Reviewerly/exHarmony huggingface dataset:
Internet of Things (IoT) and Blockchain Applications in Pharmaceutical Supply Chain Provenance to Achieve Traceability, Transparency, and Authenticity

A traceable, transparent, and authentic pharmaceutical supply chain (PSC) could provide a guarantee for drug safety, quality, and a better public health protection. A thorough review of the PSC and future research agenda are necessary to understand the IoT and blockchain applications in PSC provenance. A systematic review was conducted to study how IoT and blockchain could help to cope with the challenges faced by the current PSC. The descriptive and thematic analysis was conducted to reveal the research trends, chronological and geographical distribution of research, and themes. The main findings were focused on the challenges faced by the current PSC, opportunities for implementing IoT and blockchain in PSC, as well as the application of IoT and blockchain in pharmaceutical package and transportation processes. Additionally, the study summarizes the framework of IoT and blockchain integration in PSC.
Spatial Constraint Generation for Motion Planning in Dynamic Environments

This paper presents a novel method to generate spatial constraints for motion planning in dynamic environments. Motion planning methods for autonomous driving and mobile robots typically need to rely on the spatial constraints imposed by a map-based global planner to generate a collision-free trajectory. These methods may fail without an offline map or where the map is invalid due to dynamic changes in the environment such as road obstruction, construction, and traffic congestion. To address this problem, triangulation-based methods can be used to obtain a spatial constraint. However, the existing methods fall short when dealing with dynamic environments and may lead the motion planner to an unrecoverable state. In this paper, we propose a new method to generate a sequence of channels across different triangulation mesh topologies to serve as the spatial constraints. This can be applied to motion planning of autonomous vehicles or robots in cluttered, unstructured environments. The proposed method is evaluated and compared with other triangulation-based methods in synthetic and complex scenarios collected from a real-world autonomous driving dataset. We have shown that the proposed method results in a more stable, long-term plan with a higher task completion rate, faster arrival time, a higher rate of successful plans, and fewer collisions compared to existing methods.
Tree-based Self-adaptive Anomaly Detection by Human-Machine Interaction

Anomaly detectors are used to distinguish the difference between normal and abnormal data, which are usually implemented by evaluating and ranking anomaly scores of each instance. Static unsupervised anomaly detectors can be difficult to adjust anomaly score calculation for streaming data. In real scenarios, anomaly detection often needs to be regulated by human feedback, which benefits to adjust anomaly detectors. In this paper, we propose a human-machine interactive anomaly detection method, named ISPForest, which can be adaptively updated under the guidance of human feedback. In particular, the feedback will be used to adjust the anomaly score calculation and structure of the tree-based detector, ideally attaining more accurate anomaly scores in the future. Our main contribution is to improve the tree model that can be dynamically updated from perspectives of anomaly score calculation and the model’s structure. Our approach is instantiated for the powerful class of tree-based anomaly detectors, and we conduct experiments on a range of benchmark datasets. The results demonstrate that human expert feedback is helpful to improve the accuracy of anomaly detectors.
Fluid Collaborations. Digital Platforms to Support Creative Communities

Collaboration and relations systems have contributed to the diffusion of new places of the project by involving multidisciplinary creative teams, and local and global stakeholders’ networks. Nowadays, digital transformation and participatory design have spread the value of new relationships and synergies between disciplines making essential the use of tools that facilitate the collaboration to share communication codes and to create virtual places for interaction. The research identifies features and functionalities through the analysis of collaborative approaches and physical and digital tools used in the design process. The aim is to define a digital tool able to facilitate communication and collaboration in multidisciplinary teams and guide the different phases of the creative process.
Text vs. Graphs in Argument Analysis

The ability to understand, process and evaluate arguments made by others and ourselves is important in many personal and professional spheres, such as political debates. Analysis typically appears in written form, but a growing number of tools support analysis through diagram-based graphical representations. These UIs might support better argument analysis because arguments have non-linear structures that are difficult to convey through linear text. However, there is little empirical evidence on the advantages or mechanisms that might make graph UIs superior to traditional textual documents. We ran and analyzed a study with twenty participants who used text and graph editors to analyze political debates. Our findings demonstrate the tradeoffs between the two approaches and explain key mechanisms that support the analysis in both media.
Design, Prototype of Arduino and Android-Based Anthropometry System for Toddler’s Head and Upper Arm Circumference Measurement

