Reviewerly/exHarmony huggingface dataset:
Using Ethereum Platform to Securely Register Students' Extracurricular Activities

An official transcript is a document that is produced by the registrar's office and that provides a complete representation of a student's academic record. Yet, an official transcript does not record any of the student's extracurricular activities despite the fact that such activities can shape the student's personal and professional development. It is usually the student's responsibility to keep a running log to document the attended events and the participated activities, their dates and locations, and the number of participants in such activities. These activities, not only improve the student's communications level, expand the learned skills, and develop the pursued interests, but also open the door to present an in-depth path of the student's professional and personal growth. However, students in general, have hard time documenting and providing proof of all of their extracurricular activities during their educational years. This project addresses and solves this problem and present an innovative, reliable, secure, and even portable (possibly across educational institutions) solution. Our solution encompasses a decentralized Web3 App (dApp), Ethereum smart contract and blockchain, and Metamask crypto wallet. The dApp registers the students' activities using tokens applied to smart contracts that are saved/inserted on the Ethereum blockchain. The student can then use the combination of Etherscan block explorer and the crypto wallet's public address, to provide a detailed and certified extracurricular transcript or log of the extracurricular activities participated in or attended. The extracurricular transcript could be requested by a recruiter or an employee of the human resources department as a part of the hiring process or a promotion.
Communicability - A Software Quality Attribute Proposal

This paper builds on the known fact that there are artifacts and constructs within the software product and process that enable or inhibit communication between developer stakeholders that is critical to software quality and success. Processes such as refactoring, version control and integration (which are critical to software quality), become difficult and costly when these artifacts such as commit messages, code comments, design documents and interface descriptions are not recognised and treated as legitimate communication structures which can in fact make or break the success of the software product. The paper, therefore, proposes the software quality attribute of communicability. A group of developers were surveyed to understand the experience of developers in relation to identified communication constructs and artifacts in software development. After this, the authors collaborated on the development of realistic communicability scenarios. A general scenario for communicability is created based on the concrete scenarios that were written. Furthermore, using the input of the developers a collection of communicability tactics were documented. The findings presented in this paper will be beneficial to requirements engineers and also software architects as they document quality attribute requirements for software with special requirements for clear communication between stakeholders.
Intuitive Smart Creative Designing using Computer Vision

In the era of digital advertisements, the relevancy of advertisements and preferences of the end customer are key in determining the success of a Campaign. Understanding the underlying patterns and trends that affect the performance of Creatives (Advertisement Images) are virtually invaluable for advertisers. Most organizations don’t use any underlying technology to choose or even learn the preferred Creatives by audiences for pre-campaign setups. This study attempts to extract key features such as dominant color, text to image ratio, call to action position, presence of objects, sentiments expressed in Creative, etc. from Creatives and tie them back to Campaign performance based on Click-through rate (CTR). Our two major objectives include: 1) to be able to classify Top performing Creatives (with respect to CTR) from Low performing ones; 2) to be able to explain why certain Creatives perform better than others. The proposed framework is an ensemble of state of art technologies namely Real time Digital Binarization-Residual Networks, Convolutional Recurrent Neural Network, You Only Learn One Representation and MobileNets to extract, study and attribute Creative features to the metric of interest, CTR in an automated and customizable manner. With Decision Trees as our top performer, we were able to Classify Top performing Creatives with a Balanced Accuracy of 88.69% and 90.83% for the Ed tech Domain and an Advertiser respectively. We were also able to extract meaningful feature inferences to create Rule sets that could act as guidelines for Creative Designers.
Augmented Reality Shopping System and Experience: Overview of the Literature

This paper reviews literature related to the architecture for a proposed augmented reality shopping application under current development. The system consists of a progressive web app client that renders augmentations for the user and performs tracking, and a cloud server that performs object detection, localization, and product information retrieval and analysis. This review provides an overview of past cloud-based augmented reality applications and identifies design factors for the proposed system.
Reducing Structured Query Language Injection Vulnerabilities Through Functional Programming Principles

