Reviewerly/exHarmony huggingface dataset:
THE USE OF SNOWBALLING TECHNIQUE TO LEARN APPLYING VOCABULARY AFTER THE PANDEMIC THE USE OF SNOWBALLING TECHNIQUE TO LEARN APPLYING VOCABULARY AFTER THE PANDEMIC

The Use of Snowballing Technique to Learn Applying Vocabulary After the Pandemic. This research is aimed at finding out the effectiveness of using snowballing technique to learn applying vocabulary at the third grade students of SMAN 1 Batulayar in academic year 2020/2021. This research is an experimental research with quasi experimental and category of this research is non-equivalent control group design. The population of this research is the third grade students of SMAN 1 Batulayar consist of 62 students. The instrument that will be used to collect the data is test consists of post-test. The data will be analysed by using statistical formula of t-test.The result of data analysis showed that the value of t-test was 4.39 and t-table 2.00. The value of t-testis higher than t-table in significance level0.05 and degree of freedom 62-2. It meant that t-critical value was significant fot this confidence level. Then, alternative hypothesis (Ha) was accepeted and null hypothesis (Ho) was rejected. It could be concluded that there was an effect of using snowballing technique to teach vocabulary at the third grade students of SMAN 1 Batulayar in Academic year 2021/2022.
Development Of IoT Fall Detection System For Elderly People

In the recent time, detection of fall has become one of the most important aspect due to increase in the living standards and population of the elderly people. Nowadays, many working professionals are leaving behind their elder family members at home alone. As the fact that elderly people has the higher chances of falling down and hurting themselves due to age factor and weak joins. Falls can be very dangerous among elderly people especially those who are living alone and cannot seek any help after it. Hereby, this development of IoT Fall Detection System will ensure that the fall can be detected and the victim can be treated well at the right time. When any type of indoor falls occur, it should be correctly detected and alert the family member to take immediate action. In this case, if an individual has fallen down, this prototype device which is attached to the victim’s hand will detect the fall and send notification to the family member to alert them. Furthermore, it has become extremely necessary to adopt the Internet of Things (IoT) technology to integrate with the fall detection system and receive the alert notification using the Cayenne app. The key components in developing this project are the Wemos D1 Mini, MPU 6050 sensor and Cayenne app. The input of the system is the accelerometer/gyroscope sensor with 6 axis integrated which will detect any change in body posture or motion of the particular person while Wemos D1 Mini will integrate with the Cayenne app to send notification via IoT to the mobile phone and stores relevant information
Medicine Suggestion System from User Review Using NLP Techniques

Ever since the worldwide spread of the corona virus there has been a lot of issues regarding the non-availability of clinical resources such as health care workers, equipment and medicines. The entire population of medical professionals are in a predicament as to identifying the solution for the ongoing raging crisis. Due to these unfortunate circumstances, individuals resolved to take their own medication without proper knowledge and consultation which has made health conditions even worse and inevitably lead to the death of many people. Presently and in the future most of the applications assess Machine Learning, and there is an increase in innovative work for automation. This paper mainly confers a Medicine Suggestion system that can drastically reduce specialists’ heap. In this paper a medicine recommendation system has been built, which takes the patient reviews for sentiment prediction using some vectorization process like Bow, TF-IDF, Word2Vec and Manual Feature Analysis which will help the patients to classify and suggest the top medicine by using different classification algorithm for a particular or a given disease. Precision, recall, flscore, accuracy and AVC Score are used to evaluate the predicted sentiments. The result analysis shows that Classifier Linear SVC using TF-IDF Vectorization is the best among all the other models with 93% accuracy
Engineering Practical Lempel-Ziv Tries

The Lempel-Ziv 78 ( LZ78 ) and Lempel-Ziv-Welch ( LZW ) text factorizations are popular, not only for bare compression but also for building compressed data structures on top of them. Their regular factor structure makes them computable within space bounded by the compressed output size. In this article, we carry out the first thorough study of low-memory LZ78 and LZW text factorization algorithms, introducing more efficient alternatives to the classical methods, as well as new techniques that can run within less memory space than the necessary to hold the compressed file. Our results build on hash-based representations of tries that may have independent interest.
The Application of Artificial Intelligence (AI) in Digital Forensic Science

