Reviewerly/exHarmony huggingface dataset:
VIOLENCE AGAINST AFRICAN-AMERICAN SLAVES AS SEEN IN SOLOMON NOTHUP'S TWELVE YEARS A SLAVE

The purpose of this study was to reveal about violence against African-American slaves; the kinds of violence and impacts of violence to the slaves in Solomon Northup’s Twelve Years a Slave. This study is classified as a qualitative research because the writers gathered the data in the form of words as well as quotation from the work itself. The writers collected the data from two kinds of sources, namely primary source and secondary source. The primary source was taken from the novel Twelve Years a Slave by Solomon Northup, and the secondary sources are the other material that supports the research such as books, articles and websites. This study used a Sociological approach which regards the literary work as a social document that reflects the situation when the literary work was created. The results of this study showed that there are two kinds of violence against African-American slaves in Northup’s work; physical violence and psychological violence. The acts of violence wasreceived by the slaves result in some impacts include physical impact such as, suffering in pain, injury, and sick; and psychological impact such as, fear, trauma, and depression. Through sociological approach, it can be concluded that the novel Twelve Years a Slave really explained the real social condition of African-American slaves that occurred in the 19th century, where they experienced acts of violence from their white masters during slavery.
IMPROVING STUDENTS LISTENING SKILL USING LISTENING PRACTICE: DIRECT LISTENING THINKING ACTIVITY (DLTA)

The purpose of this study is to analyze the effectiveness of using the Direct Listening Thinking Activity (DLTA) learning strategy in improving the listening skills of eighth grade students at SMP Negeri 1 Eris. Direct Listening Thinking Activity (DLTA) is a learning strategy to improve students' understanding of the material being listened to. This study is pre-experimental design used quantitave research in one group pre-test and post-test. The subjects were 22 sttudents of eigth grade. The data were collected by using matchmaking questions wich the sampling students answer the question according to the instruction given by researcher. The result of this study indicates that the mean score of post-test was higer than pre-test. It can proof that the DLTA strategy can improve students listening skill.
Automated SLR with a Few Labeled Papers and a Fair Workload Metric

Citation screening is a crucial stage in conducting a Systematic Literature Review, where reviewers must read hundreds, if not thousands, of papers. Natural Language Processing-based models using Transformers have been successfully employed to automate this process and minimize the chances of missing relevant papers. In our research, we proposed three variations of these Transformer models, each with different pre-training techniques. With our models, reviewers only need to read 16 papers to train the model, thus saving as much as 80 $$\%$$ of the workload. In addition, we revisited the AWSS@R metric, which normalized the WSS@R index and provided a fair way to estimate the workload saved using the different datasets.
COMPARATIVE AND DOGMATIC ISSUES OF HATE SPEECH - TRADITIONAL AND MODERN ACTS OF COMMISSION

Given that the criminal offense of Public Incitement to Violence and Hatred seeks to protect public order but also certain groups of individuals and limit freedom of speech, the paper provides a comparative legal analysis of this criminal offense through Anglo-Saxon and continental law and especially refers to the law of EU and EU Member States. The paper analyzes some dogmatic principles of criminal law that explain the conditions when Public Incitement to Violence and Hatred can be committed. Especially, the development of modern technology and modern way of communication and influence considered in the paper the possibility to affect different groups of people through incitement to hate speech. Hate crime precedes different riots that in times of social turbulence can lead to different criminal offenses that affect the economic, cultural, and environmental positive development of society. The paper gives a broader insight into the way this criminal offense can be committed, especially taking into account the act of committing Public Incitement to Violence and Hatred, connecting it to forms of committing this criminal offense in a “non-public“ way through the traditional act of commission or using modern technologies (Social Networks, AI,...) and the dogmatic issue of special intent.
Overview of Data Fusion in Autonomous Driving Perception

In autonomous driving, research on data fusion has influential academic and application value. This chapter is proposed to summarize the data fusion methods of autonomous driving in recent years. Firstly, the development of deep object detection and data fusion in autonomous driving is introduced, as well as existing reviews. From three aspects of multimodal object detection, fusion levels, and calculation methods, the cutting-edge progress in this field is comprehensively shown. Finally, open issues are discussed, and the performance, challenges, and prospects are summarized.
PENGEMBANGAN SISTEM INFORMASI PENANGANAN PENDERITA GANGGUAN JIWA DENGAN PENDEKATAN ENTEPRISE SYSTEMS