Web application security breaches frequently involve exploiting structured query language (SQL) injection vulnerabilities introduced by weaknesses in the coding style. Software engineers commonly lack the expertise and experience in implementing the functional programming principles of immutability, using pure functions, and reducing side effects to prevent vulnerabilities in their code. This study explores the changes web application developers can implement following the functional programming principles of immutability, using pure functions, and reducing side effects to reduce the presence of SQL injection vulnerabilities in web applications. The solution presented in this study, design strategies, supports the nascent design theory that applying the functional programming principles of immutability, using pure functions, and reducing side effects can reduce the presence of SQL injection vulnerabilities in web applications. When applied to an intentionally vulnerable web application, the design strategies developed through this study demonstrated effectiveness in reducing SQL injection vulnerabilities from web applications. This study contributes to the practice of software development by giving web application developers a new way to secure their web Applications.
A Relation between AND and ∀, and OR and ∃

This paper explores a relation between predicate quantifiers ∀ and ∃, and logic operations AND and OR respectively.
Confidential Computing in the Post-Quantum Era

The recent advances in Quantum Computing has many industries now wondering about the long-term security of their data, networks, and data centers. Couple this with the new data privacy regulations that carry hefty fines for data breaches, and the new focused attacks by hackers on data-in-use, and you can understand their concern. Confidential Computing is focused on protecting data while in use, but many architectures are not currently considering the Post-Quantum Era in their designs. This paper describes Confidential Computing, the Post-Quantum Quandary, and provides an overview of some of the existing technologies and algorithms to create a Quantum-Safe Confidential Computing architecture.
A Deep Generative Adversarial Network (GAN)-enabled Abnormal Pedestrian Behavior Detection at Grade Crossings

This paper presents a video processing pipeline based on a deep learning generative adversarial network (GAN) for detecting and localizing abnormal behaviors of pedestrians at grade crossings. The skeleton detection and tracking algorithm captures pedestrian motion patterns of pedestrians and generates temporally-varying trajectories of posture key points for each pedestrian. The trajectories of only normal behaviors are learned by the GAN after being decomposed into local and global motion features. During the testing stage, anomalous behaviors can be readily observed as outliers of the learned features and trained models. Our pipeline can handle multiple pedestrians simultaneously presenting in a video frame by running a single model. It requires no specific location information or (minimal) model retraining, enhancing its robustness and extendibility to different grade crossings. In addition, the entire pipeline is also developed on an edge computing platform to enable salient field deployability of the technology. The experimental results demonstrate the prominent performance of the system in field tests.
Algorithm for Detecting Biological Objects and Assessment of their Parameters for Automated Bioassay

Detecting biological objects (BO) at the micro-and meso-levels and observing their vital functions are important for biodiagnostics of the environment. It can also be the basis for biotesting. In this article we propose an approach for detecting BOs moving in an aquatic environment based on the representation of the background by a mixture of Gaussian distributions, followed by applying a mathematical model of a two-dimensional Markov chain to detect BO contours. To assess the vital functions of BOs, we calculate the following parameters: the number of BOs, their size, movement speed, acceleration and movement trajectory. We present the results of the algorithm on the example of detecting cladocerans Daphnia magna and ciliates Paramecium caudatum in laboratory conditions.
Ethernet Device Authentication via Physical Layer Fingerprinting

Device authentication is an important element of any strong defense-in-depth strategy for securing cyber-physical, industrial control, and other critical infrastructure systems. However, the current stable of solutions available to perform device authentication are not suitable for deployment on many operational technology networks due to the power and processing limitations of legacy and state-of-the-art Internet-of-Things devices. In this paper, we propose a machine learning method for passively and non-invasively authenticating or fingerprinting Ethernet devices, at the physical layer (PHY), using their transmitted signals. This technique exploits the unique, intrinsic physical features of a device that are created by the operational characteristics of its discrete physical components. These characteristics are imprinted on a device’s communication signal, can be externally monitored to authenticate/authorize registered devices, and can quickly detect the introduction of unknown and/or unauthorized devices. We assess the performance of our proposed technique based on the discrimination power in a device classification scenario.
A Comparative Characteristics of Discrete Frequency Transform Variants

This study proposes mathematical models for forward and backward data sampling during block discrete transforms. A rectangular window with different processing directions is used as a shaping block. A software tool is developed to experimentally investigate the influence of discrete transform oriented scheme parameters, such as the type and variant of transform, block size, and fixed rotation angles (multiples of 90°), on the results of block discrete transform. The simulation results indicate several advantages of the proposed approach, which are presented graphically. The proposed models can significantly reduce the time required for object search in images represented in the frequency range, with comparable computational cost.
A Multispectral Imaging Algorithm Conforming to the Visual Perception of Human Eyes

