Reviewerly/exHarmony huggingface dataset:
A Simplified Traffic Flow Monitoring System Using Computer Vision Techniques

Millions of Muslims from all over the world visit the holy city of Makkah to perform Umrah throughout the year, but especially in the month of Ramadan the quantity of pilgrims increases significantly. Same conditions can be observed in the month of Dhu al-Hijjah during the Hajj season. To transfer the pilgrims from one place to another various many vehicles are used. It is very important to monitor the traffic flow during these days to avoid any congestion which may lead to any mishap. Therefore, there is a need for a traffic flow monitoring system that can be used to count vehicles, and classify them according to their size to facilitate traffic management. A computer vision based system is proposed in this research that uses MATLAB vision tool to identify and count the vehicles. The novelty of the system is its simplicity that uses a camera as a video source, then suitable algorithms are implemented on it for processing. A portion of a video is used for system training that first obtains a binary foreground from the initial frame, then removes noise and then counts the vehicles present in the frame. The functionality of the proposed system has been verified by a number of experiments with the accuracy of 90&#x0025;. It can be easily implemented in various environments for vehicles counting that can be used for better traffic management.
Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation.&#x0D; Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.
Conditional independence in max-linear Bayesian networks

Motivated by extreme value theory, max-linear Bayesian networks have been recently introduced and studied as an alternative to linear structural equation models. However, for max-linear systems the classical independence results for Bayesian networks are far from exhausting valid conditional independence statements. We use tropical linear algebra to derive a compact representation of the conditional distribution given a partial observation, and exploit this to obtain a complete description of all conditional independence relations. In the context-specific case, where conditional independence is queried relative to a specific value of the conditioning variables, we introduce the notion of a source DAG to disclose the valid conditional independence relations. In the context-free case, we characterize conditional independence through a modified separation concept, ∗-separation, combined with a tropical eigenvalue condition. We also introduce the notion of an impact graph, which describes how extreme events spread deterministically through the network and we give a complete characterization of such impact graphs. Our analysis opens up several interesting questions concerning conditional independence and tropical geometry.
GP3D: 3D NAND Based In-Memory Graph Processing Accelerator

In this work, we proposed GP3D: 3D NAND based processing-in-memory (PIM) accelerator for highly parallel and energy efficient graph processing. Although graph processing algorithms give informative and useful features from the real world graphs, innate randomness of the graph data pattern incurs inefficient processing in conventional computing architectures. Some previous works on nonvolatile memory (NVM) based PIM architecture have been proposed with vector-matrix multiplication (VMM) in memory array, but the limited memory capacity of the architecture causes frequent write operations on memory cells. Our GP3D architecture is based on the standard 3D NAND array structure without any structural changes which can utilize the ultra-high density of 3D NAND to store the large scale graphs in a single die. VMM operations on 3D NAND array enables efficient processing of PageRank algorithm, one of the representative algorithms of graph processing. To process the graphs with compressed format without conversion to the dense format, we exploited ternary content addressable memory (TCAM) function on 3D NAND. GP3D can eliminate the data loading burdens from storage device to memory which gives significant enhancement of the latency and energy efficiency. Evaluated by custom designed graph processing simulator, GP3D showed 30–61 times boosted speed and 18–64 times better energy efficiency over the GPU based system for various graph datasets.
Deep learning neural networks for emotion classification from text: enhanced leaky rectified linear unit activation and weighted loss

Accurate emotion classification for online reviews is vital for business organizations to gain deeper insights into markets. Although deep learning has been successfully implemented in this area, accuracy and processing time are still major problems preventing it from reaching its full potential. This paper proposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss (ELReLUWL) algorithm for enhanced text emotion classification and faster parameter convergence speed. This algorithm includes the definition of the inflection point and the slope for inputs on the left side of the inflection point to avoid gradient saturation. It also considers the weight of samples belonging to each class to compensate for the influence of data imbalance. Convolutional Neural Network (CNN) combined with the proposed algorithm to increase the classification accuracy and decrease the processing time by eliminating the gradient saturation problem and minimizing the negative effect of data imbalance, demonstrated on a binary sentiment problem. All work was carried out using supervised deep learning. The results for accuracy and processing time are obtained by using different datasets and different review types. The results show that the proposed solution achieves better classification performance in different data scenarios and different review types. The proposed model takes less convergence time to achieve model optimization with seven epochs against the current convergence time of 11.5 epochs on average. The proposed solution improves accuracy and reduces the processing time of text emotion classification. The solution provides an average class accuracy of 96.63% against a current average accuracy of 91.56%. It also provides a processing time of 23.3 milliseconds compared to the current average processing time of 33.2 milliseconds. Finally, this study solves the issues of gradient saturation and data imbalance. It enhances overall average class accuracy and decreases processing time.
Approximating an Artificial General Intelligence or a General Collective Intelligence

