Reviewerly/exHarmony huggingface dataset:
Research on Facial Expression Recognition Model Based on ResNet Network

Facial expression recognition is a research hot spot in computer vision, which can be widely used in automatic driving, online classroom, social networking and other aspects. In view of the problems of the convolutional neural network in facial expression recognition, such as large computer complexity, large input size and insufficient local feature extraction, a network model based on the improved ResNet network is built by using Center-Loss function to improve the ResNet network, improve feature extraction ability of the model and the accuracy of model recognition. Experiments show that there are some improvements in FER2013 and CK+ expression data sets.
Segmentation of Inhomogeneous Noisy Images via a Bayesian Model Coupled with Anisotropic Mesh Adaptation

Automatic image segmentation is a key process in many applications of science and engineering, from medical imaging to autonomous vehicle driving and smart agriculture monitoring.In these contexts, the presence of spatial inhomogeneities and noise challenges the robustness of segmentation strategies [1].In this talk, a finite element-based segmentation algorithm handling images with different spatial patterns is presented.The methodology relies on a split Bregman algorithm for the minimisation of a region-based Bayesian energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation [2].On the one hand, a Bayesian model is considered to exploit the intrinsic spatial information in inhomogeneous images [3].To address the ill-posedness of the associated optimisation problem, a convexification technique [4] is coupled with a split Bregman algorithm for the minimisation of the regularised functional [5].On the other hand, an anisotropic mesh adaptation procedure guarantees a smooth description of the interface between background and foreground of the image, without jagged details [2,6].The proper alignment, sizing and shaping of the anisotropically adapted mesh elements guarantee that the increased precision is achieved with a reduced number of degrees of freedom [2,6].Numerical experiments will be presented to showcase the performance of the resulting split-adapt Bregman algorithm on synthetic and real images featuring inhomogeneous spatial patterns.The method outperforms the standard split Bregman approach, providing accurate and robust results even in the presence of Gaussian, salt and pepper and speckle noise, as well as for a challenging medical image obtained from the ultrasound of a gallbladder.
DATran: Dual Attention Transformer for Multi-Label Image Classification

Multi-label image classification is a fundamental yet challenging task, which aims to predict the labels associated with a given image. Most of previous methods directly exploit the high-level features from the last layer of convolutional neural network for classification. However, these methods cannot obtain global features due to the limited size of convolutional kernels, and they fail to extract multi-scale features to effectively recognize small-scale objects in the images. Recent studies exploit the graph convolution network to model the label correlations for boosting the classification performance. Despite substantial progress, these methods rely on manually pre-defined graph structures. Besides, they ignore the associations between semantic labels and image regions, and do not fully explore the spatial context of images. To address above issues, we propose a novel Dual Attention Transformer (DATran) model, which adopts a dual-stream architecture that simultaneously learns spatial and channel correlations from multi-label images. Firstly, in order to solve the problem that current methods are difficult to recognize small-size objects, we develop a new multi-scale feature fusion (MSFF) module to generate multi-scale feature representation by jointly integrating both high-level semantics and low-level details. Secondly, we design a prior-enhanced spatial attention (PSA) module to learn the long-range correlation between objects from different spatial positions in images to enhance the model performance. Thirdly, we devise a prior-enhanced channel attention (PCA) module to capture the inter-dependencies between different channel maps, thus effectively improving the correlation between semantic categories. It is worth noting that PSA module and PCA module complement and promote each other to further augment the feature representations. Finally, the outputs of these two attention modules are fused to obtain the final features for classification. Performance evaluation experiments are conducted on MS-COCO 2014, PASCAL VOC 2007 and VG-500 datasets, demonstrating that DATran model achieves better performance than current state-of-the-art models.
PENGEMBANGAN MODEL PEMBELAJARAN SELF DIRECT LEARNING BERBASIS LITERASI DIGITAL PADA MASA PANDEMI COVID 19 DI SMKS TIK DARUSSALAM MEDAN

