Reviewerly/exHarmony huggingface dataset:
Wearable Based-Sensor Fall Detection System Using Machine Learning Algorithm

As nations develop and prosper economically, their population ages longer and requires extra healthcare. Falls are known to be the second major factor of deaths in elderly by accidental or unwarranted injuries. When a fall occurs, lack of immediate help or action is the main problem, especially when bleeding is involved, as fall-related injuries are a life-threatening for many people. To prevent such kinds of deadly scenarios, a reliable fall detection system must be developed to help many lives. In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. An application called `AndroSensor' on a smartphone, that retrieves real-time data from accelerometer, gyroscope and gravity sensors, is used as the input signals. The phone is placed at the most accurate position that had been done by past research which is waist position. When a fall event occurs, the real-time data is collected and placed in a *.CSV file. Then, a Machine Learning Algorithm (MLA) is used to train and test the data before a classifier is used to classify the new incoming dataset. The fall event behaviour classification classes are sleep, walk, sit, front fall, back fall, side fall, etc. MATLAB software is currently used to analyse and visualize the data too. The MLA detects fall with efficient sensitivity (SP), specificity (SP), and accuracy. An accuracy of 100% is achieved with the Support Vector Machine (SVM) classifier compared to other classifiers has been confirmed by many past research. However, other classifiers like Decision Tree and kNN had 100% accuracy too. This means that the proposed system achieved its goals. As for future work, the plan is to convert the code to an app to run on the smartphone so it can be commercialized.
The technology of rob water treatment becomes clean water by using the combination of electrolysis, filtration, and ozonation methods

Abstract Coastal residents have difficulty in fulfilling the needs of clean water due to tidal water and tidal water intrusion. Meanwhile, clean water is a crucial component in carrying out activities of daily life. This initiative aims to provide an alternative solution in the form of technology to treat water polluted by saline water into clean water. This initiative used the method of electrolysis, filtration, and ozonation. Electrolysis has functions to remove salt contained and reduce levels of heavy metals while filtration functions to filter out the impurities in the form of solid particles, and ozonation takes a role to kill bacteria. Moreover, the target output of this work is the creation of a prototype by using this technology as a technology that can treat water polluted by saline water into clean water. The parameters assessed from this initiative are physical, chemical, and biological parameters of clean water. The results of the total test of dissolved solids of water before processing were 7130 ppm while it was 2490 ppm after getting treatment. The decreasing total value of dissolved solids is 65.077%, which indicates that the test was successful. The result of an electrical conductivity test of water before processing is 310 mA while after processing is 88.6 mA. The decrease in the current value of 71.419% indicates that the test was successful. The results of the turbidity test of water before processing are 0 FAU while after getting treatment is 0 FAU. The turbidity value remains 0, which indicates the test was successful. The results of the pH test of water before processing were 5.7 while the result after treatment got 6.1. The increase in pH value towards pH 7 (neutral) indicates that the test was successful. The test results of salt and heavy metals (Fe, Cu, Cl, Na, and Pb) of water before processing were 0.073 ppm, 0.07 ppm, 0.17 ppm, 0.04 ppm, and 4.24 ppm while after processing was 0 ppm, 0 ppm, 0.07 ppm, 0.03 ppm, and 1.36 ppm. There was a decrease in Fe 100%, Cu 100%, Cl 58,823%, Na 25%, and Pb 67,924% which showed that the test was successful. Test results for fecal coliform and nonfecal coliform bacteria before processing were 43 MPN / 100 ml and 7 MPN / 100 ml while after processing was 23 MPN / 100 ml and 4 MPN / 100 ml. There was a decrease in the value of fecal coliform bacteria 46.511% and nonfecal coliform 42.857% which showed that the test was successful. The use of electrodes needs to be replaced periodically so that the results of clean water production remain optimal.
Towards Understanding How Emojis Express Solidarity in Crisis Events

We study how emojis are used to express solidarity on social media in the context of three major crisis events - a natural disaster, Hurricane Irma in 2017; terrorist attacks that occurred in November 2015 in Paris; and the Charlottesville protests in August 2017. Using annotated corpora, we first train a recurrent neural network model to classify expressions of solidarity in text. Next, we use these expressions of solidarity to characterize human behavior in online social networks, through the temporal diffusion of emojis, and their sentiment scores. Our analysis reveals that emojis are a powerful indicator of sociolinguistic behaviors (solidarity) that are exhibited on social media as the crisis events unfold. The findings from this article could help advance research on the pragmatic dimensions of emojis, which have been understudied in extant literature.
An Architecture for Blockchain-Based Cloud Banking