Human-Centric Functional Modeling (HCFM) has recently been used to define a model of Artificial General Intelligence (AGI) believed to have the capacity for human-like general problem-solving ability (intelligence), as well as a model of General Collective Intelligence (GCI) with the potential to combine individuals into a single collective intelligence that might have exponentially greater general problem-solving ability than any individual in the group. Functional modeling decouples the components of complex systems like cognition through well-defined interfaces so that they can be implemented separately, thereby breaking down the complex problem of implementing such a system into a number of much simpler problems. This paper explores how a rudimentary AGI and a rudimentary GCI might be implemented through approximating the functions of each, in order to create systems that provide sufficient value to incentivize more sophisticated implementations to be developed over time.
CRYPTOCURRENCY AS AN INSTRUMENT OF TERRORIST FINANCING

The purpose of the article is to explain the use of cryptocurrency as a terrorist financing tool. This article has emphasized the ways, which terrorists appraise for being a reliable fundraising means and their adoption. Methodology. For the purposes of the study, the methods of scientific abstraction, synthesis, observation, generalization, as well as the method of induction of literature and legal documents were used to determine the features of bitcoin, promoting and preventing its use for terrorism financing. Results. The development of the Internet and electronic devices has radically changed all spheres of human life, including criminal activity. Digitalization has led to the improvement of ordinary crimes and the emergence of new types of crime, which, in principle, cannot exist without special digital electronic devices. Among the first implementers of new technologies were terrorists, who took advantage of digitalization to increase profitability. Thus, terrorists have now significantly increased their attention to cryptocurrency as a digital means of payment, namely Bitcoin. Bitcoin has a number of features that have attracted the attention of criminals as a way to evade responsibility for a crime. In particular, decentralization avoids the need for confirmation by a central authority, and pseudo-anonymity provides a certain level of anonymity. In addition, terrorists are aware that Bitcoin's confidentiality is extremely fragile and needs to be enhanced. The paper analyzes several ways to enhance anonymity, such as software that anonymizes traffic and prevents IP identification, peer-to-peer mixers, centralized mixing services (tumbler), and other approaches. It is worth emphasizing that for the fight against crime, the main issue is the de-anonymization of the Bitcoin owner/user, which allows the identification of the criminal. Currently, law enforcement agencies use direct and indirect de-anonymization, proliferation analysis, quantitative analysis, time analysis, and transactional network analysis, among others, to achieve the above goal, which are discussed in detail in this article. In addition, agencies around the world investigated and uncovered terrorist groups and their financial facilitators. Specifically, on August 13, 2020, the U.S. Department of Justice's Office of Public Affairs announced "the largest cryptocurrency seizure in the context of terrorism in history." To investigate the agenda, the legal documents of those investigations that contain information about the terrorist fund-raising mechanism were examined and analyzed. The legal documents revealed that these investigations used the aforementioned de-anonymization approaches.
Nearly optimal time-independent reversal of a spin chain

We propose a time-independent Hamiltonian protocol for the reversal of qubit ordering in a chain of N spins. Our protocol has an easily implementable nearest-neighbor, transverse-field Ising model Hamiltonian with time-independent, nonuniform couplings. Under appropriate normalization, we implement this state reversal three times faster than a naive approach using swap gates, in time comparable to a protocol of Raussendorf [Phys. Rev. A 72, 052301 (2005)] that requires dynamical control. We also prove lower bounds on state reversal by using results on the entanglement capacity of Hamiltonians and show that we are within a factor 1.502(1+1/N) of the shortest time possible. Our lower bound holds for all nearest-neighbor qubit protocols with arbitrary finite ancilla spaces and local operations and classical communication. We give numerical evidence that the fast reversal protocols are more robust to noise than a swap-based reversal. Finally, we extend our protocol to an infinite family of nearest-neighbor, time-independent Hamiltonian protocols for state reversal. This includes chains with nearly uniform coupling that may be especially feasible for experimental implementation.Received 5 April 2020Accepted 20 September 2021DOI:https://doi.org/10.1103/PhysRevResearch.4.L012023Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement entropyMajorana fermionsQuantum controlQuantum entanglementQuantum information architectures & platformsQuantum protocolsQuantum teleportationTechniquesSpin chainsQuantum InformationAtomic, Molecular & OpticalNetworks
Design of Waste Management System Using Ensemble Neural Networks

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.
Two-stage system using item features for next-item recommendation

