Reviewerly/exHarmony huggingface dataset:
THE APOLOGY OF THE WHOLE

At the level of the whole organism, an idea of the complexity of living things is formed as a combination of levels of organization (layers) of biological and virtual reality, which develops as a space for visualization (digitization) of living objects. New digital formats of living objects, coupled with the naturalistic ethics of obtaining them, create a trend towards a complete transition of biology to a quantitatively new level of obtaining biological information information about the state of living biological objects. The development of digital biology contributes to an increasingly large-scale transition to the creation and analysis of virtual images of living biological objects, and at the same time "removes" the biologist from the actual object of research: a biologist can work with a virtual image and not destroy his research object. Digital naturalism appears, and, consequently, a digital "experiment" must also be expected, which will undoubtedly continue the eternal confrontation between naturalists and naturalists or vitalists with mechanists in the new techno environment.
Penerapan Metode Algoritma K-means Dalam Pengelompokan Angka Harapan Hidup Saat Lahir Menurut Provinsi

Life Expectancy (AHH) at birth estimates the average additional age of a person from a mother's womb during the birth process, which is expected to be able to live normally and healthy. Based on data obtained from the Government's official website address at https://bps.go.id/, which displays several amounts that vary from 2015 to 2018 according to the Province in Indonesia. For this reason, it is necessary to cluster each number of life expectancy at birth with the number from the lowest to the highest using the Data Mining method with the K-means Clustering Algorithm. In this research technique, the data will be classified based on the Province's name, which has the number of Life Expectancy at birth from 2015 to 2018. That is why the Data Mining method is used to facilitate data grouping on the number of Life Expectancy at birth according to the name of the Province in Indonesia. After grouping, the results will be obtained the number of Life Expectancy at birth, and grouping starts from the lowest to the highest cluster. In the research that has been carried out, it is expected that the Government will provide solutions of the highest life expectancy at birth that has the highest number so that in the following year, the life expectancy rate will be reduced.
Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability

When collecting annotations and labeled data from humans, a standard practice is to use inter-rater reliability (IRR) as a measure of data goodness (Hallgren, 2012).Metrics such as Krippendorff's alpha or Cohen's kappa are typically required to be above a threshold of 0.6 (Landis and Koch, 1977).These absolute thresholds are unreasonable for crowdsourced data from annotators with high cultural and training variances, especially on subjective topics.We present a new alternative to interpreting IRR that is more empirical and contextualized.It is based upon benchmarking IRR against baseline measures in a replication, one of which is a novel cross-replication reliability (xRR) measure based on Cohen's (1960) kappa.We call this approach the xRR framework.We opensource a replication dataset of 4 million human judgements of facial expressions and analyze it with the proposed framework.We argue this framework can be used to measure the quality of crowdsourced datasets.
Electronic Voting System Design

Electronic voting systems is a promising new area, however they pose a significant doubts in their integrity and ability to keep voting confidential. Moreover, security and confidentiality is often considered as a trade-off. In this whitepaper a new system design is proposed that allows both ensuring the voting integrity cannot be compromised and the voter's identity is not revealed when processing ballots. The system is subdivided into the authorization and the ballot processing servers and uses the public-key cryptography. The key idea is to cipher the ballot in a way that the ballot server can get access to the vote, but not to the voter's identity, and the authorization server can check the voter's identity but is unable to decipher the ballot. Tips to ensure software integrity while the voting is running are suggested. Two possible extensions are discussed to account for different voting rights and to include audit trails.
Effects of Flipped Classroom Technology on Students' Interest and Achievement in English Language Reading Comprehension

The paper examined the effect of Flipped Classroom Technology (FCT) on students' reading comprehension. FCT is a learner-centered instructional technology where the teachers' role and out-of-class contents are reversed in learning.The study adopted the quasi-experimental research design using a sample of 80 Junior Secondary two (JS 2) students. The instrument for data collected was English Language Reading Comprehension Test developed by the researchers and properly validated. The instrument demonstrated good inter-rater reliability index of 0.89 using Kendall coefficient of concordance. Data were analyzed using mean and Analysis of Covariance. The results indicated that FCT had significant (p< .05) effects on students' interest and achievement in English language reading comprehension in teaching reading comprehension. This implies that students' readiness, interest, achievement and mastery of reading comprehension would be greatly enhanced when teachers adopt the use of FCT. Based on the findings of the study, it was recommended that secondary school teachers should adopt FCT in teaching reading comprehension.
Parameter Selection: Why We Should Pay More Attention to It