ABSTRAKBased on the survey, the development of digital literacy-based SDL learning models at Vocational High Schools Darussalam Computer Informastion Technology (SMK TIK) has not gone well. Teachers find it difficult to measure student learning achievement because some students do not have gadgets, gadgets are brought by parents to work, 1 gadget is used interchangeably with siblings, high quota fees, uneven access to the internet, difficulties for parents in accompanying their children to study so that it affects work assignments .This digital literacy-based SDL provides a new color for teachers and students by implementing offline and online media collaboration such as communication via whatsApp groups, websites, blogs, g.classrooms, zoom meetings, video conferences, educational games, subject books and others.Based on the results of the t test, the significance value is SDL > alpha 5% or tcount = 14,028 > ttable 1,695, then H0 = rejected, so there is a significant influence between the independent and dependent variables, SDL partially has a significant positive effect in analyzing the development of digital literacy-based learning models in during the Covid 19 pandemic at Vocational High Schools Darussalam Computer Informastion Technology.The coefficient R2 is 0.864 (86.4%), SDL has a significant positive effect and the remaining 0.136 (13.6%) is influenced by other variables that are not included in the inpu The positive impact of SDL, students are able to think critically, collaboratively, communicatively so that it is no longer teacher centered learning but student centered learning. The negative impact of SDL is spending more time in front of gadgets for online learning reasons, where time playing games, stalking social media, is much longer than studying and doing assignments.Suggestion, dissemination of digital literacy to teachers, parents, students, and the community during the SDL learning process, using innovative and creative digital content applications, supervision and follow-up for students who are constrained in the parents' economic sector, making g.form school evaluation, carry out regular technical guidance for teachers to improve the quality of their teaching.Keyword : Self Direct Learning, Digital Literacy, Covid 19 Pandemic
Reflections on the use of information and communication technologies in the training of specialists in the field of culture and art during the war

The article examines the problem of self-education of culture and art experts in the context of philosophical, psychological, pedagogical, cultural studies, and art studies. The main goal of the study is to explore the role of the information field, social communication and information resources in combination with the role of the teacher and mentor in professional education and training of future specialists. Art departments of Ukrainian universities, relying on their own experience and the experience of foreign colleagues, are increasingly using innovative methods of teaching. The distance learning and the use of cloud technologies during the pandemic and the full-scale war of Russia against Ukraine became almost the only possibilities to continue the educational process. If yesterday the self-education of students of art universities was a desirable task in the implementation of educational plans of specialists in the field of culture and art, today’s challenges have proven the necessity of the proposed process in reaching the heights of personal and professional excellence. The tasks of the educational process have become more complicated, because students are required to use the existing professional training aimed at self-improvement. The ability of students to self-educate, the ability to predict the consequences of their professional activities and the search for ways to solve professional tasks through the use of existing knowledge and experience has become an urgent need of the time. Cloud technologies facilitate the distribution of various content (texts, photos, images and music files) over the Internet. The accessibility, openness, interactivity of cloud technologies enables collective use and communication both online and offline, which makes the process of self-education of future specialists in the field of culture and art meaningful, practice-oriented process and contributes to the improvement of their professional competence.
DHVSU Electrical Engineering Department Goes Green: Feasibility of Solar-Powered Building Using an Off-Grid Hybrid Solar Inverter

This research project unfolds the practicability of utilizing an Off-Grid Hybrid Solar Inverter to power up the Electrical Engineering Building in Don Honorio Ventura State University. To satisfy the aims of this research, a Solar PV System design was used to make use of the solar energy converting it to electrical energy to supply the whole building. This study was guided by doing a load schedule of all the appliances in the whole building to acquire the total power and total energy consumption, using it as a basis for the selection of the required electrical components and computation of the roof area to select a suitable solar panel in order to ensure the functionality of the whole Solar PV System. The raw data gathered was analyzed using formulas to acquire the needed electrical values. The findings resulted in the development and completion of the Solar PV System Design. The last part of the research is the results, discussions, conclusions, and recommendations. These provided the finalization and endorsement of the whole study.
Students’ Motivation in Studying English: a Case Study at The Eleventh Grade Students MA Kebunrejo Genteng

AbstractMotivation is the powerful part of learning process for studying English. By this way, students will be more diligent to go to school and engage with the subject. The purpose of this research to reveal students’ motivation in studying English. The data analysis is utilized in this research is qualitative. This method is to reveal some information that collected from questionnaire given to students. Here, there are 40 students are asked some questions. The result of this study that students have answer the questionnaire varies greatly. The top of average percentage of the choice is neutral then followed with agree. The residue are disagree, strongly agree and strongly disagree sequentially. It means some students still hesitant to say agree or disagree about the their motivation in studying English.Keywords: motivation, students’ motivation, studying English
DETAIL ENGINEERING DESIGN INFRASTRUKTUR TEKNOLOGI INFORMASI PERINGATAN DINI BANJIR DINAS PENGAIRAN PROVINSI ACEH