AI tools that work like humans and learn from examples are often integrated into many different software tools, such as object detection tools, Automated Fingerprint Identification Systems (AFIS), face recognition, speaker recognition, author recognition, and many more. We see many different tools that are combined and used in pipelines to extract evidence from big data. AI is included in many search engines and many products, and we are used to having them. Now, smart phones often include much AI for recognizing the user and predicting the information someone is interested in. Speech recognition works well enough to use for searching the internet. People are sometimes not aware that their digital devices, such as tablets and smart phones, are listening to their conversation if they have not turned the privacy modes on. Later, the user might get advertisements related to some words they were discussing, such as, for example, a vacation to Corsica if they were discussing that part of the world. In criminal settings, using certain phrases may later be used as supporting evidence in court. For facial comparison, it is well known that bias exists, depending on the training sets. There are cities, such as San Francisco, that abandoned the use of this technology by the government due to bias and other privacy concerns. However, since facial comparison algorithms have proven to be much better, and in many cases outperform humans, the use of an algorithm to assist the forensic scientist is used more often. Sometimes, we have patterns, such as the Photo Response Uniformity (PRNU) to identify cameras, that a user cannot really see, and an algorithm can compare better. In practice, we see that AI is used to make faces that never existed, based on training material, such as the deep fakes. For image and video manipulation detection, more algorithms are developed that also use deep learning techniques to detect the manipulation. It is very important that we can explain in court how these algorithms work, and that the results are validated. Also, we need to be aware that once a method for detection has been published, it is possible to attack the detection method. For this reason, it is necessary to perform continuing research on the application of methods in AI, without seeing it as a complete black box. The reproducibility of results with AI is another issue that should be addressed, since based on the starting point of a dataset, different results may be obtained.
De las TIC a las TAC: Innovación y tecnología en las escuelas

Education is an important aspect for the development of our society.Throughout history, we have seen how it has evolved considerably, both in terms of content, methodologies and materials used.Society has gone from focusing on classrooms for computing to focusing on computing for classrooms.This leads to the introduction of ICT, also known as Information and Communication Technologies, on a large scale in classrooms, becoming a pedagogical tool for teachers and a motivation for students.The aim of this work is to analyze the evolution from ICT to learning and knowledge technologies (LKT).In this work, a qualitative methodology of documentary search has been developed.From the educational point of view, the objective of ICT, and a challenge in turn, is to work on the digital competence of students so that they can be part of today's society, which is connected.This perspective is responding to the process that is leading to the use of technologies in schools, where we have gone from teaching about ICT to teaching based on technological means, thus leading us to the TAC, where the goal is to teach through technology.Also, the main objective that schools should adopt is to integrate technologies in teaching and learning to improve and ensure that students have basic digital skills.During this process it is essential that teaching and especially ICT teaching advance considerably, responding to current demands, referring not only to material resources but also to human resources.
Research Guides: Modern Languages: Books

The Department of Communication Studies & Modern Languages prepares students to excel by nurturing effective communicate skills, language proficiency, and intercultural literacy.
The Impact of Work Alienation in Organizational Commitment at the Ministry of Labor, Jordan

This study aimed to investigate the impact of the level of work alienation (Powerlessness, Meaninglessness and Self-estrangement) in organizational commitment (Affective Commitment, Continuance Commitment and Normative Commitment). The study was conducted at the Ministry of Labor in Amman / Jordan. Questionnaires were handed out to (250) employees at different levels and randomly, total of (210) valid questionnaires were analyzed. In order to achieve the objectives of this study, the descriptive analytical approach, simple and multiple regression analysis were used. The study revealed that the level of work alienation have a significant impact at level (α ≤ 0.05) in organizational commitment at the Ministry of Labor. In more details, it was found that the level of work alienation has a significant impact at level (α ≤ 0.05) in affective commitment, continuance commitment, and normative commitment at the Ministry of Labour. It is recommended to provide a positive climate by improving the relationships between managers and employees, participating in events, adopting the open door policy, encouraging creativity and creating a strong teamwork culture in order to avoid being estranged. Moreover, it is important to empower employees’ by involving them in the decision- making process to alleviate powerlessness in the Ministry of Labor.
Holistic change propagation and impact analysis in requirements management