The importance of parameter selection in supervised learning is well known.However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied.This situation may cause misleading or confusing conclusions.In this opinion paper, through an intriguing example we point out that the seriousness goes beyond what is generally recognized.In the topic of multilabel classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning.The set of frequent labels became a popular benchmark in subsequent studies, which kept pushing the state of the art.However, we discovered that most of the results in these studies cannot surpass the approach in the original paper if a parameter tuning had been conducted at the time.Thus it is unclear how much progress the subsequent developments have actually brought.The lesson clearly indicates that without enough attention on parameter selection, the research progress in our field can be uncertain or even illusive.
Esplorasi website Perpustakaan Universitas Harvard, Universitas Gadjah Mada dan Universitas Telkom

Introduction. This article evaluates library websites of world class universities in Indonesia from several aspects including features, responsibility, and speed. One library website overseas is explored as a reference: Harvard University. &#x0D; Data Collection Method. This is a literature study exploring features on library websites of Universitas Gadjah Mada (UGM) and Telkom University.&#x0D; Data analysis. The study used Harvard University library website as a reference. The results were presented into tables to discuss library websites of UGM and Telkom University.&#x0D; Results and Discussion. Harvard University library webiste has 21 features except gallery, UGM has 11 features and Telkom University has 12 features similar to Harvard University library webiste. Our result showed that Library website of UGM and Telkom University still need to be improved as required to be a world class university. All three websites has an adjustable display depending on devices in accessing websites. Those three websites were considered as responsive websites. Based on PageSpeed Insight, they showed good performance.&#x0D; Conclusion. Information on the websites are well-managed, informative, efficient, and accessible. Universities in Indonesia may use several overseas university library websites as a reference.
Social Media Law &amp;amp; Cybercrime

The evolution of social media law is playing out in a very public way, with regulatory agencies and legislative bodies inviting public comment to help them craft policies that will promote business innovation while protecting consumers. Whatever the regulatory bodies that regulate your business might be, chances are they have made and will continue to make announcements about public hearings to address compliance issues relating to new technologies and social platforms. This is particularly true within the United States and European Union.Government officials may attempt directly or obliquely to compel tech companies to suppress disfavored speech. The victims of such public‐private censorship would have little recourse apart from political struggle. The tech companies, which rank among America’s most innovative and valuable firms, would then be drawn into the swamp of a polarized and polarizing politics. To avoid politicizing tech, it is vital that private content moderators be able to ignore explicit or implicit threats to their independence from government officials.
Face Mask Detection with Raspberry Pi

In the wake of the COVID-19 epidemic, institutions such as the academy are suffering the most from global closure if the current situation haven’t rectified. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is transmitted to an infected person who talks, sneezes or coughs through respiratory droplets. This spreads quickly through close contact with anyone with the disease, or by touching objects or the infected area. By wearing a face mask under the jaws covering at crowded places or by frequently hygiene at your palms and by using at the minimum of 70% sanitizers which are based on alcohol is the best method for the against of the COVID-19. In this project we have used it ML, OpenCV and TensorFlow face recognition. This the model can be used for security purposes because of course an app that works well for use. In this way MobilenetV2 using a BN-based layout too lightweight and embedded this model with Raspberry pi to make real-time mask discovery, when, SSD (Single Shot Detector) format is used and the spinal network is light. As technology advances, Deep Learning has demonstrated its effectiveness in recognition and classification through image processing. The study uses in-depth reading techniques to distinguish facial recognition and to determine whether a person is wearing a facemask or not. The collected data contains 25,000 images using 224x224 pixel resolution and obtained 96% accuracy with the performance of a trained model. The system enhances the Raspberry Pi-based real-time recognition made by alarms and takes a facial image when the person found is not wearing a facemask. This study is beneficial in combating the spread of the virus and in avoiding contact with it.
Edge2BC: a Practical Approach for Edge-to-Blockchain IoT Transactions