Blockchain has been practiced in crypto-currencies and cross-border banking settlement. However, no clear evidence that a distributed ledger network (or Blockchain) is built within domestic payment systems, although many experts believe that Blockchain has wide applicability in various industries and disciplines. As the author’s best knowledge, no one has published a clear architecture and a feasible framework for a Blockchain-based banking network. Thus, “how Blockchain can be implemented in domestic banking systems” is a big challenge. The most important contribution of this work is to give a feasible and viable framework resolving that problem. The author investigates a Blockchain-based payment framework, more explicitly, a decentralized banking architecture running on the top of existing banking cores. The Blockchain network has two tiers: master nodes (block generators) and normal nodes (validators). The consensus mechanism is introduced as a composition of Proof of Stake, Proof of Reputation and/or practical Byzantine Fault Tolerance. In addition, nomination and approval mechanisms are added to the governance to enhance legal compliance and compatibility with real Fintech space. Some qualitative analysis is provided to show that the proposed Blockchain banking framework offers better security, scalability and decentralization, while easily adapt with different national regulation environments, among other Blockchains. In the application aspects, the framework is implementable and deployable for decentralized payment network and smartcontract infrastructure for domestic markets, then enable a complete and unified digitized space for cloud banking and financial services.
CreditX: A Decentralized and Secure Credit Platform for Higher Educational Institutes Based on Blockchain Technology

The current flow of education and industry needs, consistent convergence among students, Higher Education Institutes (HEIs) and industrial organizations. Though, maintaining the credibility and verifiability of students’ credit information is one of the vital priorities for HEIs, the current credit systems used by most of the HEIs are not sound, and lack effective means of verification. Most of these HEIs, currently utilize systems of centralized and monolithic nature which possesses limitations in availability, security, and scalability domains. Therefore, there exists an absolute requirement for a more secure, efficient, and scalable means of student’s credit information management. Blockchain technology empowers the formation of a decentralized domain where data and transactions are not heavily influenced by any outsider association. Every transaction is logged in a public ledger in a verifiable and perpetual manner allowing the visibility to all the parties involved. This enables the creation of a system that is not feasible to penetrate. Therefore, we propose a more credible and innovative solution called CreditX, a decentralized and a secure credit platform for HEIs to collaborate, share and transfer credit data among HEIs, students and industrial organizations while establishing trust.
Accelerating Human-Agent Collaborative Reinforcement Learning

In domains such as Human-Robot Collaboration artificial agents must be able to support mutual adaptation and learning. Towards this direction, we use a discrete Soft Actor-Critic agent on a real-time collaborative game with humans. We examine how different allocations of on-line and off-line gradient updates impact the game performance and the total training time. Our results suggest that early allocation of a high number of off-line g/u can accelerate learning while shortening training duration.
Path and Mechanism of Blockchain Embedded in Innovation Management of Cultural and Creative Industrial Park

As a new business form of Cultural and Creative Industrial Cluster, the management of Cultural and Creative Industrial Park is faced with many difficulties, such as imperfect credit mechanism, lagging efficiency of intellectual property management, difficulties in industrial chain integration and upgrading, and imperfect development evaluation system. The “decentralization” concept of blockchain 3.0 technology can provide guidance value for the management concept innovation of Cultural and Creative Industry Park, and the embedding of technologies such as “smart contract”, “distributed ledger”, and “digital currency” can promote the construction of “programmable management system of Cultural and Creative Industry Park”. According to the analysis path of architecture, function system, and application scenario of park management, this paper constructs the architecture from consensus layer, network layer, and data layer, constructs the function system from the public chain, alliance chain, and private chain, and locates the application scenario from the dimensions such as industrial chain management and government supervision and management; thus, the direction and focus of management innovation of blockchain 3.0 technology embedded in Cultural and Creative Industry Park are clarified. The research results of this paper have good theoretical guidance for the management subjects of Cultural and Creative Industry Park (local government, park management committee, industry organizations, etc.) to determine the path of implementing management innovation by using blockchain 3.0 and to build a scientific park management mechanism.
Evaluating the Effectiveness of A Suggested Architecture for The Real-Time Social Recommendation System