Abstract Nutritional status monitoring is a very important in toddler, because in this period children are vulnerable to trauma, infection, or developmental disorders. Nutritional status is one of the health indicators in the successful achievement of MDGs No. 4 related to the reduction of child mortality, which is 45% caused by malnutrition. Head and upper arm circumference are an important anthropometry parameter on toddler growth. The monitoring process for toddler in Posyandu usually by writing and plotting the data in KMS (Kartu Menuju Sehat) manually. Therefore, efforts to modernize Posyandu to facilitate Posyandu officers in taking measurements, as well as become one of the database systems of nutritional status of Indonesian toddler. The purpose of this study was to develop prototype of anthropmometry using ultrasonic sensor HC-SR04 and Hall sensor UGN3503 based Arduino. The system will be integrated with the database through Android applications. The results of this study had an accuracy value of 97.72% for the testing of head circumference measuring instruments and 95.35% of the testing of upper arm circumference measuring instruments. Android applications created to serve to store and provide information about the growth and development and nutritional status of a toddler
Integrating Unmanned Aerial Vehicle and Deep Learning Algorithm for Pipeline Monitoring and Inspection in the Oil and Gas Sector

Organizations invest heavily during the installation of pipeline facilities which spans across extensive geographical linkage. Also, the issue of pipeline protection and safety must be taken into consideration to have a free flow of oil and gas at inbound and outbound locations. This has necessitated the need for routine inspection and monitoring of pipelines for structural integrity, continued safe operations, and also to monitor intruders/potential trespassers at such locations. This research is focused on the elucidation of a modular unmanned aerial vehicle (UAV) integrated with deep learning algorithms for monitoring and security response to obtain very useful information around the oil and gas facility. The deep learning model employed in this study had training, validation and testing accuracies of 88.3%, 87.5%, and 83.3% respectively. Also, training and validation losses of 0.3583 and 0.3649 were obtained. The suggested integrated UAV has a low maintenance requirement, high endurance, and is cost-effective. It has a superior advantage over the manned aerial vehicles (MAV) currently in use since safety is greatly improved, the cost is reduced, adequate information is obtained and communication is enhanced.
Decentralized Document Holder Using Blockchain

The past few years have seen a spate of cyber attacks targeting multinational companies, governments, institutions and even individuals' data. Most of these attacks target the vulnerabilities of the centralized system of data storage, emphasizing that a novel approach to data storage must be developed to tackle long-standing issues with centralized systems. Blockchain technology - a decentralized ledger based system provides a viable alternative to many of these issues. The objective of this paper was to develop and test a decentralized document storage system using a private Blockchain network, Ethereum coins and the InterPlanetary File System(IPFS). Interim results suggested that the proposed solution can be implemented at scale for certain use cases including confidential documents of both private and public entities.
Hypernyms-Based Topic Discovery Using LDA

Information Technologies have created many documents, which are the basis of information systems, capable of speeding up the process for which they were developed. These processes provide results trying to imitate human knowledge, but it is still essential to generate techniques that support each other to provide even more precise results. It is essential to incorporate techniques in the automatic extraction of topics to reflect results with greater coherence according to the topic in question. In this work, the behavior of the Latent Dirichlet Analysis (LDA) algorithm is studied by incorporating the hypernymy-type semantic relationship extracted from WordNet in order to improve the results obtained when applying LDA on a set of documents without the use of an external source of knowledge. The experimental results showed an improvement when incorporating hypernyms providing a 1.23 topic coherence for GoogleNews corpus.
Towards Using Clothes Style Transfer for Scenario-Aware Person Video Generation

Clothes style transfer for person video generation is a challenging task, due to drastic variations of intra-person appearance and video scenarios. To tackle this problem, most recent AdaIN-based architectures are proposed to extract clothes and scenario features for generation. However, these approaches suffer from being short of fine-grained details and are prone to distort the origin person. To further improve the generation performance, we propose a novel framework with disentangled multi-branch encoders and a shared decoder. Moreover, to pursue the strong video spatio-temporal consistency, an inner-frame discriminator is delicately designed with input being cross-frame difference. Besides, the proposed frame-work possesses the property of scenario adaptation. Extensive experiments on the TEDXPeople benchmark demonstrate the superiority of our method over state-of-the-art approaches in terms of image quality and video coherence.
Facial Image Denoising and Reconstruction

Facial Image denoising removes the white Gaussian noise present in an image, retaining the facial features. White noise reduces the quality of images. The higher the noise present, the more difficult it is for facial recognition software to recognize the person, resulting in incorrect predictions. In this project, we aim to remove this image degrading noise from using a shallow CNN architecture. While denoising works on retaining a noisy face's features, the reconstruction model reconstructs a face's missing features, given a subset of the central portion of the facial image. We use a Generative Adversarial Network (GAN) to generate these missing portions of the face, where the Generator is involved in a Zero-Sum game with the Discriminator: both trying to minimize their loss the expense of the other.
Building a Covert Timing Channel over VoIP via Packet Length