The integration of Blockchains (BC) and Internet-of-Things (IoT) brings several benefits, such as data immutability and traceability, to a myriad of use-cases. The use of intermediary nodes, e.g., an edge node, provides benefits, but opens the possibility for attacks, especially when considering nodes handling the signing of transactions for devices. If the private key of a device is leaked, the data coming from that device cannot be trusted anymore, thus, rendering transactions signed with that private key unreliable.To tackle this concern, this paper presents Edge2BC, a practical approach to sign automatically BC transactions in an IoT device without revealing its private key to intermediary nodes, thus, increasing trust in the edge-to-BC data transit. The device relies on an edge node to communicate with a BC interoperability API to retrieve dynamic information (e.g., transaction nonce and gas price) and to send the signed raw transaction to the correct BC node. Power measurements (i.e., required current for different method steps) were performed in the prototype implemented to verify Edge2BC's feasibility. Such evaluations indicate that it is possible to have such a method in place, but the high power consumption required during the signing phase should be taken into consideration when deploying Edge2BC in a dedicated scenario.
A Serverless Approach to Federated Learning Infrastructure Oriented for IoT/Edge Data Sources (Student Abstract)

The paper proposes a Serverless and Mobile relay based architecture for a highly scalable Federated Learning system for low power IoT and Edge Devices. The aim is an easily deployable infrastructure on a public cloud platform by the end user and democratize the use of federated learning.
A User Study to Evaluate the Customization of Automatically Generated GUIs

Graphical User Interface (GUI) development is time-consuming and error-prone. Hence, automated GUI generation from higher-level interaction design models may become more and more important . In particular, automated generation can help with interface design prototyping. The usability of fully-automatically generated GUIs is considered unsatisfactory, however. Manual changes to the generated GUI itself would need to be made persistent. Hence, we proposed customization rules on a higher level of abstraction, and changes of style sheets. This paper presents a new user study on whether this kind of customization can actually improve generated GUIs. This study achieved statistically significant results that the adjusted task time of the customized version is less than that of the fully-automatically generated one. The subjective results indicated that attractiveness and wording were improved through customization.
China’s National Digital Currency: An Overview of Digital Currency Electronic Payment

Digital Currency Electronic Payment (DCEP) is the Central Bank Digital Currency (CBDC) of China. It is significantly different from electronic currency and third-party payment instruments. DCEP is issued through the dual mode which consists of the central bank and commercial banks. It operates and circulates based on the framework of Digital Currency Registration Center, Identity Authentication Center and Big Data Analysis Center. Compared with the encrypted digital currency based on blockchain technology, DCEP has the characteristics of centralized management. DCEP will have profound impacts on the effectiveness of monetary policy, the operation of banks and payment institutions, the development of digital economy, the internationalization of CNY and even social governance.
How Do Users Chain Email Accounts Together?

Recovery connections between email accounts can be exploited in manual hijacking attacks as has been shown in several incidents during the last years. Yet little is known about users’ practices of chaining email accounts together. We conducted a qualitative interview study with 23 students in which they shared their email recovery and forwarding settings with us. Altogether, we collected and analyzed information about 138 different email accounts. We used this data to map email account topologies and analyzed these topologies for recurring patterns. We found that users often make poor configuration decisions in their email recovery setups, and often create patterns in their email recovery topologies that result in security vulnerabilities. Patterns such as loops (seen in more than a quarter of our topologies) could be easily exploited in a targeted attack. We conclude that users need better guidance about how to use email based recovery settings in a robust way.
Data Movement Is All You Need: A Case Study of Transformer Networks

Transformer neural networks have become widely used for language modeling andsequence learning tasks, and are one of the most important machine learningworkloads today. Training one is a very compute-intensive task, often takingdays or weeks, and significant attention has been given to optimizingtransformers. Despite this, existing implementations do not efficiently utilizeGPUs. We find that data movement is the key bottleneck when training. Due toAmdahl's Law and massive improvements in compute performance, training has nowbecome memory-bound. Further, existing frameworks use suboptimal data layouts.Using these insights, we present a recipe for globally optimizing data movementin transformers. We reduce data movement by up to 22.91% and overall achieve a1.30x performance improvement over state-of-the-art frameworks when trainingBERT. Our approach is applicable more broadly to optimizing deep neuralnetworks, and offers insight into how to tackle emerging performancebottlenecks.
Does Flipped Learning at Fully Online Systems Enhance Self-directed Learning Attitude?