The complexity of life in the world is the most difficult to anticipate when natural phenomena occur in the form of rain, earthquakes, landslides, volcanic eruptions, and whirlwinds. The natural event that causes an area to become a natural disaster most often is flooding, basically the rain that occurs can be predicted in terms of intensity, rainfall, volume, and time of occurrence. BMKG as a government institution that researches natural phenomena usually provides daily weather forecasts and the information can be obtained on websites and online media, TV, social media and other media, the Nangroe Aceh Darussalam Provincial Irrigation Service which is responsible for planning, handling, and construction of irrigation and The use of irrigation is challenged to make innovations using information technology and incorporating technically designed hardware telemetry (DED) to combine it into a flood early warning system, especially in river flows close to residential areas so that it can anticipate or mitigate flood disasters massively and informatively. to local governments, communities, media and other parties with an interest in the main tasks and functions of rescue. In developing and combining hardware, software, and brainware for this flood early warning system, the main focus was the use of Hawk Eye, high resolution CCTV operated using Igismap software, and Dynamic software, which successfully identified garbage in rivers that caused puddles, and sent signals and data to the Command Center (EWS Command Center).
Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

Cryptocurrencies, especially Bitcoin, is a hot commodity today. High volatility is a common feature of almost all the cryptocurrencies in the world. A systematic exploration and examination of the volatility of cryptos enables the investor to earn more on their investments. After the outbreak of the COVID-19 pandemic, the crypto market witnessed a highly volatile situation with a huge increase in price. The pandemic also affected the volatility and return of Bitcoin. This research aims to analyse and compare the risk and volatility characteristics of Bitcoin after the outbreak of the COVID-19 pandemic. The study further tests the capacity of several autoregressive models, such as ARMA, GARCH, EGARCH, and TARCH in estimating and evaluating the return and volatility associated with Bitcoin. Identified models were tested and compared with the help of Akaike information criteria (AIC) and Schwarz information criteria (SIC). For this article, the data of daily adjusted closing price of Bitcoin INR (BTC-INR) were collected from Yahoo Finance during the period January, 2017 to December, 2021. We witnessed a huge change in the daily average return of Bitcoin after the COVID-19 outbreak. Also, we identified TARCH (1, 1) as the best model in the ARCH family for evaluating and estimating volatility and ARMA (10, 10) as the best model for predicting the return of Bitcoin.
Blockchain Technology in Higher Education Ecosystem: Unraveling the Good, Bad, and Ugly

The higher education management systems first identified and realized the trap of pitting innovation against privacy while first addressing COVID-19 social isolation challenges in 2020. In the age of data sprawl, we observe the situation has been exacerbating since then. Integrating blockchain technology has the potential to address the recent and emerging challenges in the higher education management system. This paper unravels the Good (scopes and benefits), Bad (limitations), and Ugly (challenges and trade-offs) of blockchain technology integration in the higher education management paradigm in the existing landscape. Our study adopts both qualitative and quantitative approaches to explore the experiences of educators, researchers, students, and other stakeholders and fully understand the blockchain's potential and contextual challenges. Our findings will envision an efficient, secure, and transparent higher education management system and help shape the debate (and trade-offs) pertaining to the recent shift in relevant business and management climate and regulatory sentiment.
On the Reliability of Watermarks for Large Language Models

As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effective strategy for mitigating such harms by enabling the detection and documentation of LLM-generated text. Yet a crucial question remains: How reliable is watermarking in realistic settings in the wild? There, watermarked text may be modified to suit a user's needs, or entirely rewritten to avoid detection. We study the robustness of watermarked text after it is re-written by humans, paraphrased by a non-watermarked LLM, or mixed into a longer hand-written document. We find that watermarks remain detectable even after human and machine paraphrasing. While these attacks dilute the strength of the watermark, paraphrases are statistically likely to leak n-grams or even longer fragments of the original text, resulting in high-confidence detections when enough tokens are observed. For example, after strong human paraphrasing the watermark is detectable after observing 800 tokens on average, when setting a 1e-5 false positive rate. We also consider a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document, and we compare the robustness of watermarking to other kinds of detectors.
Weaponizing Artificial Intelligence

Abstract: The research paper delved into the concerns of chatbot users and the potential risks lurking in the shadows—security, privacy, data protection, and social quandaries. What emerged from the study was a disquietude among users, fearing the misuse or thievery of their personal information. Additionally, apprehension surfaced regarding the sinister deployment of chatbots in the realms of phishing scams and the propagation of deceitful news. To ensure a shielded experience when engaging with chatbots, users were provided with sagacious counsel. They were admonished to only divulge their valuable information to chatbots that have garnered a reputable and trustworthy stature. Moreover, a word of caution resonated, urging users to exercise vigilance in their cyber-surfing, refraining from clicking on dubious links and showing discretion when confronted with attachments or applications proposed by chatbots. Careful advice also dictated the periodic updating of devices and software, ensconcing the latest security fixes within their digital fortresses. As an added layer of defence, users were encouraged to change their passwords at regular intervals, an act of precautionary foresight guarding against potential breaches of security.
JOSS: Joint Exploration of CPU-Memory DVFS and Task Scheduling for Energy Efficiency