The health information system is a digital system that is quite complex so that in the development and evaluation stages, it is necessary to consider cultural, political, social and organizational structure factors. In Bali, the number of people with mental disorders is considered high and the need for treatment is in a fairly long time, that is the reason a management information system is needed to organize data and information about the patients. This study aims to develop a centralized management information system that can be utilised specifically for health workers to manage information regarding people with mental disorders. The development of the management information system in this study involves doctors, volunteers, and admin or organizational staff in every critical process. The prototyping method was chosen as a system development method because it considered more adaptive in capturing the needs of prospective system users. At the evaluation stage, functional evaluation through the Black-box method was used to test the functional capabilities of the system. In addition, Technology Acceptance Model (TAM) and Task Load Index (TLX) were used to evaluate the non-functionality of the system where all users agree that the developed management information system is useful, easy to use, and requires a small cognitive workload.
PENERAPAN MODEL COOPERATIVE INTEGRATED READING AND COMPOSITION DENGAN BERBANTUAN MEDIA DIRGAM UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PEMAHAMAN SISWA SEKOLAH DASAR

This research is motivated by the results of the reading comprehension ability of class VI students who are still low. This is evidenced by the results of students' reading comprehension ability which are still below the Minimum Completeness Criteria (KKM). One of the learning methods set to overcome this problem is the CIRC method. The purpose of this study was to determine the increase in students' reading comprehension skills through the CIRC learning model. This type of research is Classroom Action Research (CAR) using the Kemmis McTaggart model, which consists of four stages, namely planning, implementing, observing, and reflecting. The research data showed an increase in the average test results and an increase in the number of students who achieved the KKM in each cycle. The average score in cycle I was 74.70 with 64.70% completeness and the average score in cycle II was 81.23% with 82.36% completeness.
An improved least squares (LS) channel estimation method based on CNN for OFDM systems

&lt;abstract&gt;&lt;p&gt;Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.&lt;/p&gt;&lt;/abstract&gt;
Sistem Informasi Geografis Pemetaan Bangunan Kota Pekanbaru Berbasis Web

Pekanbaru City Public Works and Spatial Planning Office in conveying building information that will display the location of the building along with detailed information about the location of the building so that it is easier to see which buildings are lacking building information so that the Public Works and Spatial Planning Office can pay more attention to buildings. . So with the construction of the web-based Geographic Information System for Building Mapping, it is intended that this application can provide convenience to users in viewing information about building information and the location of the building location they want to know in the city of Pekanbaru. In designing this system, an analysis is carried out using the Unified Modeling Language (UML) model which includes Use Case Diagrams, Activity Diagrams, Sequence Diagrams, Entity Relationship Diagrams, and Data Flow Diagrams. The Mapping Geographic Information System that has been built is able to provide convenience to users.
Kapabilitas Tata Kelola Keamanan Informasi Menggunakan Cobit5 Pada Perguruan Tinggi Graha Kirana

Information security management governance is needed to maintain the confidentiality, integrity and availability of information in the company. This research requires an understanding of information security that is ongoing in the company to conduct a capability assessment and provide recommendations for improvements using COBIT 5 at Graha Kirana College. In the process, the five COBIT frameworks are used to increase the effectiveness of agency security. The selection of the COBIT domain was carried out by reviewing the business documents of Graha Kirana College and interviews with IT managers. Good news security can be achieved through the implementation of a number of technical measures supported by appropriate management policies and procedures. As an activity carried out to guide and manage the company in the context of facing risks. Risk management can be understood as a process that is carried out rationally and systematically arranged to guide, identify, monitor, prepare solutions, and report hazards.
Mental Healty dan Literasi Keuangan Wanita: Antara Karier dan Ibu Rumah Tangga

Today more and more women are having careers outside the home, especially those working in offices, in certain places and this always raises pros and cons. Women work, wives work, this sometimes creates problems in the family. Some cannot accept it, some accept it, some do it with an open heart, some do it with a guilty heart. There are women who work but are constantly plagued with guilt because they think they should be at home. The purpose of the service is to explain in more detail solutions to solving problems regarding the positive and negative sides of women's dual roles and regarding the mechanisms for managing or managing time. The socialization was carried out face-to-face with Almadany Elementary School teachers. Along with presenting the material, the implementing team showed a video that had been made by the implementing team on Youtube regarding women's mental health. The final result is that participants can understand how to maintain mental health in multiple roles.
Upside-Down Selfies Look Much More Alert and Awake