With the rapid development of the Mobile Internet, the application of instant messaging technology has become increasingly abundant, among which Voice over Internet Protocol (VoIP) has played an important role. However, in the VoIP-based instant messaging scene, there is a risk of illegal eavesdropping by a third party. For this problem, we consider building a covert timing channel (CTC) to hide user messages and improve the security of instant messaging. At present, the existing methods of building covert timing channels (CTCs) are mainly applied to traditional Ethernet, and there is a lack of relevant methods for mobile networks, especially VoIP networks. In order to improve the transmission capacity of the CTC and improve the transmission security of user information, the research of this article focus on the method of building covert timing channel over VoIP according to the characteristics of packet length. And a prototype is established based on Linphone, which is an open-source VoIP application. Through reality transmission tests, covert messages are delivered effectively, which proves that the CTC based on packet length are feasible.
ODE-Inspired Image Denoiser: An End-to-End Dynamical Denoising Network

Image denoising which aims to remove noise in the given images is one of the most challenging tasks in the low level computer vision field. Especially for the real image noise, its complex distribution is difficult to be simulated by a single mathematical model. In this paper, an end-to-end dynamical denoising network (DDNet) is proposed to resolve the denoising problem of the real image. Inspired by ordinary differential equation (ODE), cascaded OI-Blocks (ODE-Inspired Blocks) are designed to iteratively approximate the distribution of noise. Moreover, non-linear affine transformation based on the attention mechanism among channels is exploited to extract the channel dependencies. Compared with the state-of-the-art works, extensive experimental results show that DDNet achieves notable improvement on both objective and subjective evaluations.
Application and improvement of Knowledge Graph in Literature Management in the Field of Information Communication

In recent years, with the vigorous development of the field of information and communication, all kinds of resources in this field are gradually increasing, and the demand of relevant practitioners for proper management of resources in this field is also increasing day by day, which puts forward higher requirements for the effect of resources in the management system. In a variety of presentation methods, knowledge graph connects and integrates different knowledge information to build an information network connected with each other, which has attracted more and more attention for its intuitiveness and good relevance. This paper mainly introduces an implementation method and perfect means of knowledge graph in the field of information and communication literature management.
k-sums Clustering

In this paper, we revisit the decades-old clustering method k -means. The egg-chicken loop in traditional k -means has been replaced by a pure stochastic optimization procedure. The optimization is undertaken from the perspective of each individual sample. Different from existing incremental k -means, an individual sample is tentatively joined into a new cluster to evaluate its distance to the corresponding new centroid, in which the contribution from this sample is accounted. The sample is moved to this new cluster concretely only after we find the reallocation makes the sample closer to the new centroid than it is to the current one. Compared with traditional k -means and other variants, this new procedure allows the clustering to converge faster to a better local minimum. This fundamental modification over the k -means loop leads to the redefinition of a family of k -means variants, such as hierarchical k -means, and Sequential k -means. As an extension, a new target function that minimizes the summation of pairwise distances within clusters is presented. Under l2-norm, it could be solved under the same stochastic optimization procedure. The re-defined traditional k -means, hierarchical k -means, as well as Sequential k-means all show considerable performance improvement over their traditional counterparts under different settings and on various types of datasets.
Factors that has Effect on Early Movie Recommendation: A Systematic Literature Review

Movies have always been a great way of entertainment for decades. Almost every movie is created focusing on some specific genres such as drama, thriller, horror, romantic, science fiction, etc. Day by day we are having new genres and one movie can belong to multiple genres. Nowadays, the number of movies has increased at a great level and movies of different genres are preferred to different types of audiences. For this reason, the movie recommendation system does a great job of recommending movies of specific genres to corresponding audiences. Modern recommendation systems follow different approaches such as user similarities, genre-based, user interest, etc. In this paper we have reviewed the most recent novel approaches on this topic to make the movie recommendation system easy and more accurate. This review work covers around 9 different movie recommendation systems. Multiple works have been done on each system. But we have found that “User Interest” is the most popular movie recommendation system.
Validating Business Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