With the growth of social media and online network sites, a large number of textual data are continuously generated every day, however, it is a challenging subject to detect, describe and analyze those unstructured and semi-structured textual data since it has the characteristics of interactivity, sociality, and real-time means. Consequently, researchers have proposed several data mining methods that are used for building effective social recommendation systems to enhance user commercial and social activities. In this paper, we evaluated the performance of our developed real-time social recommendation system called ChatWithRec that aims to analyze the user's contextual conversation dynamically, detect the topic, and then match it with a suitable advertisement to increase the accuracy of recommendations. In our evaluation, we utilized a set of textual datasets to test the conversational analysis segment by using a modified Latent Dirichlet Allocation topic modeling method. Besides, we involved Google's Mobile ad network and an adjusted advertisement database (considering only some fields which are, food and travel subjects including booking hotels and flight adverts) as a task-related output action to collect qualitative data and defining the user's behaviors within-subjects' interaction with our system. The results are encouraging and indicate that the system is fast, satisfy users by getting what they seek without interrupting their conversation flow.
The specificity and reproducibility of general Schallamach pattern on heel part of rubber outsole

Abstract The Schallamach pattern is an acquired characteristic that is sometimes observed in crime scenes. However, the detailed pattern is not always visible, although its general pattern is still recognizable. To determine the discrimination power (DP) of the general Schallamach pattern (GSP), 55 outsoles worn by 33 volunteers were chosen for the experiment. Their prints were lifted after different intervals for six months using magnetic powder and self‐adhesive lifters. After the prints were segmented into grids using Photoshop CS6, the presence/absence, angle, and density of the GSP were observed and measured in each grid. There were 18747 pairs of same‐source samples among the 1447 dust impressions. An algorithm (weight combination of the presence/absence, angle, and density) was determined when a weight combination of the parameters considered recognized most of the same‐source sample pairs. The results showed that the angles of the GSP on the left and right shoes were concentrated in the range [–5.5°, –20.43°] and [8.8°, 24.97°], respectively, and the DP was only 37.5%. With a simply optimized algorithm, the DP reached 54.82%, and the DP of sample pairs lifted within one day reached 79.04%. The study demonstrates that one can distinguish the Schallamach pattern on the heel part of the outsole without information regarding the presence/absence, angle, and density of patterns.
Sparse flow adversarial model for robust image compression

Existing learned-based image compression methods have shown impressive performance. However, they rely mostly on the consistent distribution between training and test images, which reduces the robustness of the training model. In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). SFAM employs a deep generative framework to learn a reversible and stable mapping between image distributions, thus it can work in varied scenes for robust compression. The mapping explores the sparsity of the image by combining linear and nonlinear transformations, rather than extracting the features of a particular dataset as is the case with other learning-based methods. Moreover, a sparse adversarial map is introduced into SFAM, to constrain the SFAM to generate sparser features for efficient compression. Extensive experiments are performed on different datasets, in which the effectiveness and robustness of the proposed method are verified. Meanwhile, SFAM is trained only once and it can work well on three different datasets, which also prove the robustness of the proposed SFAM.
Geospatial Data Mining Techniques Survey

Researchers from government offices, educational institutes and private organizations are generating large amounts of geospatial data. Even though they provide valuable information itself, there is a growing need for analysis of these data to obtain new insights. Many organizations are applying traditional data mining techniques to geospatial data in order to get information that is even more valuable. Here we will overview five techniques of data mining adapted to work with geographic information systems.
Applying a post-processing strategy to consider the multiple interests of users of a Paper Recommender System

Currently, the amount of information available to Web users is very large, and this situation is similar for scientific communities when searching for papers for their research. Recommender Systems (RSs) can help in this task because they combine computational techniques to select personalized items based on the users’ interests and according to the context in which users are inserted. The increase in the impact and scope of recommendations in the users’ lives, leads to the result on the ethical issues involved in the generation of recommendations and indicators for visualizing the results of the algorithms found. This paper presents a Recommender System for the Human-Computer Interaction (HCI) community, indicating papers from the Brazilian Symposium on Human Factors in Computing Systems related to the users’ profile applied to a post-processing strategy focused on fairness to balance the users’ interests. After the development of the RS and the Web environment, the results were obtained on the impact that the tool had on the community and demonstrated through the evaluation of the system.
Introduction to TensorFlow 2

Numerous open-source tools have been introduced to develop and train machine learning models. Compared to other toolkits, TensorFlow attracts the greatest number of users on GitHub. TensorFlow 2 is an improved version of TensorFlow 1 that focuses on simple execution, easy model building, and robust model deployment. This chapter introduces in detail three main updated functions of TensorFlow 2: (1) eager execution, providing a programming environment where operations can be evaluated immediately without constructing graphs; (2) Keras, a high-level application programming interface (API) for quickly and easily designing and training network models; and (3) tf.data, an API that allows users to construct complex input pipelines in a simple way.
Younger is better