Introduction: For practical reasons, cameras on cell phones and laptops are routinely located above the display. This allows better visibility, minimizes interference from the user’s hands, and affords a more natural projection angle than if located below the display. Yet, an unintended consequence is that in selfies and when video conferencing with others, the user’s eyes are typically focused below the camera on the middle of the screen. This favors recording a somewhat downward gaze direction that might be interpreted to indicate tiredness and inattention. Turning the phone upside-down or otherwise positioning the camera at the bottom of the display might then produce an image that appears more awake and alert. Methods: 100 naïve volunteers took selfies on cell phones while staring at the bridge of their nose, both with their phone upright and upside-down. They then indicated which photo appeared more awake and alert versus more tired and inattentive. Results: Over 90% of participants indicated the upside-down selfie appeared more awake and alert (z > 8, p < 0.0001, a very large effect). The few exceptions appeared to be due to paying attention to other facial features such as unintended changes in expression or their make-up appearing less flattering in the upside-down selfie. Discussion: The findings confirm a very strong tendency for upright selfies with the camera above the screen to appear more tired and inattentive compared to ones taken upside-down with the camera located below the screen. The findings are consistent with people judging attentiveness based on gaze angle relative to the camera, with practical implications in the design of visual interaction devices like cell phones, tablets, and lap tops. We suggest that most users would prefer positioning cameras on the bottom of displays once they become aware of the increased impression of attentiveness that it produces.
A Framework for Wireless Technology Classification using Crowdsensing Platforms

Spectrum crowdsensing systems do not provide labeled data near real-time yet. We propose a framework that addresses this challenge and relies solely on Power Spectrum Density (PSD) data collected by low-cost receivers. A major hurdle is to design a system that is computationally efficient for near real-time operation, yet using only the limited 2 MHz bandwidth of low-cost spectrum sensors. First, we present a method for unsupervised transmission detection that works with PSD data already collected by the backend of the crowdsensing platform, and that provides stable detection of transmission boundaries. Second, we introduce a data-driven deep learning solution to classify the wireless technology used by the transmitter, using transmission features in a compressed space extracted from single PSD measurements over at most 2 MHz band. We build an experimental platform, and evaluate our framework with real-world data collected from 47 different sensors deployed across Europe. We show that our framework yields an average classification accuracy close to 94.25% over the testing dataset, with a maximum latency of 3.4 seconds when integrated in the backend of a major crowdsensing network. Code and data have been released for reproducibility and further studies.
Contrastive learning with self-reconstruction for channel-resilient modulation classification

Despite the substantial success of deep learning for Automatic Modulation Classification (AMC), models trained on a specific transmitter configuration and channel model often fail to generalize well to other scenarios with different transmitter configurations, wireless fading channels, or receiver impairments such as clock offset. This paper proposes Contrastive Learning with Self-Reconstruction called CLSR-AMC to learn good representations of signals resilient to channel changes. While contrastive loss focuses on the differences between individual modulations, the reconstruction loss captures representative features of the signal. Additionally, we develop three data augmentation operators to emulate the impact of channel and hardware impairments without exhaustive modeling of different channel profiles. We perform extensive experimentation with commonly used realistic datasets. We show that CLSR-AMC outperforms its counterpart based on contrastive learning for the same amount of labeled data by significant average accuracy gains of 24.29%, 17.01%, and 15.97% in the Additive White Gaussian Noise (AWGN), Rayleigh, and Rician channels, respectively.
Mixed Graph Designs Do Not Improve Visual Memory

Objects that are distinct in their categories and features tend to be better for visual memory, this is thought to be due to a decreased amount of overlap for object representations and interference (Konkle et al., 2010). If this is the case, this distinct advantage may have implications for improving memory in real-world situations, such as trying to remember multiple patterns of visualized data shown across the sequence of a research presentation. Previous research on individual visualizations has shown that visualized data is more memorable when visually distinct (e.g. contains a drawing) compared to plain graphs (Bateman et al., 2010). However, no existing work has tested whether this advantage in visual distinctiveness can be applied to the data shown across a series of graphs. This study asked 144 participants to recall specific trends and higher-level patterns (i.e. overarching trends) in data within a realistic journalistic data story. The graphs were either of distinct designs (e.g., bar, pie, and line graphs in one series) or identical designs (e.g. three bar graphs in one series). We find that distinctiveness in graphs had no statistically reliable effect on recall accuracy, suggesting that mixed graphical designs do not improve memory.
Exploring the use of Genetic Algorithms Toolbox in Engineering Education: Did it Provide an Interesting Learning Experience for Students?