With traditional in-person classes canceled due to the pandemic, teachers moved classes online, adapting new teaching methods. With this background, this study is aimed at exploring if flipped learning conducted through fully online systems improves students’ self-directed learning attitudes. Comparing a conventional flipped learning model with asynchronous online before-class sessions to offline during-class ones, this study replaced offline during-class sessions with synchronous online ones and tried to see if a variant form of flipped learning will be effective in promoting students’ self-directed learning attitudes over time. Twenty-two English major students participated in a variant of flipped learning for eight weeks. A pre- and a post-test were administered using a survey questionnaire adapted from Lee and Pyo (2018)’s four characteristics of self-directed learning attitudes: autonomy, responsibility, creativity, and self-concept. The research findings are as follows. First, there was a significant increase in all four characteristics of self-directed learning attitudes in the fully online flipped classroom. Second, two themes, ‘greater work efficiency online’ and ‘a unique group mechanism online’ were found from open-ended questions, and explain well why the online-only flipped learning model is conducive to developing students’ self-directed learning attitudes.
A novel efficient automatic modulation classification algorithm using deep LSTM aided convolutional networks

Automatic modulation classifications would play an essential part in wireless spectrum anomaly detection and radio environment awareness. With the breakthrough in deep learning algorithms, this issue can be solved with unprecedented precision and effectiveness by using neural networks. Therefore, a novel neural network termed as Long short-term Convolutional Deep Neural Network(LCDNN) is proposed, which creatively combines the complimentary merits of Long Short-Term Memory(LSTM), Convolutional Neural Network(CNN) and deep network architectures. This model directly learns from raw time domain amplitude and phase samples in training dataset without requiring human engineered features. Simulation results show that the proposed model yields a classification accuracy of 93.5% at high SNRs. Further, the noise sensitivity of the proposed LCDNN model is examined and it is showed that LCDNN can outperform existing baseline models across a range of SNRs. Finally, in order to reduce the computational complexity of the LCDNN model, a 'compact' LCDNN model is proposed, which achieves the state-of-the-art classification performance with only 0.6% parameters of the original LCDNN model.
Video Semantic Keyword Extraction Based on Multi-Channel Information

Digital videos have became important multimedia information carrier. However, videos have features of large number, wide variety, lengthy content and complex structure, which make video retrieval, video query and video classification difficult. Video keyword extraction can not only quickly grasp the subject content of video, but also use key words to classify video and establish video database index. Due to the unstructured and multi-channel feature of video, it is not enough to use only the keyword extraction method for text. This paper proposes a semantic based algorithm to extract keywords from video from multi-channel information, which combines the information of visual channel and audio channel, and extracts keywords from video based on semantics. Experiment results show that the algorithm proposed in this paper is very effective for both video and plain text.
Enhancement of Interoperability in Health Care Information Systems with the Pursuit of Blockchain Implementations

Blockchain is a decentralized computation and information-sharing platform that enables multiple authoritative domains to cooperate, coordinate and collaborate in rational decision-making processes. It is an open, immutable ledger, distributed over cluster systems that are decentralized. The past transactions in the distributed ledger are maintained as a chain of blocks and hence called blockchain. The internet world is looking into the benefits of blockchain that can be procured when it is implemented in different areas like governance, healthcare, smart city management and global trade. Blockchain is becoming attractive for innovative business processes because of its characteristics: immutability, decentralization, fast settlement, security, identification and authentication. Blockchain technology can extend its services to various fields such as health management, electronic health record management, health insurance claims, clinical testing, pharmaceutical department, genomic data analysis, etc.in health care organizations. The advantages of using a blockchain based system are that patient health records are stored in a distributed way and there is no need for a centralized owner or hub that can be damaged or interfered with by hackers. The data will be updated and always available, while data from various sources is collected in one repository. There are upcoming projects that focus on specific data modalities such as genomics and imaging. The technology claims to reduce the cost of genome sequencing, controlling patient data, and sharing the value captured from the monetization of the data to patients. It has also use cases in managing clinical trials for pharmaceutical research. There is an emerging blockchain-powered health care system that uses artificial health systems to model and display patient status, diagnosis and care, then use computer experiments to analyze and evaluate various treatment regimens, and carry out parallel implementations to support real-time decisions. In all these applications, interoperability is a crucial factor, because organizations demanding technology expansion have to intersect with multiple technologies or ecosystems to achieve a common framework and better performance. In the future, different levels of blockchain interoperability approaches will unlock the diverging functionalities during integration of multiple technologies.
LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing

We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechanical Turk.Compared to existing toolkits, LEGOEval features a flexible task design by providing a Python API that maps to commonly used React.jsinterface components.Researchers can personalize their evaluation procedures easily with our built-in pages as if playing with LEGO blocks.Thus, LEGOEval provides a fast, consistent method for reproducing human evaluation results.Besides the flexible task design, LEGOEval also offers an easy API to review collected data.