What makes a quality-diversity (QD) algorithm effective? This question is increasingly studied as such algorithms gain in popularity. Recent work on Novelty Search linked its efficiency to the evolution of a population under a dynamic selection pressure defined by the novelty metric, which allows the population to converge to highly evolvable individuals, on which spending the evolutionary budget allows to navigate the behavior space. MAP-Elites, another popular QD algorithm, does not use an explicit population, instead sampling the parent population directly from the behavioral archive at each generation. This sampling can be uniform, or biased according to criteria such as curiosity, which favor individuals that previously generated successful offsprings. In this article, we show that such improved selection schemes are efficient because they create a dynamic pseudo-population that mimics the desirable qualities of a Novelty Search population. We do this by proposing another simpler selection scheme, youth, that simply focuses the evolutionary budget on such a dynamic pseudo-population, and by showing it exhibits similar behavior and performance as curiosity without explicitly selecting for previously successful parents. This has important implications for the design of future QD algorithms taking the best of both archive-based and population-based methods.
How Decentralized Systems Can Upgrade AI

Decentralized systems enhance future algorithmic applications. Centralized algorithmic automation is a very powerful tool for humanity. It fulfills increasingly important roles in society for the betterment of humanity. It optimizes human interaction, outputs, and performance and can elevate most functions of technology-enhanced designs in society. At the same time, the conveniences and benefits that derive from centralized algorithmic automation bring with them risks to humanity that cannot be fully quantified. Decentralized systems can help counteract the downsides and threats of algorithmic automation and thereby enhance the long-term benefits of technological solutions for humanity.
Using deep learning for digitally controlled STIRAP

Stimulated Raman Adiabatic Passage (STIRAP) is a technique for preparing atoms and molecules in an arbitrary preselected coherent superposition of quantum states, ordinarily controlled by Gaussian laser pulses. We search novel pulse sequences by exploiting deep reinforcement learning algorithms in order to achieve fast and flexible solutions for integer and fractional STIRAP. By using the robustness of the PPO algorithm, we impose the agent to exploit only digital pulses corresponding to on/off states of the control radiation instead of continuous amplitudes. Such method allows to adapt to detuning of the energy levels and disturbances such as dephasing.
The Development of a Multidisciplinary Cybersecurity Workforce: An Investigation

The unexpected digital transformation that was forced due to COVID-19 found many citizens and organizations unprepared to deal with the relevant technological advances and the cyber threat landscape. This outcome highlighted once more the cybersecurity skills shortage and the necessity to address this gap. A solution to this, is to consider a multidisciplinary cybersecurity workforce with professionals originating from different backgrounds, beyond the traditional ones such as computing and IT. To be able to engage people though, they need to be aware of the possibilities that exist in cybersecurity for those that originate from non-traditional disciplines. Moreover, cybersecurity professionals need to be aware of the added value when collaborating with these professionals. These are aspects that need to be extensively investigated to provide insights to academia and industry, to develop education and training curricula towards building a multidisciplinary cybersecurity workforce. This paper investigated these aspects in a Further Education and Higher Education College in the UK, where 88 students from 5 disciplines were surveyed, providing valuable observations as to the interest of students, and future professionals, to work in cybersecurity industry and their perception on the subject disciplines relevant to cybersecurity jobs.
A Deliverable Delay Management of Software Development in Railway Project using an OKR-Based Scrum Process

Delay, one of the challenging topics several corporates have to face in running the complex project, such as those implementing a railway system. This article mainly focuses on the development of the Scrum framework with the Objective and Key Results (OKRs) scheme that can assess the performance information at both team and individual levels. The technique manipulates the goals from the team to individual levels to be measurable while aligning them in the same global direction. Here, the case study of implementing the OKRs-based Scrum framework in the process occupied by the Computer-Based Interlocking adaptation (CBI) team is reported. The process was evaluated continuously during the project life cycle of the railway software development. From the total of 22 tasks, only 27.20% of which were delay from the target deliverable date while 72.80% of which were completed prior to or on the target dates. Therefore, the correct implementation of measurable OKRs has shown in providing a collaboration visibility through tracking progress activities with clear priorities of tasks, thus providing the improved time deliverable performance.