Reviewerly/exHarmony huggingface dataset:
Recognition and Recall of Chocolate Brands Based on Select Repetitive and Varied Sets of Commercials Among Teenagers

As we live in a world with an array of choices, advertisers are forced to create effective and innovative advertisements or television commercials to make a brand or product stand out from its competitors. For this purpose, advertisers use repetitive and varied sets of commercials to position the brand on the minds of the people. Teenagers are seen as early adopters, as the most significant segment of consumers who can be influenced easily and as the ones who can persuade their family to purchase something in order to be noticeable and to discover new trends. Hence, this research paper attempts to study the effect of both repetitive and varied sets of commercials on teenagers recognizing and recalling ability according to Piaget’s theory of cognitive development. ‘Age’ was chosen as the chief independent variable, to study the difference that exists in the teenagers recognizing and recalling ability. A comparative experimental study was conducted among teenagers between the age groups 11 and 19 years. The study confirms that varied sets of commercials influence teenagers better and results in higher recognition and recall. Nestle Bar-One being a less familiar brand was recognized and recalled better because of the varied commercials that were screened. Varied sets of commercials on the same brand will get the attention of the TV viewing public as the commercials convey different messages, benefits and focus on diverse concepts to make it effective. It proves to hold better attention and impact as more positive ranks were oriented towards the group of teenagers who were exposed to varied sets of commercials.
Shaping Attacker Behavior: Evaluation of an Enhanced Cyber Maneuver Framework

Recent cyberattacks highlight the limitations of perimeter-based defenses and the need for a multi-layered approach to cybersecurity. Modern cybersecurity best practices include elevated internal sensors and controls; however, these are reactive defensive measures. Cyber defenders need layered defenses to protect systems when perimeters are breached. Cyber maneuvers are a paradigm shift in cyber defense that incorporates proactive cyber actions that aim to achieve positional or temporal advantages over an adversary in the cognitive, technical, and physical domains. Cyber defenders can implement maneuvers by taking actions on the network to create situations where attackers become visible, detectable, and must reshape their planned attack steps impeding progress and mission success. Current cyber-defense frameworks primarily focus on the technical actions and response, with limited attention on adversary behavioral and cognitive effects. We discuss current frameworks and present an alternative enhanced cyber maneuver framework that integrates cognitive processing mechanisms through which an adversary's behavioral response can be shaped. Our enhanced framework provides a foundation with specific cyber maneuvers and actions mapped through the explanatory influence mechanisms and cognitive effects to shape the desired behavioral outcomes. We demonstrate the effect of proposed maneuvers on attacker behavior to validate the framework and mappings and the methodology for testing. In so doing, we define a cyber-maneuver framework that transcends cyber deception and Moving Target Defense and propose a methodology for cyber maneuver design and evaluation. Cyber maneuvers and the cognitive and behavioral responses they are designed to shape are described. We introduce and describe a preliminary study of an evaluation methodology that enables prediction and quantification of cyber maneuver effects.
A Probabilistic Approach for Spatio-Temporal Phase Unwrapping in Multi-Frequency Phase-Shift Coding

Multi-frequency techniques with temporally encoded pattern sequences are used in phase-measuring methods of 3D optical metrology to suppress phase noise but lead to ambiguities that can only be resolved by phase unwrapping. However, classical phase unwrapping methods do not use all the information to unwrap all measurements simultaneously and do not consider the periodicity of the phase, which can lead to errors. We present an approach that optimally reconstructs the phase on a pixel-by-pixel basis using a probabilistic modeling approach. The individual phase measurements are modeled using circular probability densities. Maximizing the compound density of all measurements yields the optimal decoding. Since the entire information of all phase measurements is simultaneously used and the wrapping of the phases is implicitly compensated, the reliability can be greatly increased. In addition, a spatio-temporal phase unwrapping is introduced by a probabilistic modeling of the local pixel neighborhoods. This leads to even higher robustness against noise than the conventional methods and thus to better measurement results.
A New Transparent File Encryption Method Based on SM4 for Android Platform