In developing complex technical systems, requirements are subject to continuous change. Systematic and holistic change impact analysis and proactive measures are required for reducing the number of requirement changes and their negative impact. There is no method to analyse the holistic impact of a requirement change in the context of developing complex technical systems. Holistic analysis requires to consider the local effects of requirement changes as well as effects from change propagation. To develop an approach for holistic change propagation and impact analysis, twelve performance goals are defined. Those are derived from a state of research analysis as well as an industry workshop. A three-step method is proposed. Firstly, requirement dependencies that cause change propagation are detected. Secondly, critical requirements are automatically identified based on a Page Rank algorithm. Thirdly, change impact of critical requirements is analysed based on a guideline. Validation proves that ten goals are fulfilled and two are partly fulfilled. The method addresses major shortcomings of preceding research and enables sound decision making for development engineers both before a change occurs and during decision process on a change request. This helps to reduce negative change impact in development projects and the risk of project failure.
DemaBot: A tool to automatically generate decision-support chatbots

This article presents DemaBot: a low-code solution to create chatbots designed to automate decision-making. Examples of these chatbots range from COVID-19 screening to first-line incident support, amongst others. Using DemaBot, the developer only needs to model the decision that the chatbot will automate using DMN and, optionally, customize the utterances that the chatbot will use to interact with the user. From this information, DemaBot generates automatically the complete set of components that implement a ready-to-use chatbot. Furthermore, it provides help to guide users during the conversation, and optimizes the conversation flow, being able to recognize several parameters in a single turn and asking only for those that are indispensable for the decision. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Effectiveness of cybersecurity audit

The aim of this paper is to analyze how effective internal audit of cybersecurity is. We developed a Cybersecurity Audit Index composed of three dimensions (planning, performing and reporting) to address this question. We hypothesize that CSA effectiveness is positively related to cyber risk management maturity and negatively to the probability of a successful cyber attack. We tested our hypotheses in a survey with auditors and Chief Audit Executives from various countries and industries. We found that CSA Index scores significantly vary, with a mean of 58 on a scale from 0 to 100. While planning and performing CSA are strongly and positively correlated, they are less strongly related to reporting about CS risk management effectiveness to the Board of Directors. In line with our hypothesis, the CSA Index is positively associated with CS risk maturity, but contrary to our hypothesis, it is not related to the probability of a cyber attack. This is the first paper that comprehensively measures the effectiveness of cybersecurity audit and its effects on CS risk management.
Study On Development and Configuration of Uhf and Ku Signals Measuring Instrumentation System across Climatic Zones of Nigeria

This paper presents a developed and configured Ku and UHF bands integrated&nbsp; cloud-connected, multicentre instrumentation system for real time measurement and&nbsp; storing of&nbsp; Ku and UHF signals&nbsp; data across climatic zones of Nigeria for scientist and radio researchers use. The instruments are connected to the computer via USB interfaces, and data is exchanged with the systems using logging software bonded to these interfaces. The logging program plots graphs for the various detected quantities, whereas the viewing software retrieves data from a cloud service, shows its values, and plots graphs in its graphical user interface (GUI). The cloud architecture is housed by Amazon Web Services (AWS), which operates a web application that connects to numerous centers and makes their obtained data available in real time via viewing software.&nbsp; The system configuration flexibility allows for changes in Ku signal polarization to vertical or horizontal polarization situations, as well as depicting the features of the satellite whose downlink signal is being measured together with other information about its position. Through the web application that connects the locations together, upgrading may be done throughout the logging and view locations from a particular place. Configurations also improve the ability to view information on variations of Ku and UHF signals from any other location in Nigeria's climatic areas from a particular spot. This instrumentation system provides important data for radio scientists and engineers to construct detailed and realistic models of (Ku and UHF) signal fading and attenuation, which will improve the budgeting and planning of terrestrial and satellite communications across &nbsp;climatic regions of Nigeria.
Quantum Fisher information of two atoms with dipole–dipole interaction under the environment of phase noise lasers