Multispectral fluorescence endoscopy requires combining near-infrared fluorescence images and white light images in the same field of view when performing intraoperative navigation. In this paper, we proposed a multispectral visualization algorithm. The method consists of automatic segmentation of the fluorescence region of the near-infrared image and image fusion of the fluorescence area with the white light image. The algorithm features firstly, the multi-step weighted Otsu method for image segmentation to achieve accurate segmentation of fluorescence regions. Secondly, the pseudo-color processing of the segmented areas with the help of CIEDE 2000 color difference standard to transform the fluorescence intensity difference into the color difference in pseudo-color images, while preserving the consistency of their subjective visual perception. Compared with existing algorithms, the proposed algorithm has higher accuracy in segmentation, while the final fused image is more consistent with the visual properties of the human eye.
The Network Output Background Subtraction (NOBS) Algorithm for Unattended Luggage Detection

In recent years, a number of algorithms for unattended luggage detection have been proposed. They use both classical computer vision and neural network approaches. Classical methods do not work well on crowded scenes and when long time tracking is required. Neural networks face the fact that unattended objects are very diverse, that requires large representative training dataset and heavy network architecture. In this paper, we propose the Network Output Background Subtraction (NOBS): a real-time algorithm based on the use of a lightweight neural network trained on general purpose dataset with no use of any specialized dataset of abandoned luggage. It allows you to use the advantages of the neural network approach (the ability to track objects more accurately and for a long time) and partially get rid of its disadvantages: the need for an extensive training data set that reflects the large variability of detected objects, and high resource requirements.
Piecewise Constant Signal Nonlinear Filtering based on Local Center / Surround Shrinkage

The article is devoted to the synthesis of a new nonlinear filtration methods for a piecewise constant signals distorted by Poisson noise. The proposed approach to filtering assumes the principles and concepts of statistical learning, understood as learning based on input data. (cf. adaptive filtering). The synthesis is focused on a special representation of signals by samples of counts of controlled size (sampling representation). Based on the specifics of such representation, a statistical model of the signal is concretized to a parametric probabilistic model in the form of a system of receptive fields. This model allows for a very simple procedure for estimating the count probability density, which in turn is an approximation of the underling piecewise constant signal. The resulting procedure for restoring piecewise constant signals turned out to be very similar to the algorithms of wavelet-thresholding (regression shrinkage) that are actively developing today.
2D/3D ResNet Deep Neural Network for 4G and 5G NR Wireless Channel Estimation

For estimating the MIMO channels for both the uplink and the downlink, we used residual deep neural network. The proposed residual deep neural network (ResNET) channel estimation outperforms the MMSE channel estimation by more than 2 dB under EPA uplink channels and is capable of operating on untrained wireless channels. According to the 3GPP NR release 15 standard, the proposed 3D ResNET channel estimation approach exhibits a 0.5 to 1 dB gap to the ideal channel estimation and a 1 to 1.6 dB gain over MMSE channel estimation for 5G New Radio (NR) physical layer links, including demodulation reference symbols (DM-RS Type 2) and synchronization signal (SS) bursts periodicity (30 ms), DL-SCH transport channel coding modulation and coding scheme 13 (MCS), code rate 490 over 1024 under 15 iterations of Normalize Min-sum 5G eMBB LDPC decoder with scale 0.75, HARQ off (no retransmissions), PDSCH precoding using SVD (PDSCH Mapping Type A, 4×4 antennas), 20 resource blocks (240 subcariers), clustered delay line (CDL-C) channel with subcarier spacing 30kHZ, delay spread 300e-9, doppler shift 5Hz, central frequency 3.5GHz demonstrates a 0.5 to 1 dB gap to the perfect channel estimate and a gain of between 1 and 1.6 dB over MMSE channel estimation. The suggested solution enables a 25% boost in 5G NR throughput.
Combining Computer Vision and Word Processing to Classify Film Genres

The paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie Database, IMDB (https://www.imdb.com/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. The paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric.
DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers

3D object detection plays a critical role in autonomous driving perception. Although multi-view-based perception solutions have made significant progress, their performance is still far from being ready for practical use. The estimation of pixel depth is dependent on camera intrinsic properties, which led us to explore a depth-aware model guided by camera parameters. Our contribution in this paper is the DAFormer, which incorporates camera parameters and position-aware image features to detect 3D objects. The depthaware module uses camera parameters to reweight image features and estimate depth, enhancing and offsetting the 3D position embedding. The object query uses depth information enhanced features for end-to-end 3D detection by Attention layers. DAFormer achieves impressive results on the standard nuScenes dataset without any additional embellishments.
Emotion Prediction in Conversation Based on Relationship Extraction

With the development of human-computer interaction systems, emotion recognition in conversation (ERC) has attracted increasing interest in recent years. Motivated by recent studies which have proven that generating emotional conversation responses can effectively improve the performance of the ERC model. However, accurate emotional response is complicated due to the limited number of dialogue samples. Therefore, we propose a simple and effective framework for emotion prediction in conversation based on relationship extraction (DiaRP), consisting of two curricula: (1) Dialogue Relationship Capture (DRC); and (2) Next Emotion Prediction (NEP). In DRC, we capture the current emotional self-dependence and interpersonal dependence according to the influence of self and others on the current moment emotion in the conversation. We integrate self-dependence and interpersonal dependence for NEP to predict their emotional state without current utterance. We also measure the similarity between recognized emotion distribution and predicted emotion distribution by the KL divergence. With the proposed model-agnostic DiaRP strategy, we observe a significant performance improvement over a wide range of existing ERC models and achieve new state-of-the-art results on three public ERC datasets.
An active pursuit strategy for autonomous mobile robots based on deep reinforcement learning

It is a great challenging but solvable task in an actual robot confrontation: how to effectively pursue the target robot. In this paper, a maximized entropy-based deep reinforcement learning mobile robot active pursuit strategy (APS) approach is proposed for target tracking challenges, then our strategy approach will be implemented on a physical robot. The use of maximizing entropy reinforcement learning allows the agent to have diverse strategies to adapt to dynamic environment changes. We combine learning-based algorithms with rule-based algorithm that allow agent to avoid being targeted by human players to the greatest extent possible in confrontations. We conducted experiments by transferring the learned pursuit strategies to a physical world mobile robot and analyzed the feasibility of application in ICRA RoboMaster AI Challenge (RMUA) environment. The experimental results demonstrate the feasibility and potential of the algorithm in learning multiple tracking strategies.
Improving Transferability of Adversarial Point Clouds with Model Commonalities

Although deep learning of 3D point clouds has made significant progress, the robustness of 3D models has not been fully investigated. Existing 3D attack methods show satisfactory performance under the white-box setting but frequently suffer from a low transferability to attack black-box models. A key point to improve the transferability of attacks is to find the commonalities of multiple models. To this end, a novel attack method is proposed by only leveraging perturbations in local geometric feature areas termed FAP. Specifically, local perturbations are pertinently added by calculating the curvature to sample more points in the feature areas that 3D models potentially focus on. Moreover, to address the drawback of poor isometric robustness common to 3D models, we propose a generalized framework for diversifying input samples by adding random isometric transformations (DIT) in the attack process, which can be combined with other attack methods to improve their transferability. Evaluated on a variety of typical 3D models, the proposed attack method outperforms the current gradient-based methods in transferable black-box attacks, and the effectiveness of the proposed framework is demonstrated by extending it to other attack methods.
Path-Following Guidance for Powered Wheelchair Users Using Mixed-Reality Technology with Shared Control Policy

Nowadays, the number of mobility-disabled individuals who rely on a powered wheelchair to move is increasing. They need to be guided to follow a desired route when they are learning sports skills like wheelchair dance. Some systems using mixed-reality technologies to teach them to move around space are proposed. However, these methods still face difficulties in keeping users inside of a designated path. In this paper, we propose a method for teaching powered wheelchair users to follow a specific path by utilizing mixed-reality technology. In this method, we teach users how to move along a desired path by providing visual guidance cues via a head-mounted device or haptic guidance cues via a wearable haptic interface. Moreover, we employ a shared control policy to help users keep inside of the path. We conducted an experiment to evaluate the efficacy of the proposed method and to compare results for the two mixed-reality guidance modalities (visual feedback via a head-mounted device and haptic feedback via a haptic interface). The results reveal that the proposed guidance method with the shared control policy could help users follow a desired path with less deviation. We also discovered that users of the head-mounted device have similar results compared to those of the haptic interface.