With the rapid development and wide using smart phones, the protection of privacy data in mobile phones is becoming more and more important. For the data security problem of Android platform, a new transparent encryption method based on SM4 with file filter driver is designed and implemented, according to the technology of file transparent encryption and decryption system based on hook transparent encryption technology and file filtering driven transparent encryption technology used on windows platform. The transparent encryption system is different from the traditional APP development method of Android system. By intercepting the system call function and using the secret-key converted from the host MAC address, the encryption and decryption SM4 algorithm is written into the kernel, which fundamentally guarantees the security of user information. At the same time, the user's security experience is improved by putting authentication on the screen unlocking. The system design and implementation are described in this paper from system requirement analysis to overall design and detailed design of each module. Android application development technology and cross-compiling principle are used in the coding process. The system test results show that the system can effectively transparently encrypt files and protect the privacy of mobile files.
Towards a Better Characterization of Career Paths: Sequential Job Embedding and Mixture Markov Models

Extracting typical career paths from large-scale and unstructured talent proﬁles has recently attracted increasing research attention. Such characterization of career path can support various applications such as career planning and talent management. However, various challenges arise in eﬀectively analyzing self-reported career records. Inspired by recent advances in neural networks and embedding models, we develop a novel career path clustering approach with two major components. First, we formulate an embedded Markov framework to learn job embeddings from longitudinal career records and further use them to compute dynamic embeddings of career paths. Second, to cope with heterogeneous career path clusters, we estimate a mixture of Markov models to optimize cluster-wise job embeddings with a prior embedded space shared by multiple clusters. We conduct extensive experiments with our framework to investigate its algorithmic performance and extract meaningful clustering patterns of career paths in the information technology (IT) industry. The results show that our approach can naturally discover distinct sequential path clusters and reveal valuable insights for career path characterization.
Research on Risk Assessment of Blockchain-Driven Supply Chain Finance: A Systematic Review

Recently, blockchain-driven supply chain finance have been used in the scope of risk management to solve the financing problem of small and medium-sized enterprises. Among them, risk assessment is the core part of risk management. Therefore, this paper aims to review and analyze articles associated with risk assessment of BC-driven SCF. This research also aimed to provide the best practices and identify the academic challenges, motivations and recommendations related with the research. In addition, a methodological approach followed in previous research in this domain was discussed to give some insights for future comers with what to expect. Therefore, articles related to risk assessment of BC-driven SCF were searched systematically. The search was conducted on five major databases, namely, ScienceDirect, Scopus, Web of Science, Springer, Emerald from 2016 to 2022. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected on the basis of our inclusion and exclusion criteria (n=90). All these articles form a coherent taxonomy, describing the corresponding current views in the literature according to three main categories. Risk assessment of BC-driven SCF is considered a major topic which warrants attention. This study emphasizes the current research positions and opportunities in this field and promotes the understanding of this research field.
The Impact of TRAIN Law on Small and Medium Enterprises (SMEs)

The Republic Act No. 10963 or the Tax Reform for Acceleration and Inclusion (TRAIN) Law is the first package of the Comprehensive Tax Reform Program (CTRP) created by the Duterte Administration to address the deficiencies in the taxation framework. This study assessed the effect of TRAIN Law as viewed by the various SME owners and operators in the local economy of Naga City, Camarines Sur. This study used a descriptive method of research using a research-made questionnaire. The respondents are comprised of business owners, managers, and entrepreneurs. The study was conducted in the Central Business District of Naga City, Camarines Sur that were surveyed using a random sampling method. Frequency, simple percentage, and multiple regression were used to treat the gathered data. The study revealed that the impacts of TRAIN Law are evident in the rising of the product pricing due to the operation costs.
Biologically Inspired Hexagonal Image Structure for Computer Vision

Representation of digital images in hexagonal grid has been under investigation for past 40 years. The common digital image representation makes use of rectangular grid of square-structured pixel elements known as pixels. Hence, normally, rectangular grid for displaying and processing of images is used. Advanced processing power of modern graphic devices and innovative advancements incorporated in charge-coupled device (CCD) technology have made hexagonal sampling lattice a more interesting area for research. The hexagonal grid is superior to square structure due to high packing density, equidistant pixels, higher symmetry, less aliasing, good angular resolution and consistent connectivity. In addition to these benefits, an interesting inspiration behind using a hexagonal sampled lattice image is inspired from the human visual perception as this structure closely resembles human eye. Also, hexagon structure encompasses more area than any other closed planar geometry of identical perimeter rather than a circle. Hence, this structure will have high sampling density. Since there are no full-fledged hardware devices for capturing and displaying hexagonal image structure, image conversions have to be done as the preliminary step before proceeding with hexagonal image processing. This research review will give an overview of three different methods for hexagonal image sampling and various hexagonal software simulation schemes simulated images. Finally, to show the computational efficiency, run times of different methods are taken for different sized images.
A Detailed Schematic Study on AI in managing Hypertension: A Position Paper