We investigate the parameter estimation problems of two-atom system driven by the phase noise lasers (PNLs) environment. And we give a general method of numeric solution to handle the problems of atom system under the PNLs environment. The calculation results of this method on Quantum Fisher Information (QFI) are consistent with our former results. Moreover, we consider the dipole-dipole (d-d) interaction between the atoms under PNLs environment with the collective decay, and the results show that larger d-d interaction and smaller collective decay rate lead to larger QFI of the two-atom system. So the collective decay will destroy the QFI while the d-d interaction will preserve the QFI, these results can be used to protect the QFI of two-atom system driven by the PNLs environment.
Market Microstructure of Non Fungible Tokens

Non Fungible Token (NFT) Industry has been witnessing multi-million dollar trade in recent times. With rapid innovation of the NFT market environment by technology, innovation, and decentralization, it is becoming hard to distinguish between genuine NFT from fads and scams. This article discuss the NFT market microstructure, with a focus on price formation, market structure, transparency, and applications to other financial areas. Market manipulation in NFT market with the context of wash-sale patterns has also been surveyed. The article concludes by providing pointers on due-diligence activity that can be adopted by investors to mitigate NFT trading risk.
Multistrengthening Module-Based Salient Object Detection

Object detection is a classical research problem in computer vision, and it is widely used in the automatic monitoring field of various production safety. However, current object detection techniques often suffer low detection accuracy when an image has a complex background. To solve this problem, this paper proposes a double U-shaped multireinforced unit structure network (DUMRN). The proposed network consists of a detection module (DM), a reinforced module (RM), and a salient loss function (SLF). Extensive experiments on five public datasets and a practical application dataset are conducted and compared against nine state-of-the-art methods. The experiment results show the superiority of our method over the state of the art.
No pain, no gain: taking PESCO to the gym. Egmont Security Policy Brief No. 129 September 2020.

It is fair to assume that at least one out of three readers of this paper have at some point in their life accepted to join a group of friends to do regular sporting activities together. While this idea sounds great in principle (we become fitter while building our friendships), when the day comes, only a few of those who committed actually show up. Excuses abound: more important commitments; too busy; jealous friends who were not invited in the first place. This is why a much more effective method of keeping commitments is through peer-pressure: having a designated buddy keeping tabs on you, making sure you show up and help you do the work. Vice versa, you are responsible for helping and motivating another friend, until the square is circled. So, what does Permanent Structured Cooperation (PESCO) have to do with becoming fitter? Everything.
A New Perceptive of E Voting with Blockchain

Electronic voting is most growing field in the area of voting system. Digital users are growing with the expo-national growth in every country. In the country like India where mobile or internet user are 391 million, e-voting system reduce a huge amount of current voting methods. But in this current scenario e-voting system are vulnerable and not threat proof. Blockchain is new emerging field in security and for distributed systems. Only few methods are applied in the past where E-Voting system used the blockchain technology. So, the objective of this research is to review the different algorithms applied in e-voting using blockchain through various parameters to help researchers working to provide more security using blockchain.
La clase práctica interactiva como herramienta para optimizar la coordinación de los grupos en asignaturas prácticas del ámbito universitario - [The interactive practical class as a tool to optimize the coordination of groups in practical subjects in the university environment]

Virtual digital elements allow us to save time and can facilitate communication and coordination of students with their teachers, both in pandemic and normal conditions and normal conditions.For this purpose, a new tool was generated based on a spreadsheet, with instant online update, to record the student's annotations in the practices of the subject of Optometry II of the Faculty of Optics and Optometry of the Complutense University of Madrid.The main objective was to increase the efficiency of the practical classes through the application of innovative open educational resources, implementing virtual teaching methodologies to reduce direct contact between the participants in the face-to-face classes.This work methodology allowed to recover 50% of the time to carry out the practical activities of the students.
AANet: Adjacency auxiliary network for salient object detection

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance.However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping.In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD.Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features.Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features.Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM.The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising.Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.