FA) procedure in case of an exceptional event (e.g., a particularly large transaction or a key recovery request). To enable this, the owner of the account secret-shares the answers of some security questions among a committee of users. When the 2FA mechanism is triggered, the committee members can provide the smart contract with enough information to check whether an attempt was successful, and nothing more. We then design a protocol that securely and efficiently implements such a functionality: The protocol is round-optimal, is robust to the corruption of a subset of committee members, supports low-entropy secrets, and is concretely efficient. As a stepping stone towards the design of this protocol, we introduce a new threshold homomorphic encryption scheme for linear predicates from bilinear maps, which might be of independent interest. To substantiate the practicality of our approach, we implement the above protocol as a smart contract in Ethereum and show that it can be used today as an additional safeguard for suspicious transactions, at minimal added cost. We also implement a second scheme where the smart contract additionally requests a signature from a physical hardware token, whose verification key is registered upfront by the owner of the funds. We show how to integrate the widely used universal two-factor authentication (U2F) tokens in blockchain environments, thus enabling the deployment of our system with available hardware.
Digital Transformation in Covid-19 Pandemic - What we Learned and How to Utilize What Was Learned

With the arrival of COVID-19 pandemic in March of 2020 the first lock-down and closing of schools and faculties occurred. Educational institutions had to find a solution overnight. To ensure the continuity of schooling was a great challenge, the strategy of the faculties has only indicated digital transformation, and a small number of faculty staff was ready to face this new situation (according to the research had already been carried out). Teachers that had already used digital technologies and e-learning found it easier to form their classes in online environment that they were already familiar with. But those teachers that have never done so, found themselves facing a great challenge. Faced with the inability to hold classes in a traditional classroom way, they were challenged to quickly transition to online environment and ensure the completion of the academic year. With the arrival of the new academic year, the return to the classic way of classes was expected, but this did not completely occur. The pandemic did not cease and again higher education institutions face closing and classes are more and more held remotely. Levels of preparation and experience of teachers and students at the University of Mostar differ greatly. This paper has a goal of presenting experiences and needs of the teachers of the Faculty of Humanities and Social Sciences and ascertaining how these experiences can be, and how much they have been used relating to the situation we find ourselves in again.
Tarnhelm: Isolated, Transparent &amp; Confidential Execution of Arbitrary Code in ARM's TrustZone

Protecting the confidentiality of applications on commodity operating systems, both on desktop and mobile devices, is challenging: attackers have unrestricted control over an application's processes and thus direct access to any of the application's assets. However, the application's code itself can be of great commercial value, for example in the case of proprietary code or additional functionality obtained as downloadable content and via in-app purchases, which are widely used to monetize free applications through premium content. Developers still rely heavily on obfuscation to protect their own code from unauthorized tampering or copying, providing an obstacle for an attacker, but not preventing compromise. In this paper, we present Tarnhelm, an approach to offer a practical and transparent primitive to implement code confidentiality by extending ARM's TrustZone, a TEE that so far provides limited functionality to application developers. Tarnhelm allows developers to easily designate part of their code as confidential through source code annotations. At compile time, Tarnhelm automatically partitions the application into regular application code, executed in the "normal world," and the invisible code, transparently executed in the "secure world." Tarnhelm tightly couples and secures the execution in both worlds without exposing any additional attack surface by combining a number of different techniques, such as secure code loading, system call forwarding, transparent world switching, and the enforcement of inter-world control-flow integrity. We im- plemented a proof of concept of Tarnhelm and demonstrate its feasibility in a mobile computing setting.
A Comparison Between A* &amp; RRT in Maze Solving Problem

The performance analysis of different algorithms in a specific environment is an important topic of path planning research. This study focuses on the performance analysis and pros and cons of the A * algorithm and RRT algorithm in two-dimensional maze solving problems. Matlab software was used to simulate the experiment, and the maze complexity was defined as the number of branches and wall thickness. Then, the two groups of experimental complexity were divided from simple to complex. Finally, the time and presentation path of the two algorithms in each group of experiments were observed by transverse and longitudinal comparison. In two-dimensional maze path planning, A * is suitable for going through a maze efficiently, while RRT is suitable for exploring a maze evenly. These findings are significant for emergency search and rescue and exploration of unknown areas by unmanned vehicles.
Precise and Effective Robotic Tool Change Strategy Using Visual Servoing With RGB-D Camera

Abstract In modern industrial manufacturing processes, robotic manipulators are routinely used in the assembly, packaging, and material handling operations. During production, changing end-of-arm tooling is frequently necessary for process flexibility and reuse of robotic resources. In conventional operation, a tool changer is sometimes employed to load and unload end-effectors, however, the robot must be manually taught to locate the tool changers by operators via a teach pendant. During tool change teaching, the operator takes considerable effort and time to align the master and tool side of the coupler by adjusting the motion speed of the robotic arm and observing the alignment from different viewpoints. In this paper, a custom robotic system, the NeXus, was programmed to locate and change tools automatically via an RGB-D camera. The NeXus was configured as a multi-robot system for multiple tasks including assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobot prototypes. Thus, different tools are employed by an industrial robotic arm to position grippers, printers, and other types of end-effectors in the workspace. To improve the precision and cycle-time of the robotic tool change, we mounted an eye-in-hand RGB-D camera and employed visual servoing to automate the tool change process. We then compared the teaching time of the tool location using this system and compared the cycle time with those of 6 human operators in the manual mode. We concluded that the tool location time in automated mode, on average, more than two times lower than the expert human operators.