Hypertension remains one of the reason of mortality in the last hundred years. Despite a lot of research and new pervasive medicines, still it is common to see the fatality caused by hypertension. With the evolution of Artificial Intelligence and Machine learning and its significant use in the last decade showed promising results in many fields including Medicine and health care domain and a lot of challenges has to be overcome. Availability of public Dataset has helped researchers a lot in exploration and implementation. AI has impacted everyone’s life. AI has still a long a way to cover in the field of Health care domain. In this review we provide practical approaches on Artificial intelligence as applied to hypertension. We will focus on the roadblocks and the proposed solutions for the treatment of Hypertensions. We need a robust system comprising of both scientific as well as clinical expertise followed by extensive validations to have a sustainable solution to Hypertension using Artificial Intelligence.
Convergence Analysis of ABC Algorithm Based on Difference Model

Through the theoretical analysis of the stability of the Artificial Bee Colony optimization algorithm, appropriate parameters can be selected for the algorithm under different convergence conditions and different application conditions, and then the rapid convergence of the algorithm can be achieved, so the stability analysis of the algorithm is very worth exploring. This paper firstly introduces the Artificial Bee Colony optimization algorithm (ABC), and then differentiates the motion trajectory equations of employed bees in the ABC algorithm to obtain a differential model; then uses the discrete system stability theory to analyze the stability of the system, and obtains the relationship between the value range of the parameters in the cross-mutation stage and the stability of the algorithm. Finally, the correctness of the conclusion will be verified by experiments using the test function.
Drilled and Blasted Rock Particles Originating from Tunnelling Show Distinct Elongated Morphology Compared to Naturally Eroded Particles, Independent of Geochemical Characteristics

Rock particles from drilling and blasting during tunnel construction (DB particles) are assumed to exhibit morphologies i) dependent on mineral composition and different from naturally eroded particles (NE particles) and ii) to cause greater mechanical abrasion to biota than NE particles due to difference in morphology. The objectives in the current study were to investigate the morphological differences between DB and NE particles and the influence of mineral and element content on DB particles. Particle geochemistry were characterized by inductively coupled plasma mass spectrometry (ICP-MS), micro X-ray fluorescence (µXRF), environmental scanning electron microscope interfaced with energy dispersive X-ray (ESEM-EDX), and X-ray diffraction (XRD). Size and morphology were determined by coulter counter, microscopy (stereo microscope and ESEM; static image analysis-SIA), and dynamic image analysis (DIA). DB particles (61-91% <63µm) collected from five different tunnel construction locations in Norway were more elongated (diff 8-15%) and slightly more fibrous (diff 3%) than NE particles from river water and sediments, although their angularity was similar (diff 0.3-0.8%). Despite distinct mineral and element characteristics between tunnel construction locations, DB morphology was not explained by geochemical content. This suggests that particle formation mechanisms during drilling and blasting are more influential of morphology than mineralogy.
Adversarial Attacks

Deep learning has achieved excellent performance in various multimedia and computer-vision tasks. However, its vulnerabilities against adversarial attacks have become a significant concern. During the attacks, a crafted perturbation is applied to images or speech files so that a target deep neural network (DNN)-based system outputs incorrect results. Adversarial examples include perturbed inputs such as images, videos, and speeches that are created by optimizing the input to maximize the prediction error with minimum changes so that the attacker fools the DNN-based system. In this chapter, we review adversarial attack techniques.
Autoencoder-Based Speech Features for Manipuri Dialect Identification

A dialect identification system for Manipuri using deep-stacked autoencoder-based speech features has been presented in this paper. For this work, Manipuri speech corpora have been used available at Linguistic Data Consortium for Indian Languages (LDC-IL). Mel-frequency cepstrum coefficients (MFCC) were extracted from input speech signals, and then autoencoder (AE) is used for dimensionality reduction. Support vector machines (SVM) and deep neural network (DNN) are used for classification of three major Manipuri dialects, i.e. Sekmai, Kakching and Imphal. Results have shown an average dialect identification performance in terms of accuracy of approximately 70% and 72.77% for SVM and DNN, respectively. The performance of both the classifiers has been compared with and without using autoencoder. The role of stacked autoencoder-based features is found to be significant in Manipuri dialect classification.
PhisherCop: Developing an NLP-Based Automated Tool for Phishing Detection

Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for a more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classifier (SGD) and Support Vector Classifier (SVC) which showed an average accuracy of 96%, performing better than six other popular classifiers including: Decision Tree, Logistics Regression, Random Forest, Gradient Boosting Classifier, K-Nearest Neighbors and Multinomial Naive Bayes. Our tool is significant in distinguishing between phishing and legitimate content both over emails and text messages.
Automated Crowd Size Estimation in Dense Crowd Images—Application in Detecting COVID-19 Guideline Violations

The objective of our work is to provide a solution through deep learning for automatic detection of the COVID-19 guideline violators related to the restrictions on the number of people that can gather at a particular place. Our work is a complete software-based model where the input is the videos from CCTV camera and output is the number of people present in a particular frame. The selection of the frame is based on the frequency of crowd movement in that area. We have also proposed a modification of the model proposed by Shami et. al. (IEEE Trans Circuits Syst Video Technol 29:2627–2636, 2019, [1]) for crowd size estimation that will reduce the time complexity of the existing model. In the proposed modification, we have incorporated the concept of Congested Scene Recognition Net (CSRNet) to study the highly dense images. ShanghaiTech dataset and videos collected from local crowded areas are used to demonstrate the efficacy of our model.
Analyzing the Impact of Machine Learning on Cancer Treatments

The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays, the health care industry has completely transformed by using modern technologies and their applications for medical services. Among those technologies, machine learning is one of them, which has gained attention by people that its new advanced technology would give accurate results by using modeling methods for prediction. As this is a branch of artificial intelligence, it employs various statics, probabilistic and optimistic tools. This is applied to medical services, especially which are based on proteomic and genomic measurements. The aim is to use the dataset of cancer treatment and predict the results of patients by using machine learning with its modeling methods for accurate results. Recently experts have even used this machine learning in cancer for prognosis and forecasting.
High Utility Itemset Mining Using Genetic Approach

Frequent Itemset Mining(FIM) aims to generate itemsets having their frequency of occurrence not lesser than minimum support specified by the user. FIM does not consider the itemset utility which is the it’s profit value. High-utility itemset mining(HUIM) mines high-utility itemsets(HUI) from data. HUIM is a combinatorial optimization. With HUIM algorithms, the time required to search increases exponentially with an increasing number of transactions and database items. To address this issue an efficient algorithm to mine HUIs is proposed. The proposed algorithm uses a compact form of chromosome encoding by eliminating the itemsets with low transactional utilities. The algorithm employs methodology of self mutation to reduce generation of unwanted chromosomes. Experimental results have shown that the proposed algorithm finds HUIs for a given threshold value. The proposed algorithm consumes less time as compared to another HUIM algorithm HUIM-IGA.
QDRL: QoS-Aware Deep Reinforcement Learning Approach for Tor's Circuit Scheduling

Tor is a popular anonymity network adopted by more than two million users to preserve their privacy. Tor was mainly developed as a low-latency network to support interactive web browsing and messaging applications. However, bandwidth acquisitive applications such as BitTorrent consume a considerable percentage of Tor traffic. This results in an unfair allocation of the available bandwidth and significant degradation in the Quality-of-service (QoS) delivered to users. This paper presents a QoS-aware deep reinforcement learning approach for Tor's circuit scheduling (QDRL). We propose a design that coalesces the two scheduling levels originally presented in Tor and addresses it as a single resource allocation problem. We use the QoS requirements of different applications to set the weight of active circuits passing through a relay. Furthermore, we propose a set of approaches to achieve the optimal trade-off between system fairness and efficiency. We designed and implemented a reinforcement-learning-based scheduling approach ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRLS</i> ), a convex-optimization-based scheduling approach ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CVX-OPT</i> ), and an average-rate-based proportionally fair heuristic ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AR-PF</i> ). We also compare the proposed approaches with basic heuristics and with the implemented scheduler in Tor. We show that our reinforcement-learning-based approach (TRLS) achieved the highest QoS-aware fairness level with a resilient performance to the changes in an environment with a dynamic nature, such as the Tor network.