The next-item recommendation task aims to predict the item that a user is likely to interact with next, given the previous transactions of the user. In this paper, we explore the use of discrete item features to improve the performance of a next-item recommendation system. We design a hybrid embedding method based on user transactions and item features to represent items that can capture item-item co-occurrence as well as item features. We propose a two-stage model for the task of next-item recommendation. In the first stage, features of the next item are predicted, based on which candidate items are generated. In the second stage, the candidate items are ranked, and the top item is recommended to the users. Our model is capable of recommending new items since the candidate items are selected based on item features. We evaluate our model on three different datasets, including two public datasets and show that our model achieves significant improvement compared with several state-of-the-art next-item recommendation models.
Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Eye tracking is currently a research hotspot in the territory of service robotics. There is an urgent need for machine vision technique in the territory of video surveillance, and biological visual object following is one of the important basic research problems. By tracking the object of interest and recording the tracking trajectory, we can extract a structure from a video. It can also analyze the abnormal behavior of groups or individuals in the video or assist the public security organs in inquiring and searching for evidence of criminal suspects, etc. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following in visual surveillance is easily affected by factors such as occlusion, rapid object movement, and appearance changes, and it is difficult to solve these problems effectively with single-layer features. This paper adopts a visual object following algorithm based on visual information features and few-shot learning, which effectively improves the accuracy and robustness of tracking.
Supporting farming smart documentation system by modular blockchain solutions

For more than a decade, various farm-specific models have been developed by collaborating and integrating sensing technologies as a step toward successful data-farm documentation and effective decision-making. However, the stored and gathered data continues to rely on cloud infrastructure or centralized platform control, which is particularly vulnerable to threats such as data tampering, data distortion, confidentiality, and manipulation, which caused the farm product data difficult to trace to its provenance. In this paper, we propose a farm transaction model by demonstrating a flow of farm transaction simulation implicated by MBC sensing instrument with an array of sensors, controllers, networking hardware, computing equipment, and internal memory functions to enhance data integrity and security farm object. Based on the proposed model, a proof-of-concept experimental system called Encapsulating Block Mesh (EBM) integrates blockchain technology with the specific application case of cocoa production has been implemented. Results have shown that farm objects represented by MBC take turn recording information on the process of generating, transacting, and consuming a farm product and encrypting it into a block was validated and linked in the EBM with the hash of transaction data that connected to each cocoa farm object in a simulation environment.
Splitting an Arbitrary Three-Qubit State via a Five-Qubit Cluster State and a Bell State

Quantum information splitting (QIS) provides an idea for transmitting the quantum state through a classical channel and a preshared quantum entanglement resource. This paper presents a new scheme for QIS based on a five-qubit cluster state and a Bell state. In this scheme, the sender transmits the unknown three-qubit secret state to two agents by the quantum channel with the Bell basis measurement three times and broadcasts the measurement results to the agents through the classical channel. The agent who restores the secret state can successfully recover the initial information to be transmitted through the appropriate unitary operation with the help of the other party. Firstly, our scheme's process can be accurately realized by performing the applicable Bell basis measurement, single-qubit measurement, and local unitary operation instead of a multiparticle joint measurement. The splitting process of quantum information is realized through a convenient operation. Secondly, compared with some previous schemes, the efficiency of the total scheme has been improved in principle, and the qubit consumption is reduced. Finally, the security of the quantum information splitting scheme is analyzed from the perspectives of external attacks and participant attacks. It is proved that our scheme can effectively resist internal participant attacks and external eavesdropper attacks.
A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner-MSDU-is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.
Nanoscale isotopic evidence resolves origins of giant Carlin-type ore deposits

Abstract The western North American Great Basin's Carlin-type deposits represent the largest accumulation of gold in the Northern Hemisphere. The controversy over their origins echoes the debate between Neptunists and Plutonists at the birth of modern geology: were the causative processes meteoric or magmatic? Sulfur isotopes have long been considered key to decoding metal cycling in the Earth's crust, but previous studies of Carlin-type pyrite lacked the spatial resolution to quantify differences among the numerous generations of sulfide mineralization. We developed a new dual-method, nanoscale approach to examine the fine-grained ore pyrite. The δ34S of the ore pyrite varies systematically with Au concentration at the nanoscale, indicating that both magmatic and meteoric fluids contributed during mineralization, but the magmas brought the gold. Repeated oscillations in fluid ratios upgraded the metal content, resulting in high gold endowment. Our results demonstrate that high-spatial-resolution studies are key to elucidate the spatiotemporal evolution of complex hydrothermal systems.
An IoT Device-trusted Remote Attestation Framework

Remote attestation allows the verifier to determine the level of trust for the system’s health. We propose a framework for verifying the trustworthiness of the IoT device using remote attestation. The framework provides remote device trust verification and a trust management service for a large group of devices to prevent remote control and zombization due to firmware spoofing. We show how the framework performs remote attestation for object forgery attacks under various types of heterogeneous TEE (SW) and TPM (HW) IoT platforms.