Energy-efficient execution of task-based parallel applications is crucial as tasking is a widely supported feature in many parallel programming libraries and runtimes. Currently, state-of-the-art proposals primarily rely on leveraging core asymmetry and CPU DVFS. Additionally, these proposals mostly use heuristics and lack the ability to explore the trade-offs between energy usage and performance. However, our findings demonstrate that focusing solely on CPU energy consumption for energy-efficient scheduling while neglecting memory energy consumption leaves room for further energy savings. We propose JOSS, a runtime scheduling framework that leverages both CPU DVFS and memory DVFS in conjunction with core asymmetry and task characteristics to enable energy-efficient execution of task-based applications. JOSS also enables the exploration of energy and performance trade-offs by supporting user-defined performance constraints. JOSS uses a set of models to predict task execution time, CPU and memory power consumption, and then selects the configuration for the tunable knobs to achieve the desired energy performance trade-off. Our evaluation shows that JOSS achieves 21.2% energy reduction, on average, compared to the state-of-the-art. Moreover, we demonstrate that even in the absence of a memory DVFS knob, taking energy consumption of both CPU and memory into account achieves better energy savings compared to only accounting for CPU energy. Furthermore, JOSS is able to adapt scheduling to reduce energy consumption while satisfying the desired performance constraints.
Multilingual Communication in WhatsApp Interactions: Empirical Analysis in Burundian Context

This article on "multilingual communication in interactions by whatsApp" will try to present and describe the mechanisms of code switching in the whatsApp messages of multilingual individuals in a Burundian context. We try to show the nature of these messages mediated by smartphones and their functions. After analysis, we discovered a special case. Generally, there are hybridizations by partial confixation, that is to say, in cases where an allogeneic prefix or suffix is ​​joined to an autonomous indigenous lexical unit. In the case of Kirundi and French, it is quite the opposite because it is the indigenous prefixes and suffixes that are attached to the foreign lexical units.
Analysis of Negotiation Text Writing Ability in Class X Students of SMA Nurul Iman Tanjung Morawa in the 2020/2021 Academic Year

This study aims to describe the ability to write negotiating texts for tenth grade students of SMA Nurul Iman Tanjung Morawa. The population of this study was all students of class X SMA Nurul Iman Tanjung Morawa in the 2020/2021 academic year. The population of this study was all students of class X SMA Nurul Iman Tanjung Morawa in the 2020/2021 academic year which consisted of 10 classes with a total of 132 students. For this study, the sample taken was class X MIA-1, totaling 29 students through random sampling technique. This research uses descriptive analysis method. The data collection technique in this research is documentation. The research instrument used was a negotiating text writing test. The results showed that the ability of class X students of SMA Nurul Iman Tanjung Morawa in writing negotiating texts based on the structural aspect got an average score (93) in the very good category and the linguistic feature aspect got an average score (78.15) in the good category. . Based on the distribution table of the percentage value of the ability to write negotiating texts for class X students of SMA Nurul Iman Tanjung Morawa in; (1) the structural aspect shows the very good category (79.31%), good category (20.69%), quite good category (0%), and poor category (0%). (2) the aspect of linguistic characteristics shows in the very good category (31.04%), good category (51.72%), quite good category (17.24%), and poor category (0%). (3) the aspect of structure and linguistic characteristics shows in the very good category (34.48%), good category (58.62%), quite good category (6.89%), and poor category (0%). Indonesian language teachers can determine students' writing abilities, especially in writing negotiating texts based on structural aspects and aspects of linguistic characteristics. After knowing the students' abilities, the teacher can further improve the effectiveness of learning to write negotiating texts.
A new dispersive flies optimisation algorithm for the sum of three cubes

By first solving the equation $x^3+y^3+z^3=k$ with fixed $k$ for $z$ and then considering the distance to the nearest integer function of the result, we turn the sum of three cubes problem into an optimisation one. We then apply three stochastic optimisation algorithms to this function in the case with $k=2$, where there are many known solutions. The goal is to test the effectiveness of the method in searching for integer solutions. The algorithms are a modification of particle swarm optimisation and two implementations of simulated annealing. We want to compare their effectiveness as measured by the running times of the algorithms. To this end, we model the time data by assuming two underlying probability distributions -- exponential and log-normal, and calculate some numerical characteristics for them. Finally, we evaluate the statistical distinguishability of our models with respect to the geodesic distance in the manifold with the corresponding Fisher information metric.