Validating an elicited business problem hindering a business goal is often more important than finding solutions. For example, validating the impact of a client’s account balance toward an unpaid loan would be critical as a bank can take some actions to mitigate the problem. However, business organizations face difficulties confirming whether some business events are against a business goal. Some challenges to validate a problem are discovering testable factors, preparing relevant data to validate, and analyzing relationships between the business events. This paper proposes a goal-oriented and Machine Learning(ML)-based framework, Gomphy, using a problem hypothesis for validating business problems. We present an ontology and a process, an entity modeling method for a problem hypothesis to find testable factors, a data preparation method to build an ML dataset, and an evaluation method to detect relationships among the business events and goals. To see the strength and weaknesses of our framework, we have validated banking events behind an unpaid loan in one bank as an empirical study. We feel that at least the proposed approach helps validate business events against a goal, providing some insights about the validated problem.
Ethics, Digital Rights Management, and Cyber Security

Cloud content hosting and redistribution is enabling convenient and easy access to online content thereby accelerating the adoption and penetration of internet in past two decades. The current Industry 4.0 revolution and adoption and acceleration efforts are leveraging cloud computing as a means to store, retrieve, and share data. This makes the internet a relatively vulnerable to content abuse and increase the demand of clear consent before data consumption and redistribution. The growth of cloud computing and management technologies is penetrating in the market, and digital rights management (DRM) practices are needed for better and ethically safe online space. This chapter talks about state-of-the-art DRM paradigms being proposed in the literature and critically discusses their technical performance, flexibility, and immutability challenges. This chapter will clarify internet governance implementation roadmap for Industry 4.0 revolution by critically analyzing the cloud technology stack and ethical features by advocating Cloud DRM.
Unsupervised Learning Approach for Evaluating the Impact of COVID-19 on Economic Growth in Indonesia

The spread of COVID-19 that occurred in several parts of Indonesia resulted in the economy getting worse. Almost all business fields in Indonesia experienced a contraction. Each province has a different impact from one another so that the policies taken cannot be generalized. Therefore, this study was conducted to group provinces based on the value of Gross Regional Domestic Product (GRDP) in 2019 and 2020 using unsupervised learning. The year 2019 represents the economic conditions before COVID-19 and 2020 represents the conditions during the COVID-19 pandemic. The unsupervised learning method used in this research is the K-Means, K-Medoids, SOM, as well as Hybrid SOM-K-Means methods. From the grouping results obtained, then a comparison of the results of the four methods will be carried out to obtain the best method based on the Silhouette Index. The results show that based on 2019 data, the grouping of provinces using the K-Medoids, SOM and hybrid SOM-K-Means methods is three clusters. While the results of grouping using the K-Means method are as many as two clusters. On the other hand, based on 2020 data, both the K-mean, K-Medoids, SOM, and hybrid SOM-K-Means methods show the same results, namely the grouping is carried out in two clusters. The best method based on 2019 GRDP data is K-Means with two groups. Meanwhile, in 2020, the best method obtained is the K-mean, K-Medoids, and SOM methods with two groups. In addition, all economic growth indicators have contracted or decreased due to the impact of the COVID-19 pandemic.
WaveNet modeling of distortion pedal using spectral features

The present paper proposes a distortion pedal modeling method using the so-called WaveNet. A state-of-the-art method constructs a feedforward network by modifying the original autoregressive WaveNet, and trains it so that a loss function defined by the normalized mean squared error between the high-pass filtered outputs is minimized. This method works well for pedals with low distortion, but not for those with high distortion. To solve this problem, the proposed method exploits the same WaveNet, but a novel loss function, which is defined by a weighted sum of errors in time and time-frequency (T-F) domains. The error in the time domain is defined by the mean squared error without the high-pass filtering, while that in the T-F domain is defined by a divergence between spectral features computed from the short-time Fourier transform. Numerical experiments using a pedal with high distortion, the Ibanez SD9, show that the proposed method is capable of precisely reproducing high-frequency components without attenuation of low-frequency components compared to the state-of-the-art method.
Isolation and Dispensing of DC-to-DC Power Supply to High Power Electrical Application Circuits

The suggested system is constructed using a line voltage of 220V/50Hz as input, which is stepped down, rectified, then admitted through a filter capacitor to produce an unregulated DC voltage. This uncontrolled voltage is stepped down to provide a steady 15v supply to the IC, which is controlled by a PWM signal to control the output voltage level to the various applications. To isolate the DC output from the input source, a Separation Transformer is employed. The transformer output is rectified again by the high frequency Diode bridge rectifier and filtered with a capacitor to provide the regulated DC output. To provide correct voltage output, a voltage regulator is installed. The feedback network generates a high frequency PWM signal that drives the MOSFET switch. The DC voltage at the output is determined by the width of the switching pulse. The pulse width varies in response to fluctuations in the DC output voltage level; this variation in the pulse width eliminates the output voltage variation, and the SMPS output stays constant independent of load dissimilarities