Teaching in engineering education using Genetic Algorithms (GAs) toolbox as a teaching tool in engineering education can be an effective approach, particularly in Signal Processing subjects, as it encourages students to learn how to find optimal values required in designing digital filters. This research investigates the use of GAs for teaching digital filter design, where the evaluation function of the problem is optimized using Gas. The GAs toolbox simulation is designed to yield a stable, lowest-order H[z] that meets the tolerance parameters and satisfies the design criteria. Teaching GAs involves representing the filter design problem in a way that can be accepted by genetic programming.
ADOPTION OF DIGITAL VACCINATION SERVICES FROM A LITERALLY PROFESSIONAL VIEW: A COMMENTARY ON “IT IS THE CLICK FLOW, NOT THE VALUE” FROM A HEALTH CARE WORKERS AND AGILITY PERSPECTIVE

The interplay of innovation and health care workers (HCWs) focusing on vaccination were key in mastering the COVID-19 pandemic, which led to serious health, social and economic problems worldwide (Holzmann-Littig et al., 2022). Apart from optimizing traditional approaches to contain the pandemic outbreak, such as hygiene rules, testing regime, contact tracing, quarantine (Saw et al., 2021), new mRNA-vaccines and innovative digital technologies such as vaccination platforms were introduced (Kis et al., 2021). In this context, the study “Adoption of Digital Vaccination Services: It Is the Click Flow, Not the Value—An Empirical Analysis of the Vaccination Management of the COVID-19 Pandemic in Germany” (Alscher et al., 2023) focused on the digital management of vaccination processes. Existing adoption theories and acceptance models from consumer market research were adapted to the public health sector and formed into a digital platform framework for policy makers and pandemic managers to manage public health services, such as vaccination offerings. Based on a survey in Germany’s federal state with the highest vaccination rate, results showed that (1) the usability barrier is the most important barrier which harms adoption, whereas the most emphasized value barrier does not play a dominant role in contrast to consumer market research. By analyzing the three digital platform configuration areas Communication, Data Management, Personalization, (2) Personalization stood out as the most important factor for managing the usability barrier by optimizing the best click flow on the digital health platform to address the needs, preferences, and situation of the citizens as users. &#x0D; “Yet, the endgame of the pandemic is not vaccines; it is vaccination” (Dai &amp; Song, 2021, p. 455) and thus, the fast implementation of the vaccination by health care workers (HCWs) are critical premises and antecedents for a successful vaccination adoption (Scroggins et al., 2023). In this direction, we want to highlight the role of professional HCWs in the adoption of digital vaccination services: First, their own adoption of the vaccination as a role model and their position as first priority vaccination group, and second, their contribution in adapting their service to the new pandemic situation from an agility point of view.
A survey on college students’ cybersecurity awareness and education from the perspective of China

As people increasingly rely on information and communication technology (ICT), a variety of cyber security issues are emerging, making improving cyber security awareness (CSA) an important topic. This quantitative study focuses on a group of college students from eight local public universities in China (n=1710) and aims to investigate their CSA and education levels using a 32-question questionnaire. Descriptive statistics and cross analysis were used to analyze the current situation related to cybersecurity in college. The results showed that nearly 50% of students spend more than four hours online, with female students spending more online time than males. Smartphones are currently the most popular devices, and spam is the most common issue they encounter. Descriptive statistics, independent samples t-test, and One-way ANOVA test were used to analyze the levels of CSA and education. For CSA, all students are still weak in their password practices. Male students have higher CSA levels in usage habit related to device and HTTP use, but female students perform better in social media. Majors do not make a significant difference in CSA, and freshmen perform better than juniors in device and HTTP application. Regarding cybersecurity education, almost all students believe that colleges need to strengthen information security education. Male students are more familiar with cybersecurity laws, and there are differences in the education methods chosen by students in different majors. Students majoring in computer-related fields prefer more specialized knowledge. This study not only provides valuable insights into the prevailing state of CSA among college students but also offers effective recommendations for enhancing cybersecurity education practices in colleges. The findings underscore the importance of addressing weaknesses in password practices and emphasize the need for comprehensive educational approaches that encompass various facets of cybersecurity. Institutions should consider tailoring their instructional strategies to meet the unique needs of students from diverse academic disciplines. Moreover, fostering awareness of cybersecurity laws and regulations is crucial for all students, regardless of their major.